Book Chapter10.1007/3-540-47967-8_8
Class-Specific, Top-Down Segmentation
Eran Borenstein,Shimon Ullman +1 more
- 28 May 2002
- pp 109-124
TL;DR: A novel class-based segmentation method, which is guided by a stored representation of the shape of objects within a general class (such as horse images), which leads to markedly improved segmentation results and can deal with significant variation in shape and varying backgrounds.
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Abstract: In this paper we present a novel class-based segmentation method, which is guided by a stored representation of the shape of objects within a general class (such as horse images). The approach is different from bottom-up segmentation methods that primarily use the continuity of grey-level, texture, and bounding contours. We show that the method leads to markedly improved segmentation results and can deal with significant variation in shape and varying backgrounds. We discuss the relative merits of class-specific and general image-based segmentation methods and suggest how they can be usefully combined.
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Citations
Scale & Affine Invariant Interest Point Detectors
TL;DR: A comparative evaluation of different detectors is presented and it is shown that the proposed approach for detecting interest points invariant to scale and affine transformations provides better results than existing methods.
Object class recognition by unsupervised scale-invariant learning
Rob Fergus,Pietro Perona,Andrew Zisserman +2 more
- 18 Jun 2003
TL;DR: The flexible nature of the model is demonstrated by excellent results over a range of datasets including geometrically constrained classes (e.g. faces, cars) and flexible objects (such as animals).
Hypercolumns for object segmentation and fine-grained localization
Bharath Hariharan,Pablo Arbeláez,Ross Girshick,Jitendra Malik +3 more
- 07 Jun 2015
TL;DR: In this paper, the authors define the hypercolumn at a pixel as the vector of activations of all CNN units above that pixel, and use hypercolumns as pixel descriptors.
Robust Object Detection with Interleaved Categorization and Segmentation
TL;DR: A novel method for detecting and localizing objects of a visual category in cluttered real-world scenes that is applicable to a range of different object categories, including both rigid and articulated objects and able to achieve competitive object detection performance from training sets that are between one and two orders of magnitude smaller than those used in comparable systems.
•Posted Content
Hypercolumns for Object Segmentation and Fine-grained Localization
TL;DR: Using hypercolumns as pixel descriptors, this work defines the hypercolumn at a pixel as the vector of activations of all CNN units above that pixel, and shows results on three fine-grained localization tasks: simultaneous detection and segmentation, and keypoint localization.
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A region growing and merging algorithm to color segmentation
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Contour Continuity in Region Based Image Segmentation
Thomas Leung,Jitendra Malik +1 more
- 02 Jun 1998
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