In this model we proposed on algorithm for object tracking using ‘graph –cut method’ and for labeling the pixels in graph-cut we use Markov random field (mrf) ,this theorem relies on Bayesian estimation associated with combinational optimization. The segmentation is obtained by classifying the pixels in to different pixel classes. These classes are represented by multivariate Gaussian distribut...

An efficient colorization scheme for images based on prioritized source propagation is proposed in this work. A user first scribbles colors on a set of source pixels in an image. The proposed algorithm then propagates those colors to the other non-source pixels and the subsequent frames. Specifically, the proposed algorithm identifies the non-source pixel with the highest priority, which can be...

We present an alternative to traditional texture compression techniques whereby a clear data channel is embedded within a texture using steganographic techniques. The resulting pixels are polymorphic – they can be used without decompression and the embedded data can be extracted and used for alternative purposes. The technique has fewer degenerate cases than DXT, does not suffer from high-frequ...

In this paper we present an segmentation technique which uses super pixels as the basic unit of operation. The image is first converted into a super pixel image. Then the rules framed by Gestalt based on study of cognitive science are used to extract the features of image such as texture, edges and spatial information. The rule of locality determines the neighboring pixels. The rule of proximit...

We explore design principles for general pixel-level prediction problems, from low-level edge detection to midlevel surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fullyconvolutional network (FCN), have achieved remarkable success by exploiting the spatial redundancy of neighboring pixels through convolutional processing. Though computational...