Advances in computer speed, memory capacity, and hardware graphics acceleration have made the interactive manipulation and visualization of complex, detailed (and therefore large) three-dimensional models feasible. These models are either painstakingly designed through an elaborate CAD process or reverse engineered from sculpted prototypes using modern scanning technologies and integration methods. The availability of detailed data describing the shape of an object offers the computer vision practitioner new ways to recognize and localize free-form objects. This survey reviews recent literature on both the 3D model building process and techniques used to match and identify free-form objects from imagery. c ○ 2001 Academic Press 1.

This paper explores a view-based approach to recognize free-form objects in range images. We are using a set of local features that are easy to calculate and robust to partial occlusions. By combining those features in a multidimensional histogram, we can obtain highly discriminant classifiers without the need for segmentation. Recognition is performed using either histogram matching or a probabilistic recognition algorithm. We compare the performance of both methods in the presence of occlusions and test the system on a database of almost 2000 full-sphere views of 30 free-form objects. The system achieves a recognition accuracy above 93% on ideal images, and of 89% with 20% occlusion.

Abstract — Eigendecomposition is a common technique that is performed on sets of correlated images in a number of computer vision and robotics applications. Unfortunately, the computation of an eigendecomposition can become prohibitively expensive when dealing with very high resolution images. While reducing the resolution of the images will reduce the computational expense, it is not known a priori how this will affect the quality of the resulting eigendecomposition. The work presented here provides an analysis of how different resolution reduction techniques affect the eigendecomposition. A computationally efficient algorithm for calculating the eigendecomposition based on this analysis is proposed. Examples show that this algorithm performs very well on arbitrary video sequences. 1 I.

Eigendecomposition is a common technique that is performed on sets of correlated images in a number of computer vision and robotics applications. Unfortunately, the computation of an eigendecomposition can become prohibitively expensive when dealing with very high-resolution images. While reducing the resolution of the images will reduce the computational expense, it is not known a priori how this will affect the quality of the resulting eigendecomposition. The work presented here provides an analysis of how different resolution reduction techniques affect the eigendecomposition. A computationally efficient algorithm for calculating the eigendecomposition based on this analysis is proposed. Examples show that this algorithm performs well on arbitrary video sequences.

This paper presents a novel object recognition approach based on range images. Due to its insensitivity to illumination, range data is well suited for reliable silhouette extraction. Silhouette or contour descriptions are good sources of information for object recognition. We propose a complete object recognition system, based on a 3D laser scanner, reliable contour extraction with floor interpretation, feature extraction using a new, fast Eigen-CSS method, and a supervised learning algorithm. The recognition system was successfully tested on range images acquired with a mobile robot, and the results are compared to standard techniques,

Abstract This thesis introduces a novel method for sequentially accumulating evidence as it pertains to an active observer seeking to identify an object in a known environment. First, a probabilistic framework is developed, based on a generalized inverse theory, where assertions are represented by conditional probability density functions. In order to resolve ambiguous assertions from single view measurements, a sequential recognition strategy is developed in which evidence is accumulated over successive viewpoints until a definitive assertion can be made. The main contribution of the thesis is a strategy for conditioning the inference and the measurement processes with feedback from prior information. The problem of interest is that of model-based recognition, where the task is to identify an unknown model from a database of known objects on the basis of parameter estimates. The robustness of the algorithm is illustrated through its application to two very different domains: (1) recognition of 3-D parametric models estimated directly from laser rangefinder data, (2) recognition of objects based on signatures extracted from optical flow images that they generate as they move with respect to a camera. The latter approach is completely novel and presents a major contribution to the field. Experimental results verify the strength of the approach at overcoming difficulties encountered in both contexts, as rapid convergence to the correct solution occurs in most cases.

Eigendecomposition is a common technique that is performed on sets of correlated images in a number of computer vision and robotics applications. Unfortunately, the computation of an eigendecomposition can become prohibitively expensive when dealing with very high resolution images. While reducing the resolution of the images will reduce the computational expense, it is not known how this will affect the quality of the resulting eigendecomposition. The work presented here gives the theoretical background for quantifying the effects of varying the resolution of images on the eigendecomposition that is computed from those images. A computationally efficient algorithm for this eigendecomposition is proposed using derived analytical expressions. Examples show that this algorithm performs very well on arbitrary video sequences.

This work focuses on the extraction of features from dense range images for object recognition. The object recognition process is based on a CAD model of the object. Curvature information derived from the CAD model is used to support the feature extraction process. We perform a curvature based classification of the range image to achieve a segmentation into meaningful surface patches, which are later to be matched with the surfaces of the CAD model.

Image-P)Psurgery (IGS) is a technique for localizing anatomical structures on the basis of volumetric image data and for determining the optimal surgical path to reach thesestructures, by the means of a localization device, or probe, whose position is tracked over time. The usefulness of this technology hinges on the accuracy of the transformation between the image volume and the space occupied by the patient anatomy and spanned by the probe.Unfortunately, in neurosurgery this transformation can be degraded byintra-PIUCxx8 brain shift, which often measures more than 10 mm and can exceed 25 mm. We propose a method for characterizing brain shift that is based onnon-8):P surface registration, and can be combined with a constitutively realistic finite element approach for volumetric displacement estimation. The proposed registration method integrates in a unified framework all of the stages required to estimate the movement of the cortical surface in the operating room:model-fi--VP segmentation of thepre-8U--PIU)-- brain surface in magnetic resonance image data,range-WfixPIU of the cortex in the OR, range--MR rigid transformation computation, andrange---PI8 non-e--- brain motion estimation. The brain segmentation technique is an adaptation of the surface evolution model. Its convergence to the brain boundary is the result of a speed term restricted Computer Vision and Image Understanding 89 (2003) 226--251 www.elsevier.com/locate/cviu Corresponding author.

Eigendecomposition represents one computationally efficient approach for dealing with object detection and pose estimation, as well as other vision-based problems, and has been applied to sets of correlated images for this purpose. The major drawback in using eigendecomposition is the off line computational expense incurred by computing the desired subspace. This off line expense increases drastically as the number of correlated images becomes large (which is the case when doing fully general 3-D pose estimation). Previous work has shown that for data correlated on S¹, Fourier analysis can help reduce the computational burden of this off line expense. This paper presents a method for extending this technique to data correlated on S² as well as SO(3) by sampling the sphere appropriately. An algorithm is then developed for reducing the off line computational burden associated with computing the eigenspace by exploiting the spectral information of this spherical data set using spherical harmonics and Wigner-D functions. Experimental results are presented to compare the proposed algorithm to the true eigendecomposition, as well as assess the computational savings.