Flat Zone Analysis and a Sharpening Operation for Gradual Transition Detection on Video Images
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Flat Zone Analysis and a Sharpening Operation for Gradual Transition Detection on Video Images ˜ Silvio J. F. Guimaraes ´ ´ Laboratoire Algorithmique et Architecture des Syst`emes Informatiques, Ecole Sup´erieure d’Ing´enieurs en Electronique ´ et Electrotechnique, 93162 Noisy Le Grand Cedex, Paris, France Institute of Computing, Pontifical Catholic University of Minas Gerais, 31980-110 Belo Horizonte, MG, Brazil Email: [email protected]
Neucimar J. Leite Institute of Computing, State University of Campinas, 13084-971 Campinas, SP, Brazil Email: [email protected]
Michel Couprie ´ ´ Laboratoire Algorithmique et Architecture des Syst`emes Informatiques, Ecole Sup´erieure d’Ing´enieurs en Electronique ´ et Electrotechnique, 93162 Noisy Le Grand Cedex, Paris, France Email: [email protected]
´ Arnaldo de A. Araujo Computer Science Department, Universidade Federal de Minas Gerais, 6627 Pampulha, Belo Horizonte, MG, Brazil Email: [email protected] Received 1 September 2003; Revised 28 June 2004 The boundary identification represents an interesting and difficult problem in image processing, mainly if two flat zones are separated by a gradual transition. The most common edge detection operators work properly for sharp edges, but can fail considerably for gradual transitions. In this work, we propose a method to eliminate gradual transitions, which preserves the number of the image flat zones. As an application example, we show that our method can be used to identify very common gradual video transitions such as fades and dissolves. Keywords and phrases: flat zone analysis, video transition identification, visual rhythm.
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INTRODUCTION
The boundary identification represents an interesting and difficult problem in image processing mainly if two flat zones, defined as the sets of adjacent points with the same gray-scale value, are separated by a gradual transition. The most common edge detection operators like Sobel and Roberts [1] work well for sharp edges but fail considerably for gradual transitions. These transitions can be detected, for example, by a statistical approach proposed by Canny [2]. Another approach to cope with this problem is through mathematical morphology operators which include the notion of thick gradient and multiscale morphological gradient [3]. From this approach, and depending on the size of the
transition and its neighboring flat zones, the gradual transitions cannot be well detected. In this work, we consider the problem of detecting gradual transitions on images by a sharpening process which does not change their original number of flat zones. As an application example, we consider the problem of identifying gradual transitions such as fade and dissolve on digital videos. Usually, the common approach to this problem is based on dissimilarity measures used to identify the gradual transitions between consecutive shots [4]. In literature, we can find different types of dissimilarity measures used for video segmentation, such as pixel-wise and histogram-wise comparison. If two frames belong to
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