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

In this chapter, a modified, regularized image restoration algorithm useful in reducing blocking artifacts in predictive-coded (P) pictures of compressed video, based on the corresponding image degradation model, is presented. Since most video coding standards adopt a hybrid structure of macroblock-based motion compensation (MC) and block discrete cosine transform (BDCT), the blocking artifacts occur at both the block boundary and block interior, and the degradation process due to quantization is generated on just differential images. Based on observation, a new degradation model is needed for differential images and the corresponding restoration algorithm, which directly processes the differential images before reconstructing decoded images. For further removal of both kinds of blocking artifacts, the restored differential image must satisfy two constraints: directional discontinuities on the block boundary and on the block interior. These constraints have been used for defining convex sets for restoring differential images. In-depth analysis of differential domain processing is presented in the appendix and serves as the theoretical basis for justifying differential domain image processing. Experimental results also show significant improvement over conventional methods in the sense of both objective and subjective criteria.

Enhancement of Compressed Video

This paper describes an imaging system that was developed to aid industrial bin picking tasks. The purpose of this system was to provide accurate 3D models of parts and objects in the bin, so that precise grasping operations could be performed. The technology described here is based on two types of sensors: range mapping scanners and video cameras. The geometry of bin contents was reconstructed from range maps and modeled using superquadric representations, providing location and parts surface information that can be employed to guide the robotic arm. Texture was also provided by the video streamsandappliedtothe recoveredmodels.The systemis expected to improve the accuracy and efe ciency of bin sorting and represents a step toward full automation.

https://imaging.utk.edu/publications/papers/2003/boughorbel_aa03.pdf

Research article Laser ranging and video imaging for bin picking

The overall architecture of the dual-axis manipulator element and its importance as a fundamental building block of an advanced telerobotic system are described. The design, implementation, and testing of a custom VLSI chip that provides multiaxis sensor interfacing and signal conditioning are discussed. The current chip design includes: two quadrature phase optical decoders with a 16-bit resolution, three pulse-width-modulated waveform generators with 11-bit precision, and a digital input/output microprocessor interface. Each of these components have been laid out with VLSI CAD (computer-aided design) tools and have been extensively computer simulated. A 64-pin prototype chip has been fabricated in 3- mu m SCMOS technology with an expected clock rate of 10 MHz for the pulse-width modulated waveform generators and 8 MHz for the optical decoders. The custom chip replaces over 125 standard SSI/MSI CMOS and TTL services and achieves a bit resolution far greater than what is currently available with off-the-shelf components. >

VLSI design of a robotic controller for a dual axis manipulator element

Data fusion provides tools for solving problems which are characterized by distributed and diverse information sources. Many robotic applications need to retrieve particular properties from a scene; so it is necessary to use multiple knowledge sources since a single sensory modality cannot capture all of the physical causes of a given edge feature. In this paper we focus on the problem of extracting features such as image discontinuities from both synthetic and real images. Since edge detection and surface reconstruction are ill-posed problems according to Hadamard, Tikhonov's regularization paradigm is proposed as the basic tool for solving this inversion problem and restoring well-posedness. The proposed framework includes (1) a review of 2D regularization, (2) extension of the standard Tikhonov regularization method by allowing space-variant regularization parameters, and (3) further extension of the regularization paradigm by adding multiple data sources for different sensing modalities. The theoretical approach is complemented by developing a regularized hybrid fusion algorithm for solving the early vision problems of edge detection and surface reconstruction. An evaluation of these methods reveals that this new analytical data fusion technique reconstructs a smooth filtered surface in noisy regions while preserving edge characteristics needed for extracting object features. Results indicate the fusion technique is beneficial for combining edge features from different types of sensory data to locate and identify objects of interest.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Dynamic adjustment of regularization parameters for the fusion of edge features and noisy dense surfaces

PET is the only imaging modality that provides doctors with early analytic and quantitative biochemical assessment and precise localization of pathology. In PET images, boundary information as well as local pixel intensity are both crucial for manual and/or automated feature tracing, extraction, and identification. Unfortunately, the present PET technology does not provide the necessary image quality from which such precise analytic and quantitative measurements can be made. PET images suffer from significantly high levels of radial noise present in the form of streaks caused by the inexactness of the models used in image reconstruction. In this paper, our objective is to model PET noise and remove it without altering dominant features in the image. The ultimate goal here is to enhance these dominant features to allow for automatic computer interpretation and classification of PET images by developing techniques that take into consideration PET signal characteristics, data collection, and data reconstruction. We have modeled the noise steaks in PET images in both rectangular and polar representations and have shown both analytically and through computer simulation that it exhibits consistent mapping patterns. A class of filters was designed and applied successfully. Visual inspection of the filtered images show clear enhancement over the original images.

Mongi A. Abidi

Papers

Enhancement of Compressed Video

Research article Laser ranging and video imaging for bin picking

VLSI design of a robotic controller for a dual axis manipulator element

Dynamic adjustment of regularization parameters for the fusion of edge features and noisy dense surfaces

Enhancement of PET Images