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

3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation. With the recent availability of inexpensive 2.5D depth sensors (e.g. Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop. Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding. To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network. Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representation automatically. It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning. To train our 3D deep learning model, we construct ModelNet - a large-scale 3D CAD model dataset. Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.

/pdf/3d-shapenets-a-deep-representation-for-volumetric-shapes-1ii3phcu91.pdf

3D ShapeNets: A deep representation for volumetric shapes

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

http://cvssp-data.eps.surrey.ac.uk/Personal/AdrianHilton/Publications_files/survey_techrep.pdf

A survey of advances in vision-based human motion capture and analysis

Multisensor data fusion is an emerging technology applied to Department of Defense (DoD) areas such as automated target recognition, battlefield surveillance, and guidance and control of autonomous vehicles, and to non-DoD applications such as monitoring of complex machinery, medical diagnosis, and smart buildings. Techniques for multisensor data fusion are drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation and other areas. This paper provides a tutorial on data fusion, introducing data fusion applications, process models, and identification of applicable techniques. Comments are made on the state-of-the-art in data fusion.

An introduction to multisensor data fusion

https://imaging.utk.edu/publications/papers/2013/CVIU13_sh.pdf

Occlusion filling in stereo

A major problem in the creation of multi-sensor interfaces for tele-robotics is the development of method for combining various data sources into one coherent world model for examination by the operator. However, if the images acquired from different sensing modalities can be registered with LADAR range images, texture mapping can be employed for sensor integration. Presented in this paper is a novel stereo-based method for registering color and LIDAR images acquired from externally uncalibrated sensors for use in a visualization system as described above. In our approach, corresponding features are first extracted from LADAR intensity/color image pairs. Since the computation of Euclidean range using stereoscopic techniques depends on knowledge of the relative sensor orientation, a downhill simplex algorithm is iteratively applied to minimize the sum-squared-error between the stereo and LADAR range estimates of the matched points with respect to the system's relative rotation and translation parameters. Once the system's epipolar geometry has been estimated in this manner, the LADAR range data is employed to re-project the color image to the viewpoint of the scanner for use as a texture map.

Stereo-based registration of ladar and color imagery

An algorithm is proposed for pose estimation based on the volume measurement of tetrahedra composed of feature-point triplets extracted from an arbitrary quadrangular target and the lens center of the vision system. Using a pinhole model (lens distortion is taken into account separately) and a quadrangular target, for which only the six distance measurements between all pairs of feature points are known, the complete pose is determined using an all-geometric closed-form solution for the six parameters of the pose (three translation components and three rotation components). This method has been tested using synthetic and real data and shown to be efficient, accurate, and robust. Its speed, in particular, makes it a potential candidate for real-time robotic tasks. >

/pdf/pose-estimation-for-camera-calibration-and-landmark-tracking-32pgs0o2j7.pdf

Pose estimation for camera calibration and landmark tracking

https://imaging.utk.edu/publications/papers/2010/IMAVIS_28_fb.pdf

A new method for the registration of three-dimensional point-sets: The Gaussian Fields framework

In order to achieve improved recognition performance in comparison with conventional broadband images, this paper addresses a new method that automatically specifies the optimal spectral range for multispectral face images according to given illuminations. The novelty of our method lies in the introduction of a distribution separation measure and the selection of the optimal spectral range by ranking these separation values. The selected spectral ranges are consistent with the physics analysis of the multispectral imaging process. The fused images from these chosen spectral ranges are verified to outperform the conventional broadband images by 3%-20%, based on a variety of experiments with indoor and outdoor illuminations using two well-recognized face-recognition engines. Our discovery can be practically used for a new customized sensor design associated with given illuminations for improved face-recognition performance over the conventional broadband images.

Mongi A. Abidi

Papers

Occlusion filling in stereo

Stereo-based registration of ladar and color imagery

Pose estimation for camera calibration and landmark tracking

A new method for the registration of three-dimensional point-sets: The Gaussian Fields framework

Improving Face Recognition via Narrowband Spectral Range Selection Using Jeffrey Divergence