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Using appearance and context for outdoor scene object classification
Bosch Rué, Anna; Muñoz Pujol, Xavier; Martí Bonmatí, Joan
We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal
Discriminació visual
Imatges -- Processament
Imatges -- Segmentació
Reconeixement òptic de formes
Visió per ordinador
Computer vision
Image processing
Imaging segmentation
Optical pattern recognition
Visual discrimination
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