KEYWORDS: Image segmentation, Video, Detection and tracking algorithms, Cameras, Digital filtering, Visualization, Video processing, Analytical research, Motion analysis, Semantic video
The present paper introduces a very specific and pragmatic approach to segmentation. It is driven by a particular application context: in the framework of mixed-reality, Tranfiction (“transportation into fictional spaces”) is designed to mix synthetic and natural images in real time while allowing users to interact in these input/output screens. Segmentation is therefore used to provide both the immersion and interaction capabilities. The former aspect is achieved by composing the image of the user within the projected virtual scenes, while the later is achieved thanks to basic body/gesture analysis on the segmented silhouettes. According to indoor or outdoor usages, two real-time techniques are developed. Results are analyzed with respect to the overall application, not only in terms of absolute quality but also in terms of perception by the users.
This paper introduces an automatic tool able to analyze the picture according to the semantic interest an observer attributes to its content. Its aim is to give a 'level of interest' to the distinct areas of the picture extracted by any segmentation tool. For the purpose of dealing with semantic interpretation of images, a single criterion is clearly insufficient because the human brain, due to its a priori knowledge and its huge memory of real-world concrete scenes, combines different subjective criteria in order to assess its final decision. The developed method permits such combination through a model using assumptions to express some general subjective criteria. Fuzzy logic enables the user to encode knowledge in a form that is very close the way experts think about the decision process. This fuzzy modeling is also well suited to represent multiple collaborating or even conflicting experts opinions. Actually, the assumptions are verified through a non-hierarchical strategy that considers them in a random order, each partial result contributing to the final one. Presented results prove that the tool is effective for a wide range of natural pictures. It is versatile and flexible in that it can be used stand-alone or can take into account any a priori knowledge about the scene.
This paper presents a very low bit-rate coding algorithm based on image split in order to represent it through an adaptive multigrid supported by a binary tree structure. Independently of its tree representation, the picture is segmented via a watershed procedure and several criteria are combined to automatically extract interesting areas of the image. This object information is not transmitted but used to reduce picture complexity, and therefore the bit-rate, while keeping a good subjective quality. This is achieved by a merge procedure which homogenizes values of the tree subblocks belonging to a same non-interesting object. This treatment affects both intra- and inter-images. For intra-images, the resulting tree structure is entropy coded while its leaves are encoded through a DPCM procedure followed by a multi- huffman coder. For inter-images, a motion field is adaptated by an adaptative block matching algorithm which is a kind of BMA for which blocksize is chosen in order to reach a sufficient level of confidence. Residues, essential to correct motion compensation artifacts, are sent through local intra-trees or, if the bit-rate allows it, through DCT blocks, allowing to reach an arbitrary level of quality. During the reconstruction step, an object oriented approach combined with the use of overlapping functions allows to reduce block artifacts while keeping sharp edges.
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