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We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction and baseline adjustment. We incorporate recent advances in feature matching, energy minimization, stereo vision and data clustering into our...
Shift-map image processing is a new framework based on energy minimization over a large space of labels. The optimization utilizes alpha-expansion moves and iterative refinement over a Gaussian pyramid. In this paper we extend the range of applications to image registration. To do this, new data and smoothness terms have to be constructed. We note a great improvement when we measure pixel similarities...
Images captured in foggy weather conditions exhibit losses in quality which are dependent on distance. If the depth and atmospheric conditions are known, one can enhance the images (to some degree) by compensating for the effects of the fog. Recently, several investigations have presented methods for recovering depth maps using only the information contained in a single foggy image. Each technique...
In this paper we propose a new method that addresses the problem of tracking the bitmap (silhouette) of an object in a video under very general conditions. We assume a general target, possibly non rigid, with no prior information except initialization. The target, as well as the background, may change its appearance over time and the camera may move arbitrarily. The proposed algorithm fuses different...
Graph-cut methods have become an intensive area of research in image processing, with applications to stereo vision, binary thresholding, classification and image de-noising amongst other areas. This paper describes novel graph-cut implementations for use in two applications: 2D optical flow and interactive object delineation. The optical flow implementation is based on recursive application of graph-cuts,...
This paper addresses the problem of unsupervised change detection in high spatial resolution optical remote sensing images based on Hopfield neural network (HNN). An optimization relaxation approach based on the analysis of a modified Hopfield neural network is proposed for solving the change detection problem. The modified Hopfield neural network is designed to characterize a texture in terms of...
The paper presents an extension of the gradient method for detection of red lesions on images of eye fundus, like a step in an automated system for recognition of diabetic retinopathy. Images of high resolution were corrected for shading and noise and the method was applied by calculating on every pixel the ldquoexpanding gradientrdquo of that pixel. This way, a map of the image was obtained and by...
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