Colour consistency in computer vision : a multiple image dynamic exposure colour classification system : a thesis presented to the Institute of Natural and Mathematical Sciences in fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, Auckland, New Zealand

Colour classification vision systems face difficulty when a scene contains both very bright and dark regions. An indistinguishable colour at one exposure may be distinguishable at another. The use of multiple cameras with varying levels of sensitivity is explored in this thesis, aiding the classification of colours in scenes with high illumination ranges. Titled the Multiple Image Dynamic Exposure Colour Classification (MIDECC) System, pie-slice classifiers are optimised for normalised red/green and cyan/magenta colour spaces. The MIDECC system finds a limited section of hyperspace for each classifier, resulting in a process which requires minimal manual input with the ability to filter background samples without specialised training. In experimental implementation, automatic multiple-camera exposure, data sampling, training and colour space evaluation to recognise 8 target colours across 14 different lighting scenarios is processed in approximately 30 seconds. The system provides computationally effective training and classification, outputting an overall true positive score of 92.4% with an illumination range between bright and dim regions of 880 lux. False positive classifications are minimised to 4.24%, assisted by heuristic background filtering. The limited search space classifiers and layout of the colour spaces ensures the MIDECC system is less likely to classify dissimilar colours, requiring a certain ‘confidence’ level before a match is outputted. Unfortunately the system struggles to classify colours under extremely bright illumination due to the simplistic classification building technique. Results are compared to the common machine learning algorithms Naïve Bayes, Neural Networks, Random Tree and C4.5 Tree Classifiers. These algorithms return greater than 98.5% true positives and less than 1.53% false positives, with Random Tree and Naïve Bayes providing the best and worst comparable algorithms, respectively. Although resulting in a lower classification rate, the MIDECC system trains with minimal user input, ignores background and untrained samples when classifying and trains faster than most of the studied machine learning algorithms.Colour classification vision systems face difficulty when a scene contains both very bright and dark regions. An indistinguishable colour at one exposure may be distinguishable at another. The use of multiple cameras with varying levels of sensitivity is explored in this thesis, aiding the classification of colours in scenes with high illumination ranges. Titled the Multiple Image Dynamic Exposure Colour Classification (MIDECC) System, pie-slice classifiers are optimised for normalised red/green and cyan/magenta colour spaces. The MIDECC system finds a limited section of hyperspace for each classifier, resulting in a process which requires minimal manual input with the ability to filter background samples without specialised training. In experimental implementation, automatic multiple-camera exposure, data sampling, training and colour space evaluation to recognise 8 target colours across 14 different lighting scenarios is processed in approximately 30 seconds. The system provides computationally effective training and classification, outputting an overall true positive score of 92.4% with an illumination range between bright and dim regions of 880 lux. False positive classifications are minimised to 4.24%, assisted by heuristic background filtering. The limited search space classifiers and layout of the colour spaces ensures the MIDECC system is less likely to classify dissimilar colours, requiring a certain ‘confidence’ level before a match is outputted. Unfortunately the system struggles to classify colours under extremely bright illumination due to the simplistic classification building technique. Results are compared to the common machine learning algorithms Naïve Bayes, Neural Networks, Random Tree and C4.5 Tree Classifiers. These algorithms return greater than 98.5% true positives and less than 1.53% false positives, with Random Tree and Naïve Bayes providing the best and worst comparable algorithms, respectively. Although resulting in a lower classification rate, the MIDECC system trains with minimal user input, ignores background and untrained samples when classifying and trains faster than most of the studied machine learning algorithms

Unidade de processamento e sistema de visão para um robô humanóide

Mestrado em Engenharia Electrónica e TelecomunicaçõesEste trabalho descreve a integração da Unidade Central de Processamento, um computador embebido, numa plataforma humanóide e o desenvolvimento do sistema de visão do robô. É abordado o processo de alteração da estrutura da plataforma para a integração física, e também a configuração e implementação do ambiente de desenvolvimento por forma a permitir a integra ção numa arquitectura de controlo distribuída já existente. O sistema de visão é baseado numa unidade pan-tilt que movimenta uma câmara para aquisição de imagem. A informação retirada da imagem adquirida é processada e usada para fazer o seguimento de um objecto. Para o seguimento são usados dois algoritmos de controlo baseados na imagem. ABSTRACT: This report describes the integration of the Central Control Unit, an embedded computer, on an humanoid platform and the development of the robot's vision system. The necessary changes on the physical support are shown as well as the configuration and implementation of the development environment, in order to allow the integration with the existing distributed architecture. The vision system is based on a pan and tilt unit supporting a color CCD camera for image aquisition. The visual tracking is based on the features of the acquired and processed image. Two diferent image-based algorithms are used for control

Robustness of colour detection for robot soccer

This paper outlines a method for detection and classification of the ball and the colour patches on microsoccer robots using pie slice decision region in a YUV colour map. The method is robust under changing lighting conditions

Robustness of colour detection for robot soccer

This paper outlines a method for detection and classification of the ball and the colour patches on microsoccer robots using pie slice decision region in a YUV colour map. The method is robust under changing lighting conditions