Video object segmentation introducing depth and motion information

We present a method to estimate the relative depth between objects in scenes of video sequences. The information for the estimation of the relative depth is obtained from the overlapping produced between objects when there is relative motion as well as from motion coherence between neighbouring regions. A relaxation labelling algorithm is used to solve conflicts and assign every region to a depth level. The depth estimation is used in a segmentation scheme which uses grey level information to produce a first segmentation. Regions of this partition are merged on the basis of their depth level.Peer ReviewedPostprint (published version

Semantic labeling of places using information extracted from laser and vision sensor data

Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction withhumans. As an example, natural language terms like corridor or room can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we firrst propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from range data and vision into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation procedure. We finally show how to apply associative Markov networks (AMNs) together with AdaBoost for classifying complete geometric maps. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments

Supervised semantic labeling of places using information extracted from sensor data

Indoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating interaction with humans. As an example, natural language terms like “corridor” or “room” can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we first propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from sensor range data into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. In this case we additionally use as features objects extracted from images. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation method. Alternatively, we apply associative Markov networks to classify geometric maps and compare the results with a relaxation approach. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments

Cortical sulci model and matching from 3D brain magnetic resonance images

International audiencePositron emission tomography (PET) is one of the most popular techniques for the study of brain functional activity. Several studies show that PET is an in-vivo examination technique able to produce real images of cerebral activity, and is also neither destructive nor invasive. Unfortunately, PET images offer low resolution and signal-to-noise ratio. Moreover, they do not reflect the anatomy of patients. Accurate and reproducible analysis of PET images requires other informations, coming from aliases or other images such as magnetic resonance images (MRI) of the same patient. Hence it is of great interest to superimpose functional PET data and anatomical MRI data. Here, the authors deal with representation and identification of sulci. A first step is to choose and to automatically extract anatomical knowledge from a database, in order to adapt it to any image where the recognition has to be performed. Then, the authors introduce a stochastic method using these features to recognise human cerebral sulci

Arc and path consistency revisited

Journal ArticleMackworth and Freuder have analyzed the time complexity of several constraint satisfaction algorithms [4]. We present here new algorithms for arc and path consistency and show that the arc consistency algorithm is optimal in time complexity and of the same order space complexity as the earlier algorithms. A refined solution for the path consistency problem is proposed. However, the space complexity of the path consistency algorithm makes it practicable only for small problems. These algorithms are the result of the synthesis techniques used in ALICE (a general constraint satisfaction system) and local consistency methods

Valued constraint satisfaction problems: Hard and easy problems

tschiexOtoulouse.inra.fr fargierOirit.fr verfailOcert.fr In order to deal with over-constrained Constraint Satisfaction Problems, various extensions of the CSP framework have been considered by taking into account costs, uncertainties, preferences, priorities...Each extension uses a specific mathematical operator (+, max...) to aggregate constraint violations. In this paper, we consider a simple algebraic framework, related to Partial Constraint Satisfaction, which subsumes most of these proposals and use it to characterize existing proposals in terms of rationality and computational complexity. We exhibit simple relationships between these proposals, try t

Group-wise sparse correspondences between images based on a common labelling approach

Presentado al VISAPP 2012 celebrado en Roma del 24 al 26 de febrero.Finding sparse correspondences between two images is a usual process needed for several higher-level computer vision tasks. For instance, in robot positioning, it is frequent to make use of images that the robot captures from their cameras to guide the localisation or reduce the intrinsic ambiguity of a specific localisation obtained by other methods. Nevertheless, obtaining good correspondence between two images with a high degree of dissimilarity is a complex task that may lead to important positioning errors. With the aim of increasing the accuracy with respect to the pair-wise image matching approaches, we present a new method to compute group-wise correspondences among a set of images. Thus, pair-wise errors are compensated and better correspondences between images are obtained. These correspondences can be used as a less-noisy input for the localisation process. Group-wise correspondences are computed by finding the common labelling of a set of salient points obtained from the images. Results show a clear increase in effectiveness with respect to methods that use only two images.This research is supported by “Consolider Ingenio 2010”: project CSD2007-00018, by the CICYT project DPI2010-17112 and by the Universitat Rovira I Virgili through a PhD research grant.Peer Reviewe

Segmentation of images having unimodal distributions

Journal ArticleA gradient relaxation method based on maximizing a criterion function is studied and compared to the nonlinear probabilistic relaxation method for the purpose of segmentation of images having unimodal distributions. Although both methods provide comparable segmentation results, the gradient method has the additional advantage of providing control over the relaxation process by choosing three parameters which can be tuned to obtain the desired segmentation results at a faster rate. Examples are given on two different types of scenes