Application of the Fisher-Rao metric to ellipse detection

The parameter space for the ellipses in a two dimensional image is a five dimensional manifold, where each point of the manifold corresponds to an ellipse in the image. The parameter space becomes a Riemannian manifold under a Fisher-Rao metric, which is derived from a Gaussian model for the blurring of ellipses in the image. Two points in the parameter space are close together under the Fisher-Rao metric if the corresponding ellipses are close together in the image. The Fisher-Rao metric is accurately approximated by a simpler metric under the assumption that the blurring is small compared with the sizes of the ellipses under consideration. It is shown that the parameter space for the ellipses in the image has a finite volume under the approximation to the Fisher-Rao metric. As a consequence the parameter space can be replaced, for the purpose of ellipse detection, by a finite set of points sampled from it. An efficient algorithm for sampling the parameter space is described. The algorithm uses the fact that the approximating metric is flat, and therefore locally Euclidean, on each three dimensional family of ellipses with a fixed orientation and a fixed eccentricity. Once the sample points have been obtained, ellipses are detected in a given image by checking each sample point in turn to see if the corresponding ellipse is supported by the nearby image pixel values. The resulting algorithm for ellipse detection is implemented. A multiresolution version of the algorithm is also implemented. The experimental results suggest that ellipses can be reliably detected in a given low resolution image and that the number of false detections can be reduced using the multiresolution algorithm

Prognostic factors in node-negative colorectal cancer: a retrospective study from a prospective database

PURPOSE: There is a need to identify a subgroup of high-risk patients with node-negative colorectal cancer who have a poor long-term prognosis and may benefit from adjuvant therapies. The aim of this study was to evaluate the prognostic impact of clinical and pathological parameters in a retrospective study from a prospective, continuous database of homogenously treated patients. METHODS: This study included 362 patients operated in a single institution for Dukes A and B (node-negative) colorectal cancer. The median follow-up was 140 months. The prognostic value of 13 clinical and pathological parameters was investigated. RESULTS: Multivariate analysis identified six independent prognostic factors: age at time of diagnosis (hazard ratio (HR) = 1.076), number of lymph nodes removed (HR = 0.948), perineural invasion (HR = 2.173), venous invasion (HR = 1.959), lymphatic vessel invasion (HR = 2.126), and T4 stage (HR = 5.876). CONCLUSION: These parameters could be useful in identifying patients with high-risk node-negative colorectal cancer who should be presented to adjuvant therapy

Duplication of the Gallbladder. A Case Report

Gallbladder duplication is a rare anatomic malformation, which can now be detected by preoperative imaging study. We report a case of a symptomatic duplicated gallbladder, successfully treated by laparoscopic cholecystectomy. This anomaly is important to know for surgeons because of associated anatomical variations of main bile duct and hepatic artery and increased risk of common bile duct injury

Psychophysics, Gestalts and Games

International audienceMany psychophysical studies are dedicated to the evaluation of the human gestalt detection on dot or Gabor patterns, and to model its dependence on the pattern and background parameters. Nevertheless, even for these constrained percepts, psychophysics have not yet reached the challenging prediction stage, where human detection would be quantitatively predicted by a (generic) model. On the other hand, Computer Vision has attempted at defining automatic detection thresholds. This chapter sketches a procedure to confront these two methodologies inspired in gestaltism. Using a computational quantitative version of the non-accidentalness principle, we raise the possibility that the psychophysical and the (older) gestaltist setups, both applicable on dot or Gabor patterns, find a useful complement in a Turing test. In our perceptual Turing test, human performance is compared by the scientist to the detection result given by a computer. This confrontation permits to revive the abandoned method of gestaltic games. We sketch the elaboration of such a game, where the subjects of the experiment are confronted to an alignment detection algorithm, and are invited to draw examples that will fool it. We show that in that way a more precise definition of the alignment gestalt and of its computational formulation seems to emerge. Detection algorithms might also be relevant to more classic psychophysical setups, where they can again play the role of a Turing test. To a visual experiment where subjects were invited to detect alignments in Gabor patterns, we associated a single function measuring the alignment detectability in the form of a number of false alarms (NFA). The first results indicate that the values of the NFA, as a function of all simulation parameters, are highly correlated to the human detection. This fact, that we intend to support by further experiments , might end up confirming that human alignment detection is the result of a single mechanism

Long-term successful management of an aortoesophageal fistula secondary to the ingestion of a bone

Aortoesophageal fistula (AOF) is a rare pathology but often lethal disease with a high mortality rate [1]. No consensus exists about the management of this pathology but the current trend is in favour of an endovascular treatment. The limit of this approach is the need of a specific material and a trained and experimented practitioner, not available in every center. We reported the case of a patient who presented an AOF complicated by a hemorrhagic shock successfully treated by a simple aortic suture protected by a sternocleidomasoid muscle flap

A-Contrario Horizon-First Vanishing Point Detection Using Second-Order Grouping Laws

International audienceWe show that, in images of man-made environments, the horizon line can usually be hypothesized based on a-contrario detections of second-order grouping events. This allows constraining the extraction of the horizontal vanishing points on that line, thus reducing false detections. Experiments made on three datasets show that our method, not only achieves state-of-the-art performance w.r.t. horizon line detection on two datasets, but also yields much less spurious vanishing points than the previous top-ranked methods

Foliations of Isonergy Surfaces and Singularities of Curves

It is well known that changes in the Liouville foliations of the isoenergy surfaces of an integrable system imply that the bifurcation set has singularities at the corresponding energy level. We formulate certain genericity assumptions for two degrees of freedom integrable systems and we prove the opposite statement: the essential critical points of the bifurcation set appear only if the Liouville foliations of the isoenergy surfaces change at the corresponding energy levels. Along the proof, we give full classification of the structure of the isoenergy surfaces near the critical set under our genericity assumptions and we give their complete list using Fomenko graphs. This may be viewed as a step towards completing the Smale program for relating the energy surfaces foliation structure to singularities of the momentum mappings for non-degenerate integrable two degrees of freedom systems.Comment: 30 pages, 19 figure

Thick Line Segment Detection with Fast Directional Tracking

International audienceThis paper introduces a fully discrete framework for a new straight line detector in gray-level images, where line segments are enriched with a thickness parameter intended to provide a quality criterion on the extracted feature. This study is based on a previous work on interactive line detection in gray-level images. At first, a better estimation of the segment thickness and orientation is achieved through two main improvements: adaptive directional scans and control of assigned thickness. Then, these advances are exploited for a complete unsupervised detection of all the line segments in an image. The new thick line detector is left available in an online demonstration

How to Overcome Perceptual Aliasing in ASIFT?

International audienceSIFT is one of the most popular algorithms to extract points of interest from images. It is a scale+rotation invariant method. As a consequence, if one compares points of interest between two images subject to a large viewpoint change, then only a few, if any, common points will be retrieved. This may lead subsequent algorithms to failure, especially when considering structure and motion or object recognition problems. Reaching at least affine invariance is crucial for reliable point correspondences. Successful approaches have been recently proposed by several authors to strengthen scale+rotation invariance into affine invariance, using viewpoint simulation (e.g. the ASIFT algorithm). However, almost all resulting algorithms fail in presence of repeated patterns, which are common in man-made environments, because of the so-called perceptual aliasing. Focusing on ASIFT, we show how to overcome the perceptual aliasing problem. To the best of our knowledge, the resulting algorithm performs better than any existing generic point matching procedure