Source: http://www.google.com.tw/patents/US8028234
Timestamp: 2013-05-24 21:55:53
Document Index: 646735800

Matched Legal Cases: ['art 1', 'art 2', 'art 5', 'art 5', 'art 5', 'art 5']

�M�Q US8028234 - Summarization of sumo video content - Google �M�Q�j�M �Ϥ� �a�� Play YouTube �s�D Gmail ���ݵw�� ��h »�i���M�Q�j�M | �������� | �n�J�i���M�Q�j�M�M�QSummarization of video content, such as a video that includes sumo, by a method that provides a description of the video, identifies a plurality of segments of the video based upon the provided description, and generates another video based upon the identified segments, having less frames than the original...http://www.google.com.tw/patents/US8028234?utm_source=gb-gplus-share�M�Q US8028234 - Summarization of sumo video content���}��US8028234 B2�X���������v�ӽЮѽs��11/074,860�o�G���2011�~9��27���ӽФ��2005�~3��8�� �u���v���2002�~1��28����L���}�M�Q��US7120873US20030141665US20050155053US20050155054US20050155055�o��HBaoxin Li��M�Q�v�HSharp Laboratories Of America, Inc.Sharp Kabushiki Kaisha ���M�Q������715/719386/326386/241715/723��ڱM�Q������G06T7/20H04N9/80H04N5/92G06F17/30G06F3/00 �X�@����H04N21/23418G06K9/00751G06F17/30843G06T7/20G06F17/30787G06F17/30802H04N21/8549G06F17/30796 �ڬw������G06T 7/20H04N 21/8549H04N 21/234DG06F 17/30V1AG06F 17/30V1TG06F 17/30V4SG06F 17/30V1V1G06K 9/00V3F1�ѦҤ��m�M�Q�ޥ� (111)�D�M�Q�ޥ� (94)�~���s�����M�Q�ӼЧ� ���M�Q�ӼЧ��M�Q����T�� �ڬw�M�Q��Summarization of sumo video contentUS 8028234 B2�K�n Summarization of video content, such as a video that includes sumo, by a method that provides a description of the video, identifies a plurality of segments of the video based upon the provided description, and generates another video based upon the identified segments, having less frames than the original video.
2. The method of claim 1 where said first frame is selected so as to include in said at least one segment at least a portion of a pre-bout ceremony. ����
FIG. 3 is a technique for detecting a start frame of a sumo ��play.��.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT Sumo, the national sport of Japan, is tremendously popular in eastern Asia and is growing in popularity elsewhere in the world. Sumo is a sport comprising bouts in which two contestants meet in a circular ring 4.55 meters in diameter. The rules of Sumo are uncomplicated. After the contestants and a referee have entered the circular ring, the bout begins with an initial charge�Xcalled a ��tachiai���Xwhere each contestant rushes towards, then collides with, the other. The bout will end when one of the contestant loses by either stepping outside the circular ring or touching the ground with any part of the contestant's body other than the soles of the feet. Aside from a limited number of illegal moves, such as gouging the opponent's eyes, striking with a closed fist, or intentionally pulling at the opponent's hair, there are no rules that govern a sumo bout.
After consideration of the difficulty of developing highly sophisticated models of a sumo video to analyze the content of the sumo video, as the sole basis upon which to create a sumo summarization, the present inventors determined that this technique is ultimately flawed as the models will likely never be sufficiently robust to detect all the desirable content. Moreover, the number of different types of model sequences of potentially desirable content is difficult to quantify. In contrast to attempting to detect particular model sequences, the present inventors determined that the desirable segments of the sumo match are preferably selected based upon a ��play��. A ��play�� may be defined as a sequence of events defined by the rules of sumo. In particular, and in one aspect, the sequence of events of a ��play�� may generally include the time between which the players line up to charge one another and one player loses the bout by either stepping outside the sumo ring or touching the clay surface with a part of his body other than the soles of the feet. A play may also selectively include certain pre-bout ceremonies or events, such as the time during which the contestants throw salt in the ring or stare at one another prior to charging. Normally the ��play�� should include a related series of activities that could potentially result in a victory by one contestant and a loss by the other contestant.
It is to be understood that the temporal bounds of a particular type of ��play�� does not necessarily start or end at a particular instance, but rather at a time generally coincident with the start and end of the play or otherwise based upon, at least in part, a time (e.g., event) based upon a play. For example, a ��play�� starting with the contestants throwing salt into the ring may include the times during which the contestants charge each other. A summarization of the video is created by including a plurality of video segments, where the summarization includes fewer frames than the original video from which the summarization was created. A summarization that includes a plurality of the plays of the sumo match provides the viewer with a shorted video sequence while permitting the viewer to still enjoy the game because most of the exciting portions of the video are provided, preferably in the same temporally sequential manner as in the original sumo video.
Referring to FIG. 1, a procedure for summarization of a sumo video includes receiving a video sequence 20 that includes at least a portion of a sumo match. Block 22 detects the start of a play of a video segment of a plurality of frames of the video. After detecting the start of the play, block 24 detects the end of the play, thereby defining a segment of video between the start of the play and the end of the play, namely, a ��play��. Block 26 then checks to see if the end of the video (or the portion to be processed) has been reached. If the end of the video has not been reached block 26 branches to block 22 to detect the next play. Alternatively, if the end of the video has been reached then block 26 branches to the summary description 28. The summary description defines those portions of the video sequence 20 that contain the relevant segments for the video summarization. The summary description may be compliant with the MPEG-7 Summary Description Scheme or TV-Anytime Segmentation Description Scheme. A compliant media browser, such as shown in FIG. 17, may apply the summary description to the input video to provide summarized viewing of the input video without modifying it. Alternatively, the summary description may be used to edit the input video and create a separate video sequence. The summarized video sequence may comprise the selected segments which excludes at least a portion of the original video other than the plurality of segments. Preferably, the summarized video sequence excludes all portions of the original video other than the plurality of segments.
One component of the summarization procedure depicted in FIG. 1 is the detection of an event, or ��play.�� If the start and end points of all plays are detected, then the system may string all the plays together to obtain a summary from the original video and perform some post processing to smooth the transition boundaries, such as using dissolving techniques to reduce abrupt change between plays and smoothing the audio filed for better auditory effects. Further, the summary should ideally contain only those segments comprising a ��play�� as earlier defined, thus providing a compact representation of the original tournament; the user can spend only a few minutes to watch it, yet almost all of the excitement of the original game can be appreciated.
One of the difficulties in the detection of a ��play�� in a sumo broadcast is that frames in one play may sweep a large range of color, yet all the frames belong to the same event, and form an uninterrupted video clip. Thus a generic summarization scheme that uses, for example, a color histogram as the cue for key frame detection or scene classification, may not be particularly effective. In light of such difficulties, the present inventors have developed an alternate method for detecting a ��play�� that is specifically tailored to sumo content.
Still referring to FIG. 1, a summary is to be obtained by first detecting the boundaries of a ��play.�� In a sumo bout, two contestants meet in a ring 4.55 meters across. Though they wear silk belts around their waists, the players are otherwise unclothed. There are strict rules as to where the players and the referee, called a ��Gyoji,�� are to stand in the moments immediately proceeding the initiation of the bout. Cameras are situated at fixed locations around the ring capture the sumo bout. The cameras can typically pan, tilt, and zoom. The primary camera typically is situated opposite to the side where the referee stands. Thus a bout usually starts with a scene as illustrated in FIG. 2, and the bout will almost always be broadcast in its entirety by the primary camera from this vantage. Video captured by any other camera is typically used exclusively for replays, player close-ups, or post-bout ceremonies, all of which take place after the bout has ended. This format is adhered to because the primary camera can best cover the action of the bout, which usually lasts for mere moments, making it impractical to switch camera angles during a bout.
Based on these observations, the inventors have developed a model for ��play�� detection. A play starts with a scene as in FIG. 2. The time between the scene cut at the end of a current play and the start of the following play is not usually exciting and can thus be excluded from a compact summary. Note that a scene like that shown in FIG. 2 is typically merely a necessary condition, not a sufficient condition. In a sumo tournament, there are many pre-game ceremonies that result in a scene like that shown in FIG. 2, but the contestants, are not yet ready to initiate the bout. Thus in order to detect the start of a play, in addition to finding a scene like that depicted in FIG. 2, it should determine whether the scene is an immediate precursor to the start of a bout. One test would be to determine whether the contestants charge one another and collide, because that is how each bout begins. In other words, the methodology of detecting whether the start of a ��play�� has occurred involves locating a frame similar to that shown in FIG. 2 then applying a test to determine whether the frame immediately precedes the start of a bout.
The color of the stage can be estimated from sample data; given a set of sample frames containing the stage, a set of parameters can give an estimate for the stage color. Detecting the players is a more difficult task. Theoretically, one could use complex methods such as those explicitly modeling the shape of a human body. To achieve fast computation, the present inventors have identified a simpler method in describing a player: a player is represented by a color blob with skin tone. Thus assuming that an estimate for skin tone is obtained, two blobs corresponding to the two respective players could be segmented. As mentioned earlier, in a Sumo broadcast, there are pregame ceremonies that could result in frames like a start frame. To enable this type of false alarm to be eliminated, the players should be tracked after they are detected to see if they move towards each other and eventually collide with each other, as would occur at the beginning of a ��play�� as earlier defined.
One method for detecting the beginning of a play may proceed as shown in FIG. 3. Given a stage color description Cs and skin tone description Ck, a video frame image IM can be examined to determine whether the image represents the beginning of a ��play.�� The color descriptions, may be for example, a single color, a range of colors, a set of colors, in one or more color spaces. First, the image is examined to determine if it has a dark upper portion and a lower portion dominated (25% or more, 50% or more, or 75% or more) by the color Cs+Ck. If not, then the image is determined as a non-start frame. If yes, then the image is examined to determine whether there are two dominant (25% or more, 50% or more, or 75% or more) color blobs of color Ck, nearly symmetric to each other with respect to a generally center column (+/−20% of the width of the frame off center) of the frame. If not, then the image is determined as a non-start frame. If yes, subsequent frames are examined to determine whether the two dominant color blobs move towards, and eventually collide with, one another. If so, the original frame image IM is determined a start frame, otherwise it is determined not to be a start frame. The technique may be modified to include fewer tests or additional tests, in the same or a different sequence.
To avoid the computational burden and potential inaccuracies of such a grouping procedure, the present inventors discovered that the foregoing method for detecting the beginning of a play may be implemented by representing and tracking the blobs through their one-dimensional projections. FIG. 4 shows a candidate image IM that is a representative start frame of a sumo ��play�� as earlier defined, and thus should be detected by the summarization procedure shown in FIG. 1. Given the stage color description Cs and the skin tone description Ck, the candidate image shown in FIG. 4 may be reduced to the image shown in FIG. 5 where white pixels indicate a place where there is a pixel in the candidate image corresponding to either the stage color Ck or the skin color Cs. The black pixels represent the dark background areas of the candidate image. The image may be further decomposed using skin-tone based segmentation to isolate those portions of the image corresponding to the skin color Cs. A binary image, shown in FIG. 6 may be used to represent the obtained body parts, in which numeral ones represent a pixel of that location representing skin in the original image. This binary image may be projected along vertical and horizontal axes, shown in FIGS. 7 and 8, respectively. The analysis of the blob may be performed on those projections. The proposed projection behaves effectively like an integration process, which makes the algorithm less sensitive to imperfection in the skin/stage segmentation. Note that in these projections, small and isolated peaks have been suppressed.
The foregoing method is able to detect start frames successfully in most situations. However, if the detection of a start frame is declared after finding only one candidate frame, then the method may be susceptible to false-positives. By examining a set of consecutive frames (or other temporally related frames) and accumulating evidence, the system can reduce the false-positive rate. Referring to FIG. 10, the following approach may be used to achieve temporal evidence of accumulation: when detecting the start of a ��play��, a sliding window of width w is used (e.g., w frames are considered at the same time). A start is declared only if more than p out of the w frames in the current window are determined to be start scene candidates, as previously described. A suitable value of p is such that p/w=70%. Other statistical measures may be used of a fixed number of frames or dynamic number of frames to more accurately determine start scenes.
While the start of a ��play�� may be found according to the aforementioned method, the end of a ��play�� can occur in a variety of different ways due to the numerous techniques used to either force the opposing contestant to the ground or out of the ring. Image analysis techniques may be used to analyze the image content of the frames after the beginning of a bout to attempt to determine what occurred, but with the nearly endless possibilities and the difficulty of interpreting the content of the frames, this technique is at least, extremely difficult and computationally intensive. In contrast to attempting to analyze the content of the subsequent frames of a potential play, the present inventors determined that a more efficient manner for the determination of the extent of a play in sumo is to base the end of the play on camera activities. After analysis of a sumo video the present inventors were surprised to determine that the approximate end of a play may be modeled by scene changes, normally as a result of switching to a different camera or a different camera angle. The different camera or different camera angle may be modeled by determining the amount of change between the current frame (or set of frames) to the next frame (or set of frames).
Sumo video may also include gradual transitions between plays and other activities, such as commentary. These gradual transitions tend to be computationally complex to detect in the general case. However, in the case of sumo it has been determined that detecting gradual transitions based upon the color histogram differences is especially suitable. Other techniques may likewise be used. Referring to FIG. 13, the preferred technique may include starting from a start-of-play time (to) and looking forward until a sufficiently large scene change is detected or until time to+tp is reached, whichever occurs first. Tp relates to the maximum anticipated play duration and therefore automatically sets a maximum duration to the play. This time period for processing to locate gradual transitions is denoted as tclean �X cut. If tclean �X cut<tlow then the system will not look for a gradual scene cut and set the previously detected scene cut as the end of the play. This corresponds to an anticipated minimum time duration for a play and tlow is used to denote the minimum time period. Otherwise, the system looks for the highest color histogram difference in the region tlow, tclean �X cut or other measure of a potential scene change. This region of the segment is from the minimum time duration to the next previously identified scene cut. This identifies the highest color histogram difference in the time duration which may be a potential scene change. The time of the highest color histogram difference is identified at t1. In a neighborhood of t1, [t1−c1, t2+c2], a statistical computation is performed, such as computing the mean m1 and the standard deviation F of the color histogram differences. C1 and c2 are constants or statistically calculated temporal values for the region to examine around the highest color histogram difference. A mean filtering emphasizes regions having a relatively large difference in a relatively short time interval. If the color histogram differences at t1 exceeds m1+c3*F1, where c3 is a constant (or otherwise) and some of its neighbors (or otherwise) are sufficiently large, then the system considers a gradual transition to have occurred at around time (frame) t1. The play inset to the shorter of the previously identified scene cut or the gradual transition, if any.
While an effective summarization of a sumo video may be based on the concept of the ��play��, sometimes the viewer may prefer an even shorter summarization with the most exciting plays included. One potential technique for the estimation of the excitement of a play is to perform statistical analysis on the segments to determine which durations are most likely to have the highest excitement. However, this technique will likely not provide sufficiently accurate results. Further, excitement tends to be a subjective measure that is hard to quantify. After further consideration the present inventors came to the realization that the audio provided together with the video provides a good indication of the excitement of the plays. For example, the volume of the response of the audience and/or the commentators provides a good indication of the excitement. The louder audience and/or commentator acclamations, the greater the degree of excitement.
�M�Q�ޥ� �ޥΪ��M�Q�ӽФ���o�G��� �ӽЪ��M�Q�W��US41830561977�~5��23��1980�~1��8��Kewp Electronic Systems, Inc.Apparatus and method for monitoring sports contestsUS42531081979�~6��4��1981�~2��24��Zenith Radio CorporationControl for color killer and automatic color limiterUS42988841980�~3��31��1981�~11��3��Zenith Radio CorporationChroma amplifier and color killerUS43216351979�~4��20��1982�~3��23��Teac CorporationApparatus for selective retrieval of information streams or itemsUS45204041982�~8��23��1985�~5��28��Von Kohorn; HenrySystem, apparatus and method for recording and editing broadcast transmissionsUS47290441985�~2��5��1988�~3��1��Lex Computing & Management CorporationMethod and apparatus for playing serially stored segments in an arbitrary sequenceUS49376851983�~12��2��1990�~6��26��Lex Computer And Management CorporationMethod of display presentation for video editingUS50274001989�~8��16��1991�~6��25��Hitachi Ltd.Multimedia bidirectional broadcast systemUS51013641990�~2��9��1992�~3��31��Massachusetts Institute Of TechnologyMethod and facility for dynamic video composition and viewingUS51094821991�~2��19��1992�~4��28��Bohrman; DavidInteractive video control system for displaying user-selectable clipsUS51481541990�~12��4��1992�~9��15��Sony Corporation Of AmericaMulti-dimensional user interfaceUS52008251992�~7��1��1993�~4��6��Beam Laser Systems, Inc.Commercial insertion system remotely controlling multiple video switchesUS53330911993�~1��8��1994�~7��26��Arthur D. Little Enterprises, Inc.Method and apparatus for controlling a videotape player to automatically scan past recorded commercial messagesUS53393931993�~4��15��1994�~8��16��Sony Electronics, Inc.Graphical user interface for displaying available source material for editingUS54247701994�~9��15��1995�~6��13��Cable Service Technologies, Inc.Method and apparatus for automatic insertion of a television signal from a remote sourceUS54346781993�~1��11��1995�~7��18��Nissim Corp., A Florida CorporationSeamless transmission of non-sequential video segmentsUS54520161993�~3��10��1995�~9��19��Texas InstrumentsCombined digital color control and chroma killer deviceUS55218411994�~3��31��1996�~5��28��Siemens Corporate Research, Inc.Browsing contents of a given video sequenceUS55595491993�~12��2��1996�~9��24��Discovery Communications, Inc.Television program delivery systemUS55899451994�~9��13��1996�~12��31��Nissim Corp., A Florida CorporationComputer-themed playing systemUS56003641993�~12��2��1997�~2��4��Discovery Communications, Inc.Network controller for cable television delivery systemsUS56005731994�~12��2��1997�~2��4��Discovery Communications, Inc.Operations center with video storage for a television program packaging and delivery systemUS56106531995�~4��24��1997�~3��11��Customplay LlcMethod and system for automatically tracking a zoomed video imageUS56348491995�~4��12��1997�~6��3��Abecassis; MaxContent-on-demand interactive video method and apparatusUS56359821994�~6��27��1997�~6��3��Kent Ridge Digital LabsSystem for automatic video segmentation and key frame extraction for video sequences having both sharp and gradual transitionsUS56547691995�~6��7��1997�~8��5��Texas Instruments IncorporatedDigital color control and chroma killer deviceUS56593501993�~12��2��1997�~8��19��Discovery Communications, Inc.Operations center for a television program packaging and delivery systemUS56640461995�~4��10��1997�~9��2��Customplay LlcAutoconfigurable video systemUS56642271994�~10��14��1997�~9��2��Carnegie Mellon UniversitySystem and method for skimming digital audio/video dataUS56757521994�~9��15��1997�~10��7��Sony CorporationInteractive applications generator for an interactive presentation environmentUS56821951993�~12��2��1997�~10��28��Discovery Communications, Inc.Digital cable headend for cable television delivery systemUS56849181994�~9��8��1997�~11��4��Customplay LlcSystem for integrating video and communicationsUS56968691994�~9��19��1997�~12��9��Nissim Corp.Variable-content-video provider systemUS57108841995�~3��29��1998�~1��20��Intel CorporationSystem for automatically updating personal profile server with updates to additional user information gathered from monitoring user's electronic consuming habits generated on computer during useUS57178141994�~9��16��1998�~2��10��Abecassis MaxVariable-content video retrieverUS57244721995�~5��1��1998�~3��3��Nissim Corp., A Florida CorporationContent map for seamlessly skipping a retrieval of a segment of a videoUS57348531993�~12��2��1998�~3��31��Discovery Communications, Inc.Set top terminal for cable television delivery systemsUS57618811997�~1��13��1998�~6��9��Wall; BenjaminProcess and apparatus for wrapping paper rollsUS57743571995�~6��6��1998�~6��30��Hoffberg; Steven M.Human factored interface incorporating adaptive pattern recognition based controller apparatusUS57781081996�~6��7��1998�~7��7��Electronic Data Systems CorporationMethod and system for detecting transitional markers such as uniform fields in a video signalUS57970011996�~9��26��1998�~8��18��Hitachi America, Ltd.Broadcast interactive multimedia systemUS57987851993�~12��2��1998�~8��25��Discovery Communications, Inc.Terminal for suggesting programs offered on a television program delivery systemUS58057331994�~12��12��1998�~9��8��Apple Computer, Inc.Method and system for detecting scenes and summarizing video sequencesUS58219451997�~5��15��1998�~10��13��The Trustees Of Princeton UniversityMethod and apparatus for video browsing based on content and structureUS58618811996�~2��8��1999�~1��19��Actv, Inc.Interactive computer system for providing an interactive presentation with personalized video, audio and graphics responses for multiple viewersUS58673861995�~6��6��1999�~2��2��Hoffberg; Steven M.Morphological pattern recognition based controller systemUS58751071997�~9��9��1999�~2��23��Mitsubishi Denki Kabushiki KaishaInverter apparatusUS58751081995�~6��6��1999�~2��23��Microsoft CorporationErgonomic man-machine interface incorporating adaptive pattern recognition based control systemUS58925361996�~10��3��1999�~4��6��Personal AudioSystems and methods for computer enhanced broadcast monitoringUS59008671995�~7��17��1999�~5��4��Gateway 2000, Inc.Self identifying remote control device having a television receiver for use in a computerUS59012461995�~6��6��1999�~5��4��Hoffberg; Steven M.Ergonomic man-machine interface incorporating adaptive pattern recognition based control systemUS59034541991�~12��23��1999�~5��11��Hoffberg; Linda IreneHuman-factored interface corporating adaptive pattern recognition based controller apparatusUS59130131997�~12��15��1999�~6��15��Nissim Corp., A Corp. Of FloridaSeamless transmission of non-sequential video segmentsUS59203601996�~6��7��1999�~7��6��Electronic Data Systems CorporationMethod and system for detecting fade transitions in a video signalUS59204771995�~6��6��1999�~7��6��Hoffberg; Steven M.Human factored interface incorporating adaptive pattern recognition based controller apparatusUS59233651996�~12��6��1999�~7��13��Orad Hi-Tech Systems, LtdSports event video manipulating system for highlighting movementUS59266241996�~9��12��1999�~7��20��Audible, Inc.Digital information library and delivery system with logic for generating files targeted to the playback deviceUS59338111996�~8��20��1999�~8��3��Paul D. AnglesSystem and method for delivering customized advertisements within interactive communication systemsUS59560261997�~12��19��1999�~9��21��Sharp Laboratories Of America, Inc.Method for hierarchical summarization and browsing of digital videoUS59580061995�~12��19��1999�~9��28��Motorola, Inc.Method and apparatus for communicating summarized dataUS59596811996�~12��30��1999�~9��28��Samsung Electronics Co., Ltd.Motion picture detecting methodUS59596971996�~6��7��1999�~9��28��Electronic Data Systems CorporationMethod and system for detecting dissolve transitions in a video signalUS59697551997�~2��5��1999�~10��19��Texas Instruments IncorporatedMotion based event detection system and methodUS59736831997�~11��24��1999�~10��26��International Business Machines CorporationDynamic regulation of television viewing content based on viewer profile and viewing historyUS59866901994�~11��7��1999�~11��16��Discovery Communications, Inc.Electronic book selection and delivery systemUS59866921998�~12��15��1999�~11��16��Logan; James D.Systems and methods for computer enhanced broadcast monitoringUS59872111997�~11��8��1999�~11��16��Abecassis; MaxSeamless transmission of non-sequential video segmentsUS59909271993�~12��2��1999�~11��23��Discovery Communications, Inc.Advanced set top terminal for cable television delivery systemsUS59909801998�~5��28��1999�~11��23��Sarnoff CorporationDetection of transitions in video sequencesUS59950951999�~5��21��1999�~11��30��Sharp Laboratories Of America, Inc.Method for hierarchical summarization and browsing of digital videoUS60028331997�~11��8��1999�~12��14��Nissim Corp., A Corp. Of FloridaDisc storing a variable-content-video and a user interfaceUS60118951998�~5��1��2000�~1��4��Abecassis; MaxKeyword responsive variable content video programUS60141831997�~8��6��2000�~1��11��Imagine Products, Inc.Method and apparatus for detecting scene changes in a digital video streamUS60383671999�~2��19��2000�~3��14��Nissim Corp.Playing a Video Responsive to a comparison of two sets of Content PreferencesUS60525541996�~9��10��2000�~4��18��Discovery Communications, Inc.Television program delivery systemUS60550181997�~11��4��2000�~4��25��Ati Technologies, Inc.System and method for reconstructing noninterlaced captured content for display on a progressive screenUS60674011997�~12��11��2000�~5��23��Abecassis; MaxPlaying a version of and from within a video by means of downloaded segment informationUS60729341997�~10��20��2000�~6��6��Nissim Corp. A Florida CorporationVideo previewing method and apparatusUS60817501995�~6��6��2000�~6��27��Hoffberg; Steven MarkErgonomic man-machine interface incorporating adaptive pattern recognition based control systemUS60884551997�~1��7��2000�~7��11��Digitalsmiths CorporationMethods and apparatus for selectively reproducing segments of broadcast programmingUS60918861998�~7��1��2000�~7��18��Nissim CorporationVideo viewing responsive to content and time restrictionsUS61009411998�~7��28��2000�~8��8��U.S. Philips CorporationApparatus and method for locating a commercial disposed within a video data streamUS61410411998�~6��22��2000�~10��31��Lucent Technologies Inc.Method and apparatus for determination and visualization of player field coverage in a sporting eventUS61410601999�~3��5��2000�~10��31��Fox Sports Productions, Inc.Method and apparatus for adding a graphic indication of a first down to a live video of a football gameUS61443751998�~8��14��2000�~11��7��Praja Inc.Multi-perspective viewer for content-based interactivityUS61514441998�~6��30��2000�~11��21��Nissim Corp.Motion picture including within a duplication of framesUS61609891996�~10��23��2000�~12��12��Discovery Communications, Inc.Network controller for cable television delivery systemsUS61611422000�~1��10��2000�~12��12��The Musicbooth LlcMethod and system for using a communication network to supply targeted streaming advertising in interactive mediaUS61695421998�~12��14��2001�~1��2��Gte Main Street IncorporatedMethod of delivering advertising through an interactive video distribution systemUS61813351998�~9��21��2001�~1��30��Discovery Communications, Inc.Card for a set top terminalUS61954971994�~10��24��2001�~2��27��Hitachi, Ltd.Associated image retrieving apparatus and methodUS62015361994�~12��2��2001�~3��13��Discovery Communications, Inc.Network manager for cable television system headendsUS64962281999�~11��4��2002�~12��17��Koninklijke Philips Electronics N.V.Significant scene detection and frame filtering for a visual indexing system using dynamic thresholdsUS65812071999�~6��29��2003�~6��17��Kabushiki Kaisha ToshibaInformation filtering system and methodUS71817572000�~9��29��2007�~2��20��Electronics And Telecommunications Research InstituteVideo summary description scheme and method and system of video summary description data generation for efficient overview and browsingUS200200510772001�~7��19��2002�~5��2��Das MadirakshiVideoabstracts: a system for generating video summariesUS200200834712000�~12��21��2002�~6��27��Philips Electronics North America CorporationSystem and method for providing a multimedia summary of a video programUS200201119682001�~2��12��2002�~8��15��Ching Philip WaisinHierarchical document cross-reference system and methodUS200201416192001�~3��30��2002�~10��3��Logitech Europe S.A.Motion and audio detection based webcamming and bandwidth controlUS200201442932001�~3��27��2002�~10��3��Koninklijke Philips Electronics N.V.Automatic video retriever genieUS200201639112001�~5��4��2002�~11��7��Hewlett-Packard Development Company L.P.Method and system for midstream transcoding of secure scalable packets in response to downstream requirementsUS200302256962001�~4��6��2003�~12��4��Niwa PaulCustomized multimedia content method, apparatus, media and signalsUS200402552492002�~12��6��2004�~12��16��Chang Shih-FuSystem and method for extracting text captions from video and generating video summariesUS200701012662006�~12��21��2007�~5��3��Electronics And Telecommunications Research InstituteVideo summary description scheme and method and system of video summary description data generation for efficient overview and browsingUSD3482511992�~12��9��1994�~6��28��Discovery Communications, Inc.Menu control panel for a universal remote control unitUSD3540591992�~12��3��1995�~1��3��Discovery Communications, Inc.Remote control unitUSD3682631994�~7��12��1996�~3��26��Discovery Communications, Inc.Remote control unitUSD3819911994�~7��12��1997�~8��5��Discovery Communications, Inc.Remote control unitUSD4023101994�~11��7��1998�~12��8��Discovery Communications, Inc.Electronic bookUSD4355612000�~1��11��2000�~12��26��Tivo, Inc.Replay bar icon for a displayUSRE368011996�~4��18��2000�~8��1��James LoganTime delayed digital video system using concurrent recording and playback�D�M�Q�ޥ��ѦҤ��m1"A Schema for TV-Anytime Segmentation Metadata AN195r1," myTV project, Copyright NDS Limited 2000, pp. 1-28.2"A Schema for TV-anytime: Segmentation Metadata AN195," NDS Contribution from MyTV, Copyright NDS Limited 2000, pp. 1-27.3"XML Schema Part 1: Structures," W3C Working Draft, May 6, 1999, pp. 1-60.4"XML Schema Part 2: Datatypes," World Wide Web Consortium Working Draft, May 6, 1999, pp. 1-37.5Alan E. Bell, "The dynamic digital disk," IEEE Spectrum, Oct. 1999, pp. 28-35.6Alberto Del Bimbo, Enrico Vicario and Daniele Zingoni, "A Spatial Logic for Symbolic Description of Image Contents," Journal of Visual Languages and Computing (1994) 5, pp. 267-286.7B. B. Chaudhuri, N. Sarkar, and P. Kundu, "Improved Fractal Geometry Based Texture Segmentation Technique," IEEE Proceedings-E, vol. 140, No. 5, Sep. 1993, pp. 233-241.8B.S. Manjunath and W.Y. Ma, "Texture Features for Browsing and Retrieval of Image Data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, No. 8, Aug. 1996, pp. 837-842.9Baoxin Li and M. Ibrahim Sezan, "Event Detection and Summarization in sports Video," to be presented in IEEE Workshop on Content-Based Access of Image and Video Libraries (Joint with CUPR), Kauai, Hawaii, Dec. 2001.10Bilge Gunsel, Yue Fu and A. Murat Tekalp, "Hierarchical Temporal Video Segmentation and Content Characterization," SPIE vol. 3229, pp. 46-56.11Boon-Lock Yeo and Bede Liu, "On the Extraction of DC Sequence From MPEG Compressed Video," IEEE 1995, pp. 260-263.12Boon-Lock Yeo and Minerva M. Yeung, "Retrieving and Visualizing Video," Communications of the ACM, Dec. 1997, vol. 40 No. 12, pp. 43-52.13Boreczky, John S., et al., A Hidden Markov Model Framework for video Segmentation Using Audio and Image Features, IEEE International conference on Acoustics, speech and Signal Processing, Seattle, WA 1998, consisting of four pages.14Christel, Michael G., Hauptmann, Alexander G., Warmack, Adrienne S., and Crosby, Scott S., "Adjustable Filmstrips and Skims as Abstractions for a Digital video Library," Computer Science Department, Carnegie Mellon University, Pittsburgh, PA; pp. 1-7.15Chung-Lin Huang and Chih-Yu Chang, "Video Summarization using Hidden Markov Model," IEEE 2001, pp. 473-478.16Chung-Lin Huang and Chih-Yu-Chang, "Video Summarization using Hidden Markov Model," Electrical Engineering Department, Nationala Tsing-Hua University, Hsin-Chu, Taiwan, ROC, 2001, IEEE, pp. 473-477.17Daniel Dementhon, Bikrant Kobla and David Doermann, "Video Summarization by Curve Simplification," ACM Multimedia 1998, Language and Media Processing (LAMP), Univeristy of Maryland, College Park, MD 20742-3275, pp. 211-218.18Daniel DeMenthon, Vikrant Kobla and David Doermann, "Video Summarization by Curve Simplification," ACM 1998, pp. 211-218.19Dennis Yow, Boon-Lock Yeo, Minerva Yeung and Bede Liu, "Analysis and Presentation of Soccer Highlights From Digital Video," Proceedings, Second Asian Conference on Computer Vision (ACCV '95).20Drew D. Saur, Yap-Peng Tan, Sanjeev R. Kulkarni, and Peter J. Ramadge, "Automated Analysis and Annotation of Basketball Video," SPIE vol. 3022, pp. 176-187.21Dulce Ponceleon, Savitha Srinivasan, Arnon Amir, Dragutin Petkovic, and Dan Diklic, "Key to Effective Video Retrieval: Effective Cataloging and Browsing," ACM, Aug. 1998, pp. 99-107.22Eickler, Stefan, et al., Content-Based Video Indexing of TV Broadcast News Using Hidden Markov Models, IEEE International Conference on Acoustics, speech and Signal Processing, Phoenix, AZ, 1999, consisting of four pages.23F. Arman, R. Depommier, A. Hsu, and M-Y. Chiu, "Content-based Browsing of Video Sequences," Proceedings of ACM International Conference on Multimedia '94, Oct. 15-20, San Francisco, CA, USA.24Frank R. Kschischang, Brendan J. Frey, and Hans-Andrea Loeliger, "Factor Graphs and the Sum-Product Algorithm," IEEE Transactions on Information Theory, vol. 47, No. 2, Feb. 2001, pp. 498-518.25Gargi, U.; Kasturi, R.; Strayer, S.H.; "Performance Characterization of Video-Shot-Change Detection Methods," Circuits and Systems for Video Technology, IEEE Transactions on, vol. 10, No. 1, pp. 1-13, Feb. 2000.26Giridharan Iyengar and Andrew Lippman, "Models for automatic classification of video sequences," SPIE vol. 3312, pp. 216-227.27H. Pan, P. van Beek, and M. I. Sezan, "Detection of Slow-Motion Replay Segments in Sports Video for Highlights Generation," Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Salt Lake city, UT, 2001.28H.B. Lu, Y.J. Zhang, Y.R. Yao, "Robust Gradual Scene Change Detection," Proceedings of IEEE International Conference on Image Processing, Kobe, Japan, 1999.29Hao Pan, Baoxin Li, and M. Ibrahim Sezan, "Automatic Detection of Replay Segments in Broadcast Sports Programs by Detection of Logos in Scene Transitions," IEEE ICASSP 2002, pp. 3385-3388.30Hongjiang Zhang, Stephen W. Smoliar and Jian Hua Wu, "Content-Based Video Browsing Tools," SPIE vol. 2417, pp. 389-398.31International Organisation for Standardisation, ISO/IEC JTC1/SC29/WG11/MXXXX, MPEG-7 Media/Meta DSs upgrade (V02.), Oct. 1999, Melbourne.32International Organisation for Standardisation, ISO/IEC JTC1/SC29/WG11/N3398, Coding of Moving Pictures and Associated Audio Information, "MPEG-7 Visual Part of eXperimentation Model Version 6.0," Jun. 2000, Geneva.33International Organisation for Standardisation,ISO/IEC JTC1/SC29/WG11/N2844, Coding of Moving Pictures and Audio Information, "MPEG-7 Description Schemes (V0.5)," Jul. 1999, Vancouver.34International Organization for Standardization ISO/IEC JTC 1/SC 29/WG 11/N3411, Coding of Moving Pictures and Audio, MPEG-7 Multimedia Description Schemes WD (Version 3.0), May 2000, Geneva.35International Organization for Standardization, ISO/IEC JTC 1/SC 29/WG 11/N3410, Coding of Moving Pictures and Audio, MPEG-7 Multimedia Description Schemes XM (Version 3.0), May 2000, Geneva.36International Organization for Standardization, ISO/IEC JTC 1/SC 29/WG 11/N3966, Information technology-Multimedia Content Description Interface-part 5: Multimedia Description Schemes, Mar. 12, 2001.37International Organization for Standardization, ISO/IEC JTC 1/SC 29/WG 11/N3966, Information technology�XMultimedia Content Description Interface�Xpart 5: Multimedia Description Schemes, Mar. 12, 2001.38International Organization for Standardization, ISO/IEC JTC1/SC29/WG11/N3399, Coding of Moving Pictures and Associated Audio, "Visual Working Draft 3.0," Jun. 2000, Geneva.39International Organization for Standardization,ISO/IEC JTC1/SC29/WG11/N3391, Coding of Moving Pictures and Associated Audio, "DDL Working Draft 3.0," May 2000., Geneva.40ISO/IEC JTC 1/SC 29 N3705, "Information Technology-Multimedia Content Description Interface-Part 5: Multimedia Description Schemes," Nov. 17, 2000.41ISO/IEC JTC 1/SC 29 N3705, "Information Technology�XMultimedia Content Description Interface�XPart 5: Multimedia Description Schemes," Nov. 17, 2000.42John Canny, "A Computational approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, No. 6, Nov. 1986, pp. 679-698.43John S. Boreczky and Lynn D. Wilcox, "A Hidden Markov Model Framework for Video Segmentation Using Audio and Image Features," Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Seattle, WA, 1998.44Jonathan D. Courtney, "Automatic Video Indexing via Object Motion Analysis," Pattern Recognition, vol. 30, No. 4, pp. 607-625, 1997.45Kawai, et al., "Detection of Replay in Broadcast Sports Video by Focusing on Digital Video Effects," IEICE (D-11), vol. J84-DII, No. 2, pp. 432-435, Feb. 2001, (in Japanese).46Kawashima, T.; Tateyama, K.; Lijima, T.; Aoki, Y.; "Indexing of Baseball Telecast for Content-Based Video Retrieval," Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on, vol. 1, pp. 871-874, vol. 1, Oct. 4-7, 1998.47Lawrence R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proceedings of the IEEE, vol. 77, No. 2, Feb. I989, pp. 257-286.48Lexing Xie and Shih-Fu Chang, "Structure Analysis of Soccer Video with Hidden Markov Models," ICASSP, 2002.49M. R. Naphade, R.Mehrotra, A. M. Ferman, J. Warnick, T. S. Huang, A. M. Tekalp, "A High-Performance Shot Boundary Detection Algorithm Using Multiple Cues," Proceedings of IEEE International Conference on Image Processing, Chicago, IL, 1998, pp. 884-887.50Mark T. Maybury and Andrew E. Merlino, "Multimedia Summaries of Broadcast News," IEEE 1997, pp. 442-449.51Masumitse, Ken and Echigo, Tomio, Video summarization Using Reinforcement Learning in Eigenspace; IBM Research, Tokyo Research Laboratory 1623-14, Shimotsuruma, Yamatoshi, Kanagawa, Japan.52Michael A. Smith and Takeo Kanade, "Video Skimming for Quick Browsing based on Audio and Image Characterization," Jul. 30, 1995, Carnegie Mellon University, School of Computer Science, Pittsburgh, PA.53Michael T. Chan, You Zhang and Thomas S. Huang, "Real-Time Lip Tracking and Bimodal Continuous Speech Recognition," 1998.54Minerva M. Yeung, "Video Visualization for Compact Presentation and Fast Browsing of Pictorial Content," IEEE Transactions on Circuits and Systems for Video Technology, vol. 7, No. 5, Oct. 1997, pp. 771-785.55Minerva Yeung, Boon-Lock Yeo, and Bede Liu, "Extracting Story Units from Long Programs for Video Browsing and Navigation," IEEE I996, pp. 296-305.56Nevenka Dimitrova and Forousan Golshani, "Motion Recovery for video Content Classification," ACM Transactions on Information Systems, vol. 13, No. 4, Oct. 1995, pp. 408-139.57Noboru Babaguchi, "Towards Abstracting sports Video by Highlights," IEEE 2000, pp. 1519-1522.58Noboru Babguchi, Yoshihiko Kawai, Yukinobu Yasugi, and Tadahiro Kitahashi, "Linking Live and Replay Scenes in Broadcasted Sports Video," Proceedings of ACM Multimedia 2000 Workshop on Multimedia Information Retrieval (MIR2000), pp. 205-208, Nov. 2000.59Nuno Vasconcelos and Andrew Lippman, "Bayesian Modeling of Video Editing and Structure: Semantic Features for Video Summarization and Browsing," IEEE 1998, pp. 153-157.60Padhraic Smyth, "Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View," Information and Computer Science Department, University of California, Irvine, CA, Mar. 20, 1998, pp. 1-11.61Peng Xu, Chih-Fu Chang, Ajay Divakaran, Anthony Vetro, and Huifang Sun, "Algorithms and System for High-Level Structure Analysis and Event Detection in Soccer Video," Columbia University, Advent-Technical Report #111, Jun. 2001.62Peng Xu, Chih-Fu Chang, Ajay Divakaran, Anthony Vetro, and Huifang Sun, "Algorithms and System for High-Level Structure Analysis and Event Detection in Soccer Video," Columbia University, Advent�XTechnical Report #111, Jun. 2001.63R. W. Picard, "A Society of Models for Video and Image Libraries," IBM Systems Journal, vol. 35, Nos. 3&4, 1996, pp. 292-312.64Rainer Lienhart, "Comparison of Automatic Shot Boundary Detection Algorithms," Part of the IS&T/SPIE Conference on Storage and Retrieval for Image and Video Databases VII, San Jose, California, Jan. 1999, SPIE vol. 3656.65Rainer Lienhart, Silvia Pfeiffer, and Wolfgang Effelsberg, "Video Abstracting," Communications of the ACM, vol. 40, No. 12, Dec. 1997, pp. 55-62.66Riccardo Leonardi and Perangelo Migliorati, "Semantic Indexing of Multimedia Documents," IEEE 2002 (Apr.-Jun. 2002), pp. 44-51.67Richard J. Qian, M. Ibrahim Sezan, and Kristine E. Matthews, "A Robust Real-Time Face Tracking Algorithm," IEEE 1998, pp. 131-135.68Richard O. Duda and Peter E. Hart, "Use of the Hough Transformation To Detect Lines and Curves in Pictures." Stanford Research Insitute, Menlo Park, California, 1972, Association for Computing Machinery, Inc., pp. 11-15.69Richard Qian, Niels Haering, and Ibrahim Sezan, "A Computational Approach to Semantic Event Detection," IEEE 1999, pp. 200-206.70Richard W. Conners and Charles A. Harlow, "A Theoretical Comparison of Texture Algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-2, No. 3, May 1980, pp. 204-222.71S. E. Levinson, L. R. Rabiner, and M. M. Sondhi, "An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition," American Telephone and Telegraph Company, The Bell System Technical Journal, vol. 62, No. 4, Apr. 1983, pp. 1035-1074.72Selim Aksoy and Robert M. Haralick, "Textural Features for Image Database Retrieval," Intelligent Systems Laboratory, Department of Electrical Engineering, University of Washington, Seattle.73Shin'Ichi Satoh and Takeo Kanade, "Name-It: Association of Face and Name in Video,", School of Computer Science Carnegie Mellon University, Pittsburgh, PA, Dec. 20, 1996, pp. 1-17.74Stefan Eickeler and Stefan Muller, "Content-Based Video Indexing of TV Broadcast News Using Hidden Markov Models," Proceedings of IEEE International Conference on Acoustics, speech, and Signal Processing, Phoenix, AZ 1999.75Stephen S. Intille and Aaron F. Bobick, "Visual Tracking Using Closed-Worlds," M.I.T. Media Laboratory Perceptual computing Section Technical Report No. 294, Nov. 1994, pp. 1-18.76Stephen S. Intille, "Tracking Using a Local Closed-World Assumption: Tracking in the Football Domain," M.I.T. Media Lab Perceptual Computing Group Technical Report No. 296, pp. 1-62, Submitted to the Program in Media Arts and Sciences, School of Architecture and Planning on Aug. 5, 1994.77Stuart J. Golin, "New Metric to Detect Wipes and Other Gradual Transitions in . . . ," Part of the IS&T/SPIE Conference on Visual Communications and Image Processing '99, San Jose, California, Jan. 1999, SPIE vol. 3653, pp. 1464-1474.78Sunghoon Choi, Yongduek Seo, Hyunwoo Kim, and Ki-Sang Hong, "Where are the ball and players?: Soccer Game Analysis with Color-based Tracking and Image Mosaick," Dept. of EE, Pohang University of Science and Technology, Republic of Korea, pp. 1-15.79T. Lambrou, P. Kudumakis, R. Speller, M. Sandler, and A. Linney, "Classification of Audio Signals Using Statistical Features on Time and Wavelet Transform Domains," IEEE 1998, pp. 3621-3624.80Toshio Kawashima, Kouichi Tateyama, Toshimasa Iijima, and Yoshinao Aoki, "Indexing of Baseball Telecast for Content-based Video Retrieval," IEEE 1998, pp. 871-874.81Ullas Gargi, Rangachar Kasturi, and Susan H. Strayer, "Performance Characterization of Video-Shot-Change Detection Methods," IEEE Transactions on Circuits and Systems for Video Technology, vol. 10, No. 1, Feb. 2000, pp. 1-13.82Vikrant Kobla, Daniel DeMenthon, and David Doermann, "Detection of Slow-Motion Replay Sequences for Identifying Sports Videos," IEEE Conference, I999, pp. 135-140.83Vikrant Kobla, Daniel DeMenthon, and David Doermann, "Identifying Sports Videos Using Replay, Text, and Camera Motion Features," University of Maryland, Laboratory for Language and Media Processing; at least one year prior to filing date; 12 pages.84Wayne Wolf, "Hidden Markov Model Parsing of Video Programs," Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997, pp. 2609-2611.85www.pvi.com, at least one year prior to filing.86Y. Kawai, et al., "Detection of Replay Scenes in Broadcasted Sports Video by Focusing on Digital Video Effects," IEICE (D-II), vol. J84-D-II, No. 2, pp. 432-435, Feb. 2001 (in Japanese).87Y. Rui, A. Gupta, and A. Acero, "Automatically Extracting Highlights for TV Baseball Programs," Eighth ACM International Conference on Multimedia, pp. 105-115, 2000.88Yao Wang, Zhu Liu, and Jin-Cheng Huang, "Multimedia Content Analysis," IEEE Signal Processing Magazine, Nov. 2000, pp. 12-35.89Yao Wang; Zhu Liu; Jin-Cheng Huang; "Multimedia Content Analysis-Using both Audio and Visual Clues," Signal Processing Magazine, IEEE, vol. 17, No. 6, pp. 12-36, Nov. 2000.90Yao Wang; Zhu Liu; Jin-Cheng Huang; "Multimedia Content Analysis�XUsing both Audio and Visual Clues," Signal Processing Magazine, IEEE, vol. 17, No. 6, pp. 12-36, Nov. 2000.91Yihong Gong, Lim Teck Sin, Chua Hock Chuan, Hongjiang Zhang, Masao Sakauchi, "Automatic Parsing of TV Soccer Programs," IEEE 1995, pp. 167-174.92Yong Rui, Anoop Gupta, and Alex Acero, "Automatically Extracting Highlights for TV Baseball Programs," ACM Multimedia 2000, Los Angeles, CA, USA, pp. 105-115.93Zhang Stephen W. Smoliar and Hongjiang, "Content-Based Video Indexing and Retrieval," IEEE 1994, pp. 62-72.94Zhu Liu and Quian Huang, "Detecting News Reporting Using Audio/Visual Information," IEEE 1999, pp. 324-328.������l�Ϥ�Google ���� - Sitemap - USPTO �j�q�U�� - ���p�v�F�� - �A�ȱ�� - ���� Google �M�Q - �N���^�X��ƬO�Ѭ��ӷ~�M�Q��Ʈw (IFI CLAIMS Patent Services) ����©2012 Google