1,721,089 research outputs found
Guest Editorial on Decision Making in Human and Machine Vision
No full textGranular information processing is one of the human-inspired problem-solving aspects of natural computing, as information abstraction is inherent in human thinking and reasoning processes, and plays an essential role in human cognition. Among the different facets of natural computing fuzzy sets, rough sets and their hybridization are well accepted paradigms that are based on the construction, representation and interpretation of granules, as well as the utilization of granules for problem solving. These tools are also known as primary constituents of soft computing whose objective is to provide flexible information processing capability for handling real-life ambiguous situations. They have been successfully employed in various image processing tasks, including image segmentation, enhancement and classification, both individually or in combination with other computing techniques. The reason of such success is rooted to the fact that they provide powerful tools to describe uncertainty, naturally embedded in images, which can be exploited in various image processing tasks
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
No full textMATRIOSKA: A Multi-level Approach to Fast Tracking by Learning
No full textIn this paper we propose a novel framework for the detection and tracking in real-time of unknown object in a video stream. We decompose the problem into two separate modules: detection and learning. The detection module can use multiple keypoint-based methods (ORB, FREAK, BRISK, SIFT, SURF and more) inside a fallback model, to correctly localize the object frame by frame exploiting the strengths of each method. The learning module updates the object model, with a growing and pruning approach, to account for changes in its appearance and extracts negative samples to further improve the detector performance. To show the effectiveness of the proposed tracking-by-detection algorithm, we present quantitative results on a number of challenging sequences where the target object goes through changes of pose, scale and illumination
A Two Subcycle Thinning Algorithm and its Parallel Implementation on SIMD Machines
No full textA new parallel thinning algorithm with two subcycles is proposed and compared with other parallel thinning algorithms in terms of 8-connectedness degree, erosion, stability under pattern rotation, and boundary noise sensitivity. Computational issues are also reported based on the implementation of the thinning algorithm on the SIMD machines CM-200 and MasPar MPP-12000. © 2000 IEEE
Distributed recursive learning for shape recognition through multiscale trees.
Full text linkThe paper reports an efficient and fully parallel 2D shape recognition method based on the use of a multiscale tree representation of the shape boundary and recursive learning of trees. Specifically, the shape is represented by means of a tree where each node, corresponding to a boundary segment at some level of resolution, is characterized by a real vector containing curvature, length, symmetry of the boundary segment, while the
nodes are connected by arcs when segments at successive levels are spatially related. The recognition procedure is formulated as a training procedure made by a Fuzzy recursive neural network followed by a testing procedure over unknown tree structured patterns. The proposed neural network model is able to facilitate the exchange of information between symbolic and sub-symbolic domains and deal with structured organization of information, that is typically required by symbolic processing
Salient feature based graph matching for person re-identification
No full textWe propose a person re-identification non-learning based approach that uses symmetry principles, as well as structural relations among salient features. The idea comes from the consideration that local symmetries, at different scales, also enforced by texture features, are potentially more invariant to large appearance changes than lower-level features such as SIFT, ASIFT. Finally, we formulate the re-identification problem as a graph matching problem, where each person is represented by a graph aimed not only at rejecting erroneous matches but also at selecting additional useful ones.
Experimental results on public dataset i-LIDS provide good performance compared to state-of-the-art results
Self-similarity and Points of Interest in Textured Images
No full textWe propose the application of symmetry for texture classification. First we propose a feature vector based on the distribution of local bilateral symmetry in textured images. This feature is more effective in classifying a uniform texture versus a non-uniform texture. The feature when used with a texton-based feature improves the classification rate and is tested on 4 texture datasets. Secondly, we also present a global clustering of texture based on symmetry
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