Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

The Perception of the Visual World. By James J. Gibson. U.S.A.: Houghton Mifflin Company, 1950 (George Allen & Unwin, Ltd., London). Price 35s.

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

Many ``real-world'' networks are clearly defined while most ``social'' networks are to some extent subjective. Indeed, the accuracy of empirically-determined social networks is a question of some concern because individuals may have distinct perceptions of what constitutes a social link. One unambiguous type of connection is sexual contact. Here we analyze data on the sexual behavior of a random sample of individuals, and find that the cumulative distributions of the number of sexual partners during the twelve months prior to the survey decays as a power law with similar exponents $\alpha \approx 2.4$ for females and males. The scale-free nature of the web of human sexual contacts suggests that strategic interventions aimed at preventing the spread of sexually-transmitted diseases may be the most efficient approach.

The Web of Human Sexual Contacts

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This paper provides a comprehensive review of compressed sensing or compressive sampling (CS) in bioelectric signal compression applications. The aim is to provide a detailed analysis of the current trends in CS, focusing on the advantages and disadvantages in compressing different biosignals and its suitability for deployment in embedded hardware. Performance metrics such as percent root-mean-squared difference (PRD), signal-to-noise ratio (SNR), and power consumption are used to objectively quantify the capabilities of CS. Furthermore, CS is compared to state-of-the-art compression algorithms in compressing electrocardiogram (ECG) and electroencephalography (EEG) as examples of typical biosignals. The main technical challenges associated with CS are discussed along with the predicted future trends.

Compressed Sensing for Bioelectric Signals: A Review

Point cloud matching is one of the key technologies of optical three-dimensional contour measurement. Most of the point cloud matching without landmark used the iterative closest point algorithm. In order to improve the performance of the iterative closest point algorithm, the two-step iterative closest point algorithm was proposed. The improved algorithm is divided into a rough matching step and accurate matching step. Rough matching used the principal component analysis algorithm, while the fine matching used the improved iterative closest point algorithm. Compared with the classic iterative closest point algorithm, the improved algorithm can match the partial coincident point cloud. At the same time, the experiment can validate the effectiveness of the proposed algorithm.

3D point cloud matching based on principal component analysis and iterative closest point algorithm

Pathfinding in the context of video games concerns the way in which a moving entity finds a path around obstacles; the most frequent context is real-time strategy games (in which the player directs units around a play area containing obstacles), but forms of this are found in most modern video games. It is probably the most popular but frustrating game artificial intelligence (AI) problem in game industry. This article introduces A-Star algorithm first, then describes the details of A-Star algorithm. Finally, a real example of how the A-Star pathfinding technique is used in a famous 3D game engine: Unity3D.

A pathfinding algorithm in real-time strategy game based on Unity3D

Audio fingerprint is an effective representation of an audio signal using low-level features and can be used to identify unlabeled audio based on its content. In this paper, we introduce a robust audio feature, local energy centroid (LEC), which can represent the energy conglomeration degree of the relative small region in the spectrum. Our audio fingerprint is generated based on the LEC feature which is conducive to enhance the robustness of system. In audio retrieval processing, an improved scoring strategy is proposed to resist the linear speed change. Experimental results show that the new fingerprinting system is quite robust in the present of noise and the proposed method can achieve satisfying recognition accuracy.

Audio fingerprinting based on local energy centroid

Three-dimensional articulated human motion tracking is challenging due to the high-dimensional parameter space and poor image observations. When particle swarm optimization (PSO) is used for human motion tracking, due to unreliable image likelihood, particles may be misled and be unable to find the most plausible pose space. This paper proposes a new PSO-based algorithm for human motion tracking, annealed PSO-based particle filter (APSOPF). The sampling covariance and annealing factor are incorporated into the velocity-updating equation of PSO; they are initialized with appropriate values at the beginning of the PSO iteration, and decreased (annealed) in reasonable steps. Through the sampling covariance, the motion prior is introduced into APSOPF, constraining particles to the most likely region of pose space and reducing the generation of invalid particles. By adopting decreasing coefficients in the updating principle, the leading effects of the local and global best on particles decrease with generations, making particles preserve their own divergence and self-exploration capabilities before convergence. Hence the problem of insufficiently reliable image likelihood can be mitigated in some degree. We compare APSOPF quantitatively with an annealed particle filter and a standard particle filter on the challenging HumanEvaI data set. Experimental results show that the proposed algorithm achieves lower estimation error in tracking real-world 3-D human motion. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3281669]

Annealed particle filter based on particle swarm optimization for articulated three-dimensional human motion tracking

Due to the fast reconstruction and low complexity of mathematical framework, a family of iterative greedy algorithms has been widely used in compressed sensing recently. In this paper, we focus on two types of greedy algorithms―matching pursuit and gradient pursuit, including MP, OMP, StOMP, CoSaMP, GP, CGP, etc. The mathematical framework of all types of greedy algorithms is introduced, and all of the greedy algorithms are classified according to the strategy of element selection and the update of the residual error. The performance of greedy algorithms is analyzed under the same conditions of running time, reconstruction error, SNR, etc. The relationship among the reconstruction performance, signal sparsity and the number of measurements is provided through the simulation experiments. The results show that the reconstruction error of StOMP, and CoSaMP is significantly better than the MP, OMP, GP and CGP algorithm in the case of small sparsity or more measurements, but the Gradient Pursuit approaches are much faster than Matching Pursuit.

Xiaoqing Yu

Papers

3D point cloud matching based on principal component analysis and iterative closest point algorithm

A pathfinding algorithm in real-time strategy game based on Unity3D

Audio fingerprinting based on local energy centroid

Annealed particle filter based on particle swarm optimization for articulated three-dimensional human motion tracking

Analysis on greedy reconstruction algorithms based on compressed sensing