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MOORE, WARRELL AND PRINCE: HIERARCHIAL BOUNDARY PRIORS 1 Vistas: Hierarchial boundary priors using multiscale conditional random fields.

by Alastair P. Moore, Jonathan Warrell, Simon J. D. Prince
"... Boundary detection is a fundamental problem in computer vision. However, bound-ary detection is difficult as it involves integrating multiple cues (intensity, color, texture) as well as trying to incorporate object class or scene level descriptions to mitigate the am-biguity of the local signal. In ..."
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boundary detection algorithms do not generalize well to these complex scenes. We show that our algorithm successfully learns these boundary distributions and can exploit this knowledge to improve state-of-the-art bound-ary detectors. 1

Vistas: Hierarchial boundary priors using multiscale conditional random fields.

by Jonathan Warrell, Alastair P. Moore, Simon J. D. Prince
"... Boundary detection is a fundamental problem in computer vision. However, bound-ary detection is difficult as it involves integrating multiple cues (intensity, color, texture) as well as trying to incorporate object class or scene level descriptions to mitigate the am-biguity of the local signal. In ..."
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boundary detection algorithms do not generalize well to these complex scenes. We show that our algorithm successfully learns these boundary distributions and can exploit this knowledge to improve state-of-the-art bound-ary detectors. 1

Object Detection with Discriminatively Trained Part Based Models

by Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester, Deva Ramanan
"... We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their ..."
Abstract - Cited by 1422 (49 self) - Add to MetaCart
We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular

Neural Network-Based Face Detection

by Henry A. Rowley, Shumeet Baluja, Takeo Kanade - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1998
"... We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present ..."
Abstract - Cited by 1206 (22 self) - Add to MetaCart
, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable

Scale-space and edge detection using anisotropic diffusion

by Pietro Perona, Jitendra Malik - IEEE Transactions on Pattern Analysis and Machine Intelligence , 1990
"... Abstract-The scale-space technique introduced by Witkin involves generating coarser resolution images by convolving the original image with a Gaussian kernel. This approach has a major drawback: it is difficult to obtain accurately the locations of the “semantically mean-ingful ” edges at coarse sca ..."
Abstract - Cited by 1887 (1 self) - Add to MetaCart
. The algorithm involves elementary, local operations replicated over the image making parallel hardware implementations feasible. Index Terms-Adaptive filtering, analog VLSI, edge detection, edge enhancement, nonlinear diffusion, nonlinear filtering, parallel algo-

Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multi-band Image Segmentation

by Song Chun Zhu, Alan Yuille - IEEE Transactions on Pattern Analysis and Machine Intelligence , 1996
"... We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum and c ..."
Abstract - Cited by 774 (20 self) - Add to MetaCart
and combines aspects of snakes/balloons and region growing. Indeed the classic snakes/balloons and region growing algorithms can be directly derived from our approach. We provide theoretical analysis of region competition including accuracy of boundary location, criteria for initial conditions

Geodesic Active Contours

by Vicent Caselles, Ron Kimmel, Guillermo Sapiro , 1997
"... A novel scheme for the detection of object boundaries is presented. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both in ..."
Abstract - Cited by 1425 (47 self) - Add to MetaCart
A novel scheme for the detection of object boundaries is presented. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both

Active Contours without Edges

by Tony F. Chan, Luminita A. Vese , 2001
"... In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford--Shah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. We minimize an energy ..."
Abstract - Cited by 1206 (38 self) - Add to MetaCart
In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford--Shah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. We minimize

SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

by Radhakrishna Achanta, Kevin Smith, Aurelien Lucchi, Pascal Fua - PAMI
"... Abstract—Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superp ..."
Abstract - Cited by 222 (3 self) - Add to MetaCart
Abstract—Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art

Contour Detection and Hierarchical Image Segmentation

by Pablo Arbeláez, Michael Maire, Charless Fowlkes, Jitendra Malik - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2010
"... This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentati ..."
Abstract - Cited by 389 (24 self) - Add to MetaCart
This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our
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