title: The Development of a test of science skills and processes

authors: McGrath, Dennis Michael

abstract: This study reports the development of a test of science skills and processes for use in Forms 2, 3, 4. The test was constructed to meet two of the objectives of the Form 1-5 syllabus. 
The test characteristics, mean 50.60 per cent, standard deviation 15.43 per cent and standard error of measurement 3.09 were calculated from a sample of 341 pupils in Forms 2, 3 and 4. The test-retest reliability was O. 75 and Kuder-Richardson internal consistency O. 77. Two subtests, one related to skills, the other to processes had means of 48. 31 per cent and 50. 50 per cent respectively. The standard deviations were 17 .48 per cent (skills) and 17. 00 per cent (processes) and standard error of measurement 1. 79 (skills) and 2.10 (processes). 
It is suggested that the test could be used to assess achievement and diagnose weakness in the attainment of the science skills and processes objectives of the Form 1-5 syllabus. Tables for converting raw scores to T-scores are provided in the Appendices.

text: The Development of a test of science skills and processes This study reports the development of a test of science skills and processes for use in Forms 2, 3, 4. The test was constructed to meet two of the objectives of the Form 1-5 syllabus. 
The test characteristics, mean 50.60 per cent, standard deviation 15.43 per cent and standard error of measurement 3.09 were calculated from a sample of 341 pupils in Forms 2, 3 and 4. The test-retest reliability was O. 75 and Kuder-Richardson internal consistency O. 77. Two subtests, one related to skills, the other to processes had means of 48. 31 per cent and 50. 50 per cent respectively. The standard deviations were 17 .48 per cent (skills) and 17. 00 per cent (processes) and standard error of measurement 1. 79 (skills) and 2.10 (processes). 
It is suggested that the test could be used to assess achievement and diagnose weakness in the attainment of the science skills and processes objectives of the Form 1-5 syllabus. Tables for converting raw scores to T-scores are provided in the Appendices.

year: 1973

title: Adaptive Vision Based Scene Registration for Outdoor Augmented Reality

authors: Catchpole, Jason James

subjects: computer vision

abstract: Augmented Reality (AR) involves adding virtual content into real scenes. Scenes are viewed using a Head-Mounted Display or other display type. In
order to place content into the user's view of a scene, the user's position and orientation relative to the scene, commonly referred to as their pose, must be determined accurately. This allows the objects to be placed in the correct positions and to remain there when the user moves or the scene changes. It is achieved by tracking the user in relation to their environment using a variety of technology. One technology which has proven to provide accurate results is computer vision. Computer vision involves a computer
analysing images and achieving an understanding of them. This may be locating objects such as faces in the images, or in the case of AR, determining the pose of the user.

One of the ultimate goals of AR systems is to be capable of operating under any condition. For example, a computer vision system must be robust under a range of different scene types, and under unpredictable environmental conditions due to variable illumination and weather. The majority of existing literature tests algorithms under the assumption of ideal or 'normal' imaging conditions. To ensure robustness under as many circumstances as possible it is also important to evaluate the systems under adverse conditions.

This thesis seeks to analyse the effects that variable illumination has on computer vision algorithms. To enable this analysis, test data is required to isolate weather and illumination effects, without other factors such as changes in viewpoint that would bias the results. A new dataset is presented which also allows controlled viewpoint differences in the presence of weather and illumination changes. This is achieved by capturing video from a camera undergoing a repeatable motion sequence. Ground truth data is stored per frame allowing images from the same position under differing environmental conditions, to be easily extracted from the
videos.

An in depth analysis of six detection algorithms and five matching techniques demonstrates the impact that non-uniform illumination changes can have on vision algorithms. Specifically, shadows can degrade performance and reduce confidence in the system, decrease reliability, or even completely prevent successful operation.

An investigation into approaches to improve performance yields techniques that can help reduce the impact of shadows. A novel algorithm is presented that merges reference data captured at different times, resulting in reference data with minimal shadow effects. This can significantly improve performance and reliability when operating on images containing shadow effects. These advances improve the robustness of computer vision systems and extend the range of conditions in which they can operate. This can increase the usefulness of the algorithms and the AR systems that employ them.

text: Adaptive Vision Based Scene Registration for Outdoor Augmented Reality Augmented Reality (AR) involves adding virtual content into real scenes. Scenes are viewed using a Head-Mounted Display or other display type. In
order to place content into the user's view of a scene, the user's position and orientation relative to the scene, commonly referred to as their pose, must be determined accurately. This allows the objects to be placed in the correct positions and to remain there when the user moves or the scene changes. It is achieved by tracking the user in relation to their environment using a variety of technology. One technology which has proven to provide accurate results is computer vision. Computer vision involves a computer
analysing images and achieving an understanding of them. This may be locating objects such as faces in the images, or in the case of AR, determining the pose of the user.

One of the ultimate goals of AR systems is to be capable of operating under any condition. For example, a computer vision system must be robust under a range of different scene types, and under unpredictable environmental conditions due to variable illumination and weather. The majority of existing literature tests algorithms under the assumption of ideal or 'normal' imaging conditions. To ensure robustness under as many circumstances as possible it is also important to evaluate the systems under adverse conditions.

This thesis seeks to analyse the effects that variable illumination has on computer vision algorithms. To enable this analysis, test data is required to isolate weather and illumination effects, without other factors such as changes in viewpoint that would bias the results. A new dataset is presented which also allows controlled viewpoint differences in the presence of weather and illumination changes. This is achieved by capturing video from a camera undergoing a repeatable motion sequence. Ground truth data is stored per frame allowing images from the same position under differing environmental conditions, to be easily extracted from the
videos.

An in depth analysis of six detection algorithms and five matching techniques demonstrates the impact that non-uniform illumination changes can have on vision algorithms. Specifically, shadows can degrade performance and reduce confidence in the system, decrease reliability, or even completely prevent successful operation.

An investigation into approaches to improve performance yields techniques that can help reduce the impact of shadows. A novel algorithm is presented that merges reference data captured at different times, resulting in reference data with minimal shadow effects. This can significantly improve performance and reliability when operating on images containing shadow effects. These advances improve the robustness of computer vision systems and extend the range of conditions in which they can operate. This can increase the usefulness of the algorithms and the AR systems that employ them.

year: 2008

title: Te Kuranui

authors: Maxwell, Te Kahautu

abstract: Ko te tūāpapa o tēnei kōrero Te Kuranui he titiro ki te tūāpapa o Ōpōtiki Mai Tawhiti kapa haka. He aha te take i whakatūngia ai tēnei Kuranui e karangatia ana ko Ōpōtiki Mai Tawhiti? Hai tāhūhū mō tēnei tuhinga raurūtia tēnei kupu whakaari a Te Kōoti Arikirangi; “He Kura hoki ahau”, me tēnei wehenga o te mōteatea nāna; Te Pono ki runga rā, te ara mai o te tika. Tukua mai ki raro nā te maungarongo; te whana i tū ai nā te kotahitanga; te aroha i mau ai. Ana ko te Kupu Whakaari nei; He Kura hoki ahau he wewete i tēnei Kupu Whakaari kia kite i te hōhonu o te tikanga ōna hai whakatamārama i tō Te Kōoti wawata hai kura i tana iwi Ringatū me tā te kaituhi whakaraupapa i ēnei kura ki roto i te whakairotanga i te Kuranui nei.
Ka rua kua tātaritia tēnei wehenga o te mōteatea nei kia rima ōna wāhanga hai taki i tēnei tuhinga epā ana ki te Kuranui nei a Ōpōtiki Mai Tawhiti. Tuatahi Te Pono ki runga rā, ka titiro ki te kaituhi nei me tōna whakatipuranga i ōna kaumātua me ō rātou kuranga i a ia kia tohenihorau ki tōna ao Māori. He kuranga ēnei hai maungāngākau, maungāhinengaro ki te whakaturuki i te Kuranui a Ōpōtiki Mai Tawhiti. Te ara mai o te tika, ko te whakatūtanga o te kapa haka nei a Ōpōtiki Mai Tawhiti te Kuranui,arā te whakapaparanga mai i te kapa i tōna tīmatatanga ki te wā tonu nei. Tukua mai ki raro nā te maungarongo; ko te whakapono tēnei wāhanga, ko te ū o Ōpōtiki Mai Tawhiti ki tēnei kura a Te Kooti Arikirangi. Te whana i tū ai nā te kotahitanga;ko te titiro ki te pouārahi me te whakapau kaha ki te tuitui i ngā whakaaro o te katoa, ko te ranga i te iwi, ko te pupuri i te iwi, i te kapa haka ki tōna, rautaki, whāinga ko te whakakotahitanga i te iwi i raro i te kaupapa o Ōpōtiki Mai Tawhiti. Te aroha i mau ai, ko te aroha ki te iwi, ko te aroha ki te kaupapa, ko te whakaū, whakapūmau i te moemoeā kia whakahokia mai te toheariki ki te iwi kia tū toherauariki a Te Whakatōhea i raro i te korowai o Ōpōtiki Mai Tawhiti.

text: Te Kuranui Ko te tūāpapa o tēnei kōrero Te Kuranui he titiro ki te tūāpapa o Ōpōtiki Mai Tawhiti kapa haka. He aha te take i whakatūngia ai tēnei Kuranui e karangatia ana ko Ōpōtiki Mai Tawhiti? Hai tāhūhū mō tēnei tuhinga raurūtia tēnei kupu whakaari a Te Kōoti Arikirangi; “He Kura hoki ahau”, me tēnei wehenga o te mōteatea nāna; Te Pono ki runga rā, te ara mai o te tika. Tukua mai ki raro nā te maungarongo; te whana i tū ai nā te kotahitanga; te aroha i mau ai. Ana ko te Kupu Whakaari nei; He Kura hoki ahau he wewete i tēnei Kupu Whakaari kia kite i te hōhonu o te tikanga ōna hai whakatamārama i tō Te Kōoti wawata hai kura i tana iwi Ringatū me tā te kaituhi whakaraupapa i ēnei kura ki roto i te whakairotanga i te Kuranui nei.
Ka rua kua tātaritia tēnei wehenga o te mōteatea nei kia rima ōna wāhanga hai taki i tēnei tuhinga epā ana ki te Kuranui nei a Ōpōtiki Mai Tawhiti. Tuatahi Te Pono ki runga rā, ka titiro ki te kaituhi nei me tōna whakatipuranga i ōna kaumātua me ō rātou kuranga i a ia kia tohenihorau ki tōna ao Māori. He kuranga ēnei hai maungāngākau, maungāhinengaro ki te whakaturuki i te Kuranui a Ōpōtiki Mai Tawhiti. Te ara mai o te tika, ko te whakatūtanga o te kapa haka nei a Ōpōtiki Mai Tawhiti te Kuranui,arā te whakapaparanga mai i te kapa i tōna tīmatatanga ki te wā tonu nei. Tukua mai ki raro nā te maungarongo; ko te whakapono tēnei wāhanga, ko te ū o Ōpōtiki Mai Tawhiti ki tēnei kura a Te Kooti Arikirangi. Te whana i tū ai nā te kotahitanga;ko te titiro ki te pouārahi me te whakapau kaha ki te tuitui i ngā whakaaro o te katoa, ko te ranga i te iwi, ko te pupuri i te iwi, i te kapa haka ki tōna, rautaki, whāinga ko te whakakotahitanga i te iwi i raro i te kaupapa o Ōpōtiki Mai Tawhiti. Te aroha i mau ai, ko te aroha ki te iwi, ko te aroha ki te kaupapa, ko te whakaū, whakapūmau i te moemoeā kia whakahokia mai te toheariki ki te iwi kia tū toherauariki a Te Whakatōhea i raro i te korowai o Ōpōtiki Mai Tawhiti.

year: 2014-09-27