| The LNCS series reports state-of-the-art results in computer science research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community,with numerous individuals, as well as with prestigious organizations and societies, LNCS has grown into the most comprehensive computer science research forum available. The scope of LNCS, including its subseries LNAI and LNBI, spans the whole range of computer science and information technology including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes -proceedings (published in time for the respective conference) -post-proceedings (consisting of thoroughly revised final full papers) -research monographs (which may be based on outstanding PhD work,research projects, technical reports, etc.) |
| use of context in automatic annotation of sports videos content based retrieval of 3d data clifford geometric algebra:a promising framework for computer vision, robotics and learning .. adaptive color model for figure-ground segmentation in dynamic environments real-time infrared object tracking based on mean shift optimal positioning of sensors in 2d computer vision algorithms versus traditional methods in food technology: the desired correlation radiance function estimation for object classification detecting and ranking saliency for scene description decision fusion for object detection and tracking using mobile cameras selection of an automated morphological gradient threshold for image segmentation localization of caption texts in natural scenes using a wavelet transformation a depth measurement system with the active vision of the striped lighting and rotating mirror fast noncontinuous path phase-unwrapping algorithm based on gradients and mask color active contours for tracking roads in natural environments generation of n-parametric appearance-based models through non-uniform sampling gaze detection by wide and narrow view stereo camera a new auto-associative memory based on lattice algebra image segmentation using morphological watershed applied to cartography 3d object surface reconstruction using growing self-organised networks .single layer morphological perceptron solution to the n-bit parity problem robust self-organizing maps extended associative memories for recalling gray level patterns new associative memories to recall real-valued patterns …… author index |
内容加载中，请稍后...
商品评论（0条）