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

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Mobile robots have the potential to become the ideal tool to teach a broad range of engineering disciplines. Indeed, mobile robots are getting increasingly complex and accessible. They embed elements from diverse ﬁelds such as mechanics, digital electronics, automatic control, signal processing, embedded programming, and energy management. Moreover, they are attractive for students which increases their motivation to learn. However, the requirements of an effective education tool bring new constraints to robotics. This article presents the e-puck robot design, which specifically targets engineering education at university level. Thanks to its particular design, the e-puck can be used in a large spectrum of teaching activities, not strictly related to robotics. Through a systematic evaluation by the students, we show that the e-puck ﬁts this purpose and is appreciated by 90 percent of a large sample of students.

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The e-puck, a Robot Designed for Education in Engineering

Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading modelling and control of robot manipulators as one of the reading material to finish quickly.

Modelling and Control of Robot Manipulators

/pdf/defying-the-crowd-cultivating-creativity-in-a-culture-of-21o9g85nzm.pdf

Defying the Crowd: Cultivating Creativity in a Culture of Conformity

https://people.ok.ubc.ca/lgabora/papers/conf_papers/Gabora-Firouzi-2012.pdf

Society Functions Best with an Intermediate Level of Creativity

This paper presents a real-time vision-based object tracking system consisting of a camera on a 2-DOF manipulator which, for example, can be a PT camera. The main novelties of the proposed tracking system include the ability to i) reduce the image processing load by relying on the object position as the only feature of the images acquired from a camera, and ii) estimate the distance and motion of the object without the need for an active rangefinder. The object tracking system is capable of controlling a manipulator using a feedback system based on the object position. The control rule of the feedback system is to minimize the distance between the object position in the camera image and the center point of the image. The proposed method can be readily adopted in dynamic environments, and achieve better tracking accuracies and efficiencies than the traditional methods. The formulation of the distance and motion estimation is presented in the paper. The efficiency and robustness of the proposed method in dealing with noise is verified by simulation using a PT camera.

Real-time monocular vision-based object tracking with object distance and motion estimation

https://escholarship.org/content/qt0007h14p/qt0007h14p.pdf?t=os5zw7

A computational model of two cognitive transitions underlying cultural evolution

A probabilistic framework for interactive learning in continuous and multimodal perceptual spaces is proposed. In this framework, the agent learns the task along with adaptive partitioning of its multimodal perceptual space. The learning process is formulated in a Bayesian reinforcement learning setting to facilitate the adaptive partitioning. The partitioning is gradually and softly done using Gaussian distributions. The parameters of distributions are adapted based on the agent's estimate of its actions' expected values. The probabilistic nature of the method results in experience generalization in addition to robustness against uncertainty and noise. To benefit from experience generalization diversity in different perceptual subspaces, the learning is performed in multiple perceptual subspaces-including the original space-in parallel. In every learning step, the policies learned in the subspaces are fused to select the final action. This concurrent learning in multiple spaces and the decision fusion result in faster learning, possibility of adding and/or removing sensors-i.e., gradual expansion or contraction of the perceptual space-, and appropriate robustness against probable failure of or ambiguity in the data of sensors. Results of two sets of simulations in addition to some experiments are reported to demonstrate the key properties of the framework.

Interactive Learning in Continuous Multimodal Space: A Bayesian Approach to Action-Based Soft Partitioning and Learning

Conceptualization strengthens intelligent systems in generalization skill, effective knowledge representation, real-time inference, and managing uncertain and indefinite situations in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction by which the continuous state is formed as entities called concepts which are connected to the action space and thus, they illustrate somehow the complex action space. Of computational concept learning approaches, action-based conceptualization is favored because of its simplicity and mirror neuron foundations in neuroscience. In this paper, a new biologically inspired concept learning approach based on the probabilistic framework is proposed. This approach exploits and extends the mirror neuron's role in conceptualization for a reinforcement learning agent in nondeterministic environments. In the proposed method, instead of building a huge numerical knowledge, the concepts are learnt gradually from rewards through interaction with the environment. Moreover the probabilistic formation of the concepts is employed to deal with uncertain and dynamic nature of real problems in addition to the ability of generalization. These characteristics as a whole distinguish the proposed learning algorithm from both a pure classification algorithm and typical reinforcement learning. Simulation results show advantages of the proposed framework in terms of convergence speed as well as generalization and asymptotic behavior because of utilizing both success and failures attempts through received rewards. Experimental results, on the other hand, show the applicability and effectiveness of the proposed method in continuous and noisy environments for a real robotic task such as maze as well as the benefits of implementing an incremental learning scenario in artificial agents.

Hadi Firouzi

Papers

Society Functions Best with an Intermediate Level of Creativity

Real-time monocular vision-based object tracking with object distance and motion estimation

A computational model of two cognitive transitions underlying cultural evolution

Interactive Learning in Continuous Multimodal Space: A Bayesian Approach to Action-Based Soft Partitioning and Learning

A Probabilistic Reinforcement-Based Approach to Conceptualization