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In this paper, new intra and inter shape coding algorithms are proposed. The new algorithms are based on chain code. Chain code of an object's contour can be divided into several segments by the smooth contour characteristic. By this property, some techniques can be used for the proposed intra and inter shape coding algorithms. In intra mode, each segment is encoded by specific codes, and the decoding result is lossless. Compared with MPEG-4, M4R, DCC and chain code, the compression ratio is improved. Besides, a new coding scheme is proposed for the inter shape coding. It includes finding break points in a series of chain code, correlation between break points and fine scaling with a tolerant threshold between two similar chain code segments. By detecting the segments, break points can be found. The correlation is performed by computing the curvature difference between break points and contour points. The scaling is a technique for extending or shrinking a segment of chain code. Experimental results show that in the condition of high quality or low bit rate our proposed inter shape coding algorithm obtains better performance than MPEG-4 in compression.

La segmentación de imágenes es una técnica que tiene como finalidad dividir una imagen en un conjunto de regiones, asignando a cada objeto en la escena una o varias regiones. Para obtener una segmentación correcta, cada una de las regiones debe cumplir con un criterio de homogeneidad impuesto a priori. Cuando se fija un criterio de homogeneidad, lo que implícitamente se esta haciendo es asumir un modelo matemático que caracteriza las regiones.En esta tesis se introduce un nuevo tipo de modelo denominado modelo jerárquico, ya que tiene dos niveles diferentes sobrepuestos uno sobre el otro. El nivel inferior (o subyacente) modela la posición que ocupa cada una de las regiones dentro de la imagen; mientras que, por su parte, el nivel superior (u observable) esta compuesto por un conjunto de submodelos independientes (un submodelo por región) que caracterizan el comportamiento del interior de las regiones. Para el primero se usa un campo aleatorio Markoviano de orden dos que modelara los contornos de las regiones, mientras que para el segundo nivel se usa un modelo Gausiano. En el trabajo se estudian los mejores potenciales que deben asignarse a los tipos de agrupaciones que permiten definir los contornos. Con todo ello la segmentación se realiza buscando la partición más probable (criterio MAP) para una realización concreta (imagen observable).El proceso de búsqueda de la partición optima para imágenes del tamaño habitual seria prácticamente inviable desde un punto de vista de tiempo de cálculo. Para que se pueda realizar debe partirse de una estimación inicial suficientemente buena y de una algoritmo rápido de mejora como es una búsqueda local. Para ello se introduce la técnica de segmentación piramidal (multirresolucion). La pirámide se genera con filtrado Gausiano y diezmado. En el nivel mas alto de la pirámide, al tener pocos píxels, si que se puede encontrar la partición óptima.

This paper concerns methods of verification of person's signature and handwriting. Some of commonly used techniques are resumed and described with related literature being referred. Next aim of this work is design and implementation of a simple handwriting verification application. Application is based on edge detection and comparison of a set of structural and statistical features. As a support classification tool a SVM classifier of the LIBSVM software is employed. The Application is written in C language using OpenCV graphics library. Testing and training set was extracted from samples found in the IAM Handwriting Database. Application was created and tested in the Windows XP operating system.

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This master’s thesis describes the design and implementation of the application that created the basis set pieces "puzzle" according to the shape of the folded edges of the original image. This application is developed using Matlab. The work also describes how to create a database of actual pieces of the puzzle composite photo image. Closer was also focused on finding the characteristic section points, their segmentation and appropriate description. There is dismantled procedure for selecting the types of symptoms and their extraction. On the basis of suitably described pieces of segmented parts is designed and implemented the algorithm of comparing and grouping into clusters. Using the proposed method of visualization is then displayed in the resulting composite picture puzzle.

This master’s thesis describes the design and implementation of the application that created the basis set pieces "puzzle" according to the shape of the folded edges of the original image. This application is developed using Matlab. The work also describes how to create a database of actual pieces of the puzzle composite photo image. Closer was also focused on finding the characteristic section points, their segmentation and appropriate description. There is dismantled procedure for selecting the types of symptoms and their extraction. On the basis of suitably described pieces of segmented parts is designed and implemented the algorithm of comparing and grouping into clusters. Using the proposed method of visualization is then displayed in the resulting composite picture puzzle.

Neural networks with algorithm back-propagation will be presented in this work. Theoretical background of the algorithm will be explained. The problems with training neural nets will be solving there. The work discuss some techniques of image preprocessing and image extraction features, which is one of main part in classification. Some part of work discuss few experiments with neural nets with chosen image features.