Abstract— Image segmentation is a very important step in image processing. Extracting useful information from an image is the goal of image segmentation. The purpose of image segmentation is to partition an image into meaningful regions with respect to a particular application. Local image information is crucial for accurate segmentation of images with intensity in homogeneity. A region-based active contour model that is able to utilize image information in local regions. The major contribution of Local Binary Fitting Model is the introduction of a local binary fitting energy with a kernel function, which LBF enables the extraction of accurate local image information. Therefore LBF can be used to segment images with intensity in homogeneity, which overcomes the limitation of piecewise constant models. The active contour/snake model is one of the most successful variation models in image segmentation. It consists of evolving a contour in images toward the boundaries of objects. A global minimum of the active contour model approach is based on the unification of image segmentation and image denoising tasks into a global minimization framework. Image segmentation is an important application of image processing. In Extraction Algorithm achieved the segmentation and extraction of symbols using minimum spanning tree based segmentation method. The presents the extraction of symbols and characters from document images and describes the number of symbols extracted from the images. Symbols itself include all characters and characters includes all the letters and numbers. The focus is on the black and white images. Basically this is achieved by using image segmentation in color domain. That is why each and every symbol or character in document images should be disjoint. The algorithm also extracts the handwritten symbols and characters from binary images.

Abstract— Image segmentation is a very important step in image processing. Extracting useful information from an image is the goal of image segmentation. The purpose of image segmentation is to partition an image into meaningful regions with respect to a particular application. Local image information is crucial for accurate segmentation of images with intensity in homogeneity. A region-based active contour model that is able to utilize image information in local regions. The major contribution of Local Binary Fitting Model is the introduction of a local binary fitting energy with a kernel function, which LBF enables the extraction of accurate local image information. Therefore LBF can be used to segment images with intensity in homogeneity, which overcomes the limitation of piecewise constant models. The active contour/snake model is one of the most successful variation models in image segmentation. It consists of evolving a contour in images toward the boundaries of objects. A global minimum of the active contour model approach is based on the unification of image segmentation and image denoising tasks into a global minimization framework. Image segmentation is an important application of image processing. In Extraction Algorithm achieved the segmentation and extraction of symbols using minimum spanning tree based segmentation method. The presents the extraction of symbols and characters from document images and describes the number of symbols extracted from the images. Symbols itself include all characters and characters includes all the letters and numbers. The focus is on the black and white images. Basically this is achieved by using image segmentation in color domain. That is why each and every symbol or character in document images should be disjoint. The algorithm also extracts the handwritten symbols and characters from binary images.

Abstract— Reversible logic has become one of the promising research directions in low power dissipating circuit design in the past few years and has found its applications in low power CMOS design, cryptography, digital signal processing, optical information processing and nanotechnology. This paper presents a quantum cost efficient reversible full adder gate in nanotechnology. This gate can work singly as a reversible full adder unit and requires only one clock cycle. The proposed gate is a universal gate in the sense that it can be used to synthesize any arbitrary Boolean functions.