Le Probleme de la mise en correspondance : l'etat de l'art

Le probleme de la mise en correspondance est l'un des problemes les plus difficiles en vision par ordinateur. Nous identifions trois categories de mise en correspondance : stereovision, reconnaissance d'objets et analyse de sequences d'images. Ce rapport vise a faire une revue complete sur l'ensemble de travaux dans la litterature avec une attention particuliere sur la mise en correspondance entre deux images au sein d'une sequence, bidimensionnelles ou tridimensionnelles

Extremal transmission and beating effect of acoustic wave in two-dimensional sonic crystal

The extremal transmission of acoustic wave near the Dirac point in two-dimensional (2D) sonic crystal (SC), being inversely proportional to the thickness of sample, has been demonstrated experimentally for the first time. Some unusual beating effects have been observed experimentally when the acoustic pulse transport through the 2D SC slabs. Such phenomena are completely different from the oscillations of the wave in a slab or cavity originating from the interface reflection or Fabry-Perot effect. They can be regarded as acoustic analogue to Zitterbewegung of relativistic electron. The physical origination for the phenomenon has been analyzed

Feature-Based Facial Expression Recognition: Experiments With a Multi-Layer Perceptron

In this paper, we report our experiments on feature-based facial expression recognition within an architecture based on a two-layer perceptron. We investigate the use of two types of features extracted from face images: the geometric positions of a set of fiducialpoints on a face, and a set of multi-scale and multi-orientation Gabor wavelet coefficients at these points. They can be used either independently or jointly. The recognition performance with different types of features has been compared, which shows that Gabor wavelet coefficients are much more powerful than geometric positions. Furthermore, since the first layer of the perceptron actually performs a nonlinear reduction of the dimensionality of the feature space, we have also studied the desired number of hidden units, i.e., the appropriate dimension to represent a facial expression in order to achieve a good recognition rate. It turns out that five to seven hidden units are probably enough to represent the space of feature expressions. Then, we have investigat- ed the importance ofeach individual fiducial point to facial expression recognition. Sensitivity analysis reveals that points on cheeks and on forehead carry little useful information. After discarding them, not only the computational efficiency increases, but also the generalization performanc- e slightly improves. Finally, we have studied the significance of image scales. Experiments show that facial expression recognition is mainly a low frequency process, and a spatial resolution of 64 pixels $\times$ 64 pixels is probably enough

Registration of free-form surfaces represented by point sets

A method has been developed for registering two dense 3-D maps obtained by using a correlation-based stereo system . Geometric matching in general is a difficult unsolved problem in computer vision . Fortunately, in many practical applications, some a priori knowledge exists which considerably simplifies the problem. In visual navigation, for example, the motion between successive positions is usually either small or approximately known . From this initial estimate, our algorithm can compute the motion with very good precision, which is required for environment modeling . Objects are represented by a set of 3-D points, which are considered as the samples of a surface . No contraint is imposed on the foret of the objects . The proposed algorithm is based on iteratively matching points of one view to the closest points of the another view . A statistical method based on the distance distribution is used ta discard the outliers. A least-squares technique is used to estimate 3-D motion from the point correspondences, which reduces the average distance between points in the two sets. Real data have been used to test the algorithm . The results show that it is efficient and robust, and yields an accurate motion estimate.Une méthode a été développée pour recaler deux nuages de points 3D obtenus en utilisant la stéréo par corrélation. Le recalage de deux ensembles de primitives géométriques est un problème en général très difficile et non résolu. Heureusement, dans beaucoup d'applications, des connaissances a priori simplifient considérablement le problème. Par exemple, le mouvement entre deux positions successives est généralement soit petit soit approximativement connu. A partir de cette estimée grossière, notre algorithme permet de calculer le mouvement avec une très bonne précision, nécessaire à l'obtention d'un modèle satisfaisant de l'environnement. Les objets observés sont représentés au moyen de nuages de points 3D. Ces points sont considérés comme des échantillons d'une surfac

Ground-Challenge: A Multi-sensor SLAM Dataset Focusing on Corner Cases for Ground Robots

High-quality datasets can speed up breakthroughs and reveal potential developing directions in SLAM research. To support the research on corner cases of visual SLAM systems, this paper presents Ground-Challenge: a challenging dataset comprising 36 trajectories with diverse corner cases such as aggressive motion, severe occlusion, changing illumination, few textures, pure rotation, motion blur, wheel suspension, etc. The dataset was collected by a ground robot with multiple sensors including an RGB-D camera, an inertial measurement unit (IMU), a wheel odometer and a 3D LiDAR. All of these sensors were well-calibrated and synchronized, and their data were recorded simultaneously. To evaluate the performance of cutting-edge SLAM systems, we tested them on our dataset and demonstrated that these systems are prone to drift and fail on specific sequences. We will release the full dataset and relevant materials upon paper publication to benefit the research community. For more information, visit our project website at https://github.com/sjtuyinjie/Ground-Challenge