Based on the Motor Theory of speech perception, the interaction between the auditory and motor systems plays an essential role in speech perception. Since the Motor Theory was proposed, it has received remarkable attention in the field. However, each of the three hypotheses of the theory still needs further verification. In this review, we focus on how the auditory-motor anatomical and functional associations play a role in speech perception and discuss why previous studies could not reach an agreement and particularly whether the motor system involvement in speech perception is task-load dependent. Finally, we suggest that the function of the auditory-motor link is particularly useful for speech perception under adverse listening conditions and the further revised Motor Theory is a potential solution to the ＂cocktail-party＂ problem.
This is primarily an expository paper surveying up-to-date known results on the spectral theory of1-Laplacian on graphs and its applications to the Cheeger cut, maxcut and multi-cut problems. The structure of eigenspace, nodal domains, multiplicities of eigenvalues, and algorithms for graph cuts are collected.
The Landweber scheme is a method for algebraic image reconstructions. The convergence behavior of the Landweber scheme is of both theoretical and practical importance. Using the diagonalization of matrix, we derive a neat iterative representation formula for the Landweber schemes and consequently establish the convergence conditions of Landweber iteration. This work refines our previous convergence results on the Landweber scheme.
基于HEVC(High Efficiency Video Coding)新的编码结构,本文提出了一种基于视觉特性的率失真优化方法。首先基于分歧归一化与量化之间的关系,提出了一种适合HEVC编码结构的视觉因子的计算方法,并提出使用非线性模型对视觉因子进行缩放,进而用于对量化参数的调整。其次,基于视觉因子和HEVC的四叉树结构,提出一种基于视觉特性的率失真代价模型用于模式决策,以提升视频编码的主观性能。实验结果表明,本文算法可以有效提升重构视频的主观质量,在RA和LDP配置下,平均主观性能提升为7.21%和11.46%。
We propose new techniques for 2-D shape/contour completion, which is one of the important research topics related to shape analysis and computer vision, e.g. the detection of incomplete objects due to occlusion and noises. The purpose of shape completion is to find the optimal curve segments that fill the missing contour parts, so as to acquire the best estimation of the original complete object shapes. Unlike the previous work using local smoothness or minimum curvature priors, we solve the problem under a Bayesian formulation taking advantage of global shape prior knowledge. With the priors, our methods are expert in recovering significant shape structures and dealing with large occlusion cases. There are two different priors adopted in this paper: (i) A generic prior model that prefers minimal global shape transformation (including non-rigid deformation and affine transformation with respect to a reference object shape) of the recovered complete shape; and (ii) a class-specific shape prior model learned from training examples of an object category, which prefers the reconstructed shape to follow the learned shape variation models of the category. Efficient contour completion algorithms are suggested corresponding to the two types of priors. Our experimental results demonstrate the advantage of the proposed shape completion approaches compared to the existing techniques, especially for objects with complex structure under severe occlusion.
We present a method to represent multi-resolution vector graphics such as road networks or railway networks in virtual environment. These vector data can be interactively edited and the landscape and be explored in real time at any altitude from flight view to car view. We design a context-focused vector description of linear can areal features, with associated customized definition painter to specify their appearance （color and material） and their display mode （detailed mode or simplified mode）. There are some special problems in drawing vector graphics in virtual environment. Floating-point round-off error appear when we use a low view point to observe the scene, and it leads to scene jittering. Drawing 3D wide lines turns into a problem on 3D terrain. We design a view-based self-adaptive interpolation algorithm and an offset line generating algorithm to solve it. Our results show high performance with good visual quality.
