Bu çalışmada, aktif çevrit nesne bölütleyici yöntemlerle birlikte kullanılabilecek yeni bir eşzamanlı şekil betimleme ve tanıma sistemi önerilmiştir. Aktif çevrit bölütleyici olarak Hızlı Yürüme (Fast Marching) algoritması kullanılmış, Hızlı Yürüme metodu için yeni bir hız işlevi tanımlanmış, 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 (Çapar vd., 2009). GTŞB, aktif çevrit bölütleyicilerinin 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 ortaya atılan 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 gösterilmektedir. Bu çalışmada görüntüde bulunan nesneleri bölütlerken aynı zamanda betimleyebilen tümleşik bir yöntem önerilmiştir. Anahtar Kelimeler: Şekil betimleyici, Fourier dönüşümü, hızlı yürüme, yönlendirilebilir süzgeçler.We proposed a gradient based shape description and recognition methodology to use with active contour based object segmentation systems. We selected Fast Marching method which is an active contour segmentation technique is assigned for object detection and segmentation. We proposed a new speed function using first and second order intensity derivatives. In order to obtain the shapes properly, the evolving front is asked to be stopped near real object boundaries. Nevertheless, it is impossible for ordinary Fast Marching systems because of the non-zero speed functions. One of the contributions of the thesis is providing a new FM contour stopping algorithm. The proposed algorithm uses first and second order derivatives of local image intensities to determine whether an evolving node should stop or not The proposed system is capable for both segmentation and identification of shapes simultaneously. Since we utilized an active contour based segmentation approach for detecting objects, we need a contour based shape descriptor. In this work, we proposed a contour-based shape description scheme, named Gradient Based Shape Descriptor (GBSD), using some rotated gradient filter responses along the object boundary. GBSD can be applied to both binary and grayscale images. The proposed algorithm utilizes gradient based features which are extracted along the object boundaries. We use two-dimensional steerable G-Filters to obtain gradient information at different orientations and scales, and then aggregate the gradients into a shape signature. The signature derived from the rotated object is circularly shifted version of the signature derived from the original object. This property is called the circular-shifting rule. The shape descriptor is defined as the Fourier transform of the signature. We also provide a distance measure for the proposed descriptor by taking the circular-shifting rule into account. The performance of the proposed descriptor is evaluated over two databases; one containing digits taken from vehicle license plates and the other containing MPEG-7 Core Experiment and Kimia shape data set. The experiments show that the devised method outperforms other well-known Fourier-based shape descriptors such as centroid distance and boundary curvature. When we combine the proposed shape descriptor GBSD with Fast Marching (FM) approach, we obtain a descriptor vector for each FM evolving iteration. That means we have more than one feature vectors for a single shape. Besides, each vector will be able to be fed into a classifier to obtain different decisions. Each decision result can be threaded as a different source of information and a decision fusion process can be applied to get final decision. This is another contribution of the thesis. The proposed system has following advantages comparing with other concurrent object segmentation-recognition approaches; In previous studies, the evolving front is always forced to have the prior shape. However, we stop the front near object boundaries. It is stated that, the proposed method does not work when the number of prior object classes is more than one (Paragios vd., 2002). However, our system is capable to segment and recognize different class of characters. Previous researchers obtained the shape statistics from the whole map of level set values; however we employ only the front itself for shape description. Previously proposed systems need high calculation power because they have two optimization stages, one is for minimization of image energies, and other is for minimizing shape similarity energies. On the other hand, our system has one optimization step for minimizing both energies. Misrecognitions mostly occur because of segmentation problems. An object cannot be easily recognized if we cannot extract it from the background properly. In this study, many segmentation results are employed as input of classifiers to reduce the segmentation effects on recognition. In traditional recognition systems only one recognition chance exists for a single object but we can obtain many decision results while the active contour is capturing the shape. We showed in Section 5.4 that voting among these results raises the recognition performance comparing with single decision cases. We have feedback mechanism between segmentation and description. This feedback provides better segmentation and recognition results. Keywords: Shape descriptors, fourier, fast marching, steerable filters
