A contrario patch matching, with an application to keypoint matches validation

We describe a simple metric for image patches similarity, together with a robust criterion for unsupervised patch matching. The gradient orientations at corresponding positions in the two patches are compared and the normalized errors are accumulated. Based on the a contrario framework, the matching criterion validates a match between two patches when this cumulative error is too small to have occurred as the result of an accidental agreement. The method is illustrated in the validation of keypoint matches.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICIP.2015.735093

Unsupervised smooth contour detection

An unsupervised method for detecting smooth contours in digital images is proposed. Following the a contrario approach, the starting point is dening the conditions where contours should not be detected: soft gradient regions contaminated by noise. To achieve this, low frequencies are removed from the input image. Then, contours are validated as the frontiers separating two adjacent regions, one with signicantly larger values than the other. Signicance is evalu-ted using the Mann-Whitney U test to determine whether the samples were drawn from the same distribution or not. This test makes no assumption on the distributions. The resulting algorithm is similar to the classic Marr-Hildreth edge detector, with the addition of the statistical validation step. Combined with heuristics based on the Canny and Devernay methods, an efficient algorithm is derived producing sub-pixel contours

A brief analysis of the holistically-nested edge detector

Este artículo está disponible en línea con materiales complementarios, software, conjuntos de datos y demostración en https://doi.org/10.5201/ipol.2022.422This work describes the HED method for edge detection. HED uses a neural network based on a VGG16 backbone, supplemented with some extra layers for merging the results at different scales. The training was performed on an augmented version of the BSDS500 dataset. We perform a brief analysis of the results produced by HED, highlighting its quality but also indicating its limitations. Overall, HED produces state-of-the-art results

A sub-pixel edge detector: an implementation of the Canny/Devernay Algorithm

An image edge detector is described which produces chained edge points with sub-pixel accuracy. The method incorporates the main ideas of the classic Canny and Devernay algorithms. The analysis shows that a slight modification to the original formulation improves the sub-pixel accuracy of the edge points

A brief analysis of the dense extreme inception network for edge detection

This work describes DexiNed, a Dense Extreme Inception Network for Edge Detection proposed by Xavier Soria, Edgar Riba and Angel Sappa in [IEEE Winter Conference on Applications of Computer Vision (WACV), 2020]. The network is organized in blocks that extract edges at different resolutions, which are then merged to produce a multiscale edge map. For training, the authors introduced an annotated dataset (BIPED) specifically designed for edge detection. We perform a brief analysis of the results produced by DexiNed, highlighting its quality but also indicating its limitations. Overall, DexiNed produces state-of-the-art results

Joint A Contrario Ellipse and Line Detection.

This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TPAMI.2016.2558150We propose a line segment and elliptical arc detector that produces a reduced number of false detections on various types of images without any parameter tuning. For a given region of pixels in a grey-scale image, the detector decides whether a line segment or an elliptical arc is present (model validation). If both interpretations are possible for the same region, the detector chooses the one that best explains the data (model selection ). We describe a statistical criterion based on the a contrario theory, which serves for both validation and model selection. The experimental results highlight the performance of the proposed approach compared to state-of-the-art detectors, when applied on synthetic and real images.This work was partially funded by the Qualcomm postdoctoral program at École Polytechnique Palaiseau, a Google Faculty Research Award, the Marie Curie grant CIG-334283-HRGP, a CNRS chaire d’excellence and chaire Jean Marjoulet, and EPSRC grant EP/L010917/1

A contrario 3D point alignment detection algorithm

In this article we present an algorithm for the detection of perceptually relevant alignments in 3D point clouds. The algorithm is an extension of the algorithm developed by Lezama et al. [J. Lezama, J-M. Morel, G. Randall, R. Grompone von Gioi, A Contrario 2D Point Alignment Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (3), pp. 499- 512, 2015] for the case of sets of 2D points. The algorithm is based on the a contrario detection theory that mathematically formalizes the non-accidentalness principle proposed for perception: an observed structure is relevant if it rarely occurs by chance. This framework has been widely used in different detection tasks and leads to algorithms with a single critical parameter to control the number of false detections

An unsupervised algorithm for detecting good continuation in Dot Patterns

In this article we describe an algorithm for the automatic detection of perceptually relevant configurations of `good continuation' of points in 2D point patterns. The algorithm is based on the `a contrario' detection theory and on the assumption that `good continuation' of points are locally quasi-symmetric. The algorithm has only one critical parameter, which controls the number of false detections

An a-contrario biometric fusion approach.

Fusion is a key component in many biometric systems: it is one of the most widely used techniques to improve their accuracy. Each time we need to combine the output of systems that use different biometric traits, or different samples of the same biometric trait, or even different algorithms, we need to define a fusion strategy. Independently of the fusion method used, there is always a decision step, in which it is decided if the traits being compared correspond to the same individual or not. In this work, we present a statistical decision criterion based on the a-contrario framework, which has already proven to be useful in biometric applications. The proposed method and its theoretical background is described in detail, and its application to biometric fusion is illustrated with simulated and real data