Olga Regina Pereira Bellon

Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms

This paper addresses the range image registration problem for views having low overlap and which may include substantial noise. The current state of the art in range image registration is best represented by the well-known iterative closest point (ICP) algorithm and numerous variations on it. Although this method is effective in many domains, it nevertheless suffers from two key limitations: it requires prealignment of the range surfaces to a reasonable starting point; and it is not robust to outliers arising either from noise or low surface overlap. This paper proposes a new approach that avoids these problems. To that end, there are two key, novel contributions in this work: a new, hybrid genetic algorithm (GA) technique, including hill climbing and parallel-migration, combined with a new, robust evaluation metric based on surface interpenetration. Up to now, interpenetration has been evaluated only qualitatively; we define the first quantitative measure for it. Because they search in a space of transformations, GA are capable of registering surfaces even when there is low overlap between them and without need for prealignment. The novel GA search algorithm we present offers much faster convergence than prior GA methods, while the new robust evaluation metric ensures more precise alignments, even in the presence of significant noise, than mean squared error or other well-known robust cost functions. The paper presents thorough experimental results to show the improvements realized by these two contributions.

3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure

This paper presents a novel automatic framework to perform 3D face recognition. The proposed method uses a simulated annealing-based approach (SA) for range image registration with the surface interpenetration measure (SIM), as similarity measure, in order to match two face images. The authentication score is obtained by combining the SIM values corresponding to the matching of four different face regions: circular and elliptical areas around the nose, forehead, and the entire face region. Then, a modified SA approach is proposed taking advantage of invariant face regions to better handle facial expressions. Comprehensive experiments were performed on the FRGC v2 database, the largest available database of 3D face images composed of 4,007 images with different facial expressions. The experiments simulated both verification and identification systems and the results compared to those reported by state-of-the-art works. By using all of the images in the database, a verification rate of 96.5 percent was achieved at a false acceptance rate (FAR) of 0.1 percent. In the identification scenario, a rank-one accuracy of 98.4 percent was achieved. To the best of our knowledge, this is the highest rank-one score ever achieved for the FRGC v2 database when compared to results published in the literature.

Automatic Face Segmentation and Facial Landmark Detection in Range Images

We present a methodology for face segmentation and facial landmark detection in range images. Our goal was to develop an automatic process to be embedded in a face recognition system using only depth information as input. To this end, our segmentation approach combines edge detection, region clustering, and shape analysis to extract the face region, and our landmark detection approach combines surface curvature information and depth relief curves to find the nose and eye landmarks. The experiments were performed using the two available versions of the Face Recognition Grand Challenge database and the BU-3DFE database, in order to validate our proposed methodology and its advantages for 3-D face recognition purposes. We present an analysis regarding the accuracy of our segmentation and landmark detection approaches. Our results were better compared to state-of-the-art works published in the literature. We also performed an evaluation regarding the influence of the segmentation process in our 3-D face recognition system and analyzed the improvements obtained when applying landmark-based techniques to deal with facial expressions.

New improvements to range image segmentation by edge detection

This article presents new improvements to range image segmentation based on edge detection techniques. The developed approach better preserves the objects topology and shape even to noisy images. The algorithm also does not depend on rigid threshold values, thus being useful in unsupervised systems. Experiments were performed in a popular range image database and the results were compared to four other traditional range image segmentation algorithms, demonstrating the efficiency of the proposed algorithm.

Automatic 3D facial segmentation and landmark detection

This paper presents our methodology for face and facial features detection to improve 3D face recognition in a presence of facial expression variation. Our goal was to develop an automatic process to be embedded in a face recognition system, using only range images as input. To do that, our approach combines traditional image segmentation techniques for face segmentation and detect facial features by combining an adapted method for 2D facial features extraction with the surface curvature information. The experiments were performed in a large, well-known face image database available on the Biometric Experimentation Environment (BEE), including 4,950 images. The results confirms that our method is efficient for the proposed application.

Range image segmentation into planar and quadric surfaces using an improved robust estimator and genetic algorithm

This paper presents a novel range image segmentation method employing an improved robust estimator to iteratively detect and extract distinct planar and quadric surfaces. Our robust estimator extends M-estimator Sample Consensus/Random Sample Consensus (MSAC/RANSAC) to use local surface orientation information, enhancing the accuracy of inlier/outlier classification when processing noisy range data describing multiple structures. An efficient approximation to the true geometric distance between a point and a quadric surface also contributes to effectively reject weak surface hypotheses and avoid the extraction of false surface components. Additionally, a genetic algorithm was specifically designed to accelerate the optimization process of surface extraction, while avoiding premature convergence. We present thorough experimental results with quantitative evaluation against ground truth. The segmentation algorithm was applied to three real range image databases and competes favorably against eleven other segmenters using the most popular evaluation framework in the literature. Our approach lends itself naturally to parallel implementation and application in real-time tasks. The method fits well into several of todays applications in man-made environments, such as target detection and autonomous navigation, for which obstacle detection, but not description or reconstruction, is required. It can also be extended to process point clouds resulting from range image registration.

3D reconstruction methods for digital preservation of cultural heritage

A overview of the state-of-the-art approaches for 3D reconstruction.We focus this overview on cultural heritage preservation.We point open problems and difficulties on this field. 3D reconstruction, refers to capturing and reproducing the shape and appearance of an arbitrary object or scene given depth and color information. This is a broad research area within the computer vision field involving many stages and still open problems. The digital preservation of cultural heritage is a specially challenging application of 3D reconstruction. Cultural heritage objects and sites greatly differ from each other and a maximized fidelity of the 3D reconstruction is a core requirement. The literature on this topic has substantially increased in the past years, mostly due to the variety of scenarios and the development of new depth sensing devices as well as techniques able to deal with this issue. In our search to develop a complete 3D reconstruction pipeline, we have comprehensively studied techniques related to this topic and divided the 3D digitization process in four major overviews: image acquisition, view registration, mesh integration and texture generation. We present the state-of-the-art approaches and challenges of each stage.

Enhanced, robust genetic algorithms for multiview range image registration

We present a new method for precise registration of multiple range images with low overlap based on genetic algorithms (GAs). The proposed method minimizes the alignment error within the common overlap area among a set of views, which is computed by a novel robust evaluation metric, called the surface interpenetration measure. Because they search in a space of transformations, GAs are capable of registering surfaces without need for prealignment, as opposed to methods based on the iterative closest point (ICP) algorithm, the most popular to date. The experimental results confirm that the new method ensures more precise alignments than combined sequential pairwise alignments for multiview registration, providing accurate global alignment among overlapping views.

Edge detection to guide range image segmentation by clustering techniques

Edge detection is an unsolved problem in that, so far, there is no general optimal solution. However, edge detection provides rich information about the scene being observed. This is particularly true in range images, where 3D information is explicit. Many researchers have been taking advantage of edge detection information to improve the segmentation of range images by integrating edge detection with other different segmentation techniques. This paper presents a methodology to perform edge detection in range images in order to provide a reliable and meaningful edge map, which helps to guide and improve range image segmentation by clustering techniques. The obtained edge map leads to three important improvements: (1) the definition of the ideal number of regions to initialize the clustering algorithm; (2) the selection of suitable initial cluster centers; and (3) the successful identification of distinct regions with similar features. Experimental results that substantiate the effectiveness of this work are presented.

A 3D reconstruction pipeline for digital preservation

We present a new 3D reconstruction pipeline for digital preservation of natural and cultural assets. This application requires high quality results, making time and space constraints less important than the achievable precision. Besides the high quality models generated, our work allows an overview of the entire reconstruction process, from range image acquisition to texture generation. Several contributions are shown, which improve the overall quality of the obtained 3D models. We also identify and discuss many practical problems found during the pipeline implementation. Our objective is to help future works of other researchers facing the challenge of creating accurate 3D models of real objects.