The time course of texture segregation was studied for three different measures of segregation (detection, localization and identification of an embedded texture region) under three different raster width conditions (26$ sp prime$, 43$ sp prime$ and 61$ sp prime$ of arc) by using a backward masking paradigm. The masking data were described with an exponential model the parameters of which represent rate of performance improvement and asymptotic performance level. The results indicate that: (1) information supporting localization accrues more rapidly than information supporting identification, (2) increasing element spacing had a more detrimental effect on identification than on localization, (3) under most spacing conditions, performance on detection fell between that for localization and identification. In conclusion, these three widely used texture segregation tasks cannot be considered equivalent measures of a single process. However, comparisons among their respective time courses may enable us to better characterize the mechanisms underlying the segregation process.

No description available.

Thesis (M.A.)--York University, 2003. Graduate Programme in Psychology. / Typescript. Includes bibliographical references (leaves 102-107). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pMQ82940.

Thesis (M.S.)--Rochester Institute of Technology, 1994. / Typescript. Bibliography: leaves 57-58.

Abstract not available

No description available.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 116-119). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 116-119). Also available on the Internet.

As redes complexas têm recebido um crescente interesse nas mais diversas áreas do conhecimento. Esse crescimento se deve principalmente a sua flexibilidade em modelar e simular estruturas topológicas que aparecem em nosso cotidiano. Na maioria das vezes, a caracterização das redes complexas é baseada em medidas básicas, como média dos graus, graus hierárquicos, coeficiente de aglomeração, entre outras. Muitas das medidas propostas são correlacionadas, implicando em redundância. Este trabalho propõe o uso das caminhadas determinísticas do turista como uma medida de representação robusta e eficiente de redes complexas. Nesta medida, caminhadas são iniciadas por exploradores que partem de um dos vértices da rede e em seguida, informações são extraídas sobre essas caminhadas. Experimentos foram realizados em redes complexas artificiais e em redes modelando imagens de textura. No reconhecimento de redes artificiais, o método proposto foi aplicado em quatro modelos de redes complexas teóricos: redes aleatórias, pequenomundo, livre de escala e geográficas. No reconhecimento de textura, o método foi avaliado em bancos de texturas sintéticas e reais (texturas de folhas de plantas). Em ambas as aplicações, o método alcançou excelentes resultados comparados com o estado da arte / Complex networks have received a growing interest in several areas of knowledge. This growth is mainly due to its flexibility in modeling and simulating topological structures that appear in our daily life. In most cases, complex networks characterization are based on basic measurements such as average degree, hierarchical degree, clustering coefficient, among others. Many of the measures are correlated, resulting in redundancy. This dissertation proposes the use of deterministic walks as a robust and efficient complex network measurement. In this measurement, walks are initiated by explorers starting from each vertex and then, informations are extracted on these walks. Experiments were performed on artificial complex networks and network modeling texture images. In artificial network recognition, the proposed method was applied to four theoretical complex network models: random, small-world, free-scale and geographical networks. In texture recognition, the method was evaluated in synthetic and real (texture of leaves) databases. In both applications, the method achieved excellent results compared with the state of the art methods

Textured motion - generally known as dynamic or temporal texture - is a popular research area for synthesis, segmentation and recognition. Dynamic texture is a spatially repetitive, time-varying visual pattern that forms an image sequence with certain temporal stationarity. In dynamic texture, the notion of self-similarity central to conventional image texture is extended to the spatiotemporal domain. Dynamic textures are typically videos of processes, such as waves, smoke, fire, a flag blowing in the wind, a moving escalator, or a walking crowd. Creation of synthetic frames is a key issue especially for movie screen industry to enrich their scenes from a white screen into a shining reality. In robotics world, for example an autonomous vehicle must decide what is traversable terrain (e.g. grass) and what is not (e.g. water). This problem can be addressed by classifying portions of the image into a number of categories, for instance grass, dirt, bushes or water. If these parts are identifiable, then segmentation and recognition of these textures results with an efficient path planning for the autonomous vehicle. In this thesis, we aimed to characterize these textured motions like mentioned above. We tried to implement several known techniques and compared the results.