The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces. >

A method for registration of 3-D shapes

We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.

/pdf/object-detection-with-discriminatively-trained-part-based-4cnosvuqcp.pdf

Object Detection with Discriminatively Trained Part-Based Models

Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can be easily extended to include new algorithms. We have also produced several new multiframe stereo data sets with ground truth, and are making both the code and data sets available on the Web.

/pdf/a-taxonomy-and-evaluation-of-dense-two-frame-stereo-4c20etkubp.pdf

A taxonomy and evaluation of dense two-frame stereo correspondence algorithms

A comparative study of texture measures with classification based on featured distributions

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

Cyclic motion is formally defined as repeating curvature values along a path of motion. A procedure is presented for cyclic motion detection using spatiotemporal (ST) surfaces and ST curves. The projected movement of an object generates ST surfaces. ST curves are detected on the ST surfaces, providing an accurate, compact, qualitative description of the ST surfaces. Curvature scale-space of the ST curves is then used to detect intervals of repeating curvature values. The successful detection of cyclic motion in two data sets is presented. >

/pdf/cyclic-motion-detection-using-spatiotemporal-surfaces-and-475ol8dufo.pdf

Cyclic motion detection using spatiotemporal surfaces and curves

A technique is presented for computing 3D scene structure from point and line features in monocular image sequences. Unlike previous methods, the technique guarantees the completeness of the recovered scene, ensuring that every scene feature that is detected in each image is reconstructed. The approach relies on the presence of four or more reference features whose correspondences are known in all the images. Under an orthographic or affine camera model, the parallax of the reference features provides constraints that simplify the recovery of the rest of the visible scene. An efficient recursive algorithm is described that uses a unified framework for point and line features. The algorithm integrates the tasks of feature correspondence and structure recovery, ensuring that all reconstructible features are tracked. In addition, the algorithm is immune to outliers and feature drift, two weaknesses of existing structure from motion techniques. Experimental results are presented for real images. >

/pdf/complete-scene-structure-from-four-point-correspondences-4qc3taacjk.pdf

Complete scene structure from four point correspondences

A shape smoothing algorithm is presented which uses properties of the medial axis to define segments of the shape border and their prominence relative to the local radius of the shape. Prominence values are used to classify axes as either major or minor, and minor axes are then deleted. An augmented medial axis transform is also defined using an approximate Euclidean distance transform, and medial axis interpolation and linking.

Shape Smoothing Using Medial Axis Properties

The question of which views map be inferred from a set of basis images is addressed. Under certain conditions, a discrete set of images implicitly describes scene appearance for a continuous range of viewpoints. In particular it is demonstrated that two basis views of a static scene determine the set of all views an the line between their optical centers. Additional basis views further extend the range of predictable views to a two- or three-dimensional region of viewspace. These results are shown to apply under perspective projection subject to a generic visibility constraint called monotonicity. In addition, a simple scanline algorithm is presented for actually generating these views from a set of basis images. The technique, called view morphing map be applied to both calibrated and uncalibrated images. At a minimum, two basis views and their fundamental matrix are needed. Experimental results are presented an real images. This work provides a theoretical foundation for image-based representations of 3D scenes by demonstrating that perspective view synthesis is a theoretically well-posed problem.

/pdf/toward-image-based-scene-representation-using-view-morphing-1xdp05ge3c.pdf

Toward image-based scene representation using view morphing

Head pose estimation has many useful applications in practice. How to estimate the head pose automatically and robustly is still a challenging problem. In pose estimation, different pose angles can be used as regression values or viewed as different class labels. Thus a question is raised in our study: which is proper for pose estimation - classification or regression? We investigate representative classification and regression methods on the same problem to see any difference. A method that combines regression and classification approaches is also examined. Preliminary experiments show some interesting results which might prompt further exploration of related issues in pose estimation.

/pdf/head-pose-estimation-classification-or-regression-umvzilw3c6.pdf

Charles R. Dyer

Papers

Cyclic motion detection using spatiotemporal surfaces and curves

Complete scene structure from four point correspondences

Shape Smoothing Using Medial Axis Properties

Toward image-based scene representation using view morphing

Head pose estimation: Classification or regression?