Détection de primitives linéaires et circulaires par une approche a contrario

National audienceLow-level image understanding requires the use of different detectors for basic primitives, such as line segments or circular arcs. Most of the existent detectors deal with problems that have been (and still are) extensively studied like parameter tunning, control of number of false detections or execution time. In this paper, we focus on detecting simultaneously lines and circles in an image, while controlling the number of false detections and without any need of parameter tunning. We present an algorithm which extends the Line Segment Detector (LSD) for circles, both being based on the a contrario approach. Due to the fact that the proposed detector targets two different types of primitives, the a contrario validation is used as a criterion for model selection, which is a novelty in the a contrario-based works. In addition, we propose a new algebraic method for estimating a circle, which benefits equally from the direction of the gradient of the contour points, and not only from their position.La compréhension bas niveau d'une image exige l'usage des différents détecteurs de primitives de base, telles que des segments de droite ou arcs de cercles. La plupart des détecteurs existants se confrontent à des problèmes qui ont été (et sont toujours) considérablement étudiés comme le réglage de paramètres, le contrôle du nombre de fausses détections ou le temps d'exécution. Dans cet article, nous nous intéressons à la détection à la fois des droites et des cercles dans une image, tout en contrôlant le nombre de fausses détections et sans réglage particulier de paramètres. Nous présentons un détecteur qui étend l'algorithme LSD (Line Segment Detector) aux cercles, les deux étant fondés sur une approche a contrario. Du fait que le détecteur proposé vise deux types différents de primitives, la validation a contrario est utilisée comme méthode de sélection du modèle, ce qui représente une nouveauté dans les travaux fondés sur l'approche a contrario. De plus, nous proposons une nouvelle méthode d'estimation algébrique d'un cercle, qui profite également de la direction du gradient des points contour, et non pas uniquement de la position de ceux-ci

Dense urban elevation models from stereo images by an affine region merging approach

The main subject of this thesis is the computation of Dense Disparity Maps from a pair of satelite or aerial stereo images from an urban scene, taken from two different viewpoints. Several steps are needed to obtain the ?nal disparity map from the pair of images. We focus here on one of these steps: how to match the points in one image with the points in the other one. This matching process is closely related to the computation of the altitudes of the objects present in the scene. Indeed, the precision we can obtain in these altitude values is directly proportional to the precision in the matching process. This precision in the altitude is also inversely proportional to the distance between both viewpoints where the images are taken(baseline). The matching process is a widely studied ?eld in the Computer Vision Community and several methods and algorithms have been developed so far ([31, 27, 49]). Most of them consider a big base- line con?guration, which increases the performance in the altitude and also simpli?es the matching process. However, this assumption presents a major drawback with objects that are occluded in one image but appear in the other one. The bigger the baseline is, the more objects are occluded in one image and are not occluded in the other one. Recently, a different approach in which the images are taken with a very small baseline started to be analyzed ([19, 20]). This approach has the advantage of eliminating most of the ambiguities presented when one object occluded in one image is not occluded in the other one. Indeed, if we consider that we have a very small baseline, the occlusions presented in both images are almost the same. Now, this con?guration obviously decreases the precision in the ?nal altitude. In order to continue obtaining highly accurate altitude values, the precision in the matching process must be im- proved. The methods developed so far which consider the small baseline approach, compute altitude values with a high precision at some points, but leave the rest of them with no altitude values at all, generating a non-dense disparity map. Based on the fact that piecewise-a?ne models are reasonable for the elevation in urban areas, we propose a new method to interpolate and denoise those non-dense disparity maps. Under lambertian illumination hypothesis 1 , it is reasonable to assume that homogeneous regions in the graylevel image, correspond to the same a?ne elevation model. In other words, the borders between the piecewise a?ne elevation model are included to a large extent within contrasted graylevel borders. Hence, it is reasonable to look for an piecewise a?ne ?t to the elevation model where the borders between regions are taken from a graylevel segmenation of the image We present a region-merging algorithm that starts with an over-segmentation of the gray-level im- age. The disparity values at each region are approximated by an a?ne model, and a meaningfulness measure of the ?t is assigned to each of them. Using this meaningfulness as a merging order, the method iterates until no new merge is possible, according to a merging criterion which is also based on the meaningfulness of each pair of neighboring regions. In the last step, the algorithm performs a validation of the ?nal regions using again the meaningfulness of the ?t. The regions validated in this last step are those for which the a?ne model is a good approximation. The region-merging algorithm presented in this work can be seen as an attempt to incorporate a semantical meaning to real scenes: we have developed a validation method to determine whether the data within a region is well approximated by an a?ne model or not. Hence, we could analyze more complex models, de?ning a suitable validation criterion for each of them. In this way, we can search for the model that best explains a given data set in terms of its meaningfulness

Detectando agrupamientos y contornos: un estudio doble sobre representación de formas

Las formas juegan un rol clave en nuestro sistema cognitivo: en la percepción de las formas yace el principio de la formación de conceptos. Siguiendo esta línea de pensamiento, la escuela de la Gestalt ha estudiado extensivamente la percep- ción de formas como el proceso de asir características estructurales encontradas o impuestas sobre el material de estímulo.En resumen, tenemos dos modelos de formas: pueden existir físicamente o ser un producto de nuestros procesos cogni- tivos. El primer grupo está compuesto por formas que pueden ser definidas extra- yendo los contornos de objetos sólidos. En este trabajo nos restringiremos al caso bidimensional. Decimos entonces que las formas del primer tipo son formas planares. Atacamos el problema de detectar y reconocer formas planares. Cier- tas restricciones teóricas y prácticas nos llevan a definir una forma planar como cualquier pedazo de línea de nivel de una imagen. Comenzamos por establecer que los métodos a contrario existentes para de- tectar líneas de nivel son usualmente muy restrictivos: una curva debe ser enter- amente saliente para ser detectada. Esto se encuentra en clara contradicción con la observación de que pedazos de líneas de nivel coinciden con los contornos de los objetos. Por lo tanto proponemos una modificación en la que el algoritmo de detección es relajado, permitiendo la detección de curvas parcialmente salientes. En un segundo acercamiento, estudiamos la interacción entre dos maneras diferentes de determinar la prominencia de una línea de nivel. Proponemos un esquema para competición de características donde el contraste y la regularidad compiten entre ellos, resultando en que solamente las líneas de nivel contrastadas y regulares son consderedas salientes. Una tercera contribución es un algoritmo de limpieza que analiza líneas de nivel salientes, descartando los pedazos no salientes y conservando los salientes. Está basado en un algoritmo para detección de multisegmentos que fue extendido para trabajar con entradas periódicas. Finalmente, proponemos un descriptor de formas para codificar las formas detectadas, basado en el Shape Context global. Cada línea de nivel es codificada usando shape contexts, generando así un nuevo descriptor semi-local. A contin- uación adaptamos un algoritmShape plays a key role in our cognitive system: in the perception of shape lies the beginning of concept formation. Following this lines of thought, the Gestalt school has extensively studied shape perception as the grasping of structural fea- tures found in or imposed upon the stimulus material. In summary, we have two models for shapes: they can exist physically or be a product of our cognitive pro- cesses. The first group is formed by shapes that can be defined by extracting contours from solid objects. In this work we will restrict ourselves to the two dimensional case. Therefore we say that these shapes of the first type are planar shapes. We ad- dress the problem of detecting and recognizing planar shapes. A few theoretical and practical restrictions lead us to define a planar shape as any piece of mean- ingful level line of an image. We begin by stating that previous a contrario methods to detect level lines are often too restrictive: a curve must be entirely salient to be detected. This is clearly in contradiction with the observation that pieces to level lines coincide with object boundaries. Therefore we propose a modification in which the detection criterion is relaxed by permitting the detection of partially salient level lines. As a second approach, we study the interaction between two different ways of determining level line saliency: contrast and regularity. We propose a scheme for feature competition where contrast and regularity contend with each other, resulting in that only contrasted and regular level lines are considered salient. A third contribution is a clean-up algorithm that analyses salient level lines, discarding the non-salient pieces and returning the salient ones. It is based on an algorithm for multisegment detection, which was extended to work with periodic inputs. Finally, we propose a shape descriptor to encode the detected shapes, based on the global Shape Context. Each level line is encoded using shape contexts, thus generating a new semi-local descriptor. We then adapt an existing a contrario shape matching algorithm to our particular case. The second group is composed by shapes that do not correspond to a solid object but are formed by integrating several solid objects. The simplest shapes in this group are arrangements of points in two dimensions. Clustering techniques might be helpful in these situations. In a seminal work from 1971, Zahn faced the problem of finding perceptual clusters according to the proximity gestalt and proposed three basic principles for clustering algorithms: (1) only inter-point distances matter, (2) stable results across executions and (3) independence from the exploration strategy. A last implicit requirement is crucial: clusters may have arbitrary shapes and detection algorithms must be capable of dealing with this. In this part we will focus on designing clustering methods that completely fulfils the aforementioned requirements and that impose minimal assumptions on the data to be clustered. We begin by assessing the problem of validating clusters in a hierarchical struc- ture. Based on nonparametric density estimation methods, we propose to com- pute the saliency of a given cluster. Then, it is possible to select the most salient clusters in the hierarchy. In practice, the method shows a preference toward com- pact clusters and we propose a simple heuristic to correct this issue. In general, graph-based hierarchical methods require to first compute the com- plete graph of interpoint distances. For this reason, hierarchical methods are often considered slow. The most usually used, and the fastest hierarchical clustering al- gorithm is based on the Minimum Spanning Tree (MST). We therefore propose an algorithm to compute the MST while avoiding the intermediate step of computing the complete set of interpoint distances. Moreover, the algorithm can be fully par- allelized with ease. The algorithm exhibits good performance for low-dimensional datasets and allows for an approximate but robust solution for higher dimensions. Finally we propose a method to select clustered subtrees from the MST, by computing simple edge statistics. The method allows naturally to retrieve clus- ters with arbitrary shapes. It also works well in noisy situations, where noise is regarded as unclustered data, allowing to separate it from clustered data. We also show that the iterative application of the algorithm allows to solve a phenomenon called masking, where highly populated clusters avoid the detection less popu- lated ones.Fil:Tepper, Mariano. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina

Consideration of Driver Behavior in the Design of Red Light Extension Systems

Red Light Running (RLR) is a safety hazard at signalized intersections in Oregon and throughout the United States. Various methods of mitigating factors contributing to RLR have been implemented but few protect against a conflict when a vehicles runs a red light. Additionally, current prediction algorithms are complicated; there is a need for a simplified method which uses input values that are easily measured at an intersection. This study investigated current RLR countermeasures, specifically red clearance extension systems which extend the red clearance interval when a RLR vehicle is detected with the goal of reducing RLR crashes. A field study was completed which investigated driver behavior at five intersections in Oregon in regards to the development of a red clearance extension system. Specifically, driver behavior in response to the yellow and red indications on the major approach, and the green indication on the minor approach were observed using 149 hours of transcribed video data. Analysis determined RLR rates ranging from 0 to 1.263 RLR vehicles/hour and 5th %ile time to conflict (TTC) times ranging from 2 to 4s. These factors, which are simple to determine, are then used to develop an equation which calculates a red extension time input for a red clearance extension system

Αλγόριθμοι ανάλυσης και επεξεργασίας αυτοστερεοσκοπικών εικόνων

Οι σημερινές μέθοδοι λήψης και απεικόνισης τρισδιάστατου (3Δ) περιεχομένου απαιτούν τη χρήση συσκευών εντοπισμού ή ειδικών γυαλιών. Υπάρχουν όμως τεχνικές οι οποίες παρέχουν τη δυνατότητα προβολής 3Δ περιεχομένου στο χρήστη χωρίς τη χρήση ειδικών διατάξεων. Αυτές ονομάζονται «αυτοστερεοσκοπικές» και οι αντίστοιχες εικόνες που προκύπτουν χαρακτηρίζονται «αυτοστερεοσκοπικές εικόνες». Ένα ιδιαίτερα υποσχόμενο είδος αυτοστερεοσκοπικής 3Δ απεικόνισης ονομάζεται Ολοκληρωτική Απεικόνιση (ΟΑ). Η ΟΑ δίνει τη δυνατότητα λήψης Ολοκληρωτικών Εικόνων (ΟΕ) οι οποίες περιέχουν ενσωματωμένη την 3Δ πληροφορία και μπορούν μέσω κατάλληλων συσκευών να την μεταφέρουν στο θεατή χωρίς τη χρήση από αυτόν ειδικού εξοπλισμού. Όμως η οποιαδήποτε απώλεια ευθυγράμμισης μεταξύ των μηχανικών τμημάτων της συσκευής λήψης έχει ως αποτέλεσμα γεωμετρικές παραμορφώσεις στη δομή της ληφθείσας ΟΕ. Το αποτέλεσμα είναι η απώλεια του 3Δ περιεχομένου καθώς και η ολοκληρωτική αποτυχία των αλγορίθμων ανάλυσης οι οποίοι βασίζονται στις καθορισμένες γεωμετρικές διαστάσεις της ληφθείσας ΟΕ. Στην διδακτορική αυτή διατριβή αναπτύχθηκαν εύρωστες μέθοδοι επεξεργασίας εικόνας για την επιτυχή αντιμετώπιση των εν λόγω γεωμετρικών παραμορφώσεων. Χρησιμοποιώντας μεθόδους από το χώρο της τεχνητής όρασης μελετήθηκαν και επιλύθηκαν τα προβλήματα γεωμετρικών παραμορφώσεων ΟΕ οι οποίες προκύπτουν από συστοιχίες τετράγωνων, εξαγωνικών, τριγωνικών καθώς και κυκλικών φακών. Το αποτέλεσμα της μελέτης αυτής ήταν η δημιουργία ευέλικτων αλγορίθμων επεξεργασίας και επιδιόρθωσης ΟΕ. Nowadays, acquisition and display of three-dimensional (3D) images requires the use of special tracking devices or glasses. However specialized techniques provide the ability of 3D content delivery to end users without such limitations. These methods are called autostereoscopic and the resulting images are called autostereoscopic images. A promising type of autostereoscopic imaging is called Integral Imaging (InI). InI provides the ability of capturing Integral Images (InIms) that contain embedded 3D information and are additionally able to display it to the end user without the need for specialized equipment. But the existence of even slight misalignments between the optical components in the acquisition device results in geometrical aberrations in the structure of the acquired InIm. These result in total loss of the displayed 3D content as well as failure of all InIm analysis and processing algorithms that depend on pre-determined geometric dimensions of the acquired InIm. In this doctoral dissertation robust image processing frameworks were developed in order to successfully correct these geometrical aberrations. In detail, using computer vision methodologies, the problems of geometrical aberrations in arrays of square, hexagonal and triangular lenses were extensively studied, resulting in the development of robust InIm processing and rectification algorithms

Αλγόριθμοι Ανάλυσης και Επεξεργασίας Αυτοστερεοσκοπικών Εικόνων

Οι σημερινές μέθοδοι λήψης και απεικόνισης τρισδιάστατου (3Δ) περιεχομένου απαιτούν τη χρήση συσκευών εντοπισμού ή ειδικών γυαλιών. Υπάρχουν όμως τεχνικές οι οποίες παρέχουν τη δυνατότητα προβολής 3Δ περιεχομένου στο χρήστη χωρίς τη χρήση ειδικών διατάξεων. Αυτές ονομάζονται «αυτοστερεοσκοπικές» και οι αντίστοιχες εικόνες που προκύπτουν χαρακτηρίζονται «αυτοστερεοσκοπικές εικόνες». Ένα ιδιαίτερα υποσχόμενο είδος αυτοστερεοσκοπικής 3Δ απεικόνισης ονομάζεται Ολοκληρωτική Απεικόνιση (ΟΑ). Η ΟΑ δίνει τη δυνατότητα λήψης Ολοκληρωτικών Εικόνων (ΟΕ) οι οποίες περιέχουν ενσωματωμένη την 3Δ πληροφορία και μπορούν μέσω κατάλληλων συσκευών να την μεταφέρουν στο θεατή χωρίς τη χρήση από αυτόν ειδικού εξοπλισμού. Όμως η οποιαδήποτε απώλεια ευθυγράμμισης μεταξύ των μηχανικών τμημάτων της συσκευής λήψης έχει ως αποτέλεσμα γεωμετρικές παραμορφώσεις στη δομή της ληφθείσας ΟΕ. Το αποτέλεσμα είναι η απώλεια του 3Δ περιεχομένου καθώς και η ολοκληρωτική αποτυχία των αλγορίθμων ανάλυσης οι οποίοι βασίζονται στις καθορισμένες γεωμετρικές διαστάσεις της ληφθείσας ΟΕ. Στην διδακτορική αυτή διατριβή αναπτύχθηκαν εύρωστες μέθοδοι επεξεργασίας εικόνας για την επιτυχή αντιμετώπιση των εν λόγω γεωμετρικών παραμορφώσεων. Χρησιμοποιώντας μεθόδους από το χώρο της τεχνητής όρασης μελετήθηκαν και επιλύθηκαν τα προβλήματα γεωμετρικών παραμορφώσεων ΟΕ οι οποίες προκύπτουν από συστοιχίες τετράγωνων, εξαγωνικών, τριγωνικών καθώς και κυκλικών φακών. Το αποτέλεσμα της μελέτης αυτής ήταν η δημιουργία ευέλικτων αλγορίθμων επεξεργασίας και επιδιόρθωσης ΟΕ.Nowadays, acquisition and display of three-dimensional (3D) images requires the use of special tracking devices or glasses. However specialized techniques provide the ability of 3D content delivery to end users without such limitations. These methods are called autostereoscopic and the resulting images are called autostereoscopic images. A promising type of autostereoscopic imaging is called Integral Imaging (InI). InI provides the ability of capturing Integral Images (InIms) that contain embedded 3D information and are additionally able to display it to the end user without the need for specialized equipment. But the existence of even slight misalignments between the optical components in the acquisition device results in geometrical aberrations in the structure of the acquired InIm. These result in total loss of the displayed 3D content as well as failure of all InIm analysis and processing algorithms that depend on predetermined geometric dimensions of the acquired InIm. In this doctoral dissertation robust image processing frameworks were developed in order to successfully correct these geometrical aberrations. In detail, using computer vision methodologies, the problems of geometrical aberrations in arrays of square, hexagonal, triangular and circular lenses were extensively studied, resulting in the development of robust InIm processing and rectification algorithms

Detection and identification of elliptical structure arrangements in images: theory and algorithms

Cette thèse porte sur différentes problématiques liées à la détection, l'ajustement et l'identification de structures elliptiques en images. Nous plaçons la détection de primitives géométriques dans le cadre statistique des méthodes a contrario afin d'obtenir un détecteur de segments de droites et d'arcs circulaires/elliptiques sans paramètres et capable de contrôler le nombre de fausses détections. Pour améliorer la précision des primitives détectées, une technique analytique simple d'ajustement de coniques est proposée ; elle combine la distance algébrique et l'orientation du gradient. L'identification d'une configuration de cercles coplanaires en images par une signature discriminante demande normalement la rectification Euclidienne du plan contenant les cercles. Nous proposons une technique efficace de calcul de la signature qui s'affranchit de l'étape de rectification ; elle est fondée exclusivement sur des propriétés invariantes du plan projectif, devenant elle même projectivement invariante. ABSTRACT : This thesis deals with different aspects concerning the detection, fitting, and identification of elliptical features in digital images. We put the geometric feature detection in the a contrario statistical framework in order to obtain a combined parameter-free line segment, circular/elliptical arc detector, which controls the number of false detections. To improve the accuracy of the detected features, especially in cases of occluded circles/ellipses, a simple closed-form technique for conic fitting is introduced, which merges efficiently the algebraic distance with the gradient orientation. Identifying a configuration of coplanar circles in images through a discriminant signature usually requires the Euclidean reconstruction of the plane containing the circles. We propose an efficient signature computation method that bypasses the Euclidean reconstruction; it relies exclusively on invariant properties of the projective plane, being thus itself invariant under perspective

Description of vehicle passage through a multisegment detection field

This paper presents the video-based description method for vehicles passing a detection field. A sequence of source images is created by consecutive frames of the input video stream. The source images are converted into binary target images using the analysis of small gradients. Binary values of the target images represent edges and surfaces comprised in the source images. For all images, the same detection field composed of segments is defined. Inside each segment of the detection field, the sum of edge values is calculated. For the entire detection field, an adjusted sum of the edge values is determined. A vehicle passing the detection field changes the number of edge values within individual segments and the adjusted sum of the edge values for the entire detection field. Vehicle passage through the detection field is described by a discrete function that associates the adjusted sum of the edge values determined for the entire detection field in the current binary image to the ordinal number of the current image in the sequence of source images