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

red and color video data acquired by moving cameras under the constraints of small and large motion parallaxes. We distinguish between techniques for image sequences with small motion parallaxes and techniques for image sequences with large motion parallaxes and we describe techniques for building the mosaics for the purpose of under vehicle inspection and visualization of roadside sequences. For the under vehicle sequences, the goal is to create a large, high-resolution mosaic that may used to quickly inspect the entire scene shot by a camera making a single pass underneath the vehicle. The generated mosaics provide efficient and complete representations of video sequences.

Multi-Perspective Mosaics For Inspection and Visualization

Analysis of range data is important for performing scene interpretation in robotic environments. The first step applied to range images for feature extraction is segmentation, which reveals the inner borders between three-dimensional scene elements. Segmentation of range images can be performed by detecting edges and interpreting them as boundaries of the different surfaces. A segmentation method for range images is proposed using the a trous algorithm implementation of the discrete wavelet transform. This algorithm applies oriented band pass filters at multiple scales for derivative estimations. The wavelet transform (WT) is used as a smoothing multiscale differentiation operator. A model for range image features is developed based on the differential properties of step and roof edges in range images. We show how specific families of wavelets are used for investigating the local and scale properties of the differentials of two-dimensional signals, with emphasis on the higher derivative singularities and zero-crossings usually found in range data. These features are detected, then combined into a binary edge map. Following the modulus maxima of the wavelet transform in the direction of the gradient vector obviates the need for thresholding. The resulting binary edge map provides a basis for complete segmentation. The final result is a segmented image with labeled regions indicating different surface patches. This segmented image can be used as input to a higher-level recognition or for a three-dimensional reconstruction algorithm. The technique is applied to synthetic and real range images with different features and is shown to yield consistent and reliable results.

Multiple scale segmentation of range images using wavelets

This paper presents a human model-based feature extraction method for a video surveillance retrieval system. The proposed method extracts, from a normalized scene, object features such as height, speed, and representative color using a simple human model based on multiple-ellipse. Experimental results show that the proposed system can effectively track moving routes of people such as a missing child, an absconder, and a suspect after events.

Object retrieval using scene normalized human model for video surveillance system

In this paper, we present a novel denoising algorithm based on the Rodin-Osher-Fatemi (ROF) model. The goal is to ensure maximum noise removal while preserving image details. To achieve this goal, we developed a new edge detector based on the structure tensor, Non-Local Mean filtering and fuzzy complement. This edge detector is incorporated in the objective function of the ROF model to introduce more control over the amount of regularization allowing more denoising in smooth regions and less denoising when processing edge regions. Experiments on synthetic images demonstrate the efficiency of the edge detector. Furthermore, denoising experiments and comparison with other algorithms show that the proposed method presents good performance in terms of Peak Signal-to-Noise Ratio and Structure Similarity Index.

Edge guided total variation for image denoising

Many applications require the use of 3D graphics to create models of real environments. These models are usually built from range or depth images. In the scene modeling process, the use of additional 2D digital sensorial information leads to multimodal scene representation, where an image acquired by a 2D sensor is used as a texture map for a geometric model of a scene. In this chapter we present, as an example of optimization, a photo-realistic scene reconstruction procedure using laser range data and color photographs.

Mongi A. Abidi

Papers

Multi-Perspective Mosaics For Inspection and Visualization

Multiple scale segmentation of range images using wavelets

Object retrieval using scene normalized human model for video surveillance system

Edge guided total variation for image denoising

Multimodal Scene Reconstruction Using Genetic Algorithm-Based Optimization