Browsing by Author "Melo-Pinto, Pedro"
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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.
- Hierarchical fuzzy logic based approach for object trackingPublication . Lopes, Nuno Vieira; Couto, Pedro M.; Jurio, Aranzazu; Melo-Pinto, PedroIn this paper a novel tracking approach based on fuzzy concepts is introduced. A methodology for both single and multiple object tracking is presented. The aim of this methodology is to use these concepts as a tool to, while maintaining the needed accuracy, reduce the complexity usually involved in object tracking problems. Several dynamic fuzzy sets are constructed according to both kinematic and non-kinematic properties that distinguish the object to be tracked. Meanwhile kinematic related fuzzy sets model the object's motion pattern, the non-kinematic fuzzy sets model the object's appearance. The tracking task is performed through the fusion of these fuzzy models by means of an inference engine. This way, object detection and matching steps are performed exclusively using inference rules on fuzzy sets. In the multiple object methodology, each object is associated with a confidence degree and a hierarchical implementation is performed based on that confidence degree.
- Multi-feature Tracking Approach Using Dynamic Fuzzy SetsPublication . Lopes, Nuno Vieira; Couto, Pedro; Melo-Pinto, PedroIn this paper a new tracking approach based in fuzzy concepts is introduced. The aim of this methodology is to incorporate in the proposed model the uncertainty underlying any problem of feature tracking, through the use of fuzzy sets. Several dynamic fuzzy sets are constructed according both cinematic (movement model) and non cinematic properties (image gray levels) that distinguish the feature. Meanwhile cinematic related fuzzy sets model the feature movement characteristics, the non cinematic fuzzy sets model the feature visible image related properties. The tracking task is performed through the fusion of these fuzzy models by means of a fuzzy inference engine. This way feature detection and matching steps are performed exclusively using inference rules on fuzzy sets.