Road Extraction from High Resolution Remote Sensing Image based on Shape Analysis with Gestalt Theory

Simulating the psychological experience of human vision，a road extraction model based on the format tower is proposed to extract the road in the high resolution remote sensing image from the perspective of morphology.Firstly，based on the spectral and texture information，the suspected road targets are extracted by using segmentation technology.Then these targets are classified according to their reliability and extract the road targets for each category.Finally，three types of identified road information are verified and merged，and the continuous smooth road extraction results are obtained.Experiments on real high resolution images show that the results are consistent with the visual perception of the human eye，and the overall classification accuracy is higher，indicating that the algorithm is effective and feasible and has good use value.     

Evaluating dynamic performance: The influence of salient Gestalt characteristics on performance ratings.

It is well recognized that performance changes over time. However, the effect of these changes on overall assessments of performance is largely unknown. In a laboratory experiment, we examined the influence of salient Gestalt characteristics of a dynamic performance profile on supervisory ratings. We manipulated performance trend (flat, linear-improving, linear-deteriorating, U-shaped, and ∩-shaped), performance variation (small, large), and performance mean (negative, zero, positive) within subjects and display format (graphic, tabular) between subjects. Participants received and evaluated information about the weekly performance of different employees over a simulated 26-week period. Results showed strong main effects on performance ratings of both performance mean and performance trend, as well as interactions with display format. Theoretical and practical implications of the results are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Analyzing subjectivity in therapeutic discourse: Rogers, Perls, Ellis, and Gloria revisited.

Analyzed the film Three Approaches to Psychotherapy (E. L. Shostrom, 1966), which depicts C. Rogers (client-centered therapy), A. Ellis (rational-emotive therapy), and F. S. Perls (Gestalt therapy) interviewing the same client, Gloria. Verbal exchanges between participants were analyzed, using T. S. Essig and R. L. Russell's (1988) categories for analyzing psychological relations between subjects and events in discourse. Predictable patterns in how therapists of different theoretical orientations represent subjectivity in discourse can be screened with these categories. Classification categories are appended. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

论格式塔心理学原理在语言综合测试中的运用

通过格式塔心理学"整体决定部分,部分依从于整体"的原理,论述了格式塔心理学基本原理和语言综合测试的关系,提出格式塔心理学是语言综合测试的理论基础;并对当前中国学生在英语完形测试中出现的问题进行了研究分析,指出教师应在教学过程中运用"整体感知"和"补缺心理"等理论,指导学生"破解"语言综合性测试这类语言教学过程和测试过程中的"难题"。     

VICUR: A human-vision-based algorithm for curve reconstruction

In this paper, a new algorithm, named VICUR, is presented for curve reconstruction problem. From a set of unorganized points, the proposed algorithm can construct curves that look natural to human vision. The VICUR algorithm is based on two connectivity criteria: proximity and good continuation from the prominent Gestalt principles of perception. Experimental results are presented to show the effectiveness of VICUR.     

A Multi-parameter Approach to Automated Building Grouping and Generalization

This paper presents an approach to automated building grouping and generalization. Three principles of Gestalt theories, i.e. proximity, similarity, and common directions, are employed as guidelines, and six parameters, i.e. minimum distance, area of visible scope, area ratio, edge number ratio, smallest minimum bounding rectangle (SMBR), directional Voronoi diagram (DVD), are selected to describe spatial patterns, distributions and relations of buildings. Based on these principles and parameters, an approach to building grouping and generalization is developed. First, buildings are triangulated based on Delaunay triangulation rules, by which topological adjacency relations between buildings are obtained and the six parameters are calculated and recorded. Every two topologically adjacent buildings form a potential group. Three criteria from previous experience and Gestalt principles are employed to tell whether a 2-building group is ‘strong,’ ‘average’ or ‘weak.’ The ‘weak’ groups are deleted from the group array. Secondly, the retained groups with common buildings are organized to form intermediate groups according to their relations. After this step, the intermediate groups with common buildings are aggregated or separated and the final groups are formed. Finally, appropriate operators/algorithms are selected for each group and the generalized buildings are achieved. This approach is fully automatic. As our experiments show, it can be used primarily in the generalization of buildings arranged in blocks.

完形心理理论对产品形态的作用与意义

完形心理学是探讨人类对于图像的认知反应的科学,以强调知觉活动过程中的整体性、独特性为特征.从工业设计角度出发,通过其知觉组织原则与产品形态特征的分析和理解,可以进一步了解和把握产品形态的视觉规律,对于产品形态的设计与创造有着重要的作用和意义.     

Collinearity and Parallelism are Statistically Significant Second-Order Relations of Complex Cell Responses

By investigating the second-order statistics of Gabor wavelet responses derived from natural images, we show that collinearity and parallelism are conspicuous relations. We give a precise mathematical characterization of these Gestalt principles by the conditional probability of two responses. Essential for our investigations is a non-linear transformation, initially utilized within the object recognition system [5], which transforms continuous Gabor wavelet responses into a binary code indicating the presence or absence of local oriented line segments.     

Perceptual grouping of line features in 3-D space: a model-based framework

In this paper, we propose a novel model-based perceptual grouping algorithm for the line features of 3-D polyhedral objects. Given a 3-D polyhedral model, perceptual grouping is performed to extract a set of 3-D line segments which are geometrically consistent with the 3-D model. Unlike the conventional approaches, grouping is done in 3-D space in a model-based framework. In our unique approach, a decision tree classifier is employed for encoding and retrieving the geometric information of the 3-D model. A Gestalt graph is constructed by classifying input instances into proper Gestalt relations using the decision tree. The Gestalt graph is then decomposed into a few subgraphs, yielding appropriate groups of features. As an application, we suggest a 3-D object recognition system which can be accomplished by selecting a best-matched group. In order to evaluate the performance of the proposed algorithm, experiments are carried out on both synthetic and real scenes.