Edge and Line Feature Extraction Based on Covariance Models

age segmentation based on contour extraction usually involves three stages of image operations: feature extraction, edge detection and edge linking. This paper is devoted to the first stage: a method to design feature extractors used to detect edges from noisy and/or blurred images. The method relies on a model that describes the existence of image discontinuities (e.g. edges) in terms of covariance functions. The feature extractor transforms the input image into a “log-likelihood ratio” image. Such an image is a good starting point of the edge detection stage since it represents a balanced trade-off between signal-to-noise ratio and the ability to resolve detailed structures. For 1-D signals, the performance of the edge detector based on this feature extractor is quantitatively assessed by the so called “average risk measure”. The results are compared with the performances of 1-D edge detectors known from literature. Generalizations to 2-D operators are given. Applications on real world images are presented showing the capability of the covariance model to build edge and line feature extractors. Finally it is shown that the covariance model can be coupled to a MRF-model of edge configurations so as to arrive at a maximum a posteriori estimate of the edges or lines in the image

From uncertainty to adaptivity : multiscale edge detection and image segmentation

This thesis presents the research on two different tasks in computer vision: edge detection and image segmentation (including texture segmentation and motion field segmentation). The central issue of this thesis is the uncertainty of the joint space-frequency image analysis, which motivates the design of the adaptive multiscale/multiresolution schemes for edge detection and image segmentation. Edge detectors capture most of the local features in an image, including the object boundaries and the details of surface textures. Apart from these edge features, the region properties of surface textures and motion fields are also important for segmenting an image into disjoint regions. The major theoretical achievements of this thesis are twofold. First, a scale parameter for the local processing of an image (e.g. edge detection) is proposed. The corresponding edge behaviour in the scale space, referred to as Bounded Diffusion, is the basis of a multiscale edge detector where the scale is adjusted adaptively according to the local noise level. Second, an adaptive multiresolution clustering scheme is proposed for texture segmentation (referred to as Texture Focusing) and motion field segmentation. In this scheme, the central regions of homogeneous textures (motion fields) are analysed using coarse resolutions so as to achieve a better estimation of the textural content (optical flow), and the border region of a texture (motion field) is analysed using fine resolutions so as to achieve a better estimation of the boundary between textures (moving objects). Both of the above two achievements are the logical consequences of the uncertainty principle. Four algorithms, including a roof edge detector, a multiscale step edge detector, a texture segmentation scheme and a motion field segmentation scheme are proposed to address various aspects of edge detection and image segmentation. These algorithms have been implemented and extensively evaluated

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

An interactive approach to the identification and extraction of visual events

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 171-175).by William F. Stasior.Ph.D

Object Segmentation And Recognition Using Gradient Based Descriptors And Shape Driven Fast Marching Methods

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2010Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2010Bu çalışmada, aktif çevrit nesne bölütleyici yöntemlerle birlikte kullanılabilecek yeni bir şekil betimleme ve tanıma sistemi önerilmiştir. Önerilen sistem daha önce yapılan çalışmalar gibi aktif çevriti önceden tanımlı şekillerden birine zorlamak yerine, çevrit nesne sınırlarına yapışırken aynı zamanda şekil betimleme yapmayı amaçlamıştır. Aktif çevrit bölütleyici olarak Hızlı Yürüme (Fast Marching) algoritması kullanılmış, Hızlı Yürüme metodu için yeni bir hız işlevi tanımlanmıştır. Ayrıca çevriti nesne sınırlarından geçtiği sırada durdurmayı amaçlayan özgün yaklaşımlar önerilmiştir. Çalışmanın en önemli katkılarından birisi yeni ortaya atılan Gradyan Temelli Şekil Betimleyicisi (GTŞB) dir [1]. GTŞB, aktif çevrit bölütleyicilerin yapısına uygun, sınır tabanlı, hem ikili hem de gri-seviyeli görüntülerle rahatça kullanılabilecek başarılı bir şekil betimleyicidir. GTŞB nin araç plaka karakter veritabanı, MPEG-7 şekil veritabanı, Kimia şekil veritabanı gibi farklı şekil veritabanlarında elde ettiği başarılar diğer çok bilinen sınır tabanlı betimleyicilerle de karşılaştırılarak verilmiştir. Elde edilen sonuçlar GTŞB nin tüm veritabanlarında diğer yöntemlere göre daha başarılı olduğunu işaret etmektedir. Çalışmada geliştirilen bir diğer önemli yaklaşım da Hızlı Yürüme çevritinin nesne sınırına yaklaşırken örneklenerek şeklin birden fazla defa betimlenmesine olanak veren yeni sınıflandırıcı yapıdır. Bu yaklaşım nesne tanımayı bir denemede sonuçlandıran geleneksel yöntemlerin bu sınırlamasını aşarak aynı nesneyi birçok kez tanıma olanağı sunmaktadır. Bu tanıma sonuçlarının tümleştirilmesiyle tek tanımaya göre daha yüksek başarılar elde edildiği çalışmanın ilgili bölümlerinde başarıları karşılaştıran tablolar yardımıyla gösterilmektedir.In this thesis, a gradient based shape description and recognition methodology to use with active contour-based object segmentation systems has been proposed. The Fast Marching (FM) active contour evolving model is utilized for boundary segmentation. A new speed functional has been defined to use first and second order image intensity derivatives. A local front stopping algorithm has also been proposed to improve the boundary handling performance of the FM model. The most critical improvement of the thesis is defining a new shape descriptor called the Gradient Based Shape Descriptor (GBSD) [1]. GBSD is a new boundary-based shape descriptor that can operate on both binary and gray-scaled images. The recognition performance of GBSD is measured on a license plate character database, MPEG-7 Core Experiments shape data set and Kimia data Set. The success rates are compared with other well-known boundary-based shape descriptors and it is shown that GBSD achieves better recognition percentages. A new recognition approach that utilizes the progressive active contours while iterating towards the real object boundaries has been proposed. This approach provides the recognizer many trials for shape description; it removes the limitation of traditional recognition systems that have only one chance for shape classification. Test results shown in this study prove that the voted decision result among these iterated contours outperforms the ordinary individual shape recognizers.DoktoraPh