This thesis presents a system to recognise and classify road and traffic signs for the purpose of developing an inventory of them which could assist the highway engineers’ tasks of updating and maintaining them. It uses images taken by a camera from a moving vehicle. The system is based on three major stages: colour segmentation, recognition, and classification. Four colour segmentation algorithms are developed and tested. They are a shadow and highlight invariant, a dynamic threshold, a modification of de la Escalera’s algorithm and a Fuzzy colour segmentation algorithm. All algorithms are tested using hundreds of images and the shadow-highlight invariant algorithm is eventually chosen as the best performer. This is because it is immune to shadows and highlights. It is also robust as it was tested in different lighting conditions, weather conditions, and times of the day. Approximately 97% successful segmentation rate was achieved using this algorithm.Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recogniser is based on the combination of both the colour and shape of the sign. The recogniser was tested in a variety of testing conditions giving an overall performance of approximately 88%.Classification was undertaken using a Support Vector Machine (SVM) classifier. The classification is carried out in two stages: rim’s shape classification followed by the classification of interior of the sign. The classifier was trained and tested using binary images in addition to five different types of moments which are Geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments, and Binary Haar features. The performance of the SVM was tested using different features, kernels, SVM types, SVM parameters, and moment’s orders. The average classification rate achieved is about 97%. Binary images show the best testing results followed by Legendre moments. Linear kernel gives the best testing results followed by RBF. C-SVM shows very good performance, but ?-SVM gives better results in some case.

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Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.

This research focuses on extracting road models from stereo video sequences taken from a moving vehicle. The proposed method combines color histogram based segmentation, active contours (snakes) and morphological processing to extract road boundary coordinates for conversion into Matlab or Multigen OpenFlight compatible polygonal representations. Color segmentation uses an initial truth frame to develop a color probability density function (PDF) of the road versus the terrain. Subsequent frames are segmented using a Maximum Apostiori Probability (MAP) criteria and the resulting templates are used to update the PDFs. Color segmentation worked well where there was minimal shadowing and occlusion by other cars. A snake algorithm was used to find the road edges which were converted to 3D coordinates using stereo disparity and vehicle position information. The resulting 3D road models were accurate to within 1 meter.

3D representation nowadays has attracted much more public attention than ever before. One of the most important techniques in this field is depth extraction. In this thesis, we first introduce a well-known stereo matching method using color segmentation and belief propagation, and make an implementation of this framework. The color-segmentation based stereo matching method performs well recently, since this method can keep the object boundaries accurate, which is very important to depth map. Based on the implemented framework of segmentation based stereo matching, we proposed a color segmentation based 2D-to-3D video conversion method using high quality motion information. In our proposed scheme, the original depth map is generated from motion parallax by optical flow calculation. After that we employ color segmentation and plane estimation to optimize the original depth map to get an improved depth map with sharp object boundaries. We also make some adjustments for optical flow calculation to improve its efficiency and accuracy. By using the motion vectors extracted from compressed video as initial values for optical flow calculation, the calculated motion vectors are more accurate within a shorter time compared with the same process without initial values. The experimental results shows that our proposed method indeed gives much more accurate depth maps with high quality edge information. Optical flow with initial values provides good original depth map, and color segmentation with plane estimation further improves the depth map by sharpening its boundaries.

O greening ou Huanglongbing (HLB) é uma das mais graves doenças dos citros presentes nos pomares do Brasil. Causada pela bactéria Candidatus Liberibacter spp, é transmitida pelo inseto psilídeo Diaphorina citri, que ao se alimentar de uma planta doente transmite a doença às demais plantas. O greening apresenta como sintoma, manchas amareladas nas folhas, muitas vezes confundidas com deficiências nutricionais. A erradicação da planta e o controle do inseto transmissor são as únicas formas de prevenção para evitar a sua propagação. Este trabalho teve por objetivo avaliar uma metodologia baseada em segmentação por cor e outra baseada em análise de textura para avaliação de folhas de citros sintomáticas, identificando se estão contaminadas com o greening ou outras doenças e deficiências nutricionais. Foram fornecidas pelo grupo FISHER, 324 amostras de folhas cítricas, contendo folhas com doenças (greening, CVC e rubelose) e deficiências nutricionais (manganês, magnésio e zinco). As folhas foram digitalizadas por um scanner de mesa, com duas resoluções, utilizando somente a parte frontal da folha. Foram montados três bancos de imagens. Os resultados gerados com a metodologia baseada em segmentação por cor utilizando RNA PMC, mostraram que essa metodologia não é eficiente. Na metodologia baseada na análise por textura foram avaliados os descritores LBP, LFP e os de Haralick. Para estes descritores foram extraídas amostras por folha e por quadrantes das folhas nos canais de cores vermelho e verde e amostras em níveis de cinza. Os resultados gerados pelos descritores foram classificados pela distância &#9672 e pelos algoritmos IBK e RNA PMC do toolbox Weka. Os melhores resultados foram para os descritores LBP e LFP-s para distância &#9672, com valores de sensibilidade acima de 97% e 93%, respectivamente, e para o LBP com o algoritmo IBK, com valores de sensibilidade acima de 98,5%. Os resultados obtidos evidenciam que o descritor LBP é o mais eficiente seguido pelo LFP-s na diferenciação do greening das outras pragas. / The greening or Huanglongbing (HLB) is one of the most serious diseases of citrus orchards present in Brazil. HLB is caused by the bacterium Candidatus Liberibacter spp, it is transmitted by the psyllid insect (Diaphorina citri) that, when feeding on a diseased plant, it transmits the disease to other plants. One of the symptoms of the greening are yellowish spots on the leaves, often confused with nutritional deficiencies. The eradication of plants and control of insect are the only forms of prevention. This work aims to evaluate two methodologies: one based on color segmentation and the other based on texture analysis for assessment of symptomatic citrus leaves, identifying whether they are infected with greening and other diseases and nutritional deficiencies. A number of 324 samples of citrus leaves were provided by FISHER group, infected with diseases (greening, CVC, rubelose) and nutritional deficiencies ( manganese, magnesium, zinc) . The leaves were acquired by a flatbed scanner with two different resolutions, using only the front side of the leaf. Three datasets of images were constructed. The results generated using the methodology based on color segmentation with ANN MLP, showed that this methodology is not efficient. In the methodology based on texture analysis it was evaluated the LBP, LFP and the Haralick descriptors. For these descriptors it was extracted samples from the leaves and quadrants of leaves, in red and green color channels and grayscale. The results generated by the descriptors were classified by &#9672 distance and the algorithms IBK and ANN MLP from the toolbox Weka. The best results were for LBP descriptor and LFP-s for &#9672 distance with values of sensitivity above 97% and 93%, respectively, and the LBP with IBK algorithm, with values of sensitivity above 98.5%. The results showed that the LBP descriptor is the most efficient followed by LFP-s in the differentiation of the greening from other pests.

O greening ou Huanglongbing (HLB) é uma das mais graves doenças dos citros presentes nos pomares do Brasil. Causada pela bactéria Candidatus Liberibacter spp, é transmitida pelo inseto psilídeo Diaphorina citri, que ao se alimentar de uma planta doente transmite a doença às demais plantas. O greening apresenta como sintoma, manchas amareladas nas folhas, muitas vezes confundidas com deficiências nutricionais. A erradicação da planta e o controle do inseto transmissor são as únicas formas de prevenção para evitar a sua propagação. Este trabalho teve por objetivo avaliar uma metodologia baseada em segmentação por cor e outra baseada em análise de textura para avaliação de folhas de citros sintomáticas, identificando se estão contaminadas com o greening ou outras doenças e deficiências nutricionais. Foram fornecidas pelo grupo FISHER, 324 amostras de folhas cítricas, contendo folhas com doenças (greening, CVC e rubelose) e deficiências nutricionais (manganês, magnésio e zinco). As folhas foram digitalizadas por um scanner de mesa, com duas resoluções, utilizando somente a parte frontal da folha. Foram montados três bancos de imagens. Os resultados gerados com a metodologia baseada em segmentação por cor utilizando RNA PMC, mostraram que essa metodologia não é eficiente. Na metodologia baseada na análise por textura foram avaliados os descritores LBP, LFP e os de Haralick. Para estes descritores foram extraídas amostras por folha e por quadrantes das folhas nos canais de cores vermelho e verde e amostras em níveis de cinza. Os resultados gerados pelos descritores foram classificados pela distância &#9672 e pelos algoritmos IBK e RNA PMC do toolbox Weka. Os melhores resultados foram para os descritores LBP e LFP-s para distância &#9672, com valores de sensibilidade acima de 97% e 93%, respectivamente, e para o LBP com o algoritmo IBK, com valores de sensibilidade acima de 98,5%. Os resultados obtidos evidenciam que o descritor LBP é o mais eficiente seguido pelo LFP-s na diferenciação do greening das outras pragas. / The greening or Huanglongbing (HLB) is one of the most serious diseases of citrus orchards present in Brazil. HLB is caused by the bacterium Candidatus Liberibacter spp, it is transmitted by the psyllid insect (Diaphorina citri) that, when feeding on a diseased plant, it transmits the disease to other plants. One of the symptoms of the greening are yellowish spots on the leaves, often confused with nutritional deficiencies. The eradication of plants and control of insect are the only forms of prevention. This work aims to evaluate two methodologies: one based on color segmentation and the other based on texture analysis for assessment of symptomatic citrus leaves, identifying whether they are infected with greening and other diseases and nutritional deficiencies. A number of 324 samples of citrus leaves were provided by FISHER group, infected with diseases (greening, CVC, rubelose) and nutritional deficiencies ( manganese, magnesium, zinc) . The leaves were acquired by a flatbed scanner with two different resolutions, using only the front side of the leaf. Three datasets of images were constructed. The results generated using the methodology based on color segmentation with ANN MLP, showed that this methodology is not efficient. In the methodology based on texture analysis it was evaluated the LBP, LFP and the Haralick descriptors. For these descriptors it was extracted samples from the leaves and quadrants of leaves, in red and green color channels and grayscale. The results generated by the descriptors were classified by &#9672 distance and the algorithms IBK and ANN MLP from the toolbox Weka. The best results were for LBP descriptor and LFP-s for &#9672 distance with values of sensitivity above 97% and 93%, respectively, and the LBP with IBK algorithm, with values of sensitivity above 98.5%. The results showed that the LBP descriptor is the most efficient followed by LFP-s in the differentiation of the greening from other pests.

3D representation nowadays has attracted much more public attention than ever before. One of the most important techniques in this field is depth extraction. In this thesis, we first introduce a well-known stereo matching method using color segmentation and belief propagation, and make an implementation of this framework. The color-segmentation based stereo matching method performs well recently, since this method can keep the object boundaries accurate, which is very important to depth map. Based on the implemented framework of segmentation based stereo matching, we proposed a color segmentation based 2D-to-3D video conversion method using high quality motion information. In our proposed scheme, the original depth map is generated from motion parallax by optical flow calculation. After that we employ color segmentation and plane estimation to optimize the original depth map to get an improved depth map with sharp object boundaries. We also make some adjustments for optical flow calculation to improve its efficiency and accuracy. By using the motion vectors extracted from compressed video as initial values for optical flow calculation, the calculated motion vectors are more accurate within a shorter time compared with the same process without initial values. The experimental results shows that our proposed method indeed gives much more accurate depth maps with high quality edge information. Optical flow with initial values provides good original depth map, and color segmentation with plane estimation further improves the depth map by sharpening its boundaries.

The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions.This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.

In this thesis, real time detection of traffic signs using FPGA hardware is presented. Traffic signs have distinctive color and shape properties. Therefore, color and shape based algorithms are chosen to implemented on FPGA. FPGA supports sufficient logic to implement complete systems and sub-systems. Color information of images/frames is used to minimize the search domain of detection process. Using FPGA, real time conversion of YUV space to RGB space is performed. Furthermore, color thresholding algorithm is used to localize the sign in the image/video depending on the color. Edges are the most important image/frame attributes that provide valuable information about the shape of the objects. Sobel edge detection algorithm is implemented on FPGA. After color segmentation, FPGA implementation of Sobel algorithm is used to find the edges of candidate traffic signs in real time. Later, radial symmetry based shape detection algorithm is used to determine circular traffic signs. Each FPGA implemented algorithm is tested by using video sequences and static images. In addition, combined implementation of color based and shape based algorithms are tested. Joint application of color and shape based algorithms are used in order to reduce search domain and the processing time of detection process. Designing architecture on FPGA makes traffic sign detection system portable as a final product and relatively more efficient than the computer based detection systems. The resulting hardware is suitable where cost and compactness constraints are important.