Techniques from the theory of partial differential equations are often used to design filter methods that are locally adapted to the image structure. These techniques are usually used in the investigation of gray-value images. The extension to color images is non-trivial, where the choice of an appropriate color space is crucial. The RGB color space is often used although it is known that the space of human color perception is best described in terms of non-euclidean geometry, which is fundamentally different from the structure of the RGB space. Instead of the standard RGB space, we use a simple color transformation based on the theory of finite groups. It is shown that this transformation reduces the color artifacts originating from the diffusion processes on RGB images. The developed algorithm is evaluated on a set of real-world images, and it is shown that our approach exhibits fewer color artifacts compared to state-of-the-art techniques. Also, our approach preserves details in the image for a larger number of iterations. / Original Publication:Åström Freddie, Felsberg Michael and Lenz Reiner, Color Persistent Anisotropic Diffusion of Images, 2011, Image Analysis, SCIA conference, 23-27 May 2011, Ystad Sweden, 262-272.http://dx.doi.org/10.1007/978-3-642-21227-7_25Copyright: Springer

Digital cameras use a single image sensor array with a color filter array (CFA) to measure a color image. Instead of measuring a red, green, and blue value at every pixel, these cameras have a filter built onto each pixel so that only one portion of the visible spectrum is measured. To generate a full-color image, the camera must estimate the missing two values at every pixel. This process is known as color filter array interpolation. The Bayer CFA pattern samples the green image on half of the pixels of the imaging sensor on a quincunx grid. The other half of the pixels measure the red and blue images equally on interleaved rectangular sampling grids. This thesis analyzes this problem with sampling theory. The red and blue images are sampled at half the rate of the green image and therefore have a higher probability of aliasing in the output image. This is apparent when simple interpolation algorithms like bilinear interpolation are used for CFA interpolation. Two reference algorithms, a projections onto convex sets (POCS) algorithm and an edge-directed algorithm by Adams and Hamilton (AH), are studied. Both algorithms address aliasing in the green image. Because of the high correlation among the red, green, and blue images, information from the red and blue images can be used to better interpolate the green image. The reference algorithms are studied to learn how this information is used. This leads to two new interpolation algorithms for the green image. The red and blue interpolation algorithm of AH is also studied to determine how the inter-image correlation is used when interpolating these images. This study shows that because the green image is sampled at a higher rate, it retains much of the high-frequency information in the original image. This information is used to estimate aliasing in the red and blue images. We present a general algorithm based on the AH algorithm to interpolate the red and blue images. This algorithm is able to provide results that are on average, better than both reference algorithms, POCS and AH.

As emerging portable multimedia applications demand more and more computational throughput with limited energy consumption, the need for high-efficiency, high-throughput embedded processing is becoming an important challenge in computer architecture. In this regard, this dissertation addresses application-, architecture-, and technology-level issues in existing processing systems to provide efficient processing of multimedia in many, or ideally all, of its form. In particular, this dissertation explores color imaging in multimedia while focusing on two architectural enhancements for memory- and performance-hungry embedded applications: (1) a pixel-truncation technique and (2) a color-aware instruction set (CAX) for embedded multimedia systems. The pixel-truncation technique differs from previous techniques (e.g., 4:2:2 and 4:2:0 subsampling) used in image and video compression applications (e.g., JPEG and MPEG) in that it reduces the information content in individual pixel word sizes rather than in each dimension. Thus, this technique drastically reduces the bandwidth and memory required to transport and store color images without perceivable distortion in color. At the same time, it maintains the pixel storage format of color image processing in which each pixel computation is performed simultaneously on 3-D YCbCr components, which are widely used in the image and video processing community. CAX supports parallel operations on two-packed 16-bit (6:5:5) YCbCr data in a 32-bit datapath processor, providing greater concurrency and efficiency for processing color image sequences. This dissertation presents the impact of CAX on processing performance and on both area and energy efficiency for color imaging applications in three major processor architectures: dynamically scheduled (superscalar), statically scheduled (very long instruction word, VLIW), and embedded single instruction multiple data (SIMD) array processors. Unlike typical multimedia extensions, CAX obtains substantial performance and code density improvements through direct support for color data processing rather than depending solely on generic subword parallelism. In addition, the ability to reduce data format size reduces system cost. The reduction in data bandwidth also simplifies system design. In summary, CAX, coupled with the pixel-truncation technique, provides an efficient mechanism that meets the computational requirements and cost goals for future embedded multimedia products.

A segmentação de imagens consiste em urna tarefa de fundamental importância para diferentes aplicações em visão computacional, tais como por exemplo, o reconhecimento e o rastreamento de objetos, a segmentação de tomores/lesões em aplicações médicas, podendo também servir de auxílio em sistemas de reconhecimento facial. Embora exista uma extensa literatora abordando o problema de segmentação de imagens, tal tópico ainda continua em aberto para pesquisa. Particularmente, a tarefa de segmentar imagens coloridas é desafiadora devido as diversas inomogeneidades de cor, texturas e formas presentes nas feições descritivas das imagens. Este trabalho apresenta um novo método de clustering para abordar o problema da segmentação de imagens coloridas. Nós desenvolvemos uma abordagem Bayesiana para procura de máximos de densidade em urna distribuição discreta de dados, e representamos os dados de forma hierárquica originando clusters adaptativos a cada nível da hierarquia. Nós aplicamos o método de clustering proposto no problema de segmentação de imagens coloridas, aproveitando sua estrutura hierárquica, baseada em propriedades de árvores direcionadas, para representar hierarquicamente uma imagem colorida. Os experimentos realizados revelaram que o método de clustering proposto, aplicado ao problema de segmentação de imagens coloridas, obteve para a medida de performance Probabilistic Rand lndex (PRI) o valor de 0.8148 e para a medida Global Consistency Error (GCE) o valor 0.1701, superando um total de vinte e um métodos previamente propostos na literatura para o banco de dados BSD300. Comparações visuais confirmaram a competitividade da nossa abordagem em relação aos demais métodos testados. Estes resultados enfatizam a potencialidade do nosso método de clustering para abordar outras aplicações no domínio de Visão Computacional e Reconhecimento de Padrões. / Image segmentation is an essential task for several computer vision applications, such as object recognition, tracking and image retrieval. Although extensively studied in the literature, the problem of image segmentation remains an open topic of research. Particularly, the task of segmenting color images is challenging due to the inhomogeneities in the color regions encountered in natural scenes, often caused by the shapes of surfaces and their interactions with the illumination sources (e.g. causing shading and highlights) This work presents a novel non-supervised classification method. We develop a Bayesian framework for seeking modes on the underlying discrete distribution of data and we represent data hierarchically originating adaptive clusters at each levei of hierarchy. We apply the prnposal clustering technique for tackling the problem of color irnage segmentation, taking advantage of its hierarchical structure based on hierarchy properties of directed trees for representing fine to coarse leveis of details in an image. The experiments herein conducted revealed that the proposed clustering method applied to the color image segmentation problem, achieved for the Probabilistic Rand Index (PRI) performance measure the value of 0.8148 and for the Global Consistency Error (GCE) the value of 0.1701, outperforming twenty-three methods previously proposed in the literature for the BSD300 dataset. Visual comparison confirmed the competitiveness of our approach towards state-of-art methods publicly available in the literature. These results emphasize the great potential of our proposed clustering technique for tackling other applications in computer vision and pattem recognition.

A segmentação de imagens consiste em urna tarefa de fundamental importância para diferentes aplicações em visão computacional, tais como por exemplo, o reconhecimento e o rastreamento de objetos, a segmentação de tomores/lesões em aplicações médicas, podendo também servir de auxílio em sistemas de reconhecimento facial. Embora exista uma extensa literatora abordando o problema de segmentação de imagens, tal tópico ainda continua em aberto para pesquisa. Particularmente, a tarefa de segmentar imagens coloridas é desafiadora devido as diversas inomogeneidades de cor, texturas e formas presentes nas feições descritivas das imagens. Este trabalho apresenta um novo método de clustering para abordar o problema da segmentação de imagens coloridas. Nós desenvolvemos uma abordagem Bayesiana para procura de máximos de densidade em urna distribuição discreta de dados, e representamos os dados de forma hierárquica originando clusters adaptativos a cada nível da hierarquia. Nós aplicamos o método de clustering proposto no problema de segmentação de imagens coloridas, aproveitando sua estrutura hierárquica, baseada em propriedades de árvores direcionadas, para representar hierarquicamente uma imagem colorida. Os experimentos realizados revelaram que o método de clustering proposto, aplicado ao problema de segmentação de imagens coloridas, obteve para a medida de performance Probabilistic Rand lndex (PRI) o valor de 0.8148 e para a medida Global Consistency Error (GCE) o valor 0.1701, superando um total de vinte e um métodos previamente propostos na literatura para o banco de dados BSD300. Comparações visuais confirmaram a competitividade da nossa abordagem em relação aos demais métodos testados. Estes resultados enfatizam a potencialidade do nosso método de clustering para abordar outras aplicações no domínio de Visão Computacional e Reconhecimento de Padrões. / Image segmentation is an essential task for several computer vision applications, such as object recognition, tracking and image retrieval. Although extensively studied in the literature, the problem of image segmentation remains an open topic of research. Particularly, the task of segmenting color images is challenging due to the inhomogeneities in the color regions encountered in natural scenes, often caused by the shapes of surfaces and their interactions with the illumination sources (e.g. causing shading and highlights) This work presents a novel non-supervised classification method. We develop a Bayesian framework for seeking modes on the underlying discrete distribution of data and we represent data hierarchically originating adaptive clusters at each levei of hierarchy. We apply the prnposal clustering technique for tackling the problem of color irnage segmentation, taking advantage of its hierarchical structure based on hierarchy properties of directed trees for representing fine to coarse leveis of details in an image. The experiments herein conducted revealed that the proposed clustering method applied to the color image segmentation problem, achieved for the Probabilistic Rand Index (PRI) performance measure the value of 0.8148 and for the Global Consistency Error (GCE) the value of 0.1701, outperforming twenty-three methods previously proposed in the literature for the BSD300 dataset. Visual comparison confirmed the competitiveness of our approach towards state-of-art methods publicly available in the literature. These results emphasize the great potential of our proposed clustering technique for tackling other applications in computer vision and pattem recognition.

This dissertation develops an innovative approach towards less-constrained iris biometrics. Two major contributions are made in this research endeavor: (1) Designed an award-winning segmentation algorithm in the less-constrained environment where image acquisition is made of subjects on the move and taken under visible lighting conditions, and (2) Developed a pioneering iris biometrics method coupling segmentation and recognition of the iris based on video of moving persons under different acquisitions scenarios. The first part of the dissertation introduces a robust and fast segmentation approach using still images contained in the UBIRIS (version 2) noisy iris database. The results show accuracy estimated at 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at 97% in a Noisy Iris Challenge Evaluation (NICE.I) in an international competition that involved 97 participants worldwide involving 35 countries, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time. The second part of this dissertation presents an innovative segmentation and recognition approach using video-based iris images. Following the segmentation stage which delineats the iris region through a novel segmentation strategy, some pioneering experiments on the recognition stage of the less-constrained video iris biometrics have been accomplished. In the video-based and less-constrained iris recognition, the test or subject iris videos/images and the enrolled iris images are acquired with different acquisition systems. In the matching step, the verification/identification result was accomplished by comparing the similarity distance of encoded signature from test images with each of the signature dataset from the enrolled iris images. With the improvements gained, the results proved to be highly accurate under the unconstrained environment which is more challenging. This has led to a false acceptance rate (FAR) of 0% and a false rejection rate (FRR) of 17.64% for 85 tested users with 305 test images from the video, which shows great promise and high practical implications for iris biometrics research and system design.

No description available.

No description available.

License plate recognition/identification (LPR/I) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. In the United States, however, each state has its own standard-issue plates, plus several optional styles, which are referred to as special license plates or varieties. There is a clear absence of standardization and multi-colored, complex backgrounds are becoming more frequent in license plates. Commercially available optical character recognition (OCR) systems generally fail when confronted with textured or poorly contrasted backgrounds, therefore creating the need for proper image segmentation prior to classification. The image segmentation problem in LPR is examined in two stages: license plate region detection and license plate character extraction from background. Three different approaches for license plate detection in a scene are presented: region distance from eigenspace, border location by edge detection and the Hough transform, and text detection by spectral analysis. The experiments for character segmentation involve the RGB, HSV/HSI and 1976 CIE L*a*b* color spaces as well as their Karhunen-Loéve transforms. The segmentation techniques applied include multivariate hierarchical agglomerative clustering and minimum-variance color quantization. The trade-off between accuracy and computational expense is used to select a final reliable algorithm for license plate detection and character segmentation. The spectral analysis approach together with the K-L L*a*b* transformed color quantization are found experimentally as the best alternatives for the two identified image segmentation stages for US license plate recognition. / Master of Science

Les méthodes d'analyse de données et d'apprentissage ont connu un développement très important ces dernières années. En effet, après les réseaux de neurones, les machines à noyaux (années 1990), les années 2000 ont vu l'apparition de méthodes spectrales qui ont fourni un cadre mathématique unifié pour développer des méthodes de classification originales. Parmi celles-ci ont peut citer la méthode LLE pour la réduction de dimension non linéaire et la méthode LDA pour la discrimination de classes. Une nouvelle méthode de classification est proposée dans cette thèse, méthode issue d'une combinaison des méthodes LLE et LDA. Cette méthode a donné des résultats intéressants sur des ensembles de données synthétiques. Elle permet une réduction de dimension non-linéaire suivie d'une discrimination efficace. Ensuite nous avons montré que cette méthode pouvait être étendue à l'apprentissage semi-supervisé. Les propriétés de réduction de dimension et de discrimination de cette nouvelle méthode, ainsi que la propriété de parcimonie inhérente à la méthode LLE nous ont permis de l'appliquer à la segmentation d'images couleur avec succès. La propriété d'apprentissage semi-supervisé nous a enfin permis de segmenter des images bruitées avec de bonnes performances. Ces résultats doivent être confortés mais nous pouvons d'ores et déjà dégager des perspectives de poursuite de travaux intéressantes. / Data analysis and learning methods have known a huge development during these last years. Indeed, after neural networks, kernel methods in the 90', spectral methods appeared in the years 2000. Spectral methods provide an unified mathematical framework to expand new original classification methods. Among these new techniques, two methods can be highlighted : LLE for non-linear dimension reduction and LDA as discriminating classification method. In this thesis document a new classification technique is proposed combining LLE and LDA methods. This new method makes it possible to provide efficient non-linear dimension reduction and discrimination. Then an extension of the method to semi-supervised learning is proposed. Good properties of dimension reduction and discrimination associated with the sparsity property of the LLE technique make it possible to apply our method to color images segmentation with success. Semi-supervised version of our method leads to efficient segmentation of noisy color images. These results have to be extended and compared with other state-of-the-art methods. Nevertheless interesting perspectives of this work are proposed in conclusion for future developments.