Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to algorithms
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Edge Detection by Helmholtz Principle
Journal of Mathematical Imaging and Vision
Statistical Edge Detection: Learning and Evaluating Edge Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Object Recognition with Informative Features and Linear Classification
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
An A Contrario Decision Method for Shape Element Recognition
International Journal of Computer Vision
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
International Journal of Computer Vision
International Journal of Computer Vision
On Straight Line Segment Detection
Journal of Mathematical Imaging and Vision
A-contrario Detectability of Spots in Textured Backgrounds
Journal of Mathematical Imaging and Vision
From Gestalt Theory to Image Analysis: A Probabilistic Approach
From Gestalt Theory to Image Analysis: A Probabilistic Approach
A Statistical Approach to the Matching of Local Features
SIAM Journal on Imaging Sciences
LSD: A Fast Line Segment Detector with a False Detection Control
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Imaging Sciences
Meaningful Matches in Stereovision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
Random Phase Textures: Theory and Synthesis
IEEE Transactions on Image Processing
Hi-index | 0.00 |
The a contrario approach is a principled method for making algorithmic decisions that has been applied successfully to many tasks in image analysis. The method is based on a background model (or null hypothesis) for the image. This model relies on independence assumptions and characterizes images in which no detection should be made. It is often image dependent, relying on statistics gathered from the image, and therefore adaptive. In this paper we propose a generalization for background models which relaxes the independence assumption and instead uses image dependent second order properties. The second order properties are accounted for thanks to graphical models. The modified a contrario technique is applied to two tasks: line segment detection and part-based object detection, and its advantages are demonstrated. In particular, we show that the proposed method enables reasonably accurate prediction of the false detection rate with no need for training data.