Visual surveillance and activity analysis is an active research field of computer vision. As a result, there are several different algorithms produced for this purpose. To obtain more robust systems it is desirable to...
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We propose novel pixel dense modeling of motion of urban traffic in noisy environments with the help of multidimensional Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs). In our approach there is no need...
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We propose a new content based image retrieval method. The novelty of our approach lies in the applied image similarity measure: unlike traditional features, such as color, texture or shape, our measure is based on a ...
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We propose a new content based image retrieval method. The novelty of our approach lies in the applied image similarity measure: unlike traditional features, such as color, texture or shape, our measure is based on a painted representation of the original image. We use paintbrush stroke parameters as features. These strokes are produced by a stochastic paintbrush algorithm which simulates a painting process. Stroke parameters include color, orientation and location. Therefore, it provides information not only about the color content but also about the structural properties of an image. Experimental results on a database of more than 500 images show that the CBIR method using paintbrush features has a higher retrieval rate than methods using color features only.
Object-oriented motion segmentation is a basic step of the effective coding of image-series. Following the MPEG-4 standard we should define such objects. In this paper, a fully parallel and locally connected computati...
Object-oriented motion segmentation is a basic step of the effective coding of image-series. Following the MPEG-4 standard we should define such objects. In this paper, a fully parallel and locally connected computation model is described for segmenting frames of image sequences based on spatial and motion information. The first type of the algorithm is called early segmentation. It is based on spatial information only and aims at providing an over segmentation of the frame in real-time. Even if the obtained results do not minimize the number of regions, it is a good starting point for higher level post processing, when the decision on how to regroup regions in object can rely on both spatial and temporal information. In the second type of the algorithm stochastic optimization methods are used to form homogenous dense optical vector fields which act directly on motion vectors instead of 2D or 3D motion parameters. This makes the algorithm simple and less time consuming than many other relaxation methods. Then we apply morphological operators to handle disocclusion effects and to map the motion field to the spatial content. Computer simulations of the CNN architecture demonstrate the usefulness of our methods. All solutions in our approach suggest a fully parallel implementation in a newly developed CNN-UM VLSI chip architecture.
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