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

Superquadrics are able to represent a large variety of objects with only a few parameters and a single equation. We present a superquadric representation strategy for automotive parts composed of 3-D triangle meshes. Our strategy consists of two ma- jor steps of part decomposition and superquadric fitting. The origi- nalities of this approach include the following two features. First, our approach can represent multipart objects with superquadrics suc- cessfully by applying part decomposition. Second, superquadrics re- covered from our approach have the highest confidence and accu- racy due to the 3-D watertight surfaces utilized. A novel, generic 3-D part decomposition algorithm based on curvature analysis is also proposed. Experimental results demonstrate that the proposed part decomposition algorithm is able to segment multipart objects into meaningful single parts efficiently. The proposed superquadric rep- resentation strategy can then represent each individual part of the original objects with a superquadric model successfully. © 2004

https://imaging.utk.edu/publications/papers/2004/zhang_jei04.pdf

Superquadric representation of automotive parts applying part decomposition

Multithresholding and data clustering techniques are used to segment X-ray images for low intensity threat detection in carry-on luggage. The widely used statistical validity indexes methods do not generate a reasonable estimation of the optimal number of clusters and produce a biased evaluation of the segmented images acquired by different segmentation methods. We propose a method based on the Radon transform to determine the optimal number of clusters and to evaluate the segmented images. The method utilizes both statistical and spatial information from the image and is computationally efficient. Experimental results show that the proposed method produces results consistent with human visual assessment.

/pdf/automatic-x-ray-image-segmentation-for-threat-detection-195fht4tgi.pdf

Automatic X-ray image segmentation for threat detection

A general approach is presented for the integration of vision, range, proximity, and touch sensory data to derive a better estimate of the position and orientation (pose) of an object appearing in the work space. Efficient and robust methods for analyzing vision and range data to derive an interpretation of input images are discussed. Vision information analysis includes a model-based object recognition module and an image-to-world coordinate transformation module to identify the three-dimensional (3-D) coordinates of the recognized objects. The range information processing includes modules for reprocessing, segmentation, and 3-D primitive extraction. The multisensory information integration approach represents sensory information in a sensor-independent form and formulates an optimization problem to find a minimum-error solution to the problem. The capabilities of a multisensor robotic system are demonstrated by performing a number of experiments using an industrial robot equipped with several sensors of differing types. >

Mongi A. Abidi

Papers

Superquadric representation of automotive parts applying part decomposition

Automatic X-ray image segmentation for threat detection

Developing robotic systems with multiple sensors

Range Image Segmentation through Pattern Analysis of the Multiscale Wavelet Transform

Multi-scale analysis of shell growth increments using wavelet transform