Photometric reconstruction of a dynamic textured surface from just one color image acquisition

http://www.opticsinfobase.org/josaa/abstract.cfm?msid=85528 This article has been selected for inclusion in the Virtual Journal for Biomedical Optics (Vol. 3, Iss. 4)International audienceTextured surface analysis is essential for many applications. We present a three-dimensional recovery approach for real textured surfaces based on photometric stereo. The aim is to be able to measure the textured surfaces with a high degree of accuracy. For this, we use a color digital sensor and principles of color photometric stereo. This method uses a single color image, instead of a sequence of gray-scale images, to recover the surface of the three dimensions. It can thus be integrated into dynamic systems where there is significant relative motion between the object and the camera. To evaluate the performances of our method, we compare it on real textured surfaces to traditional photometric stereo using three images. We show thus that it is possible to have similar results with just one color image

Shape and Illumination from Shading Using the Generic Viewpoint Assumption

The Generic Viewpoint Assumption (GVA) states that the position of the viewer or the light in a scene is not special. Thus, any estimated parameters from an observation should be stable under small perturbations such as object, viewpoint or light positions. The GVA has been analyzed and quantified in previous works, but has not been put to practical use in actual vision tasks. In this paper, we show how to utilize the GVA to estimate shape and illumination from a single shading image, without the use of other priors. We propose a novel linearized Spherical Harmonics (SH) shading model which enables us to obtain a computationally efficient form of the GVA term. Together with a data term, we build a model whose unknowns are shape and SH illumination. The model parameters are estimated using the Alternating Direction Method of Multipliers embedded in a multi-scale estimation framework. In this prior-free framework, we obtain competitive shape and illumination estimation results under a variety of models and lighting conditions, requiring fewer assumptions than competing methods.National Science Foundation (U.S.). Directorate for Computer and Information Science and Engineering/Division of Information & Intelligent Systems (Award 1212928)Qatar Computing Research Institut

Shape from bandwidth: the 2-D orthogonal projection case

Could bandwidth—one of the most classic concepts in signal processing—have a new purpose? In this paper, we investigate the feasibility of using bandwidth to infer shape from a single image. As a first analysis, we limit our attention to orthographic projection and assume a 2-D world. We show that, under certain conditions, a single image of a surface, painted with a bandlimited texture, is enough to deduce the surface up to an equivalence class. This equivalence class is unavoidable, since it stems from surface transformations that are invisible to orthographic projections. A proof of concept algorithm is presented and tested with both a simulation and a simple practical experiment

Régularisation parcimonieuse pour le problème d'intégration en traitement d'images

International audienceLa reconstruction d'une surface ou image à partir d'un champ de gradient corrompu est une étape primordiale dans plusieurs applications en traitement d'images. Un tel champ peut contenir du bruit et des données aberrantes qui nuisent à la qualité de la reconstruction. On propose dans ce papier d'utiliser la parcimonie pour régulariser le problème, ainsi qu'une méthode efficace pour estimer une bonne solution du problème d'optimisation qui en résulte. Les expériences montrent que la méthode proposée permet d'améliorer considérablement la qualité de la reconstruction comparée aux méthodes précédentes

Detecting Pedestrians by Learning Shapelet Features

In this paper, we address the problem of detecting pedes-trians in still images. We introduce an algorithm for learn-ing shapelet features, a set of mid–level features. These fea-tures are focused on local regions of the image and are built from low–level gradient information that discriminates be-tween pedestrian and non–pedestrian classes. Using Ad-aBoost, these shapelet features are created as a combina-tion of oriented gradient responses. To train the final classi-fier, we use AdaBoost for a second time to select a subset of our learned shapelets. By first focusing locally on smaller feature sets, our algorithm attempts to harvest more use-ful information than by examining all the low–level features together. We present quantitative results demonstrating the effectiveness of our algorithm. In particular, we obtain an error rate 14 percentage points lower (at 10−6 FPPW) than the previous state of the art detector of Dalal and Triggs [1] on the INRIA dataset. 1