Browsing by Author "Bustince, Humberto"
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- Automatic histogram threshold using fuzzy measuresPublication . Lopes, Nuno Vieira; Couto, Pedro M.; Bustince, Humberto; Melo-Pinto, PedroIn this paper, an automatic histogram threshold approach based on a fuzziness measure is presented. This work is an improvement of an existing method. Using fuzzy logic concepts, the problems involved in finding the minimum of a criterion function are avoided. Similarity between gray levels is the key to find an optimal threshold. Two initial regions of gray levels, located at the boundaries of the histogram, are defined. Then, using an index of fuzziness, a similarity process is started to find the threshold point. A significant contrast between objects and background is assumed. Previous histogram equalization is used in small contrast images. No prior knowledge of the image is required.
- Fuzziness measure approach to automatic histogram thresholdPublication . Lopes, Nuno Vieira; Bustince, Humberto; Filipe, Vitor; Melo-Pinto, PedroIn this paper, an automatic histogram threshold approach based on a fuzziness measure is presented. This work is an improvement of an existing method. Using fuzzy logic concepts, the problems to find a minima of a criterion function are avoided. Similarity between gray levels is the key to find an optimal threshold. Two initial regions of gray levels, located at the boundaries of the histogram, are defined. Then, using an index of fuzziness, a similarity process is started to find the threshold point. A significant contrast between objects and background is assumed. Previous histogram equalization is used in small contrast images. No prior knowledge of the image is required.
- Fuzzy Dynamic Matching Approach for Multi-Feature TrackingPublication . Lopes, Nuno Vieira; Couto, Pedro A.; Bustince, Humberto; Melo-Pinto, PedroFeature tracking is one of the most challenging and important tasks in computer vision playing an important role in several areas. In this paper, a new approach for multi feature tracking is presented. Information from the image gray levels and the features movement model is aggregated through the use of fuzzy sets with a fuzzy inference engine to give the final output. Experimental results are presented showing that the approach successfully copes with usual difficulties within this problem.
- Fuzzy dynamic model for feature trackingPublication . Couto, Pedro; Lopes, Nuno Vieira; Bustince, Humberto; Melo-Pinto, PedroFeature tracking is one of the most challenging and important tasks in Motion Analysis which plays an important role in several areas of Computer Vision. In this work, a novel approach for feature tracking based on Fuzzy concepts is introduced. Fuzzy Sets related with both cinematic (movement model) and non cinematic (image gray levels) properties are constructed in order to model the feature motion. Meanwhile cinematic related fuzzy sets model the feature movement characteristics, the non cinematic fuzzy sets model the feature visible image related properties. The final motion model is obtained through the fusion of these fuzzy models by means of a fuzzy inference engine. Experimental results are presented showing that the approach successfully copes with usual difficulties within this problem.