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

Abstract

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 hillclimbing 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, GAs 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.

Fast and Accurate 3D Face Recognition

“…To this end, a distance measure must be defined between the two point clouds. Examples of such distance measures are the Mean Square Error (MSE) between the surfaces and the Surface Interpenetration Measure (SIM), see Silva et al (2005), Queirolo et al (2010). Based on the distance between the point clouds (or the change in distance due to a change in the registration parameters) the registration parameters (θ, φ, γ, t) are updated and the probe is transformed again etc.…”

An Analysis of Registration of Brain Images using Fast Walsh Hadamard Transform

“…Bentoutou et al in [5] offered an automatic image registration for applications in Remote Sensing. A novel approach that addresses the range image registration problem for views having low overlap and which may include substantial noise for image registration was described by Silva et al in [6]. Matungka et al proposed an approach that involved Adaptive Polar Transform (APT) for Image registration in [7,10].…”

“…There is no universal registration [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] algorithm that can work reasonably well for all images. An appropriate registration algorithm for the particular problem must be chosen or developed, as they are adhoc in nature.…”

Automatic registration of overlapping 3D point clouds using closest points

“…As a result, the key to successfully applying the ICP algorithm for 3D free form shape registration lies in eliminating the correspondences with relatively lower qualities. For this purpose, the following techniques have been proposed: † Increase the dimensionality of points from 3D [3,4] to higher dimensions by incorporating other geometric or optical features, such as normal vectors [12], invariants [44], curvature [55], laser reflectance strength value [35], and colours [22]; † Sample points uniformly [51], in normal space [42], or based on covariance matrix [14]; † Establish correspondences from matching points to matching curves [48,53] to matching 2D images [20,23,54], from matching local structural features to examining motion consistency [28,31,41] to combining both [27]; † Consider the reliability of point correspondences as a function of the cosine of the including angle between vertex normals and their viewing directions [51]; † Eliminate false matches through removing boundary points [42,51], checking interpoint distance consistency [11] or orientation consistency [38,57], examining motion consistency [29][30][31]41], or examining both the motion and structural consistency [28,27]; † Estimate motion parameters from using least squares to using weighted least squares [51], genetic algorithm [45], M-estimator [35] or simulated annealing [33], or from the Euclidean space to the frequency space [32].…”