A computational model for visual selection.

We propose a computational model for detecting and localizing instances from an object class in static gray-level images. We divide detection into visual selection and final classification, concentrating on the former: drastically reducing the number of candidate regions that require further, usually more intensive, processing, but with a minimum of computation and missed detections. Bottom-up processing is based on local groupings of edge fragments constrained by loose geometrical relationships. They have no a priori semantic or geometric interpretation. The role of training is to select special groupings that are moderately likely at certain places on the object but rate in the background. We show that the statistics in both populations are stable. The candidate regions are those that contain global arrangements of several local groupings. Whereas our model was not conceived to explain brain functions, it does cohere with evidence about the functions of neurons in V1 and V2, such as responses to coarse or incomplete patterns (e.g., illusory contours) and to scale and translation invariance in IT. Finally, the algorithm is applied to face and symbol detection.     

电子商务环境中一种基于多主体系统的信任模型

为了解决电子商务环境的动态性和不确定性带来的信任问题,提出一种基于多主体系统的信任模型来处理电子商务环境中Agent之间的信任关系。与现有的信任模型相比,该模型能自主处理Agent间的欺诈信息,同时在估价Agent的信任关系时引入了时间退化因子。模拟实验表明,时间退化因子和信任的精确度即信心的引入,能更加有效地评估Agent之间的信任关系。此外,模型中引入了信任的本体性概念。     

A coherent computational approach to model bottom-up visual attention

Visual attention is a mechanism which filters out redundant visual information and detects the most relevant parts of our visual field. Automatic determination of the most visually relevant areas would be useful in many applications such as image and video coding, watermarking, video browsing, and quality assessment. Many research groups are currently investigating computational modeling of the visual attention system. The first published computational models have been based on some basic and well-understood human visual system (HVS) properties. These models feature a single perceptual layer that simulates only one aspect of the visual system. More recent models integrate complex features of the HVS and simulate hierarchical perceptual representation of the visual input. The bottom-up mechanism is the most occurring feature found in modern models. This mechanism refers to involuntary attention (i.e., salient spatial visual features that effortlessly or involuntary attract our attention). This paper presents a coherent computational approach to the modeling of the bottom-up visual attention. This model is mainly based on the current understanding of the HVS behavior. Contrast sensitivity functions, perceptual decomposition, visual masking, and center-surround interactions are some of the features implemented in this model. The performances of this algorithm are assessed by using natural images and experimental measurements from an eye-tracking system. Two adequate well-known metrics (correlation coefficient and Kullbacl-Leibler divergence) are used to validate this model. A further metric is also defined. The results from this model are finally compared to those from a reference bottom-up model.     

基于DNA折纸术求解可满足性问题的计算模型

DNA折纸术是一种全新的DNA自组装方法,具有可编程性、纳米可寻址性等优点,被广泛地应用于DNA计算中.利用DNA折纸术可折叠出特殊结构的特点,在DNA折纸基底上设计了一种求解可满足性问题的计算模型,该模型采用分子信标原理,通过观察荧光的明灭排除非解,从而找出可满足性问题的解.最后通过实例和模拟仿真表明了模型的可行性.     

A computational model of topological and geometric recovery for visual curve completion

Computational Visual Media - Visual curve completion is a fundamental problem in understanding the principles of the human visual system. This problem is usually divided into two problems: a...     

Advanced computational reasoning based on the NKRL conceptual model

After having recalled some well-known shortcomings linked with the Semantic Web approach to the creation of (application oriented) systems of “rules” – e.g., limited expressiveness, adoption of an Open World Assumption (OWA) paradigm, absence of variables in the original definition of OWL – this paper examines the technical solutions successfully used for implementing advanced reasoning systems according to the NKRL’s methodology. NKRL (Narrative Knowledge Representation Language) is a conceptual meta-model and a Computer Science environment expressly created to deal, in an ‘intelligent’ and complete way, with complex and content-rich non-fictional ‘narrative’ data sources. These last include corporate memory documents, news stories, normative and legal texts, medical records, surveillance videos, actuality photos for newspapers and magazines, etc. In this context, we will expound first the need for distinguishing between “plain/static” and “structured/dynamic” knowledge and for introducing appropriate (and different) knowledge representation structures for these two types of knowledge. In a structured/dynamic context, we will then show how the introduction of “functional roles” – associated with the possibility of making use of n-ary structures – allows us to build up highly ‘expressive’ rules whose “atoms” can directly represent complex situations, actions, etc. without being restricted to the use of binary clauses. In an NKRL context, “functional roles” are primitive symbols interpreted as “relations” – like “subject”, “object”, “source”, “beneficiary”, etc. – that link a semantic predicate with its arguments within an n-ary conceptual formula. Functional roles contrast then with the “semantic roles” that are equated to ordinary concepts like “student”, to be inserted into the “non-sortal” (no direct instances) branch of a traditional ontology.     

基于视皮层V1模型的随机点视频序列运动特征提取

针对复杂场景中视频序列目标运动特征提取困难的问题,借鉴生物视觉系统对视频动态目标的运动感知机制,改进初级视皮层(V1)细胞模型,提出一种基于生物视皮层机制的视频运动特征提取方法。采用时空滤波器与半平方加归一化分别模拟神经元感受野的线性与非线性特性,再通过在输出权值中加入方向选择性调节参数得到普适性的V1细胞模型,从而解决传统模型方向选择性单一、多方向选择能力偏弱的问题。仿真结果表明所提模型模拟输出与生物实验数据较为吻合,能够模拟不同方向选择性的V1细胞,对复杂运动形态的随机点视频序列具有良好的运动特征提取能力。依靠该方法可以为处理特征光流信息提供新的思路,进而实现对视频序列目标的运动特征提取和有效跟踪。     

Trust beyond reputation: A computational trust model based on stereotypes

Models of computational trust support users in taking decisions. They are commonly used to guide users’ judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger’s actions in absence of the knowledge of such behavioral history, we often use our “instinct”—essentially stereotypes developed from our past interactions with other “similar” persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger’s profile. Since stereotypes are formed locally, recommendations stem from the trustor’s own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information.     

A human vision based computational model for chromatic texturesegregation

We have developed a computational model for texture perception which has physiological relevance and correlates well with human performance. The model attempts to simulate the visual processing characteristics by incorporating mechanisms tuned to detect luminance-polarity, orientation, spatial frequency and color, which are characteristic features of any textural image. We obtained a very good correlation between the model's simulation results and data from psychophysical experiments with a systematically selected set of visual stimuli with texture patterns defined by spatial variations in color, luminance, and orientation. In addition, the model predicts correctly texture segregation performance with key benchmarks and natural textures. This represents a first effort to incorporate chromatic signals in texture segregation models of psychophysical relevance, most of which have treated grey-level images so far. Another novel feature of the model is the extension or the concept of spatial double opponency to domains beyond color, such as orientation and spatial frequency. The model has potential applications in the areas of image processing, machine vision and pattern recognition, and scientific visualization.     

Transformers in computational visual media: A survey

Computational Visual Media - Transformers, the dominant architecture for natural language processing, have also recently attracted much attention from computational visual media researchers due to...     

Tilt aftereffects in a self-organizing model of the primary visual cortex

RF-LISSOM, a self-organizing model of laterally connected orientation maps in the primary visual cortex, was used to study the psychological phenomenon known as the tilt aftereffect. The same self-organizing processes that are responsible for the long-term development of the map are shown to result in tilt aftereffects over short timescales in the adult. The model permits simultaneous observation of large numbers of neurons and connections, making it possible to relate high-level phenomena to low-level events, which is difficult to do experimentally. The results give detailed computational support for the long-standing conjecture that the direct tilt aftereffect arises from adaptive lateral interactions between feature detectors. They also make a new prediction that the indirect effect results from the normalization of synaptic efficacies during this process. The model thus provides a unified computational explanation of self-organization and both the direct and indirect tilt aftereffect in the primary visual cortex.     

A visual discomfort recognition model based on the fusion of multisource information

An experiment was performed to explore the influence of image attributes (white level, black level, color saturation, and sharpness) on visual discomfort, with measurements of the subjective questionnaire, eye movement, and electrocardiogram. The objective and subjective results showed that these image attributes influenced visual discomfort significantly. A visual discomfort recognition model based on Dempster–Shafer evidence theory was proposed according to the experiment results. With multiple features and weighting coefficients, the model could recognize visual discomfort states well.     

The computational exploration of visual word recognition in a split model

We argue that the projection of the visual field to the cortex constrains and informs the modeling of visual word recognition. On the basis of anatomical and psychological evidence, we claim that the higher-level cognition involved in word recognition does not completely transcend initial foveal splitting. We present a schematic connectionist model of word recognition that instantiates the precise splitting of the visual field and the contralateral projection of the two hemifields. We explore the special nature of the exterior (i.e., first and last) letters of words in reading. The model produces the correct behavior spontaneously and robustly. We analyze this behavior of the model with respect to words and random patterns and conclude that the systematic division of the visual input has predictable, general informational consequences and is chiefly responsible for the exterior letters effect.     

A coarse-to-fine image registration method based on visual attention model

Image registration is fundamental and crucial to remote sensing. However getting highly accurate registration performance automatically and fast for large-field images consistently is a challenge. As a work around to this problem, we propose a new image registration concept based on visual attention in this paper. This concept employs the advantages of feature-based or area-based methods to improve the precision and efficiency of image registration. The key concept of proposed integrated scheme is to make optimum use of the highly prominent details in the full scene by means of visual attention computational mechanism. To testify the validation, comparisons with other classical methods are carried out on real-world images. The experimental results show that the proposed method can effectively perform on multi-view/multi-temporal remote sensing images with outstanding precision and time saving performance.     

融合HVS计算模型的视频感知哈希算法研究

感知哈希(perceptual hashing)是多媒体数据集到摘要集的单向映射,为多媒体数字内容的标识、检索、认证等应用提供了安全可靠的技术支撑。目前关于感知哈希算法的研究主要集中在不断提高其鲁棒性和安全性上,忽略了人的主要视觉感知特性,导致了算法的过鲁棒性问题。将人类视觉系统可计算模型融入视频感知哈希算法框架中,用模拟人眼感受野特征提取特性的Cortex变换进行通道分解,并使用时-空域对比度敏感函数、眼球移动函数、亮度适应性调整函数、子带内和子带间对比度掩蔽函数综合计算最小视觉差提取感知特征。在保证较好鲁棒性的前提下,算法中使用扩散分块的机制提高安全性,通过与已有算法之间的比较,结果表明,本文提出的算法在鲁棒性和安全性方面取得了有效折衷,同时也体现了主观感知与客观评测上的一致性。     

基于视觉感知模型的行人再识别

行人再识别是多摄像机协同监控系统中需要解决的关键问题之一。针对行人再识别问题的影响因素,根据人类视觉系统对行人进行识别的过程,提出一种基于视觉感知模型的行人再识别方法。该方法根据行人的局部对称性将行人分为头部、躯干和腿部,分别以行人的躯干和腿部的垂直对称轴为中心建立基于感知均匀颜色空间CIELAB的局部加权空间直方图,结合贝叶斯框架下基于局部统计特征的显著区域检测方法描述行人外观特征。两种特征分别采用不同的距离测度计算相似度,并通过自适应选取权值的方法进行线性融合。基于VIPeR数据库的实验比较和分析验证了该方法的行人再识别性能。     

异或视觉密码方案目标优化研究*

通过建立群结构的视觉密码目标优化模型,设计了一种基于异或的(k, n)门限方案。该方案将基础矩阵构造问题转换为共享份中出现黑白像素概率的求解问题,通过概率矩阵对加密规则进行选择得到共享份。实验结果表明,该方案在像素不扩展的同时,使相对差大幅改善。     

A computational model for periodic pattern perception based on frieze and wallpaper groups

We present a computational model for periodic pattern perception based on the mathematical theory of crystallographic groups. In each N-dimensional Euclidean space, a finite number of symmetry groups can characterize the structures of an infinite variety of periodic patterns. In 2D space, there are seven frieze groups describing monochrome patterns that repeat along one direction and 17 wallpaper groups for patterns that repeat along two linearly independent directions to tile the plane. We develop a set of computer algorithms that "understand" a given periodic pattern by automatically finding its underlying lattice, identifying its symmetry group, and extracting its representative motifs. We also extend this computational model for near-periodic patterns using geometric AIC. Applications of such a computational model include pattern indexing, texture synthesis, image compression, and gait analysis.     

A novel trust evolution algorithm based on a quantum-like model of computational trust

Cognition, Technology & Work - Trust models play an important role in decision support systems and computational environments in general. The common goal of the existing trust models is to...