One of the main tasks in the problem of engineering drawing (ED) automatic input and interpretation is recognition of ED primitives to obtain 2D image representation for further use in CAD systems. this paper presents...
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the goal of this work is the fast extraction of relevant information from document images. Examples of interesting information are the type of document (e.g. form, report, letter), the title of an article or the sende...
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the proceedings contain 113 papers. the special focus in this conference is on Structural Matching, Grammatical Inference and recognition of 2D and 3D Objects. the topics include: Error-tolerant graph matching;semanti...
ISBN:
(纸本)3540648585
the proceedings contain 113 papers. the special focus in this conference is on Structural Matching, Grammatical Inference and recognition of 2D and 3D Objects. the topics include: Error-tolerant graph matching;semantic content based image retrieval using object-process diagrams;patternrecognition methods in image and video databases;efficient matching with invariant local descriptors;integrating numerical and syntactic learning models for patternrecognition;synthesis of function-described graphs;marked subgraph isomorphism of ordered graphs;distance evaluation in pattern matching based on frontier topological graph;syntactic interpolation of fractal sequences;minimizing the topological structure of line images;genetic algorithms for structural editing;the noisy subsequence tree recognition problem;object recognition from large structural libraries;acquisition of 2-d shape models from scenes with overlapping objects using string matching;a taxonomy of occlusion in view signature ii representations;a survey of non-thinning based vectorization methods;a benchmark for raster to vector conversion systems;network-based recognition of architectural symbols;recovering image structure by model-based interaction map;an improved scheme to fingerprint classification;character recognition with k-head finite array automata;using semantics in matching cursive chinese handwritten annotations;concavity detection using a binary mask-based approach;structural indexing of line pictures with feature generation models;nonlinear covariance for multi-band image data;a neural network for image smoothing and segmentation;prototyping structural descriptions and neural network based learning of local compatibilities for segment grouping.
Function-described graphs (FDGs) have been introduced very recently as a representation of an ensemble of attributed relational graphs (ARGs) for structural patternrecognition [1, 2]. In this paper, the relationship ...
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the nonlinear classifier is effective for many practical problems. We have already proposed a method for constructing a nonlinear classifier using Legendre polynomials and have obtained good results on many actual dat...
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In this paper we consider the problem of recognizing ordered labeled trees by processing their noisy subsequence-trees which are "patched-up" noisy portions of their fragments. We assume that we are given H,...
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A model registration system capable of tracking an object, the model of which is known, in an image sequence is presented. It integrates tracking, pose determination and updating of the visible features. the heart of ...
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A model registration system capable of tracking an object, the model of which is known, in an image sequence is presented. It integrates tracking, pose determination and updating of the visible features. the heart of our system is the pose computation method, which handles various features (points, lines and free-form curves) in a very robust way and is able to give a correct estimate of the pose even when tracking errors occur. the reliability of the system is shown on an augmented reality project.
A face recognition system is described which employs a fuzzy information fusion technique to increase the overall recognition rate. the face images are searched for locating head area and face boundary. the eyes, and ...
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A face recognition system is described which employs a fuzzy information fusion technique to increase the overall recognition rate. the face images are searched for locating head area and face boundary. the eyes, and mouth are detected using rigid and deformable templates. Assuming a 3D head model, the face rotations are estimated, which allows for compensating rotated facial features back to a front, upright view. Each facial feature forms a source of information for classification. Based on a correlation technique using eye-forehead, mouth and nose windows, three classifiers are established. the output of each classifier is taken as a partial evidence in classification. the importance of each source is measured using a fuzzy density measure and the final classification is achieved using a fuzzy evidence aggregation method. the performance of the system is evaluated using a combined match score.
Most of the research on face recognition addresses the MATCH problem and it assumes a closed universe where there is no need for a REJECT ('false positive') option. the SURVEILLANCE problem is addressed indire...
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ISBN:
(纸本)8173192219
Most of the research on face recognition addresses the MATCH problem and it assumes a closed universe where there is no need for a REJECT ('false positive') option. the SURVEILLANCE problem is addressed indirectly, if at all, through the MATCH problem, where the size of the gallery rather than that of the probe set is very large. this paper addresses the. proper surveillance problem where the size of the probe ('un known image') set vs gallery ('known image') set is 450 vs 50 frontal images. We developed robust face ID verification ('classification') and retrieval schemes based on hybrid classifiers and showed their feasibility using the FERET face data base. the hybrid classifier architecture consists of an ensemble of connectionist networks - Radial Basis Functions (RBF) and inductive decision-trees (DT). Experimental results prove the feasibility of our approach and yield 97% accuracy using the probe and gallery sets specified above.
We introduce a novel view-based object representation, called the saliency map graph (SMG), which captures the salient regions of an object view at multiple scales using a wavelet transform. this compact representatio...
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We introduce a novel view-based object representation, called the saliency map graph (SMG), which captures the salient regions of an object view at multiple scales using a wavelet transform. this compact representation is highly invariant to translation, rotation (image and depth), and scaling, and offers the locality of representation required for occluded object recognition. To compare two saliency map graphs, we introduce two graph similarity algorithms. the first computes the topological similarity between two SMG's, providing a coarse-level matching of two graphs. the second computes the geometrical similarity between two SMG's, providing a fine-level matching of two graphs. We test and compare these two algorithms on a large database of model object views.
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