Estudo comparativo da estereopsia com três testes diferentes: TNO®, Fly Stereo Acuity Test® e StereoTAB® em estudantes do ensino superior

Estereopsia define-se como a perceção de profundidade baseada na disparidade retiniana. A estereopsia global depende do processamento de estímulos de pontos aleatórios e a estereopsia local depende da perceção de contornos. O objetivo deste estudo é correlacionar três testes de estereopsia: TNO®, StereoTAB® e Fly Stereo Acuity Test® e verificar a sensibilidade e correlação entre eles, tendo o TNO® como gold standard. Incluíram-se 49 estudantes da Escola Superior de Tecnologia da Saúde de Lisboa (ESTeSL) entre os 18 e 26 anos. As variáveis ponto próximo de convergência (ppc), vergências, sintomatologia e correção ótica foram correlacionadas com os três testes. Os valores médios (desvios-padrão) de estereopsia foram: TNO® = 87,04’’ ±84,09’’; FlyTest® = 38,18’’ ±34,59’’; StereoTAB® = 124,89’’ ±137,38’’. Coeficiente de determinação: TNO® e StereoTAB® com R2=0,6 e TNO® e FlyTest® com R2=0,2. O coeficiente de correlação de Pearson mostra uma correlação positiva de entre o TNO® e o StereoTAB® (r=0,784 com α=0,01). O coeficiente de associação de Phi mostrou uma relação positiva forte entre o TNO® e StereoTAB® (Φ=0,848 com α=0,01). Na curva ROC, o StereoTAB® possui uma área sob a curva maior que o FlyTest®, apresentando valor de sensibilidade de 92,3% para uma especificidade de 94,4%, tornando-o num teste sensível e com bom poder discriminativo.

Tracking people in 3D using position, size and shape

This paper presents a prototype tracking system for tracking people in enclosed indoor environments where there is a high rate of occlusions. The system uses a stereo camera for acquisition, and is capable of disambiguating occlusions using a combination of depth map analysis, a two step ellipse fitting people detection process, the use of motion models and Kalman filters and a novel fit metric, based on computationally simple object statistics. Testing shows that our fit metric outperforms commonly used position based metrics and histogram based metrics, resulting in more accurate tracking of people.

Improvements in sustainable energy and water practice in the food processing industry : an in depth analysis of the manufacture of Ghee at the Butter Producers' Cooperative Federation Limited, Brisbane

This thesis is a documented energy audit and long term study of energy and water reduction in a ghee factory. Global production of ghee exceeds 4 million tonnes annually. The factory in this study refines dairy products by non-traditional centrifugal separation and produces 99.9% pure, canned, crystallised Anhydrous Milk Fat (Ghee). Ghee is traditionally made by batch processing methods. The traditional method is less efficient, than centrifugal separation. An in depth systematic investigation was conducted of each item of major equipment including; ammonia refrigeration, a steam boiler, canning equipment, pumps, heat exchangers and compressed air were all fine-tuned. Continuous monitoring of electrical usage showed that not every initiative worked, others had pay back periods of less than a year. In 1994-95 energy consumption was 6,582GJ and in 2003-04 it was 5,552GJ down 16% for a similar output. A significant reduction in water usage was achieved by reducing the airflow in the refrigeration evaporative condensers to match the refrigeration load. Water usage has fallen 68% from18ML in 1994-95 to 5.78ML in 2003-04. The methods reported in this thesis could be applied to other industries, which have similar equipment, and other ghee manufacturers.

An in-depth study of a teacher engaged in an innovative primary science trial professional development project

The implementation of effective science programmes in primary schools is of continuing interest and concern for professional developers. As part of the Australian Academy of Science's approach to creating an awareness of Primary Investigations, a project team trialled a series of satellite television broadcasts of lessons related to two units of the curriculum for Year 3 and 4 children in 48 participating schools. The professional development project entitled Simply Science, included a focused component for the respective classroom teachers, which was also conducted by satellite. This paper reports the involvement of a Year 4 teacher in the project and describes her professional growth. Already an experienced and confident teacher, no quantitative changes in science teaching self efficacy were detected. However, her pedagogical content knowledge and confidence to teach science in the concept areas of matter and energy were enhanced. Changes in the teacher's views about the co-operative learning strategies espoused by Primary Investigations were also evident. Implications for the design of professional development programmes for primary science teachers are discussed.

Correlating eye gaze direction, depth and vehicle information on an interactive map for driver training

The over represented number of novice drivers involved in crashes is alarming. Driver training is one of the interventions aimed at mitigating the number of crashes that involve young drivers. Experienced drivers have better hazard perception ability compared to inexperienced drivers. Eye gaze patterns have been found to be an indicator of the driver's competency level. The aim of this paper is to develop an in-vehicle system which correlates information about the driver's gaze and vehicle dynamics, which is then used to assist driver trainers in assessing driving competency. This system allows visualization of the complete driving manoeuvre data on interactive maps. It uses an eye tracker and perspective projection algorithms to compute the depth of gaze and plots it on Google maps. This interactive map also features the trajectory of the vehicle and turn indicator usage. This system allows efficient and user friendly analysis of the driving task. It can be used by driver trainers and trainees to understand objectively the risks encountered during driving manoeuvres. This paper presents a prototype that plots the driver's eye gaze depth and direction on an interactive map along with the vehicle dynamics information. This prototype will be used in future to study the difference in gaze patterns in novice and experienced drivers prior to a certain manoeuvre.

Enhancing digital road map with lane details extracted from large-scale stereo aerial imagery using object-oriented image analysis

Precise, up-to-date and increasingly detailed road maps are crucial for various advanced road applications, such as lane-level vehicle navigation, and advanced driver assistant systems. With the very high resolution (VHR) imagery from digital airborne sources, it will greatly facilitate the data acquisition, data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lane information from aerial images with employment of the object-oriented image analysis method. Our proposed algorithm starts with constructing the DSM and true orthophotos from the stereo images. The road lane details are detected using an object-oriented rule based image classification approach. Due to the affection of other objects with similar spectral and geometrical attributes, the extracted road lanes are filtered with the road surface obtained by a progressive two-class decision classifier. The generated road network is evaluated using the datasets provided by Queensland department of Main Roads. The evaluation shows completeness values that range between 76% and 98% and correctness values that range between 82% and 97%.

Extraction of road lanes from high-resolution stereo aerial imagery based on maximum likelihood segmentation and texture enhancement

Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.