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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

The PASCAL Visual Object Classes Challenge

http://www.cs.unibo.it/~montesi/CBD/Articoli/SurvyBigData2.pdf

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

Two people with long-standing tetraplegia use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. John Donoghue and colleagues have previously demonstrated that people with tetraplegia can learn to use neural signals from the motor cortex to control a computer cursor. Work from another lab has also shown that monkeys can learn to use such signals to feed themselves with a robotic arm. Now, Donoghue and colleagues have advanced the technology to a level at which two people with long-standing paralysis — a 58-year-old woman and a 66-year-old man — are able to use a neural interface to direct a robotic arm to reach for and grasp objects. One subject was able to learn to pick up and drink from a bottle using a device implanted 5 years earlier, demonstrating not only that subjects can use the brain–machine interface, but also that it has potential longevity. Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system1,2,3,4,5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices6,7,8. Able-bodied monkeys have used a neural interface system to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.

/pdf/reach-and-grasp-by-people-with-tetraplegia-using-a-neurally-u1xyn3on0d.pdf

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

This paper continues to explore the potential of newly introduced Fuzzy Gaussian Inference (FGI) [1]. It aims at constructing fuzzy membership functions by modelling hidden probability distributions underlying human motions. A fuzzy rule-based system has been employed to assist boxing motion classification from natural human Motion Capture data. In this experiment, FGI alone is able to recognise seven different boxing stances simultaneously with an accuracy superior to a GMM-based classifier. Results indicate that adding a Fuzzy Inference Engine on top of FGI improves the accuracy of the classifier in a consistent way.

Boxing motions classification through combining fuzzy gaussian inference with a context-aware rule-based system

Scene material recognition is a valuable perceptual ability for robots. However, current methods fail to provide robust solution for indoor surrounding material recognition. As human beings are able to immediately understand properties of surrounding materials, especially when it concerns smooth fluffy materials, we believe robots should be equipped with similar abilities. In this paper we explore a new idea for fluffy surface perception for robots based on above hypothesis. The aim is to enable robots to distinguish smooth and fluffy surfaces without touching them. This is achieved through calculating image match ability map from video cameras. Through measuring the similarity of images captured from different viewpoints, robots are able to immediately recognize whether it is a fluffy material. The method has been validated by primary experiments. Our results show that robots can have a sense of material properties for fluffy materials without touching them or any prior knowledge.

/pdf/new-perception-of-fluffy-surfaces-1uj7zlp30p.pdf

New Perception of Fluffy Surfaces

Electrical resistance tomography (ERT) is considered a novel sensing technique for monitoring conductivity distribution. Image reconstruction of ERT is an ill-posed inverse problem. In this paper, an improved regularization reconstruction method is presented to solve this issue. We adopted homotopic mapping to choose the regularization parameter of the iterative Tikhonov algorithm. The standard normal distribution function was used to continuously adjust the regularization parameter. Subsequently, the resultant image vector was deployed as the initial value of the iterative Tikhonov algorithm to improve the image quality. Finally, the improved method was combined with a projection algorithm based on the Krylov subspace, which was also effective in reducing the computational time. Both simulation and experimental results indicated that the new algorithm could improve the real-time performance and imaging quality.

A fast Tikhonov regularization method based on homotopic mapping for electrical resistance tomography.

DLR Multisensory Articulated Hand I and II

In this paper, feature selection is used to allow the identification of critical attributes before the reconstruction of occluded data in 3d human motion classification. This work presents Fuzzy Laplace Similarity: a new fuzzy similarity relation used in the context of fuzzy rough feature selection. The measure of similarity is critical to the performance of the selection phase, which in turn will determine the performance of a classifier. Results over various datasets show that Fuzzy Laplace Similarity improves consistently the quality of the fuzzy rough feature selection process and performs well compared to other fuzzy similarity relations.

http://ieeexplore.ieee.org/iel5/5626921/5665275/05665648.pdf

Honghai Liu

Papers

Boxing motions classification through combining fuzzy gaussian inference with a context-aware rule-based system

New Perception of Fluffy Surfaces

A fast Tikhonov regularization method based on homotopic mapping for electrical resistance tomography.

DLR Multisensory Articulated Hand I and II

Optimizing feature selection using laplace similarity in occluded human motion recognition