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- W2768898137 abstract "This chapter introduces the use of shape models for object segmentation. Although the latter has often been carried out using raw thresholding or edge detection, such processes are liable to stray far-off course because of random image artifacts such as noise and shadows. The idea of using shape models is to control the situation so that the segmentations obtained are more meaningful. Active contour models or “snakes” provide a means for tackling this problem: principal components analysis (PCA)-based methods provide another. The snake approach is subject to difficulties because all the necessary rules have to be incorporated into the snake analytically without acknowledging exactly what type of shape it will have: PCA-based methods are more powerful as they train the system to know exactly what type of shape is being sought. However, they involve substantial amounts of complication and computation, though as a result, they can extract objects from regions of considerable noise." @default.
- W2768898137 created "2017-12-04" @default.
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- W2768898137 date "2018-01-01" @default.
- W2768898137 modified "2023-09-24" @default.
- W2768898137 title "Object segmentation and shape models" @default.
- W2768898137 doi "https://doi.org/10.1016/b978-0-12-809284-2.00012-5" @default.
- W2768898137 hasPublicationYear "2018" @default.
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