1,721,022 research outputs found
Generalized Hough Transform for Shape Matching
Full text linkIn this paper we propose a novel approach towards shape matching for image retrieval. The system takes advantages of generalized Hough transform, as it works well in detecting arbitrary shapes even in the presence of gaps and in handling rotation, scaling and shift variations, and solves the heavy computational aspect by introducing a preliminary automatic selection of the appropriate contour points to consider in the matching phase. The numerical simulations and comparisons have confirmed the effectiveness and the efficiency of the method proposed
Recognition of Shapes by Attributed Skeletal Graphs
No full textIn this paper, we propose a framework to address the problem of generic 2-D shape recognition. The aim is mainly on using the potential strength of skeleton of discrete objects in computer vision and pattern recognition where features of objects are needed for classification. We propose to represent the medial axis characteristic points as an attributed skeletal graph to model the shape. The information about the object shape and its topology is totally embedded in them and this allows the comparison of different objects by graph matching algorithms. The experimental results demonstrate the correctness in detecting its characteristic points and in computing a more regular and effective representation for a perceptual indexing. The matching process, based on a revised graduated assignment algorithm, has produced encouraging results, showing the potential of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate its robustness in the presence of scale, reflection and rotation transformations and prove the ability to handle noise and occlusions
Decomposition of two-dimensional shapes for efficient retrieval
No full textThis paper presents a novel approach to address the problem of generic 2D shape recognition. We propose a morphological method to decompose a binary shape into entities in correspondence with their protrusions. Each entity is associated with a set of perceptual features that can be used in indexing into image databases. The matching process, based on the softassign algorithm, has produced encouraging results, showing the potential of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate its robustness in the presence of scale, reflection and rotation transformations and prove the ability to handle noise and articulated structures. In order to increase efficiency, the retrieval process is applied after a coarse scale grouping of objects, without sacrificing effectiveness and allowing indexing into large shape databases
Dominant points detection on digital curves: A comparison between optimal and exact approaches
No full textIn this work we address the problem of closed digital curves polygonal approximation by locating a set of relevant points having high curvature, the so-called dominant points. This set of feature points plays a dominant role in shape perception by humans and contains almost complete information of a given contour. There are several methods to extract dominant points based on different approaches; we look over two heuristic techniques, based on Ant Colony Optimization (ACO) and based on Genetic Algorithm (GAs), and an original method based on Dominant Points Iterative Localization (DP1L). We compare the three algorithms by evaluating the approximation error and testing their affine transformations invariance
Circularity measures based on mathematical morphology
No full textThe authors describe their study of the problem of circularity of a shape, and their experiments with new types of circularity functions designed for shape description and classification. The measures proposed are translation, rotation and scale invariant. The experimental results have proved very encouraging
A new iterative approach for dominant points extraction in planar curves
No full textIn this paper the problem of dominant point detection on digital curves is addressed. Based on an initial set of curvature points, our approach adds iteratively significant points by looking for the higher curvature contour points. The process continues until all the sums of the distances of contour points in the arcs subtended to the chord between two next dominant points is less then a predefined threshold. A final refinement process adjusts the position of located dominant points by a minimum integral square error criterion. We test our method by comparing its performance with other well known dominant point extraction techniques succesfully. In the last section some examples of polygonal approximation are shown
Histogram of Radon transform and texton matrix for texture analysis and classification
No full textIn this study, the authors introduce a new and efficient method to classify texture images. From the histogram of the Radon transform, a texture orientation matrix is obtained and combined with a texton matrix for generating a new type of co-occurrence matrix. From the co-occurrences matrix, 20 statistical features for texture images classification have been extracted: seven statistics of the first-level order and 13 of the second-level one. K-Nearest neighbour and support vector machine models are used for classification. The proposed approach has been tested on widely used texture datasets (Brodatz and University KTH Royal Institute of Technology Textures under varying Illumination, Pose and Scale) and compared with several different alternative methods. The experimental results show a very high-accuracy level, confirming the strength of the developed method which overcomes the state-of-the-art methods for texture classification
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