title: F.T. Island

text: After releasing a tragic music video for their title track "Severely", F.T. Island released their fourth mini-album, entitled Grown-Up. Featuring five songs, it is composed of slow-tempo rock ballads. The album was released on January 31, 2012, and secured the No. 1 position on the Hanteo album chart for total album sales on both the daily and weekly charts, with title track "Severely" topping various online music charts. The album was completely sold out ten days after its release, recording some 50,000 copies sold. F.T. Island also achieved the "Triple Crown" recognition for "Severely" on JTBC's "Music On Top". "Triple Crown" is a recognition for any artist who have won a music programme three times consecutively and the song would be removed from further trophies.


title: F.T. Island

text: On March 9, F.T. Island and fellow labelmate CNBLUE performed in America for the first time at Los Angeles Nokia Theater.


title: F.T. Island

text: In April, the band returned to Japan to promote their 7th Japanese single "Neverland", which was released on the 18th. “Neverland”, a fast tempo rock song, was chosen as the opening theme song of the animation Ozuma, a celebration of the 20th anniversary of Japanese broadcasting system WOWOW.


title: F.T. Island

text: F.T. Island released their second studio Japanese album "20 [Twenty]" on May 16, 2012. It debuted at number four on the weekly Oricon albums chart, selling 41,726 copies in its first week, earning the band their best first-week sales in Japan.


title: F.T. Island

text: The same year, F.T. Island released their fourth Korean studio album "Five Treasure Box" and its single "I Wish" on September 10. Within the first week of its release, the album sold over 30,000 copies in South Korea.


title: F.T. Island

text: The band continued their TAKE FTISLAND tour which started the year before in Seoul, Korea and went to Taiwan in December 2012 and continued the tour in Shanghai and Beijing, China.


title: F.T. Island

text: On March 27, 2013, F.T. Island released their single, "You Are My Life" with 2 tracks, "Beat It" and "Come Into My Dream". All three songs ended up in the top 5 of Japanese music site Dwango's K-Pop weekly ringtone chart. Also, their M/V for "You Are My Life" took first on Chinese video site YinYueTai's Korea chart.


title: F.T. Island

text: Later on April 9, the band's 2009 album "Jump Up" topped Taiwanese music charts for Korean and Japanese releases, with "Bad Woman" ranking first on the songs chart, as well.


title: F.T. Island

text: The group released their Japanese album "Rated-FT" in June 2013, where members composed tracks such as "Time To", "Hold My Hand", "Black Chocolate" and "Orange Sky". The album charted at third on Japan's Oricon Daily Album Chart on the day it is released.


title: F.T. Island

text: F.T. Island released their 11th Japanese single "Theory of Happiness" on July 24, 2013. It included two tracks composed by the members which were "Eyes On Me" and "Rainy Day" which both topped the Oricon Daily Chart.


title: F.T. Island

text: The band became the first Korean band to perform three years in a row on Fuji TV's "Mezamashi Live!" on July 28, 2013. F.T. Island also performed live at rock festival, Summer Sonic 2013 in Tokyo on August 10 and in Osaka on August 11 which was their second appearance at the festival, after 2010. The band also completed their first Japan arena tour of seven cities, titled "F.T. Island Arena Tour 2013 ~Freedom~", drawing an audience of 100,000.


title: F.T. Island

text: F.T. Island returned to Korea in late September 2013, and released their self-composed mini-album "Thanks To", dedicated to their fans and supporters. "Memory" and "Always With You" was composed by main singer Hong-gi, "Try Again" by Jong-hoon, and "Falling Star" by Jae-jin. They held a two-day concert in Seoul on 28 and 29 September, in front of 6000 fans. The concert was called "FTHX", combining the first initials of the band FT with THX (thanks). 'Memory' by Hong-gi ranked No.1 on the Taiwanese Omusic chart. "According to the Taiwanese G-Music Chart, F.T. Island’s 6th anniversary album ‘Thanks To’ ranked the weekly chart No. 1 recording 28.84% sales among Asian album sales."


title: F.T. Island

text: Their Japanese Zepp Tour started on October 1, 2013, in Tokyo, and later performed in Sapporo, Osaka, Fukuoka, Shizuoka and Nagoya, as well.


title: F.T. Island

text: The band made their official Korean comeback with their fifth mini album "The Mood" on November 18, 2013. It topped the weekly Hanteo chart and Gaon chart; and ranked first on the online HMV chart in Japan for pre-released sales.


title: F.T. Island

text: F.T. Island started 2014 with a concert in Shanghai which was part of their FTIsland’s 6th-anniversary tour. The band released multiple albums and singles in Japan. "New Page" was released on May 2014, it sold an estimated 22,000 copies worldwide and charted at 3rd on the Oricon Daily Charts and 6th on the Oricon Weekly Charts.


title: F.T. Island

text: A few months later, F.T. Island then released a second album, titled "Japan Best – All About" in October with the title track "Be Free". It topped the Gaon physical album chart during October 5 and 11 periods, Tower Records Daily Pre-Order Charts and HMV Korean-Asia Daily Charts. It also achieved 8th position on the Gaon album chart for that month.


title: F.T. Island

text: The band then released a new single, "To The Light" in October, which consists of three fully composed tracks. "Mitaiken Future" was released on March 16 and "Beautiful" on January 15. "Mitaken Future" was later included in the album "New Page". "Beautiful" charted at 4th position in Oricon's Daily Singles chart on the day same day when the album was released.


title: F.T. Island

text: F.T. Island also held several tours and announced partnership with Taiwanese clothing brand, STAYREAL. They were invited and performed in one of China's most biggest music festival - Zebra Music Festival; where the band sang songs such as "Flower Rock" and "Freedom". Its ticket sales were also long sold out before the festival even started proving F.T. Island's immense popularity in China. Moreover, the band were also invited to perform in the Japanese MTV - Unplugged segment that only top artists could perform. Also, Hong-gi was also chosen to be the MC for M Countdown's 10th-anniversary special which proves the member's talent.


title: F.T. Island

text: The members also promoted themselves individually throughout 2014. Min-hwan was cast in a musical "Joseph and the Amazing Technicolour Dreamcoat", Seung-hyun collaborated with comedian Song Eun-i for "Two Song Place" and starred in the film, "No One to Trust in the World". Hong-gi resumed filming "Bride of the Century" after his fall on an icy road. He also starred in the drama, "Modern Farmer". and released his new accessory brand Skullhong. "Hongstargram" was also released as a travel book where he took pictures in his first solo tour in different countries. Jong-hoon was later announced in a new upcoming drama “Heroes”, said to premiere in 2015. He also launched his first Art Expedition In Japan where he showcased his artworks to the public. Jae-jin starred in Fashion Killa alongside Hong-gi as a special guest and also sang the title song for Bride of the Century, where member Hong-gi was starring in the drama.


title: F.T. Island

text: F.T. Island started 2015 with concerts in France, Brazil, Chile, Mexico, and the US. "I Will" was released on March 23. The album peaked at 7th on the Billboard World Albums Chart in the second week of April. "I Will" also performed well on the Gaon Chart, peaking 1st in physical album ranking and 3rd on the Gaon Social Charts in the third week of March. F.T. Island sold 31,000 copies of the album peaking in 7th position for the highest number of album copies sold in the entire month of March. For "Pray", they were awarded the "Most Popular Band in Asia". F.T. Island then toured China, Japan, Taiwan and more.


title: F.T. Island

text: On the Japanese market, "5...Go" was released on May 13. The title song of the album, "Primavera" is a collaboration with Japanese rock singer Takahiro Moriuchi. "Puppy", a Japanese single, was released three months later and reached sixth position in Billboard's Japan Hot 100. F.T. Island then released the Korean version of the song. The band then held an autumn tour in 13 countries within 32 days.


title: F.T. Island

text: In 2016, a Japanese studio album, "N.W.U", was released on April 6. The album ranked first on the Japanese's Tower Record, which is the largest record store in Japan, and second on the Oricon daily album chart. It also topped the HMV Asia daily charts and K-pop charts for famous Japanese sound sources such as Dwango. The band performed in Osaka, Tokyo, and Aichi.


title: F.T. Island

text: Their sixth Korean studio album, "Where's the Truth?", was released on July 18. "Where's the Truth?" ranked 5th position on Billboard's World Album Charts. The album also charted at 1st in the Gaon Album Chart for the week of July 23.


title: F.T. Island

text: "Just Do It" was released in July and topped HMV's Asian album chart, Amazon Japan's Asian pop chart, and landed in the top 3 on Tower Records' world music chart and hit No.5 on the Oricon Singles Chart. The song also came atop in Tower Records Japan for the top 10 K-pop singles and albums for the second week of September.


title: F.T. Island

text: On March 14, 2019, Jong-hoon left the band and retired from the entertainment industry after being involved in the Burning Sun scandal, where he was part of a group chat in which some members shared videos of sexual activities recorded without the consent of the other party. Hong-gi began his mandatory military service on September 30, 2019, becoming the first F.T. Island member to enlist. On December 24, 2019, it was announced that Seung-hyun would leave the group and FNC following the end of his contract on December 31 in order to focus primarily on his acting career, while Hong-gi, Jae-jin, and Min-hwan renewed their contracts with their label. Jae-jin enlisted as an active duty soldier on January 21, 2020, while Min-hwan enlisted on February 24 as a reserve soldier. Hong-gi was discharged on April 18, 2021. Jae-jin was discharged on August 1, 2021. Min-hwan was discharged on September 2, 2021.


title: F.T. Island

text: On April 25, 2022, FT Island will hold 'THE CON 2022: FT Island' at 6 PM on May 15, 2022 at the Jangchung Gymnasium in Jung-gu, Seoul. The show will be held after the 2019 concert held by FT Island before military service.


title: F.T. Island

text: In 2009, Jong-hoon, Min-hwan, and Jae-jin formed a sub-group called F.T. Triple. Some of the reasons behind this lineup were to showcase more of the members' skills and to prevent main singer, Lee Hong-gi, from overstraining his voice again. Another reason was to continue promoting F.T. Island while Hong-gi was filming "You're Beautiful" while Seung-hyun was having a break.


title: F.T. Island

text: At first, the trio was unofficially announced at the Dream Concert in Japan as "A3". They almost named themselves A3 because they all are of blood type 'A'. They also wanted to keep the F.T. name hence adding Triple at the end of it to represent a trio.


title: F.T. Island

text: In F.T. Triple, Jae-jin originally in charge of bass for F.T. Island becomes a guitarist and the main vocalist; Jong-hoon plays the piano and Min-hwan continues to play the drums. Their first single was titled "Love Letter" and was released with F.T. Island's 2CD Repackaged Album "Double Date" and a second CD named "Two Date" that features all of F.T. Triple's songs.


title: F.T. Island

text: F.T. Island have won several major industry awards in South Korea, including several Golden Disk Awards, Mnet Asian Music Awards and Seoul Music Awards. They have also topped popular television music program charts like "Inkigayo", "M! Countdown" and "Music Bank".


title: Malaysian passport

text: The Malaysian passport () is the passport issued to citizens of Malaysia by the Immigration Department of Malaysia.


title: Malaysian passport

text: The main legislation governing the production of passports and travel documents, their possession by persons entering and leaving Malaysia, and related matters is the Passport Act 1966.


title: Malaysian passport

text: Processing of Malaysian passport applications and renewals is very rapid, with new passports usually issued one hour after payment for normal cases. The introduction of passport renewal kiosks (KiPPas) at Immigration Department branches across the country allow passport applicants to apply and pay for their passports without queuing at the passport application counters.


title: Malaysian passport

text: Malaysia was the first country in the world to issue biometric passports in March 1998, after a local company, IRIS Corporation, developed the technology. In December 2002, thumbprint data was added to the biometric data on the passport chip. Similar technology is used in the Malaysian identity card, MyKad.


title: Malaysian passport

text: The biometric data included on the Malaysian passport is a digital photograph of the bearer's face, and images of their two thumbprints. Malaysian immigration checkpoints were the only ones with the technology to read and authenticate the data from the RFID chip using a fingerprint scanner and facial recognition technology, but widespread adoption of ePassport technology around the world has seen the technology installed in international airports in the US, the UK and other countries.


title: Malaysian passport

text: In addition to biometric data and the personal information stored on the information page, the chip also records the bearer's travel history of the last ten entry and exits at Malaysia border control points.


title: Malaysian passport

text: Concern about possible "cloning" of the data from the passport chip for the purposes of identity theft prompted IRIS to issue a press release in 2006, stating that the chip and data had never been successfully cloned, and that digital keys stored on each chip made such duplication and forgery impossible.


title: Malaysian passport

text: On 2 February 2010, Malaysia started issuing ICAO compliant e-Passports, valid for five years or two years. It was the 75th nation in the world to adopt the ICAO standard. The implementation of the new passport began at offices across Klang Valley, Johor and Pahang before expanding nationwide between March and May 2010 and to foreign missions abroad between July and August 2010.


title: Malaysian passport

text: In Malay language, the name were formerly designated '"Paspot Malaysia'", but the spelling was changed to "'Pasport" "Malaysia"' in the 1980s.


title: Malaysian passport

text: The regular international passport (Malay: "pasport antarabangsa") is the ordinary passport issued to Malaysian citizens for international travel. It features a red cover.


title: Malaysian passport

text: The regular international passport has incorporated biometric features since 1998. The biometric passport contains an 8 kB microchip which was developed by a Malaysian technology firm, IRIS Corporation. In February 2010, the passport was updated to comply with the ICAO standard on biometric and machine-readable passports, while the pages in a 32-page passport was increased to 48 pages. Beginning April 2013, the passport underwent another round of updating by introducing a polycarbonate sheet that holds the passport bearer's information. The information is laser engraved into the polycarbonate sheet for added security, including a hologram mini-photo of the passport bearer. With this the passport now holds 50 pages instead of 48 pages. There is no more 64 pages option since 2011. A redesign of the passport's interior pages along with new security features was launched on 15 November 2017.


title: Malaysian passport

text: A 50-page ICAO e-passport valid for five years costs RM200. Senior citizens, children below 12 years old, Hajj pilgrims, and students below 21 years old with proof to study abroad are entitled to half price from normal price. Disabled people are entitled to have their passports issued free of charge.


title: Malaysian passport

text: Previously, a 50-page ICAO e-passport valid for two years was available for RM100. On 15 January 2015, the two-year passport option was scrapped in conjunction with the announcement that reduced the normal five-year passport issuance fee from RM300 to RM200.


title: Malaysian passport

text: The passport is also used by citizens from Peninsular Malaysia to enter the East Malaysian states of Sabah and Sarawak, as these two states have autonomy in immigration affairs. However, citizens travelling directly from Peninsular Malaysia may produce a Malaysian identity card, or birth certificate for children below 12 years, obtain a special immigration printout form (Document in Lieu of Internal Travel Document, IMM.114) at immigration counters for social/business visits up to 3 months, and keep the form until departure.


title: Malaysian passport

text: As of July 2022, Malaysian passports were ranked #13th in the World's Most Powerful Passports Ranking, falling 1 rank from the year before.


title: Malaysian passport

text: The restricted passport ("pasport terhad") is issued to Malaysian citizens for travel to a specific country only. It has a blue cover.


title: Malaysian passport

text: Currently only a restricted passport for travel to Brunei is issued. Malaysian citizens residing in the East Malaysian areas of Sarawak, Sabah and Labuan are eligible to apply for the restricted passport. The passport is valid for five years and costs RM50.


title: Malaysian passport

text: The official passport ("pasport rasmi") is issued exclusively to Malaysian government officials travelling on official business. It is issued by the Immigration Department of Malaysia upon request through the Ministry of Foreign Affairs (Wisma Putra).


title: Malaysian passport

text: Previously, a restricted passport was issued for travel to Singapore. From 1 January 2005, the Immigration Department stopped issuing and renewing the restricted passport to Singapore, with existing passports to remain valid until 31 December 2006. However, as Singapore requires travel documents to be valid for a minimum of six months, Singapore announced it would stop accepting Malaysian restricted passports from 1 July 2006. Following a meeting between the home affairs ministers of both countries, Singapore agreed to extend the deadline to 1 November 2006. Malaysian citizens travelling to Singapore now use the regular international passport.


title: Malaysian passport

text: The green cover Hajj passport ("pasport haji") was formerly issued to Malaysian Muslim citizens for a Hajj pilgrimage to Mecca, Saudi Arabia. Applications were made through Tabung Haji, the Malaysian Hajj pilgrims fund board. Hajj passports were discontinued in 2009 and pilgrims now use the regular international passport.


title: Malaysian passport

text: The British-era Federation of Malaya passports were issued when Malaysia was Malaya and under British rule.


title: Malaysian passport

text: The passports contain a note, written in Malay and English, from the issuing state that is addressed to the authorities of all other states, identifying the bearer as a citizen of that state and requesting that he or she be allowed to pass and be treated according to international norms. In Malay, the note inside Malaysian passports reads:


title: Malaysian passport

text: The passport number is the serial number that uniquely identifies a passport. The passport number changes every time a person is issued a new passport, with the previous passport number noted in an endorsement on the last page of the new passport.


title: Malaysian passport

text: The passport number is alphanumeric, with a letter followed by an eight-digit number, e.g. A00000000. The letter prefix depends on the residency status of the bearer: "A" for Peninsular Malaysia and Labuan, "H" for Sabah and "K" for Sarawak. From 1964 to 1965, when Singapore was a part of Malaysia, Singapore citizens were issued Malaysian passports with the prefix "E".


title: Malaysian passport

text: Due to Malaysia's heterogeneous ethnic demographic, including substantial Chinese and Indian minorities as well as Malays, the name of the bearer on the Malaysian passport is displayed using that person's customary naming practice as it is on the person's identity card (MyKad) or birth certificate (with exception of ethnic Indian and Thai names). Surname and given name fields are not differentiated on the passport, and this can cause difficulties or confusion in some countries as the placement of the surname is not consistent.


title: Malaysian passport

text: Technically speaking, every Malaysian name regardless of the ethnicity is of this type: SURNAME, FIRST NAME UNKNOWN (FNU) when only the Machine Readable Zone (MRZ) area of the Passport Biodata Page is considered. There is no '«' to isolate what is technically a 'Surname' from the 'Given Name' (Please note that ICAO standards require that the name that immediately follows the three-letters country code in the Machine Readable Zone (MRZ) to be the surname of the passport holder). When swiped at international airports for border security purposes, for example: the Advanced Passenger Information System (APIS) for countries such as Australia, New Zealand and United States, the name of a Malaysian passport holder in its entirety will be captured in the 'Surname' field of the border security system, and this can sometimes cause a mismatch with how the passport holder's name is captured in the country of destination's visa system or electronic travel authority protocol. Examples of Malaysian names as printed on the Machine Readable Zone (MRZ) of the passport:


title: Malaysian passport

text: As can be seen from the above examples, it's evident that a name in a Malaysian Passport technically does not contain 'Given Name(s)' because '«' is not used at all to isolate Surname from Given Names.


title: Malaysian passport

text: The Australian Passport also does not explicitly differentiate 'Surname' from 'Given Name'. However, the Machine Readable Zone (MRZ) is very clear as regards to the passport holder's Surnames. Examples of Australian names as printed on the Machine Readable Zone (MRZ) of the passport:


title: Malaysian passport

text: Notes: 'P' stands for Passport. 'MYS' and 'AUS' stand for Malaysia and Australia respectively in the Machine Readable Zone. For clarity: Surnames in block letters and Given Names in title case for examples of Australian names.


title: Malaysian passport

text: The Malaysian identity number is a unique ID number allocated to each Malaysian, and is the same number on the bearer's MyKad, the Malaysian national identity card. The number is in the following format:


title: Malaysian passport

text: On the passport information page, the identity number is written without hyphens, e.g. YYMMDD-BP-###G is written as YYMMDDBP###G.


title: Malaysian passport

text: Visa requirements for Malaysian citizens are administrative entry restrictions by the authorities of other states placed on citizens of Malaysia. As of 13 April 2021, Malaysian citizens had visa-free or visa-on-arrival access to 179 countries and territories, ranking the Malaysian passport the 12th most powerful in the world (tied with Liechtenstein passport), the 2nd highest-ranked passport in Southeast Asia after the Republic of Singapore, and the 4th highest-ranked in Asia, according to the Henley Passport Index. Additionally, Arton Capital's Passport Index ranked the Malaysian passport 15th in the world in terms of travel freedom, with a visa-free score of 136 countries & territories due COVID-19 pandemic travel restrictions, as of January 2022.


title: Malaysian passport

text: As Malaysia does not recognise nor have diplomatic relations with the state of Israel, Malaysian passports bear the inscription: ""This passport is valid for all countries except Israel"". Israeli passport holders are not permitted to enter Malaysia without written permission from the Malaysian Ministry of Home Affairs.


title: Malaysian passport

text: Officially, the Malaysian government allows Christians to visit Israel for religious purposes. In 2009, the government imposed a ban on visits to Israel, ostensibly due to heightened security risks posed by the Israeli–Palestinian conflict. The ban was lifted in 2011, albeit with restrictions such as a quota of 700 pilgrims per year with not more than 40 pilgrims per church group, and pilgrims must be at least 18 years old and not visiting Israel more than once every three years with each stay a maximum of 10 days. On 20 December 2013, the government announced a relaxation of the ban, which lifted most of the restrictions while increasing the maximum duration of stay to 21 days, subject to the security situation in Israel.


title: Malaysian passport

text: However, the restrictions imposed by the Malaysian government do not prevent Israel from issuing visa on a separate sheet of paper to Malaysian citizens for entering Israel, and Malaysians are known to have visited Israel with or without permission from the Malaysian government.


title: Malaysian passport

text: In September 2017, Malaysia announced a ban on all Malaysian citizens from travelling to North Korea, in the wake of strained Malaysia–North Korea relations following the assassination of Kim Jong-nam at Kuala Lumpur International Airport.


title: Malaysian passport

text: Previously, Malaysian passports were not valid for travel to various communist countries, and South Africa due to its apartheid system. Following the fall of communism in Eastern Europe and improving ties with the remaining communist countries, as well as the end of apartheid in 1994, the countries were removed from the list.


title: Simple past

text: The simple past, past simple or past indefinite, sometimes called the preterite, is the basic form of the past tense in Modern English. It is used principally to describe events in the past, although it also has some other uses. Regular English verbs form the simple past in "-ed"; however, there are a few hundred irregular verbs with different forms.


title: Simple past

text: The term "simple" is used to distinguish the syntactical construction whose basic form uses the plain past tense alone, from other past tense constructions which use auxiliaries in combination with participles, such as the past perfect and past progressive.


title: Simple past

text: Regular verbs form the simple past end"-ed"; however there are a few hundred irregular verbs with different forms. The spelling rules for forming the past simple of regular verbs are as follows: verbs ending in -e add only –d to the end (e.g. live – lived, not *liveed), verbs ending in -y change to -ied (e.g. study – studied) and verbs ending in a group of a consonant + a vowel + a consonant double the final consonant (e.g. stop – stopped). For details see .


title: Simple past

text: Most verbs have a single form of the simple past, independent of the person or number of the subject (there is no addition of "-s" for the third person singular as in the simple present). However, the copula verb "be" has two past tense forms: "was" for the first and third persons singular, and "were" in other instances. The form "were" can also be used in place of "was" in conditional clauses and the like; for information on this, see English subjunctive. This is the only case in modern English where a distinction in form is made between inversion, negations with "not", and emphatic forms of the simple past use the auxiliary "did". For details of this mechanism, see "do"-support. A full list of forms is given below, using the (regular) verb "help" as an example:


title: Simple past

text: The simple past is used for a single event (or sequence of such events) in the past, and also for past habitual actions:


title: Simple past

text: For actions that were ongoing at the time referred to, the past progressive is generally used instead (e.g. "I was cooking"). The same can apply to states, if temporary (e.g. "the ball was lying on the sidewalk"), but some stative verbs do not generally use the progressive aspect at all, typically verbs of mental states (know, believe, need), of emotional states (love, dislike, prefer), of possession (have, own), of senses (hear) and some others (consist, exist, promise) – see – and in these cases the simple past is used even for a temporary state:


title: Simple past

text: However, with verbs of sensing, it is common in such circumstances to use "could see" in place of "saw", "could hear" in place of "heard", etc. For more on this, see "can see".


title: Simple past

text: If one action interrupts another, then it is usual for the interrupted (ongoing) action to be expressed with the past progressive, and the action that interrupted it to be in the simple past:


title: Simple past

text: The simple past is often close in meaning to the present perfect. The simple past is used when the event happened at a particular time in the past, or during a period which ended in the past (i.e. a period that does not last up until the present time). This time frame may be explicitly stated, or implicit in the context (for example the past tense is often used when describing a sequence of past events).


title: Simple past

text: These examples can be contrasted with those given at . Also, for past actions that occurred "before" the relevant past time frame, the past perfect is used.


title: Simple past

text: Various compound constructions exist for denoting past habitual action. The sentence "When I was young, I played football every Saturday" might alternatively be phrased using "used to" ("... I used to play ...") or using "would" ("... I would play...").


title: Simple past

text: The simple past form also has some uses in which it does not refer to a past time. These are generally in condition clauses and some other dependent clauses referring to hypothetical circumstances, as well as certain expressions of wish:


title: Simple past

text: For more details see the sections on conditionals, dependent clauses and expressions of wish in the article on uses of English verb forms.


title: Mundian To Bach Ke

text: "Mundian To Bach Ke" (, ), also titled "Beware of the Boys (Mundian To Bach Ke)" or "Beware", is a bhangra music song produced by British musician Panjabi MC, with vocals and lyrics by Punjabi artist Labh Janjua. The song was produced by Panjabi MC in Birmingham, England, for his 1998 album "Legalised".


title: Mundian To Bach Ke

text: Following its release as a single in November 2002, "Mundian To Bach Ke" achieved worldwide success, topping the singles charts in Italy and Wallonia and charting highly in many other countries. A remix of the song, released in 2003 and featuring American rapper Jay-Z, also charted highly in North America and Australia. The song sold an estimated copies worldwide, making it one of the best-selling singles of all time.


title: Mundian To Bach Ke

text: In addition to features of bhangra music, "Mundian To Bach Ke" also uses the bass line and part of the beat from "Fire It Up" (1997) by Busta Rhymes, which in turn is based around a sample from the television theme song for "Knight Rider", written by Glen A. Larson and Stu Phillips, as an underlying element and lyrics originally written by Channi Singh. The lyrics of the song are in Punjabi. The video of this song was shot in the streets of Kuala Lumpur, Malaysia.


title: Mundian To Bach Ke

text: Partially fueled by Internet downloads, "Mundian To Bach Ke" was officially released as a single in Germany on 25 November 2002. It sold over 100,000 in the first two days alone and debuted at number two on the German Singles Chart. On the Italian Singles Chart, the song reached number one for three weeks. In the UK, it was issued through Instant Karma and debuted at its peak of number five on the UK Singles Chart; it was the first bhangra song to reach the UK top 10. The remix, featuring American rapper Jay-Z, also reached number 33 on the US "Billboard" Hot 100, number 10 in Canada, and number 12 in Australia.


title: Mundian To Bach Ke

text: "The Washington Post" estimated that there may have been 10 million units sold worldwide. However, given that many of these copies may have been bootlegged, an exact number is not known, though sales are at least in the millions.


title: Universal Soldier: Day of Reckoning

text: Universal Soldier: Day of Reckoning is a 2012 American science fiction action film directed by John Hyams, who co-edited with Andrew Drazek, and wrote the screenplay with Doug Magnuson and Jon Greenlagh. It stars Scott Adkins with Jean-Claude Van Damme and Dolph Lundgren, who both reprise their roles from the first film, alongside newcomer Andrei Arlovski. It is the sixth and final installment in the "Universal Soldier" film series.


title: Universal Soldier: Day of Reckoning

text: The film acts as a sequel to 2009's "", unrelated to the two and ignoring the events of the 1999 theatrical sequel "". In the film, former military man John awakens from a coma and finds out his wife and daughter have been murdered in a home invasion. John goes on a personal vendetta against the man behind the killings, revealed to be Luc Deveraux, the eponymous Universal Soldier from the previous films who has now become a powerful terrorist military leader.


title: Universal Soldier: Day of Reckoning

text: Though criticized for its "brutal" violence, others gave high praise for its direction, action, and "haunting" atmosphere, with some comparisons made to the works of Michael Haneke and David Lynch. "Day of Reckoning" was first released on VOD in the United States before receiving a theatrical release on November 30, 2012. The film grossed $1.4 million against a production budget of $8 million. A reboot of the series has been in development since 2018.


title: Universal Soldier: Day of Reckoning

text: John awakens from a coma to learn that his wife and daughter were brutally murdered in a home invasion. Questioned by FBI Agent Gorman, the still amnesiac John identifies the perpetrator as former Universal Soldier (UniSol) Luc Deveraux, now a wanted man. Gorman activates sleeper agent Magnus, one of the cloned Next Generation UniSols. Magnus enters a brothel and slaughters both the female employees and most of the male patrons, who all display exceptional physical resistance but are no match for Magnus. His final adversary, a clone of UniSol Andrew Scott, overpowers Magnus and injects him with a serum that frees him from government control. Magnus is introduced to a separatist group led by Deveraux and Scott, who are taking in wayward UniSols to turn them against the U.S. government, with the ultimate goal of establishing a UniSol-led new order.


title: Universal Soldier: Day of Reckoning

text: John receives a call from someone claiming to be his friend Isaac, asking to meet. At his house, he finds Isaac long dead and evidence of his involvement with the UniSol program. A matchbox leads John to a strip club, where he is recognized by a stripper named Sarah, whom he cannot remember. Magnus injects John with Scott's serum, whereupon John hallucinates about Deveraux but retains his will. John follows Sarah to her apartment, where they are attacked by Magnus again. Though in the confrontation John loses some phalanges, they escape. Sarah tells John she remembers him working as a truck driver, living in a riverside cabin, and that the two were romantically involved.


title: Universal Soldier: Day of Reckoning

text: Calling his own memories into question, John meets with Agent Gorman. He learns Deveraux was often seen at the docks from where John used to take shipments. John goes to the docks, inspects the last unshipped cargo and meets with local manager Ron Castellano, who plays hidden camera footage that shows John brutally murdering Isaac.


title: Universal Soldier: Day of Reckoning

text: As John and Sarah drive towards the cabin, they are once more intercepted by Magnus. In the ensuing fight, John realizes he possesses superior strength, resistance and fighting abilities, eventually killing Magnus by decapitating him with a metal bat. Later, he discovers his severed fingers have regrown. John and Sarah reach the cabin to find it inhabited by an exact duplicate of John, who claims to be the original; it is this John whom Sarah and Castellano recall, and who killed Isaac. The original John had been mind-controlled into hunting down Deveraux, but was turned and hired as a transporter and assassin for his organization, until he met Sarah and deserted Deveraux. He tries to kill Sarah but is shot dead by the other John, now suspecting himself to be another UniSol sleeper agent.


title: Universal Soldier: Day of Reckoning

text: After sending Sarah away, John meets a rogue UniSol at the river and is taken to the separatists' underground headquarters. There, he is greeted by Dr. Su, a former scientist of the UniSol program, who reveals John had been synthetically created a few weeks earlier and as such his family is nothing more than an implanted memory. Dr. Su also says the missing shipment contains hardware that will enable Deveraux to create clones. John accepts Su's offer to surgically sever his emotional bond with the fake memories of his family, but the pain and attachment to those memories drive John insane. He kills every UniSol in his path, culminating with Andrew Scott in a one-on-one confrontation. John then reaches Deveraux himself; in the ensuing fight, Deveraux eventually gains the upper hand. Realizing the government will not stop sending John clones until he is dead, and seeing this John as a worthy successor, Deveraux allows John to kill him.


title: Universal Soldier: Day of Reckoning

text: Later, John meets with Agent Gorman again. Gorman admits to his involvement with the UniSol program and that he had purposefully put an unaware John on Deveraux's trail. Gorman ascribes John's success to his familial attachment, as opposed to the patriotism implanted in his predecessors. John kills Gorman, before a clone of Gorman and three UniSols emerge from John's van. The clone leaves in Gorman's car, hinting that John has completed the cloning equipment and taken over the separatist group, now determined to infiltrate the government which he holds responsible for his pain.


title: Universal Soldier: Day of Reckoning

text: "Universal Soldier: A New Dimension" (as it was initially titled) was to be the first in the series filmed in 3-D. John Hyams, who had directed "Universal Soldier: Regeneration", returned as director along with Jean-Claude Van Damme and Dolph Lundgren. Hyams cited films such as "Apocalypse Now", "The Manchurian Candidate", "Chinatown", and "Invasion of the Body Snatchers" as inspirations.


title: Universal Soldier: Day of Reckoning

text: When first submitted to the MPAA, the film received an NC-17 rating due to its violence. An edited R-rated version was released in theaters. The NC-17 director's cut has been released overseas.


title: Universal Soldier: Day of Reckoning

text: Michael Jai White, who previously appeared in the first two "Universal Soldier" films, joined the cast along with Van Damme and Lundgren, but ultimately dropped out.


title: Universal Soldier: Day of Reckoning

text: "Universal Soldier: Day of Reckoning" premiered on VOD on October 25, 2012 followed by a theatrical run starting on November 30. The film premiered on HDNet Movies on November 28 the same year.


title: Universal Soldier: Day of Reckoning

text: The film was released in Russia and Malaysia on October 4 and grossed $624,724. The film opened a week later in Ukraine and ended up grossing $31,349. It was released on November 30, 2012 in the United States in three screens, grossing $3,181 in its opening weekend, and as of the December 6, the film has grossed $4,928. It also opened on the same day in Turkey, finishing 8th with $75,919 for the weekend, as of the December 9, the film has grossed $138,232 in Turkey. The film was also released in the United Arab Emirates and as of the December 9 it has grossed $193,274. The worldwide total as of the December 12 is $992,507.


title: Universal Soldier: Day of Reckoning

text: Review aggregator Rotten Tomatoes reports a 56% approval rating for "Universal Soldier: Day of Reckoning" based on 50 reviews; the weighted average is 5.05/10. The site's critical consensus reads: "The mooted final installment in the long-running series is a hyper-violent, often grim throwback to action movies of yore – which will appeal to some audiences just as emphatically as it deters others".


title: Universal Soldier: Day of Reckoning

text: Negative reviews tended to focus on the film's violence, which was considered excessive. Clark Collis of "Entertainment Weekly" stated that the film "is so gruelingly violent you half wonder if director John Hyams’ goal is to make the audience get up and leave the theater rather than be party to the brutality". Scott Bowles of "USA Today" also criticized the film for being "so mean-spirited and joyfully violent" that it leans toward the torture porn genre. Elizabeth Weitzman of the "New York Daily News" wrote that unprepared viewers are "bound to leave shellshocked. Not just because of the movie's brutal violence, but from the stunning realization that this grim franchise will never stop regenerating itself".


title: Universal Soldier: Day of Reckoning

text: Positive reviews suggested that "Day of Reckoning" successfully tweaked the series formula by introducing more horror and surreal elements. Nick Antosca of "The Paris Review" wrote that "Day of Reckoning" is "less an action film than a horror film", likening it to the films of David Lynch and early David Cronenberg and describing it as "strange, haunting, sometimes even beautiful odyssey that lingered with me more than any American movie in recent memory". He concluded that it is his "favorite movie of last year–the "best" movie of [2012], I would argue". Eric Kohn of IndieWire shared this sentiment, declaring it one of 2012's best action films: "Hyams delivers a remarkably satisfying action-thriller hybrid that constantly pushes ahead". Film critic Matt Zoller Seitz praised the film for its "elegant, committed action direction" and stated that "it just radiates menace". He also went on to say that the "Universal Soldier" film series "is a rare series that takes more creative risks as it goes along". Bilge Ebiri, writing for the website Vulture, also gave praises to the film, writing that with the intro being reminiscent of a Michael Haneke film, "the entire thing has a mesmeric, unreal quality, where things can turn on a dime, and often do". Overall, he stated that "if more action movies were like "Universal Soldier: Day of Reckoning", the world would probably be a better place".


title: Universal Soldier: Day of Reckoning

text: A reboot was in development and Richard Wenk was set to write the film as of October 2018, which will focus on one resurrected soldier.


title: Scale-invariant feature transform

text: The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local "features" in images, invented by David Lowe in 1999.


title: Scale-invariant feature transform

text: Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.


title: Scale-invariant feature transform

text: SIFT keypoints of objects are first extracted from a set of reference images and stored in a database. An object is recognized in a new image by individually comparing each feature from the new image to this database and finding candidate matching features based on Euclidean distance of their feature vectors. From the full set of matches, subsets of keypoints that agree on the object and its location, scale, and orientation in the new image are identified to filter out good matches. The determination of consistent clusters is performed rapidly by using an efficient hash table implementation of the generalised Hough transform. Each cluster of 3 or more features that agree on an object and its pose is then subject to further detailed model verification and subsequently outliers are discarded. Finally the probability that a particular set of features indicates the presence of an object is computed, given the accuracy of fit and number of probable false matches. Object matches that pass all these tests can be identified as correct with high confidence.


title: Scale-invariant feature transform

text: For any object in an image, interesting points on the object can be extracted to provide a "feature description" of the object. This description, extracted from a training image, can then be used to identify the object when attempting to locate the object in a test image containing many other objects. To perform reliable recognition, it is important that the features extracted from the training image be detectable even under changes in image scale, noise and illumination. Such points usually lie on high-contrast regions of the image, such as object edges.


title: Scale-invariant feature transform

text: Another important characteristic of these features is that the relative positions between them in the original scene shouldn't change from one image to another. For example, if only the four corners of a door were used as features, they would work regardless of the door's position; but if points in the frame were also used, the recognition would fail if the door is opened or closed. Similarly, features located in articulated or flexible objects would typically not work if any change in their internal geometry happens between two images in the set being processed. However, in practice SIFT detects and uses a much larger number of features from the images, which reduces the contribution of the errors caused by these local variations in the average error of all feature matching errors.


title: Scale-invariant feature transform

text: SIFT can robustly identify objects even among clutter and under partial occlusion, because the SIFT feature descriptor is invariant to uniform scaling, orientation, illumination changes, and partially invariant to affine distortion. This section summarizes the original SIFT algorithm and mentions a few competing techniques available for object recognition under clutter and partial occlusion.


title: Scale-invariant feature transform

text: The SIFT descriptor is based on image measurements in terms of "receptive fields" over which "local scale invariant reference frames" are established by "local scale selection". A general theoretical explanation about this is given in the Scholarpedia article on SIFT.


title: Scale-invariant feature transform

text: The detection and description of local image features can help in object recognition. The SIFT features are local and based on the appearance of the object at particular interest points, and are invariant to image scale and rotation. They are also robust to changes in illumination, noise, and minor changes in viewpoint. In addition to these properties, they are highly distinctive, relatively easy to extract and allow for correct object identification with low probability of mismatch. They are relatively easy to match against a (large) database of local features but, however, the high dimensionality can be an issue, and generally probabilistic algorithms such as k-d trees with best bin first search are used. Object description by set of SIFT features is also robust to partial occlusion; as few as 3 SIFT features from an object are enough to compute its location and pose. Recognition can be performed in close-to-real time, at least for small databases and on modern computer hardware.


title: Scale-invariant feature transform

text: Lowe's method for image feature generation transforms an image into a large collection of feature vectors, each of which is invariant to image translation, scaling, and rotation, partially invariant to illumination changes, and robust to local geometric distortion. These features share similar properties with neurons in the primary visual cortex that encode basic forms, color, and movement for object detection in primate vision. Key locations are defined as maxima and minima of the result of difference of Gaussians function applied in scale space to a series of smoothed and resampled images. Low-contrast candidate points and edge response points along an edge are discarded. Dominant orientations are assigned to localized key points. These steps ensure that the key points are more stable for matching and recognition. SIFT descriptors robust to local affine distortion are then obtained by considering pixels around a radius of the key location, blurring, and resampling local image orientation planes.


title: Scale-invariant feature transform

text: Indexing consists of storing SIFT keys and identifying matching keys from the new image. Lowe used a modification of the k-d tree algorithm called the best-bin-first search method that can identify the nearest neighbors with high probability using only a limited amount of computation. The BBF algorithm uses a modified search ordering for the k-d tree algorithm so that bins in feature space are searched in the order of their closest distance from the query location. This search order requires the use of a heap-based priority queue for efficient determination of the search order. The best candidate match for each keypoint is found by identifying its nearest neighbor in the database of keypoints from training images. The nearest neighbors are defined as the keypoints with minimum Euclidean distance from the given descriptor vector. The probability that a match is correct can be determined by taking the ratio of distance from the closest neighbor to the distance of the second closest.


title: Scale-invariant feature transform

text: Lowe rejected all matches in which the distance ratio is greater than 0.8, which eliminates 90% of the false matches while discarding less than 5% of the correct matches. To further improve the efficiency of the best-bin-first algorithm search was cut off after checking the first 200 nearest neighbor candidates. For a database of 100,000 keypoints, this provides a speedup over exact nearest neighbor search by about 2 orders of magnitude, yet results in less than a 5% loss in the number of correct matches.


title: Scale-invariant feature transform

text: Hough transform is used to cluster reliable model hypotheses to search for keys that agree upon a particular model pose. Hough transform identifies clusters of features with a consistent interpretation by using each feature to vote for all object poses that are consistent with the feature. When clusters of features are found to vote for the same pose of an object, the probability of the interpretation being correct is much higher than for any single feature. An entry in a hash table is created predicting the model location, orientation, and scale from the match hypothesis. The hash table is searched to identify all clusters of at least 3 entries in a bin, and the bins are sorted into decreasing order of size.


title: Scale-invariant feature transform

text: Each of the SIFT keypoints specifies 2D location, scale, and orientation, and each matched keypoint in the database has a record of its parameters relative to the training image in which it was found. The similarity transform implied by these 4 parameters is only an approximation to the full 6 degree-of-freedom pose space for a 3D object and also does not account for any non-rigid deformations. Therefore, Lowe used broad bin sizes of 30 degrees for orientation, a factor of 2 for scale, and 0.25 times the maximum projected training image dimension (using the predicted scale) for location. The SIFT key samples generated at the larger scale are given twice the weight of those at the smaller scale. This means that the larger scale is in effect able to filter the most likely neighbors for checking at the smaller scale. This also improves recognition performance by giving more weight to the least-noisy scale. To avoid the problem of boundary effects in bin assignment, each keypoint match votes for the 2 closest bins in each dimension, giving a total of 16 entries for each hypothesis and further broadening the pose range.


title: Scale-invariant feature transform

text: Each identified cluster is then subject to a verification procedure in which a linear least squares solution is performed for the parameters of the affine transformation relating the model to the image. The affine transformation of a model point [x y] to an image point [u v] can be written as below


title: Scale-invariant feature transform

text: where the model translation is [tx ty] and the affine rotation, scale, and stretch are represented by the parameters m1, m2, m3 and m4. To solve for the transformation parameters the equation above can be rewritten to gather the unknowns into a column vector.


title: Scale-invariant feature transform

text: This equation shows a single match, but any number of further matches can be added, with each match contributing two more rows to the first and last matrix. At least 3 matches are needed to provide a solution.


title: Scale-invariant feature transform

text: where "A" is a known "m"-by-"n" matrix (usually with "m" > "n"), x is an unknown "n"-dimensional parameter vector, and b is a known "m"-dimensional measurement vector.


title: Scale-invariant feature transform

text: The solution of the system of linear equations is given in terms of the matrix formula_6, called the pseudoinverse of "A", by


title: Scale-invariant feature transform

text: which minimizes the sum of the squares of the distances from the projected model locations to the corresponding image locations.


title: Scale-invariant feature transform

text: Outliers can now be removed by checking for agreement between each image feature and the model, given the parameter solution. Given the linear least squares solution, each match is required to agree within half the error range that was used for the parameters in the Hough transform bins. As outliers are discarded, the linear least squares solution is re-solved with the remaining points, and the process iterated. If fewer than 3 points remain after discarding outliers, then the match is rejected. In addition, a top-down matching phase is used to add any further matches that agree with the projected model position, which may have been missed from the Hough transform bin due to the similarity transform approximation or other errors.


title: Scale-invariant feature transform

text: The final decision to accept or reject a model hypothesis is based on a detailed probabilistic model. This method first computes the expected number of false matches to the model pose, given the projected size of the model, the number of features within the region, and the accuracy of the fit. A Bayesian probability analysis then gives the probability that the object is present based on the actual number of matching features found. A model is accepted if the final probability for a correct interpretation is greater than 0.98. Lowe's SIFT based object recognition gives excellent results except under wide illumination variations and under non-rigid transformations.


title: Scale-invariant feature transform

text: We begin by detecting points of interest, which are termed "keypoints" in the SIFT framework. The image is convolved with Gaussian filters at different scales, and then the difference of successive Gaussian-blurred images are taken. Keypoints are then taken as maxima/minima of the Difference of Gaussians (DoG) that occur at multiple scales. Specifically, a DoG image formula_8 is given by


title: Scale-invariant feature transform

text: Hence a DoG image between scales formula_15 and formula_16 is just the difference of the Gaussian-blurred images at scales formula_15 and formula_16. For scale space extrema detection in the SIFT algorithm, the image is first convolved with Gaussian-blurs at different scales. The convolved images are grouped by octave (an octave corresponds to doubling the value of formula_19), and the value of formula_20 is selected so that we obtain a fixed number of convolved images per octave. Then the Difference-of-Gaussian images are taken from adjacent Gaussian-blurred images per octave.


title: Scale-invariant feature transform

text: Once DoG images have been obtained, keypoints are identified as local minima/maxima of the DoG images across scales. This is done by comparing each pixel in the DoG images to its eight neighbors at the same scale and nine corresponding neighboring pixels in each of the neighboring scales. If the pixel value is the maximum or minimum among all compared pixels, it is selected as a candidate keypoint.


title: Scale-invariant feature transform

text: This keypoint detection step is a variation of one of the blob detection methods developed by Lindeberg by detecting scale-space extrema of the scale normalized Laplacian; that is, detecting points that are local extrema with respect to both space and scale, in the discrete case by comparisons with the nearest 26 neighbors in a discretized scale-space volume. The difference of Gaussians operator can be seen as an approximation to the Laplacian, with the implicit normalization in the pyramid also constituting a discrete approximation of the scale-normalized Laplacian. Another real-time implementation of scale-space extrema of the Laplacian operator has been presented by Lindeberg and Bretzner based on a hybrid pyramid representation, which was used for human-computer interaction by real-time gesture recognition in Bretzner et al. (2002).


title: Scale-invariant feature transform

text: Scale-space extrema detection produces too many keypoint candidates, some of which are unstable. The next step in the algorithm is to perform a detailed fit to the nearby data for accurate location, scale, and ratio of principal curvatures. This information allows the rejection of points which are low contrast (and are therefore sensitive to noise) or poorly localized along an edge.


title: Scale-invariant feature transform

text: First, for each candidate keypoint, interpolation of nearby data is used to accurately determine its position. The initial approach was to just locate each keypoint at the location and scale of the candidate keypoint. The new approach calculates the interpolated location of the extremum, which substantially improves matching and stability. The interpolation is done using the quadratic Taylor expansion of the Difference-of-Gaussian scale-space function, formula_8 with the candidate keypoint as the origin. This Taylor expansion is given by:


title: Scale-invariant feature transform

text: where D and its derivatives are evaluated at the candidate keypoint and formula_23 is the offset from this point. The location of the extremum, formula_24, is determined by taking the derivative of this function with respect to formula_25 and setting it to zero. If the offset formula_24 is larger than formula_27 in any dimension, then that's an indication that the extremum lies closer to another candidate keypoint. In this case, the candidate keypoint is changed and the interpolation performed instead about that point. Otherwise the offset is added to its candidate keypoint to get the interpolated estimate for the location of the extremum. A similar subpixel determination of the locations of scale-space extrema is performed in the real-time implementation based on hybrid pyramids developed by Lindeberg and his co-workers.


title: Scale-invariant feature transform

text: To discard the keypoints with low contrast, the value of the second-order Taylor expansion formula_28 is computed at the offset formula_24. If this value is less than formula_30, the candidate keypoint is discarded. Otherwise it is kept, with final scale-space location formula_31, where formula_32 is the original location of the keypoint.


title: Scale-invariant feature transform

text: The DoG function will have strong responses along edges, even if the candidate keypoint is not robust to small amounts of noise. Therefore, in order to increase stability, we need to eliminate the keypoints that have poorly determined locations but have high edge responses.


title: Scale-invariant feature transform

text: For poorly defined peaks in the DoG function, the principal curvature across the edge would be much larger than the principal curvature along it. Finding these principal curvatures amounts to solving for the eigenvalues of the second-order Hessian matrix, H:


title: Scale-invariant feature transform

text: The eigenvalues of H are proportional to the principal curvatures of D. It turns out that the ratio of the two eigenvalues, say formula_34 is the larger one, and formula_35 the smaller one, with ratio formula_36, is sufficient for SIFT's purposes. The trace of H, i.e., formula_37, gives us the sum of the two eigenvalues, while its determinant, i.e., formula_38, yields the product. The ratio formula_39 can be shown to be equal to formula_40, which depends only on the ratio of the eigenvalues rather than their individual values. R is minimum when the eigenvalues are equal to each other. Therefore, the higher the absolute difference between the two eigenvalues, which is equivalent to a higher absolute difference between the two principal curvatures of D, the higher the value of R. It follows that, for some threshold eigenvalue ratio formula_41, if R for a candidate keypoint is larger than formula_42, that keypoint is poorly localized and hence rejected. The new approach uses formula_43.


title: Scale-invariant feature transform

text: This processing step for suppressing responses at edges is a transfer of a corresponding approach in the Harris operator for corner detection. The difference is that the measure for thresholding is computed from the Hessian matrix instead of a second-moment matrix.


title: Scale-invariant feature transform

text: In this step, each keypoint is assigned one or more orientations based on local image gradient directions. This is the key step in achieving invariance to rotation as the keypoint descriptor can be represented relative to this orientation and therefore achieve invariance to image rotation.


title: Scale-invariant feature transform

text: First, the Gaussian-smoothed image formula_44 at the keypoint's scale formula_19 is taken so that all computations are performed in a scale-invariant manner. For an image sample formula_46 at scale formula_19, the gradient magnitude, formula_48, and orientation, formula_49, are precomputed using pixel


title: Scale-invariant feature transform

text: The magnitude and direction calculations for the gradient are done for every pixel in a neighboring region around the keypoint in the Gaussian-blurred image L. An orientation histogram with 36 bins is formed, with each bin covering 10 degrees. Each sample in the neighboring window added to a histogram bin is weighted by its gradient magnitude and by a Gaussian-weighted circular window with a formula_19 that is 1.5 times that of the scale of the keypoint. The peaks in this histogram correspond to dominant orientations. Once the histogram is filled, the orientations corresponding to the highest peak and local peaks that are within 80% of the highest peaks are assigned to the keypoint. In the case of multiple orientations being assigned, an additional keypoint is created having the same location and scale as the original keypoint for each additional orientation.


title: Scale-invariant feature transform

text: Previous steps found keypoint locations at particular scales and assigned orientations to them. This ensured invariance to image location, scale and rotation. Now we want to compute a descriptor vector for each keypoint such that the descriptor is highly distinctive and partially invariant to the remaining variations such as illumination, 3D viewpoint, etc. This step is performed on the image closest in scale to the keypoint's scale.


title: Scale-invariant feature transform

text: First a set of orientation histograms is created on 4×4 pixel neighborhoods with 8 bins each. These histograms are computed from magnitude and orientation values of samples in a 16×16 region around the keypoint such that each histogram contains samples from a 4×4 subregion of the original neighborhood region. The image gradient magnitudes and orientations are sampled around the keypoint location, using the scale of the keypoint to select the level of Gaussian blur for the image. In order to achieve orientation invariance, the coordinates of the descriptor and the gradient orientations are rotated relative to the keypoint orientation. The magnitudes are further weighted by a Gaussian function with formula_19 equal to one half the width of the descriptor window. The descriptor then becomes a vector of all the values of these histograms. Since there are 4 × 4 = 16 histograms each with 8 bins the vector has 128 elements. This vector is then normalized to unit length in order to enhance invariance to affine changes in illumination. To reduce the effects of non-linear illumination a threshold of 0.2 is applied and the vector is again normalized. The thresholding process, also referred to as clamping, can improve matching results even when non-linear illumination effects are not present. The threshold of 0.2 was empirically chosen, and by replacing the fixed threshold with one systematically calculated, matching results can be improved.


title: Scale-invariant feature transform

text: Although the dimension of the descriptor, i.e. 128, seems high, descriptors with lower dimension than this don't perform as well across the range of matching tasks and the computational cost remains low due to the approximate BBF (see below) method used for finding the nearest neighbor. Longer descriptors continue to do better but not by much and there is an additional danger of increased sensitivity to distortion and occlusion. It is also shown that feature matching accuracy is above 50% for viewpoint changes of up to 50 degrees. Therefore, SIFT descriptors are invariant to minor affine changes. To test the distinctiveness of the SIFT descriptors, matching accuracy is also measured against varying number of keypoints in the testing database, and it is shown that matching accuracy decreases only very slightly for very large database sizes, thus indicating that SIFT features are highly distinctive.


title: Scale-invariant feature transform

text: There has been an extensive study done on the performance evaluation of different local descriptors, including SIFT, using a range of detectors. The main results are summarized below:


title: Scale-invariant feature transform

text: The evaluations carried out suggests strongly that SIFT-based descriptors, which are region-based, are the most robust and distinctive, and are therefore best suited for feature matching. However, most recent feature descriptors such as SURF have not been evaluated in this study.


title: Scale-invariant feature transform

text: SURF has later been shown to have similar performance to SIFT, while at the same time being much faster. Other studies conclude that when speed is not critical, SIFT outperforms SURF. Specifically, disregarding discretization effects the pure image descriptor in SIFT is significantly better than the pure image descriptor in SURF, whereas the scale-space extrema of the determinant of the Hessian underlying the pure interest point detector in SURF constitute significantly better interest points compared to the scale-space extrema of the Laplacian to which the interest point detector in SIFT constitutes a numerical approximation.


title: Scale-invariant feature transform

text: The performance of image matching by SIFT descriptors can be improved in the sense of achieving higher efficiency scores and lower 1-precision scores by replacing the scale-space extrema of the difference-of-Gaussians operator in original SIFT by scale-space extrema of the determinant of the Hessian, or more generally considering a more general family of generalized scale-space interest points.


title: Scale-invariant feature transform

text: Recently, a slight variation of the descriptor employing an irregular histogram grid has been proposed that significantly improves its performance. Instead of using a 4×4 grid of histogram bins, all bins extend to the center of the feature. This improves the descriptor's robustness to scale changes.


title: Scale-invariant feature transform

text: The SIFT-Rank descriptor was shown to improve the performance of the standard SIFT descriptor for affine feature matching. A SIFT-Rank descriptor is generated from a standard SIFT descriptor, by setting each histogram bin to its rank in a sorted array of bins. The Euclidean distance between SIFT-Rank descriptors is invariant to arbitrary monotonic changes in histogram bin values, and is related to Spearman's rank correlation coefficient.


title: Scale-invariant feature transform

text: Given SIFT's ability to find distinctive keypoints that are invariant to location, scale and rotation, and robust to affine transformations (changes in scale, rotation, shear, and position) and changes in illumination, they are usable for object recognition. The steps are given below.


title: Scale-invariant feature transform

text: SIFT features can essentially be applied to any task that requires identification of matching locations between images. Work has been done on applications such as recognition of particular object categories in 2D images, 3D reconstruction,


title: Scale-invariant feature transform

text: motion tracking and segmentation, robot localization, image panorama stitching and epipolar calibration. Some of these are discussed in more detail below.


title: Scale-invariant feature transform

text: In this application, a trinocular stereo system is used to determine 3D estimates for keypoint locations. Keypoints are used only when they appear in all 3 images with consistent disparities, resulting in very few outliers. As the robot moves, it localizes itself using feature matches to the existing 3D map, and then incrementally adds features to the map while updating their 3D positions using a Kalman filter. This provides a robust and accurate solution to the problem of robot localization in unknown environments. Recent 3D solvers leverage the use of keypoint directions to solve trinocular geometry from three keypoints and absolute pose from only two keypoints, an often disregarded but useful measurement available in SIFT. These orientation measurements reduce the number of required correspondences, further increasing robustness exponentially.


title: Scale-invariant feature transform

text: SIFT feature matching can be used in image stitching for fully automated panorama reconstruction from non-panoramic images. The SIFT features extracted from the input images are matched against each other to find "k" nearest-neighbors for each feature. These correspondences are then used to find "m" candidate matching images for each image. Homographies between pairs of images are then computed using RANSAC and a probabilistic model is used for verification. Because there is no restriction on the input images, graph search is applied to find connected components of image matches such that each connected component will correspond to a panorama. Finally for each connected component bundle adjustment is performed to solve for joint camera parameters, and the panorama is rendered using multi-band blending. Because of the SIFT-inspired object recognition approach to panorama stitching, the resulting system is insensitive to the ordering, orientation, scale and illumination of the images. The input images can contain multiple panoramas and noise images (some of which may not even be part of the composite image), and panoramic sequences are recognized and rendered as output.


title: Scale-invariant feature transform

text: This application uses SIFT features for 3D object recognition and 3D modeling in context of augmented reality, in which synthetic objects with accurate pose are superimposed on real images. SIFT matching is done for a number of 2D images of a scene or object taken from different angles. This is used with bundle adjustment initialized from an essential matrix or trifocal tensor to build a sparse 3D model of the viewed scene and to simultaneously recover camera poses and calibration parameters. Then the position, orientation and size of the virtual object are defined relative to the coordinate frame of the recovered model. For online match moving, SIFT features again are extracted from the current video frame and matched to the features already computed for the world mode, resulting in a set of 2D-to-3D correspondences. These correspondences are then used to compute the current camera pose for the virtual projection and final rendering. A regularization technique is used to reduce the jitter in the virtual projection. The use of SIFT directions have also been used to increase robustness of this process. 3D extensions of SIFT have also been evaluated for true 3D object recognition and retrieval.


title: Scale-invariant feature transform

text: Extensions of the SIFT descriptor to 2+1-dimensional spatio-temporal data in context of human action recognition in video sequences have been studied. The computation of local position-dependent histograms in the 2D SIFT algorithm are extended from two to three dimensions to describe SIFT features in a spatio-temporal domain. For application to human action recognition in a video sequence, sampling of the training videos is carried out either at spatio-temporal interest points or at randomly determined locations, times and scales. The spatio-temporal regions around these interest points are then described using the 3D SIFT descriptor. These descriptors are then clustered to form a spatio-temporal Bag of words model. 3D SIFT descriptors extracted from the test videos are then matched against these "words" for human action classification.


title: Scale-invariant feature transform

text: The authors report much better results with their 3D SIFT descriptor approach than with other approaches like simple 2D SIFT descriptors and Gradient Magnitude.


title: Scale-invariant feature transform

text: The Feature-based Morphometry (FBM) technique uses extrema in a difference of Gaussian scale-space to analyze and classify 3D magnetic resonance images (MRIs) of the human brain. FBM models the image probabilistically as a collage of independent features, conditional on image geometry and group labels, e.g. healthy subjects and subjects with Alzheimer's disease (AD). Features are first extracted in individual images from a 4D difference of Gaussian scale-space, then modeled in terms of their appearance, geometry and group co-occurrence statistics across a set of images. FBM was validated in the analysis of AD using a set of ~200 volumetric MRIs of the human brain, automatically identifying established indicators of AD in the brain and classifying mild AD in new images with a rate of 80%.


title: Scale-invariant feature transform

text: Competing methods for scale invariant object recognition under clutter / partial occlusion include the following.


title: Scale-invariant feature transform

text: RIFT is a rotation-invariant generalization of SIFT. The RIFT descriptor is constructed using circular normalized patches divided into concentric rings of equal width and within each ring a gradient orientation histogram is computed. To maintain rotation invariance, the orientation is measured at each point relative to the direction pointing outward from the center.


title: Scale-invariant feature transform

text: RootSIFT is a variant of SIFT that modifies descriptor normalization. Since SIFT descriptors are histograms (and as such probability distributions), employing Euclidean distance to determine their similarity is not a natural choice. Comparing such descriptors using similarity measures tailored to probability distributions such as Bhattacharyya coefficient (also known as Hellinger kernel) turns out to be more beneficial. For this purpose, the originally formula_54 normalized descriptor is first formula_55 normalized and the square root of each element is computed followed by formula_54 renormalization. After these algebraic manipulations, RootSIFT descriptors can be normally compared using Euclidean distance which is equivalent to using the Hellinger kernel on the original SIFT descriptors. This normalization scheme termed “L1-sqrt” was previously introduced for the block normalization of HOG features whose rectangular block arrangement descriptor variant (R-HOG) is conceptually similar to the SIFT descriptor.


title: Scale-invariant feature transform

text: G-RIF: Generalized Robust Invariant Feature is a general context descriptor which encodes edge orientation, edge density and hue information in a unified form combining perceptual information with spatial encoding. The object recognition scheme uses neighboring context based voting to estimate object models.


title: Scale-invariant feature transform

text: "SURF: Speeded Up Robust Features" is a high-performance scale- and rotation-invariant interest point detector / descriptor claimed to approximate or even outperform previously proposed schemes with respect to repeatability, distinctiveness, and robustness. SURF relies on integral images for image convolutions to reduce computation time, builds on the strengths of the leading existing detectors and descriptors (using a fast Hessian matrix-based measure for the detector and a distribution-based descriptor). It describes a distribution of Haar wavelet responses within the interest point neighborhood. Integral images are used for speed and only 64 dimensions are used reducing the time for feature computation and matching. The indexing step is based on the sign of the Laplacian, which increases the matching speed and the robustness of the descriptor.


title: Scale-invariant feature transform

text: PCA-SIFT and GLOH are variants of SIFT. PCA-SIFT descriptor is a vector of image gradients in x and y direction computed within the support region. The gradient region is sampled at 39×39 locations, therefore the vector is of dimension 3042. The dimension is reduced to 36 with PCA. Gradient location-orientation histogram (GLOH) is an extension of the SIFT descriptor designed to increase its robustness and distinctiveness. The SIFT descriptor is computed for a log-polar location grid with three bins in radial direction (the radius set to 6, 11, and 15) and 8 in angular direction, which results in 17 location bins. The central bin is not divided in angular directions. The gradient orientations are quantized in 16 bins resulting in 272-bin histogram. The size of this descriptor is reduced with PCA. The covariance matrix for PCA is estimated on image patches collected from various images. The 128 largest eigenvectors are used for description.


title: Scale-invariant feature transform

text: Gauss-SIFT is a pure image descriptor defined by performing all image measurements underlying the pure image descriptor in SIFT by Gaussian derivative responses as opposed to derivative approximations in an image pyramid as done in regular SIFT. In this way, discretization effects over space and scale can be reduced to a minimum allowing for potentially more accurate image descriptors. In Lindeberg (2015) such pure Gauss-SIFT image descriptors were combined with a set of generalized scale-space interest points comprising the Laplacian of the Gaussian, the determinant of the Hessian, four new unsigned or signed Hessian feature strength measures as well as Harris-Laplace and Shi-and-Tomasi interests points. In an extensive experimental evaluation on a poster dataset comprising multiple views of 12 posters over scaling transformations up to a factor of 6 and viewing direction variations up to a slant angle of 45 degrees, it was shown that substantial increase in performance of image matching (higher efficiency scores and lower 1-precision scores) could be obtained by replacing Laplacian of Gaussian interest points by determinant of the Hessian interest points. Since difference-of-Gaussians interest points constitute a numerical approximation of Laplacian of the Gaussian interest points, this shows that a substantial increase in matching performance is possible by replacing the difference-of-Gaussians interest points in SIFT by determinant of the Hessian interest points. Additional increase in performance can furthermore be obtained by considering the unsigned Hessian feature strength measure formula_57. A quantitative comparison between the Gauss-SIFT descriptor and a corresponding Gauss-SURF descriptor did also show that Gauss-SIFT does generally perform significantly better than Gauss-SURF for a large number of different scale-space interest point detectors. This study therefore shows that discregarding discretization effects the pure image descriptor in SIFT is significantly better than the pure image descriptor in SURF, whereas the underlying interest point detector in SURF, which can be seen as numerical approximation to scale-space extrema of the determinant of the Hessian, is significantly better than the underlying interest point detector in SIFT.


title: Scale-invariant feature transform

text: Wagner et al. developed two object recognition algorithms especially designed with the limitations of current mobile phones in mind. In contrast to the classic SIFT approach, Wagner et al. use the FAST corner detector for feature detection. The algorithm also distinguishes between the off-line preparation phase where features are created at different scale levels and the on-line phase where features are only created at the current fixed scale level of the phone's camera image. In addition, features are created from a fixed patch size of 15×15 pixels and form a SIFT descriptor with only 36 dimensions. The approach has been further extended by integrating a Scalable Vocabulary Tree in the recognition pipeline. This allows the efficient recognition of a larger number of objects on mobile phones. The approach is mainly restricted by the amount of available RAM.


title: Scale-invariant feature transform

text: KAZE and A-KAZE "(KAZE Features and Accelerated-Kaze Features)" is a new 2D feature detection and description method that perform better compared to SIFT and SURF. It gains a lot of popularity due to its open source code. KAZE was originally made by Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison.


title: Iron Man: Armored Adventures

text: Iron Man: Armored Adventures (also known in early promotional materials as Iron Man: The Animated Series) is a 3D CGI-animated series based on the Marvel Comics superhero Iron Man. It debuted in the United States on Nicktoons on April 24, 2009, and it aired on Teletoon in Canada. The series is story edited by showrunner Christopher Yost, who also worked on "Wolverine and the X-Men", and numerous other Marvel Animation projects. The television show is not related to the 2007 animated film "The Invincible Iron Man"; it has a different voice cast, but some story elements are similar and the show uses the same musical score as the film in some instances. It is the first Iron Man television series since "Iron Man" from 1994 to 1996, and started airing after the success of the live action "Iron Man" film.


title: Iron Man: Armored Adventures

text: The series follows the adventures of a younger version of Tony Stark and his alter ego of Iron Man. As Iron Man, he uses his technological inventions to fight other similarly technologically advanced threats. His friends James "Rhodey" Rhodes and Pepper Potts help him on his courageous and dangerous adventures.


title: Iron Man: Armored Adventures

text: The second season of this series premiered on July 13, 2011, running concurrently with the English dub of the completely separate "Marvel Anime: Iron Man" anime series, which had already finished airing on Japan's Animax in 2010.


title: Iron Man: Armored Adventures

text: On August 25, 2012, it was revealed that "Iron Man: Armored Adventures" would air as part of The CW's new Saturday morning children's block Vortexx. After November 24, 2012, Vortexx ceased airing the show and replaced it with "" on December 8, 2012.


title: Iron Man: Armored Adventures

text: After a plane crash in which his industrialist father Howard Stark disappears after refusing to weaponize the Earth Mover at Obadiah Stane's behest, 16-year-old genius Tony uses a high-tech suit of armor he has constructed and investigates a charge that Stane may have been involved in his father's death. As Iron Man, Tony spends his time stopping Stane's plans and saving the world from other villains such as Mandarin, Mr. Fix, Whiplash, A.I.M., Living Laser, the Maggia, Controller, Crimson Dynamo, Blizzard, Killer Shrike, Unicorn, M.O.D.O.K., Ghost, Black Knight, and Technovore. He is assisted in his crime fighting efforts by James Rhodes and Pepper Potts. Tony's activities as Iron Man usually result in his needing to make up excuses as to why he is constantly late or missing from school and other activities. Dependent on his phenomenal technology for survival, Tony must balance the pressures of teenage life with the duties of being a superhero.


title: Iron Man: Armored Adventures

text: The first season of "Iron Man: Armored Adventures" has a total of 26 episodes. Tony Stark, James "Rhodey" Rhodes, Pepper Potts, Gene Khan, Happy Hogan, Whitney Stane, Black Panther, The Hulk, and S.H.I.E.L.D. all appear in this season.


title: Iron Man: Armored Adventures

text: The first season focuses on the Makluan Rings saga as Tony Stark, Pepper, James Rhodes, and Gene Khan work together to get the 5 rings. Upon overthrowing his stepfather Xin Zhang, Gene secretly works undercover to steal the rings from his friends, and ends up betraying them (which upsets Pepper). The season also features the Madame Masque Saga, which comes to a conclusion in the episode "Best Served Cold". Tony's feud with Obadiah Stane comes to a partial conclusion in that episode as well. The season ends with two primary cliffhangers in the episode "Tales of Suspense". The now-friendless Gene discovers that the original Mandarin had 5 other rings besides the original 5. Tony finds out that his father, Howard, survived the plane crash and is being held prisoner, while the armory is destroyed during Xin Zhang's attack limiting Tony's resources to find and rescue his father.


title: Iron Man: Armored Adventures

text: This season featured Makluan Guardian versions of Dreadknight, Ultimo, Firebrand, and Fin Fang Foom who guard the rings the Mandarin hasn't obtained yet.


title: Iron Man: Armored Adventures

text: The second season of "Iron Man: Armored Adventures" has a total of 26 episodes, just like the first season. Black Widow / Natasha Romanoff, Hawkeye, Doctor Doom, Magneto and Justin Hammer appear in this season. General Nick Fury, Black Panther, Mr. Fix, Whiplash and Obadiah Stane return.


title: Iron Man: Armored Adventures

text: The second season covers the Armor Wars saga and Stane International storylines. The first half of Season 2 reflecting the Armor Wars has Tony and Rhodey as his definite partner War Machine fighting many people who have stolen Stark's armor tech and seek to exploit the stolen Iron Man specs for their own purposes. The enemies young Stark fights during this version of the Armor Wars include the Ghost who steals Iron Man specs and knows that Tony Stark is Iron Man. Ghost sells the specs to both Justin Hammer and Obadiah Stane but says he will not reveal Iron Man's true identity until Tony turns 18. Justin Hammer makes an armor with the Iron Man specs with the armor being called Titanium Man. Doctor Doom joins forces with Stane to attain the Iron Man armor operating system. Stane builds the Iron Monger armor which is revealed actually to be a direct upgrade from Crimson Dynamo armor (version 3) and is much larger than in the comic book and live action movie realities. The Armor Wars conclude as Obadiah Stane discovers the identity of Iron Man. Stane steals Iron Monger and is intent on destroying Tony once and for all.


title: Iron Man: Armored Adventures

text: While Tony is fighting the Armor Wars, Howard Stark is shown to be alive and forced by Gene to find the other 5 Makluan Rings. Gene continues to find and secured the sixth, seventh, eighth, and ninth rings for himself during this time. This part of the season also included Makluan Guardian versions of Melter, Sunturion, Grim Reaper, and Grey Gargoyle who guard the remaining Makluan Rings.


title: Iron Man: Armored Adventures

text: The second half of season 2 showcases the printed page storyline of loosely based on the "Stane International" story arc. Justin Hammer (instead of Obadiah Stane) successfully buys control of Stark International. Stark, Rhodey and Potts all agree to fight against Hammer and his weaponization of Stark International's projects. Unlike the printed page version, Stark and Rhodes reject the title of Circuits Maximus for the new start-up and settle on "Stark Solutions" (opposite to Stark Enterprises of the printed page).


title: Iron Man: Armored Adventures

text: On March 25, 2013, Marvel Animation announced April 23, 2013, the release of the "Complete Season 2" 4-DVD box set.


title: Iron Man: Armored Adventures

text: Months before the show's television debut, Marvel had a screening of the first episode of the series at the San Diego Comic-Con. There was also a room where the public could meet, and talk with the Co-Producer Josh Fine and Head Writer Chris Yost among others involved in the production of the show. The trailer of the show was also released on Marvel's official website as well as numerous behind-the-scene and teaser trailers on the website in the weeks before the US television debut.


title: Iron Man: Armored Adventures

text: Popular rock band Rooney recorded the theme song to the series. The song originally could be downloaded from Teletoon's website. A provided "secret code", Tony would be needed to access the theme. This "secret code" was shown onscreen during Canadian broadcasts of the first few episodes of the first season. The music video for the theme song features clips of Rooney singing, along with clips of Iron Man from the show.


title: Iron Man: Armored Adventures

text: The series is made primarily using computer-generated imagery (CGI), in a similar style to MTV's "" and Nicktoons' original series "Skyland". The technique is similar to cel-shading animation technique although the detail and resolution are lower.


title: Iron Man: Armored Adventures

text: In "Iron Man: Armored Adventures", Tony Stark becomes Iron Man as a teenager, and Pepper Potts and James Rhodes are also teenagers. In the mainstream Iron Man continuity, Tony Stark becomes Iron Man later on in his life as a grown man, and is a long time friend with Pepper, whereas in this series, Pepper and Tony have just met.


title: Iron Man: Armored Adventures

text: Traditionally, Tony's parents were accidentally killed in a car crash, whereas in this series, Tony's father, Howard Stark, disappears in the first episode in a plane crash suggesting Howard's death. Howard lives to see his son become the armored hero Iron Man and even helps his son in his adventures in this reality. Howard uses the advanced technology of one of the alien Mandarin rings that he discovers for the basis of his many creations that he later locks away in his vault such as the light armor of the Living Laser. In the mainstream incarnation, Howard never met the Mandarin nor had any of his ten rings.


title: Iron Man: Armored Adventures

text: Tony also injures his heart in this crash, as opposed to being injured by an explosion in war. Tony invents the Iron Man armor at home. In previous incarnations, Tony invented an alpha version of the suit to escape after being captured due to the trap explosion leaving him defenseless. In the show, Ho Yinsen was a Stark International medical specialist who saved Tony by installing the technology that saved him instead of Yinsen meeting Tony in the war being held captive. Ho Yinsen is different from his comics counterpart; he is unaware of Tony's double life as Iron Man.


title: Iron Man: Armored Adventures

text: Iron Man's original armor in the series is very similar to Iron Man's armor in the 2008 live action film, although the helmet design is different, and the armor is less yellow.


title: Iron Man: Armored Adventures

text: The armor appears to be convertible from its ability to spread over Tony from various parts he puts on. This armor generates a force field around Iron Man from his repulsors.


title: Iron Man: Armored Adventures

text: There are also numerous alternate armors that Stark uses in special occasions, including the Silver Centurion Armor, the Stealth Armor and the Hulkbuster armor (which was originally named the Dynamo Buster armor).


title: Iron Man: Armored Adventures

text: In the second season, the armor was changed to look similar to the second live action film's armor, but sharing more similarities with the Extremis armor. The abilities of the armor in how Tony can call it to himself are also reminiscent of the Extremis storyline. One of the other main changes of the armor is the circular unibeam is now a triangular unibeam.


title: Iron Man: Armored Adventures

text: The pilot episode was released as a bonus feature in Wal-Mart's edition of the live-action Iron Man movie.


title: Iron Man: Armored Adventures

text: Volume One was released on DVD in the United States on October 20, 2009. The Blu-ray edition was released exclusively through Best Buy. In the UK, Volume One contains the first 13 episodes (the one-hour pilot being split into two separate episodes) on two discs. Volume Two was released in the United States on January 5, 2010. The Complete Season One was released in the United States on May 4, 2010. The Complete Season One set included an unreleased pair of Volume Three and Four DVDs. The Complete First Season was released on Blu-ray in Australia.


title: Iron Man: Armored Adventures

text: Season Two, Volume One was released on DVD on June 26, 2012, with Volume Two released on September 25, 2012. The Season 2, Volume 3 DVD was released on January 22, 2013. Season 2 Volume 4 and the complete second season set became available April 23, 2013.


title: Iron Man: Armored Adventures

text: The hour-long premiere of "Iron Man: Armored Adventures" broke Nicktoons Network's record of highest-rated original series by premiering with over 125,000 viewers.


title: Iron Man: Armored Adventures

text: Reviews of the pilot episode have been mixed. Some praise the series for its detailed and layered writing, strong continuity, and character designs. "Entertainment Weekly" gave the series debut a B+ grade, saying, "What could've been a clunky retrograde reboot works surprisingly well, thanks to some smart writing and stellar CG butt-kickery."


title: Max Cantor

text: Michael "Max" Cantor (May 15, 1959 – October 3, 1991) was an American journalist and actor in films such as "Dirty Dancing" (1987) and "Fear, Anxiety & Depression" (1989).


title: Max Cantor

text: Cantor's father was the theatrical producer Arthur Cantor. He grew up in the Dakota Apartments on West 72nd Street in Manhattan, New York City. Cantor attended Collegiate School but graduated from Buxton School in Williamstown, Massachusetts. He spent his summers until age 14 at Camp Hillcroft in Billings, New York, alongside fellow campers such as the children of American Federation of Teachers president Albert Shanker and actor Burt Lancaster. At camp, he won top roles in "Winnie the Pooh" and "The Velveteen Rabbit". Cantor was a 1982 graduate of Harvard University, where he lived in Adams House and starred in several productions by student director Peter Sellars.


title: Max Cantor

text: Cantor wrote for "The Village Voice" about ibogaine as a cure for heroin addiction, and had taken an interest in the cult surrounding East Village cannibal/murderer Daniel Rakowitz.


title: Max Cantor

text: He died from a heroin overdose at the age of 32. At the time he died, he was conducting research and writing a book about Rakowitz and the murder of dancer Monika Beerle.


title: Culture of France

text: The culture of France has been shaped by geography, by historical events, and by foreign and internal forces and groups. France, and in particular Paris, has played an important role as a center of high culture since the 17th century and from the 19th century on, worldwide. From the late 19th century, France has also played an important role in cinema, fashion, cuisine, literature, technology, the social sciences, and mathematics. The importance of French culture has waxed and waned over the centuries, depending on its economic, political and military importance. French culture today is marked both by great regional and socioeconomic differences and strong unifying tendencies. A global opinion poll for the BBC saw France ranked as the country with the fourth most positive influence in the world (behind Germany, Canada and the UK) in 2014.


title: Culture of France

text: The Académie Française sets an official standard of linguistic purism; however, this standard, which is not mandatory, is occasionally ignored by the government itself: for instance, the left-wing government of Lionel Jospin pushed for the feminisation of the names of some functions ("madame la ministre") while the Académie pushed for some more traditional "madame le ministre".


title: Culture of France

text: Some action has been taken by the government to promote French culture and the French language. For instance, they have established a system of subsidies and preferential loans for supporting French cinema. The Toubon law, from the name of the conservative culture minister who promoted it, makes it mandatory to use French in advertisements directed to the general public. Note that contrary to some misconceptions sometimes found in the Anglophone media, the French government neither regulates the language used by private parties in commercial settings, nor makes it compulsory that France-based WWW sites should be in French.