Transformée de Hough | EPFL Graph Search

La transformée de Hough est une technique de reconnaissance de formes inventée en 1959 par Paul Hough, faisant l'objet d'un brevet, et utilisée dans le traitement d'. L'application la plus simple permet de détecter les lignes présentes dans une image, mais des modifications peuvent être apportées à cette technique pour détecter d'autres formes géométriques : c'est la transformée généralisée de Hough développée par Richard Duda et Peter Hart en 1972. Approche théorique Le problème posé est celui de la recherche et de la détection de lignes qui seraient éventuellement présentes dans une image analysée malgré les imperfections de l'image : points manquants (la ligne pouvant être partiellement masquée par un objet), bruit. La transformée de Hough consiste à représenter chaque point de contour détecté dans un espace de paramètres à deux dimensions :

une droite est caractérisée par deux paramètres, elle est donc représentée par un point dans cet espace de paramètres ;
si l'

Localizing Polygonal Objects in Man-Made Environments

Xiaolu Sun

Object detection is a significant challenge in Computer Vision and has received a lot of attention in the field. One such challenge addressed in this thesis is the detection of polygonal objects, which are prevalent in man-made environments. Shape analysis is an important cue to detect these objects. We propose a contour-based object detection framework to deal with the related challenges, including how to efficiently detect polygonal shapes and how to exploit them for object detection. First, we propose an efficient component tree segmentation framework for stable region extraction and a multi-resolution line segment detection algorithm, which form the bases of our detection framework. Our component tree segmentation algorithm explores the optimal threshold for each branch of the component tree, and achieves a significant improvement over image thresholding segmentation, and comparable performance to more sophisticated methods but only at a fraction of computation time. Our line segment detector overcomes several inherent limitations of the Hough transform, and achieves a comparable performance to the state-of-the-art line segment detectors. However, our approach can better capture dominant structures and is more stable against low-quality imaging conditions. Second, we propose a global shape analysis measurement for simple polygon detection and use it to develop an approach for real-time landing site detection in unconstrained man-made environments. Since the task of detecting landing sites must be performed in a few seconds or less, existing methods are often limited to simple local intensity and edge variation cues. By contrast, we show how to efficiently take into account the potential sitesâ global shape, which is a critical cue in man-made scenes. Our method relies on component tree segmentation algorithm and a new shape regularity measure to look for polygonal regions in video sequences. In this way we enforce both temporal consistency and geometric regularity, resulting in reliable and consistent detections. Third, we propose a generic contour grouping based object detection approach by exploring promising cycles in a line fragment graph. Previous contour-based methods are limited to use additive scoring functions. In this thesis, we propose an approximate search approach that eliminates this restriction. Given a weighted line fragment graph, we prune its cycle space by removing cycles containing weak nodes or weak edges, until the upper bound of the cycle space is less than the threshold defined by the cyclomatic number. Object contours are then detected as maximally scoring elementary circuits in the pruned cycle space. Furthermore, we propose another more efficient algorithm, which reconstructs the graph by grouping the strongest edges iteratively until the number of the cycles reaches the upper bound. Our approximate search approaches can be used with any cycle scoring function. Moreover, unlike other contour grouping based approaches, our approach does not rely on a greedy strategy for finding multiple candidates and is capable of finding multiple candidates sharing common line fragments. We demonstrate that our approach significantly outperforms the state-of-the-art.

EPFL2015

Edge Defect Detection in Ceramic Tile Based on Boundary Analysis Using Fuzzy Thresholding and Radon Transform

Gelareh Mohammadi, Mohsen Pashna

Applying image processing technology and machine vision in industry have had significant development in recent decade. Tile and ceramic industry was not excluded form this matter. By using image processing techniques in production line of this industry, it is possible to detect surface defections such as edge defections, cracks and coloring defections. Surface defection is the most common type of defection in tile and ceramic industry. In this article a new and simple approach for detecting surface defections using Fuzzy thersholding and morphological operand and Radon transforms as well as boundary analysis is introduced. The available algorithm in the articles has good ability in determining number and place of defections and displaying them. Compared with results of other methods, the used algorithm in this article has better results on actual data base, in presence of camera noise and environmental lighting effects.

2008

Splines and wavelets for image warping and projection

Stefan Horbelt

This thesis investigates advanced signal processing concepts and their application to geometric processing and transformations of images and volumes. In the first part, we discuss the class of transformations that project volume data onto a plane using parallel line integrals; it is called the X-ray transform. In computer tomography (CT) the problem is to reconstruct the volume from these projections. We consider a basic setup with parallel projection and a geometry model in which the CT scanner rotates around one main axis of the volume. In this case, the problem is separable and reduces to the reconstruction of parallel images (slices of the volume). Each image is reconstructible from a series of 1D projections taken at different angular positions. The standard reconstruction algorithm is the filtered back-projection (FBP). We propose an alternative discretization of the Radon transform and of its inverse that is based on least-squares approximation and the convolution of splines. It improves the quality of the transform significantly. Next we discuss volume rendering based on the X-ray transform. The volume is represented by a multiresolution wavelet decomposition. The wavelets are projected onto an adaptive multiresolution 2D grid. The multiresolution grid allows to speed up the rendering process especially at coarse scales. In the second part of the thesis, we discuss transformations that warp images. In computer graphics, this is called texture mapping. Simple warps, such as shear, rotation, or zoom, can be computed by least-squares sampling, e.g. again with convolutions of splines. For more general warps there is not an easy continuous solution, if an analytical one exists at all. After a review of existing texture mapping methods, we propose a novel recursive one, which minimizes information loss. For this purpose, the texture is reconstructed from the mapped image and compared to the original texture. This algorithm outperforms the existing methods in terms of quality, but its complexity can be very high. A multiresolution version of the algorithm allows to keep the storage requirements and computational complexity within acceptable range. Fast transmission of textures and 3D models over communication links requires low bitrate and progressive compression. We can achieve very low bitrate if we code textures only with the information necessary for a given view of the 3D scene. If the view changes, the missing information will be transmitted. This results in a progressive bitstream for an animated 3D scene. In contrast to recursive texture mapping, we do not back-project the texture, but come up with a heuristic that predicts the information loss. This concept is called view-dependent scalability and we show how to apply it on DCT-based (as part of MPEG-4) and wavelet-based coders. Last, we inspect the question of how to balance the bit budget of a jointly coded mesh and texture for the progressive and view-dependent transmission of a 3D model. By exhaustive search, we find the rate-distortion optimal path. By marginal analysis we find a close solution but at much lower costs (only two evaluated frames per step as compared to a full search).

EPFL2001