Titlebook: Computer Vision - ECCV ‘96; Fourth European Conf Bernard Buxton,Roberto Cipolla Conference proceedings 1996 Springer-Verlag Berlin Heidelbe

严峻

书目名称Computer Vision - ECCV ‘96影响因子(影响力)




书目名称Computer Vision - ECCV ‘96影响因子(影响力)学科排名




书目名称Computer Vision - ECCV ‘96网络公开度




书目名称Computer Vision - ECCV ‘96网络公开度学科排名




书目名称Computer Vision - ECCV ‘96被引频次




书目名称Computer Vision - ECCV ‘96被引频次学科排名




书目名称Computer Vision - ECCV ‘96年度引用




书目名称Computer Vision - ECCV ‘96年度引用学科排名




书目名称Computer Vision - ECCV ‘96读者反馈




书目名称Computer Vision - ECCV ‘96读者反馈学科排名

微不足道

Texture feature coding method for classification of liver sonography,d a . co-occurrence matrix which will produce texture feature descriptors. By coupling with a supervised maximum likelihood (ML) classifier, these descriptors form a classification system to discriminate the three above-mentioned liver classes. The TFCM-supervised ML system is trained by 30 liver sa

存在主义

https://doi.org/10.1007/978-3-642-79739-2nsional subspace even under severe variation in lighting and facial expressions. The Eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the propos

pancreas

mydriatic

Self-calibration from image triplets,ly via the trifocal tensor between image triplets; third, a robust and automatic implementation of the method..Results are included of affine and metric calibration and structure recovery using images of real scenes.

Aviary

Class based reconstruction techniques using singular apparent contours,bility even under large noise. This work has added to the accumulating body of work that has arisen in the computer vision community, concerning the differential geometric aspects of special surface classes.

Aviary

watertight,

KIN

Oriented projective geometry for computer vision, system of cameras, while extending its adequation to model realistic situations..We discuss the mathematical and practical issues raised by this new framework for a number of computer vision algorithms. We present different experiments where this new tool clearly helps.

藐视

Fast computation of the fundamental matrix for an active stereo vision system,form for an active stereo system. We demonstrate that typical variations in camera intrinsic parameters do not much affect the epipolar geometry in the image. This motivates us to calibrate the camera intrinsic parameters approximately and then to use the calibration results to compute the epipolar geometry directly in real time.