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The problems of dense stereo reconstruction and object class segmentation can both be formulated as Random Field labeling problems, in which every pixel in the image is assigned a label corresponding to either its disparity, or an object class such as road or building. While these two problems are mutually informative, no attempt has been made to jointly(More)
BACKGROUND Previous systems for dot (signal) counting in fluorescence in situ hybridization (FISH) images have relied on an auto-focusing method for obtaining a clearly defined image. Because signals are distributed in three dimensions within the nucleus and artifacts such as debris and background fluorescence can attract the focusing method, valid signals(More)
This paper aims to improve the performance of an HMM-based offline Thai handwriting recognition system through discriminative training and the use of fine-tuned feature extraction methods. The discriminative training is implemented by maximizing the mutual information between the data and their classes. The feature extraction is based on our proposed(More)
The problems of object class segmentation [2], which assigns an object label such as road or building to every pixel in the image and dense stereo reconstruction, in which every pixel within an image is labelled with a disparity [1], are well suited for being solved jointly. Both approaches formulate the problem of providing a correct labelling of an image(More)