Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

/pdf/image-quality-assessment-from-error-visibility-to-structural-1rlwcqe34t.pdf

Image quality assessment: from error visibility to structural similarity

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour. Estimations are Approximations. Transform Coding. Appendix A: Mathematical Complements. Appendix B: Software Toolboxes.

http://ahvazdownload.persiangig.com/document/article/39.pdf

A wavelet tour of signal processing

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Mean shift: a robust approach toward feature space analysis

Publisher Summary This chapter focuses on the modern notion of short-term memory, called working memory. Working memory refers to the temporary maintenance of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be maintained for longer periods of time through active rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behavior. Working memory is a system that is critically important in cognition and seems necessary in the course of performing many other cognitive functions, such as reasoning, language comprehension, planning, and spatial processing. This chapter demonstrates the functional importance of dopamine to working memory function in several ways. Elucidation of the cognitive and neural mechanisms underlying human working memory is an important focus of cognitive neuroscience and neurology for much of the past decade. One conclusion that arises from research is that working memory, a faculty that enables temporary storage and manipulation of information in the service of behavioral goals, can be viewed as neither a unitary, nor a dedicated system. Data from numerous neuropsychological and neurophysiological studies in animals and humans demonstrates that a network of brain regions, including the prefrontal cortex, is critical for the active maintenance of internal representations.

Chapter 11 Working memory

the Behaving Monkey in Cortex Obtained by Voltage-Sensitive Dye Imaging Rapid and Precise Retinotopic Mapping of the Visual

Curriculum vitae and list of publications

Multistage Model for Binocular Rivalry

[PDF]  [Full Text]  [Abstract] , November 1, 2002; 12 (11): 1132-1145. Cereb Cortex J. A. Bourne, R. Tweedale and M. G.P. Rosa Coherent Motion Physiological Responses of New World Monkey V1 Neurons to Stimuli Defined by [PDF]  [Full Text]  [Abstract] , February 1, 2003; 13 (2): 189-202. Cereb Cortex S. Zeki, R.J. Perry and A. Bartels The Processing of Kinetic Contours in the Brain [PDF]  [Full Text]  [Abstract] , February 1, 2006; 95 (2): 862-881. J Neurophysiol J. Larsson, M. S. Landy and D. J. Heeger Cortex Orientation-Selective Adaptation to Firstand Second-Order Patterns in Human Visual [PDF]  [Full Text]  [Abstract] , March 1, 2006; 95 (3): 1864-1880. J Neurophysiol S. G. Mysore, R. Vogels, S. E. Raiguel and G. A. Orban Processing of Kinetic Boundaries in Macaque V4

http://www.informatik.uni-ulm.de/ni/forschung/vision/VisionClub/MotionClub/KinDefBoundaries_V1V2Macaque_XiaoEtAl.pdf

V1 and V2 of the Macaque Monkey Processing of Kinetically Defined Boundaries in Areas

Motor learning occurs over long periods of practice during which motor acuity - the ability to execute actions more accurately, precisely, and within a shorter amount of time - improves. Laboratory-based motor learning studies are typically limited to a small number of participants and a time frame of minutes to several hours per participant. Thus, there is a need to assess the generalizability of theories and findings from lab-based motor learning studies on much larger samples across longer time scales. In addition, laboratory-based studies of motor learning use relatively simple motor tasks which participants are unlikely to be intrinsically motivated to learn, limiting the interpretation of their findings in more ecologically valid settings. We studied the acquisition and longitudinal refinement of a complex sensorimotor skill embodied in a first-person shooter video game scenario, with a large sample size (N = 7174 participants, 682,564 repeats of the 60 sec game) over a period of months. Participants voluntarily practiced the gaming scenario for as much as several hours per day up to 100 days. We found improvement in performance accuracy (quantified as hit rate) was modest over time but motor acuity (quantified as hits per second) improved considerably, with 40-60% retention from one day to the next. We observed steady improvements in motor acuity across multiple days of video game practice, unlike most motor learning tasks studied in the lab that hit a performance ceiling rather quickly. Learning rate was a nonlinear function of baseline performance level, amount of daily practice, and to a lesser extent, number of days between practice sessions. In addition, we found that the benefit of additional practice on any given day was non-monotonic; the greatest improvements in motor acuity were evident with about an hour of practice and 90% of the learning benefit was achieved by practicing 30 minutes per day. Taken together, these results provide a proof-of-concept in studying motor skill acquisition outside the confines of the traditional laboratory and provide new insights into how a complex motor skill is acquired in an ecologically valid setting and refined across much longer time scales than typically explored.

/pdf/long-term-motor-learning-in-the-wild-with-high-volume-video-416174qxds.pdf

David J. Heeger

Papers

the Behaving Monkey in Cortex Obtained by Voltage-Sensitive Dye Imaging Rapid and Precise Retinotopic Mapping of the Visual

Curriculum vitae and list of publications

Multistage Model for Binocular Rivalry

V1 and V2 of the Macaque Monkey Processing of Kinetically Defined Boundaries in Areas

Long-term Motor Learning in the Wild with High Volume Video Game Data