Titlebook: Computer Vision, Graphics and Image Processing; 5th Indian Conferenc Prem K. Kalra,Shmuel Peleg Conference proceedings 2006 Springer-Verlag

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書目名稱Computer Vision, Graphics and Image Processing影響因子(影響力)




書目名稱Computer Vision, Graphics and Image Processing影響因子(影響力)學科排名




書目名稱Computer Vision, Graphics and Image Processing網(wǎng)絡公開度




書目名稱Computer Vision, Graphics and Image Processing網(wǎng)絡公開度學科排名




書目名稱Computer Vision, Graphics and Image Processing被引頻次




書目名稱Computer Vision, Graphics and Image Processing被引頻次學科排名




書目名稱Computer Vision, Graphics and Image Processing年度引用




書目名稱Computer Vision, Graphics and Image Processing年度引用學科排名




書目名稱Computer Vision, Graphics and Image Processing讀者反饋




書目名稱Computer Vision, Graphics and Image Processing讀者反饋學科排名

極少

Greyscale Photograph Geometry Informed by Dodging and Burningpart, to the liberal use of dodging and burning in photography. Measurements which are invariant to these transformations can be used to extract information from photographs which is not sensitive to certain alterations in the development process. These measurements are explored through the construc

罵人有污點

A Discontinuity Adaptive Method for Super-Resolution of License Platesithm uses the information available from multiple, sub-pixel shifted, and noisy low-resolution observations to reconstruct a high-resolution image of the number plate. The image to be super-resolved is modeled as a Markov random field and is estimated from the low-resolution observations by a gradua

Commonwealth

谷類

DEFT

DEFT

An Automatic Image Segmentation Technique Based on Pseudo-convex Hull including noisy and poor quality images. It is fully automatic and has low computational cost. It may be noted that the proposed segmentation technique may not produce optimal result in some cases but it gives reasonably good result for almost all images of a large class. Hence, the method is found

FID

Single-Histogram Class Models for Image Segmentation to represent an object class by a set of histograms, each one corresponding to a training exemplar. Classification is then achieved by k-nearest neighbour search over the exemplars..In this paper we introduce two novelties on this approach: (i) we show that new compact . histogram models estimated

乳白光

泛濫