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

With the recent rapid development in the field of multispectral face imaging, the use of image fusion has emerged as a new and important research area. Many studies have attempted to improve the performance of face recognition by fusing the infrared (IR) and visible face images, yet few comparison studies have been conducted to examine which fusion method is preferable over another. In this paper, we provide an overview of the most widely used pixel level fusion algorithms, and establish a comparison to evaluate each fusion method Our experiments were validated by comparing cumulative match characteristics (CMC) of multispectral image fusion by weighted sum, principal component analysis, empirical mode decomposition, and wavelet transform.

Pixel level fusion of multispectral face images: Short review

Data acquired form multiple sensors provides a means for defining a knowledge base and a current situation scenario. The data is accepted and integrated as intelligence with the use of signal- and symbol-level fusion to translate the raw data into intelligence information that can be used to baseline the knowledge of a control system. An application of this technique is applied to a robotic inspection and dismantlement system. This system is used to dismantle material sin a potentially hazardous environment that involves nuclear waste. The objective is to gather information about the environment using a suite of sensors to include range, electro-optical and proximity sensors to develop a current situation and initiate cues to the control system. By including evidential reasoning in the fusion process, all of the data that is gathered can be used to build the knowledge base where lower belief factors are attributed to things with significant uncertainty. Logical inferences are also incorporated to develop certainty measures and truth values. The results suggest an approach to multisensor fusion for decision-based control using a knowledge base and current situation scenario framework.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Multisensor fusion for decision-based control cues

We propose a novel algorithm to smooth and simplify simultaneously range images and also triangle meshes derived from those images. These data sets often suffer from noise and over-sampling. To overcome these issues, smoothing from image processing and simplification from computer graphics attempt to minimize noise and reduce complexity, respectively. Typically, these algorithms are separate and distinct steps, but we combine them into one algorithm. We employ surface normal voting to generate robust orientation estimates and then extend the quadric error metric framework to smooth noise while simplifying the surface. We demonstrate the capabilities of this algorithm with both synthetic and real data. The proposed algorithm provides significant noise smoothing improvement when compared to the standard Garland and Heckbert (1998) quadric simplification algorithm.

https://imaging.utk.edu/publications/papers/2002/page_icip02.pdf

Simultaneous mesh simplification and noise smoothing of range images

The estimation of cloud motion is an active area of computer vision; it addresses the recovery of motion clues from a time-varying image sequence of a cloud cover, usually tracked by radar. Three major issues in this field are: (1)the choice of a measurement procedure and an area within the image where the measurement constraint is guaranteed to achieve minimum error, (2)the choice of a topological constraint and an area where this constraint is guaranteed to achieve minimum error, and (3)the identification of a procedure of coupling the two constraints mentioned above in order to obtain an optimal approximation to the velocity field. In this paper, we show that the retrieval of a reliable motion clue is closely related to the manner in which the measurement and topological constraints are coupled. An example using the gradient vector as a measurement, smoothness as a topological constraint, and the temporal gradient as a coupling factor is given. As shown when this approach is compared against a correspondence-based technique, the differential approach measures only a local average of the true motion. The performance of this differential technique and a correspondence-based technique are compared using a real radar image sequence.

Cloud Motion Measurement From Radar Image Sequences

In size preserving video tracking, the camera's focal length (zoom) is adjusted automatically to compensate for the changes in the target's image size caused by the relative motion between the camera and the target. The accurate estimation of these changes is paramount to the system performance. Structure from motion (SFM) based on the weak perspective projection model has been applied to real time target scale estimation (B. Tordoff, et al., 2004). In this paper we design target scale estimation algorithms with linear solutions based on more advanced projection models: paraperspective and perspective. The performances of the algorithms using three projection models are examined and compared. Experimental results show that the proposed algorithm based on the paraperspective projection model produces the best performance with respect to accuracy, complexity, and robustness.

https://imaging.utk.edu/publications/papers/2006/236_ICIP06_yy.pdf

Mongi A. Abidi

Papers

Pixel level fusion of multispectral face images: Short review

Multisensor fusion for decision-based control cues

Simultaneous mesh simplification and noise smoothing of range images

Cloud Motion Measurement From Radar Image Sequences

3D Target Scale Estimation for Size Preserving in PTZ Video Tracking