We present a new method (MIDES) to determine contraction kernels for the construction of graph pyramids. Experimentally the new method has a reduction factor higher than 2.0. thus, the new method yields a higher reduc...
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In the paper we propose an approach to the realization of models inspired to biological solutions for patternrecognition. the approach is based on a hierarchical modular structure capable to learn by examples and rec...
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In the paper we propose an approach to the realization of models inspired to biological solutions for patternrecognition. the approach is based on a hierarchical modular structure capable to learn by examples and recognize objects in digital images. the adopted techniques are based on multiresolution image correlation and neural networks. Performance on two different data sets and experimental timings on a SIMD machine are also reported.
Implementing Neural Networks in hardware has been a major problem due to the complexity involved in generating non-linear functions. the high hardware costs incurred in real time applications can be substantially redu...
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Implementing Neural Networks in hardware has been a major problem due to the complexity involved in generating non-linear functions. the high hardware costs incurred in real time applications can be substantially reduced by adopting a suitable reuse methodology of the neurons. In addition, neurons with high speed of operation are necessitated to realize hardware efficient real time patternrecognition for images with higher resolution. In this regard, the response time and area of a neuron becomes critical in realizing VLSI efficient neural networks. In this paper, the digital architecture of a multiple valued logic neuron has been proposed to realize a neural network implementation for real-time patternrecognition purposes. the proposed neuron uses a multi-level sigmoidal function as the activation function. Flat CORDIC, a new variation of the CORDIC algorithm, has been employed to generate the complex multi-level activation function in a VLSI efficient manner. the proposed neuron operates with a 200 MHz clock and has significant hardware and latency savings when compared to conventional CORDIC based neurons.
In dealing with large volume image data, sequential methods usually are too slow and unsatisfactory. this paper introduces a new system employing parallel matching in high-level recognition of 3D articulated objects. ...
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In dealing with large volume image data, sequential methods usually are too slow and unsatisfactory. this paper introduces a new system employing parallel matching in high-level recognition of 3D articulated objects. A new structural strategy using linear combination. and parallel graphic matching techniques is presented for 3D polyhedral objects representable by 2D line-drawings. It solves one of the basic concerns in diffusion tomography complexities, i.e. patterns can be reconstructed through fewer projections, and 3D objects can be recognized by a few learning sample views. It also improves some of the current methods while overcoming their drawbacks. Furthermore, it can distinguish very similar objects and is more accurate than other methods in the literature. An online webpage system for understanding and recognizing 3D objects is also illustrated.
this paper describes a procedure for data extraction and interpretation of section representations in vectorized line drawings. Vectorized drawings are considered as a graph representation. the hatch areas are analyse...
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We study the important issue of choosing representationsthat are suitable for recognizing pen based handwriting of characters in Tamil, a language of India. Four different choices, based on the following set of featu...
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We present a new algorithm based on Dual graph Contraction (DGC) to transform the Run graph into its Minimum Line Property Preserving (MLPP) form which, when implemented in parallel, requires O(log(longestcurve)) step...
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We focus to an application of control of printing quality in industrial environment. We try to value the quality in the sense of the readability of a text printed in disturbed conditions. We propose in this paper, top...
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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...
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(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-basedrecognition 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.
the authors describe an active 3D object recognition system that can learn complex 3D objects completely unsupervised and that can recognize previously learnt objects from different views. First a decision of which is...
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the authors describe an active 3D object recognition system that can learn complex 3D objects completely unsupervised and that can recognize previously learnt objects from different views. First a decision of which is the best next view is taken. the system- developed for this task is an iterative active perception system that executes the acquisition of several views of the object, builds a stochastic 3D model of the object and decides is the best next view to be acquired, based on an entropy measure. In this paper, we are focusing on a module for the recognition of objects in image sequences. We evaluate the optical flow in the sequence and extract a set of invariant features. As a pattern recognizer we suggest the Cellular Neural Network (CNN) architecture and generate an associative memory. the CNN paradigm is considered as a unifying model for spatio-temporal properties of the visual system.
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