First- and second-order stimulus length selectivity in New World monkey striate cortex

Motion is a powerful cue for figure-ground segregation, allowing the recognition of shapes even if the luminance and texture characteristics of the stimulus and background are matched. In order to investigate the neural processes underlying early stages of the cue-invariant processing of form, we compared the responses of neurons in the striate cortex (V1) of anaesthetized marmosets to two types of moving stimuli: bars defined by differences in luminance, and bars defined solely by the coherent motion of random patterns that matched the texture and temporal modulation of the background. A population of form-cue-invariant (FCI) neurons was identified, which demonstrated similar tuning to the length of contours defined by first- and second-order cues. FCI neurons were relatively common in the supragranular layers (where they corresponded to 28% of the recorded units), but were absent from layer 4. Most had complex receptive fields, which were significantly larger than those of other V1 neurons. The majority of FCI neurons demonstrated end-inhibition in response to long first- and second-order bars, and were strongly direction selective, Thus, even at the level of V1 there are cells whose variations in response level appear to be determined by the shape and motion of the entire second-order object, rather than by its parts (i.e. the individual textural components). These results are compatible with the existence of an output channel from V1 to the ventral stream of extrastriate areas, which already encodes the basic building blocks of the image in an invariant manner.

Dynamic texture analysis and segmentation using deterministic partially self-avoiding walks

Dynamic texture is a recent field of investigation that has received growing attention from computer vision community in the last years. These patterns are moving texture in which the concept of selfsimilarity for static textures is extended to the spatiotemporal domain. In this paper, we propose a novel approach for dynamic texture representation, that can be used for both texture analysis and segmentation. In this method, deterministic partially self-avoiding walks are performed in three orthogonal planes of the video in order to combine appearance and motion features. We validate our method on three applications of dynamic texture that present interesting challenges: recognition, clustering and segmentation. Experimental results on these applications indicate that the proposed method improves the dynamic texture representation compared to the state of the art.

Eye-movements aid the control of locomotion

Eye-movements have long been considered a problem when trying to understand the visual control of locomotion. They transform the retinal image from a simple expanding pattern of moving texture elements (pure optic flow), into a complex combination of translation and rotation components (retinal flow). In this article we investigate whether there are measurable advantages to having an active free gaze, over a static gaze or tracking gaze, when steering along a winding path. We also examine patterns of free gaze behavior to determine preferred gaze strategies during active locomotion. Participants were asked to steer along a computer-simulated textured roadway with free gaze, fixed gaze, or gaze tracking the center of the roadway. Deviation of position from the center of the road was recorded along with their point of gaze. It was found that visually tracking the middle of the road produced smaller steering errors than for fixed gaze. Participants performed best at the steering task when allowed to sample naturally from the road ahead with free gaze. There was some variation in the gaze strategies used, but sampling was predominantly of areas proximal to the center of the road. These results diverge from traditional models of flow analysis.

Automatic estimation of crowd density using texture

This paper considers the role of automatic estimation of crowd density and its importance for the automatic monitoring of areas where crowds are expected to be present. A new technique is proposed which is able to estimate densities ranging from very low to very high concentration of people, which is a difficult problem because in a crowd only parts of people's body appear. The new technique is based on the differences of texture patterns of the images of crowds. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. The image pixels are classified in different texture classes and statistics of such classes are used to estimate the number of people. The texture classification and the estimation of people density are carried out by means of self organising neural networks. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented. (C) 1998 Elsevier B.V. Ltd. All rights reserved.

Laser Printer Attribution: Exploring New Features And Beyond.

With a huge amount of printed documents nowadays, identifying their source is useful for criminal investigations and also to authenticate digital copies of a document. In this paper, we propose novel techniques for laser printer attribution. Our solutions do not need very high resolution scanning of the investigated document and explore the multidirectional, multiscale and low-level gradient texture patterns yielded by printing devices. The main contributions of this work are: (1) the description of printed areas using multidirectional and multiscale co-occurring texture patterns; (2) description of texture on low-level gradient areas by a convolution texture gradient filter that emphasizes textures in specific transition areas and (3) the analysis of printer patterns in segments of interest, which we call frames, instead of whole documents or only printed letters. We show by experiments in a well documented dataset that the proposed methods outperform techniques described in the literature and present near-perfect classification accuracy being very promising for deployment in real-world forensic investigations.

2D position system for a mobile robot in unstructured environments

Nowadays, several sensors and mechanisms are available to estimate a mobile robot trajectory and location with respect to its surroundings. Usually absolute positioning mechanisms are the most accurate, but they also are the most expensive ones, and require pre installed equipment in the environment. Therefore, a system capable of measuring its motion and location within the environment (relative positioning) has been a research goal since the beginning of autonomous vehicles. With the increasing of the computational performance, computer vision has become faster and, therefore, became possible to incorporate it in a mobile robot. In visual odometry feature based approaches, the model estimation requires absence of feature association outliers for an accurate motion. Outliers rejection is a delicate process considering there is always a trade-off between speed and reliability of the system. This dissertation proposes an indoor 2D position system using Visual Odometry. The mobile robot has a camera pointed to the ceiling, for image analysis. As requirements, the ceiling and the oor (where the robot moves) must be planes. In the literature, RANSAC is a widely used method for outlier rejection. However, it might be slow in critical circumstances. Therefore, it is proposed a new algorithm that accelerates RANSAC, maintaining its reliability. The algorithm, called FMBF, consists on comparing image texture patterns between pictures, preserving the most similar ones. There are several types of comparisons, with different computational cost and reliability. FMBF manages those comparisons in order to optimize the trade-off between speed and reliability.

Detection of Flock Movement in Spatio-Temporal Database using Clustering Techniques - an Experience

In this paper, moving flock patterns are mined from spatio- temporal datasets by incorporating a clustering algorithm. A flock is defined as the set of data that move together for a certain continuous amount of time. Finding out moving flock patterns using clustering algorithms is a potential method to find out frequent patterns of movement in large trajectory datasets. In this approach, SPatial clusteRing algoRithm thrOugh sWarm intelligence (SPARROW) is the clustering algorithm used. The advantage of using SPARROW algorithm is that it can effectively discover clusters of widely varying sizes and shapes from large databases. Variations of the proposed method are addressed and also the experimental results show that the problem of scalability and duplicate pattern formation is addressed. This method also reduces the number of patterns produced

Level Identification of Brain MR Images using Histogram of a LBP variant

Axial brain slices containing similar anatomical structures are retrieved using features derived from the histogram of Local binary pattern (LBP). A rotation invariant description of texture in terms of texture patterns and their strength is obtained with the incorporation of local variance to the LBP, called Modified LBP (MOD-LBP). In this paper, we compare Histogram based Features of LBP (HF/LBP), against Histogram based Features of MOD-LBP (HF/MOD-LBP) in retrieving similar axial brain images. We show that replacing local histogram with a local distance transform based similarity metric further improves the performance of MOD-LBP based image retrieval

Attentional Selection in Object Recognition

A key problem in object recognition is selection, namely, the problem of identifying regions in an image within which to start the recognition process, ideally by isolating regions that are likely to come from a single object. Such a selection mechanism has been found to be crucial in reducing the combinatorial search involved in the matching stage of object recognition. Even though selection is of help in recognition, it has largely remained unsolved because of the difficulty in isolating regions belonging to objects under complex imaging conditions involving occlusions, changing illumination, and object appearances. This thesis presents a novel approach to the selection problem by proposing a computational model of visual attentional selection as a paradigm for selection in recognition. In particular, it proposes two modes of attentional selection, namely, attracted and pay attention modes as being appropriate for data and model-driven selection in recognition. An implementation of this model has led to new ways of extracting color, texture and line group information in images, and their subsequent use in isolating areas of the scene likely to contain the model object. Among the specific results in this thesis are: a method of specifying color by perceptual color categories for fast color region segmentation and color-based localization of objects, and a result showing that the recognition of texture patterns on model objects is possible under changes in orientation and occlusions without detailed segmentation. The thesis also presents an evaluation of the proposed model by integrating with a 3D from 2D object recognition system and recording the improvement in performance. These results indicate that attentional selection can significantly overcome the computational bottleneck in object recognition, both due to a reduction in the number of features, and due to a reduction in the number of matches during recognition using the information derived during selection. Finally, these studies have revealed a surprising use of selection, namely, in the partial solution of the pose of a 3D object.

Caracterização de núcleos celulares no adenocarcinoma primário de reto por análise de imagem digital

O câncer colorretal é um tumor maligno freqüente no mundo ocidental. É o terceiro em freqüência e o segundo em mortalidade nos países desenvolvidos. No Brasil está entre as seis neoplasias malignas mais encontradas e a quinta em mortalidade. Dos tumores colorretais, aproximadamente 40% estão localizados no reto. A sobrevida, em cinco anos, dos pacientes operados por câncer do reto varia entre 40% e 50%, estando os principais fatores prognósticos, utilizados na prática clínica corrente, baseados em critérios de avaliação clínico-patológicos. A avaliação das alterações morfométricas e densimétricas nas neoplasias malignas tem, recentemente, sido estudadas e avaliadas através da análise de imagem digital e demonstrado possibilidades de utilização diagnóstica e prognóstica. A assinatura digital é um histograma representativo de conjuntos de características de textura da cromatina do núcleo celular obtida através da imagem computadorizada. O objetivo deste estudo foi a caracterização dos núcleos celulares neoplásicos no adenocarcinoma primário de reto pelo método da assinatura digital e verificar o valor prognóstico das alterações nucleares da textura da cromatina nuclear para esta doença. Foram avaliados, pelo método de análise de imagem digital, 51 casos de pacientes operados no Hospital de Clínicas de Porto Alegre (HCPA) entre 1988 e 1996 e submetidos à ressecção eletiva do adenocarcinoma primário de reto, com seguimento de cinco anos pós-operatório, ou até o óbito antes deste período determinado pela doença, e 22 casos de biópsias normais de reto obtidas de pacientes submetidos a procedimentos endoscópicos, para controle do método da assinatura digital. A partir dos blocos de parafina dos espécimes estocados no Serviço de Patologia do HCPA, foram realizadas lâminas coradas com hematoxilina e eosina das quais foram selecionados 3.635 núcleos dos adenocarcinomas de reto e 2.366 núcleos dos controles da assinatura digital, totalizando 6.001 núcleos estudados por análise de imagem digital. De cada um destes núcleos foram verificadas 93 características, sendo identificadas 11 características cariométricas com maior poder de discriminação entre as células normais e neoplásicas. Desta forma, através da verificação da textura da cromatina nuclear, foram obtidos os histogramas representativos de cada núcleo ou conjunto de núcleos dos grupos ou subgrupos estudados, também no estadiamento modificado de Dukes, dando origem às assinaturas digitais correspondentes. Foram verificadas as assinaturas nucleares, assinaturas de padrão histológico ou de lesões e a distribuição da Densidade Óptica Total. Houve diferença significativa das características entre o grupo normal e o grupo com câncer, com maior significância para três delas, a Área, a Densidade Óptica Total e a Granularidade nuclear. Os valores das assinaturas médias nucleares foram: no grupo normal 0,0009 e nos estadiamentos; 0,9681 no A, 4,6185 no B, 2,3957 no C e 2,1025 no D e diferiram com significância estatística (P=0,001). A maior diferença do normal ocorreu no subgrupo B de Dukes-Turnbull. As assinaturas nucleares e de padrão histológico mostraram-se distintas no grupo normal e adenocarcinoma, assim como a distribuição da Densidade Óptica Total a qual mostra um afastamento progressivo da normalidade no grupo com câncer. Foi possível a caracterização do adenocarcinoma de reto, que apresentou assinaturas digitais específicas. Em relação ao prognóstico, a Densidade Óptica Total representou a variável que obteve o melhor desempenho, além do estadiamento, como preditor do desfecho.

Higher Visual Functions in the Upper and Lower Visual Fields: a pilot study in healthy subjects

Visual perception is not identical in the upper and lower visual hemifields. The mechanisms behind this difference can be found at the retinal, cortical, or higher attentional level. In this study, a new visual test battery, that involves real-time comparisons of complex visual stimuli, such as shape of objects, and speed of moving dot patterns, in the upper and lower visual hemifields, is presented. This study represents, to our knowledge, the first to implement such a visual test battery in an immersive environment composed of a hemisphere, in order to present visual stimuli in precise regions of the visual field. Ten healthy volunteers were tested in this pilot study. The results showed a higher accuracy in the image matching when the visual test was performed in the lower visual hemifield.

Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex.

The stages of integration leading from local feature analysis to object recognition were explored in human visual cortex by using the technique of functional magnetic resonance imaging. Here we report evidence for object-related activation. Such activation was located at the lateral-posterior aspect of the occipital lobe, just abutting the posterior aspect of the motion-sensitive area MT/V5, in a region termed the lateral occipital complex (LO). LO showed preferential activation to images of objects, compared to a wide range of texture patterns. This activation was not caused by a global difference in the Fourier spatial frequency content of objects versus texture images, since object images produced enhanced LO activation compared to textures matched in power spectra but randomized in phase. The preferential activation to objects also could not be explained by different patterns of eye movements: similar levels of activation were observed when subjects fixated on the objects and when they scanned the objects with their eyes. Additional manipulations such as spatial frequency filtering and a 4-fold change in visual size did not affect LO activation. These results suggest that the enhanced responses to objects were not a manifestation of low-level visual processing. A striking demonstration that activity in LO is uniquely correlated to object detectability was produced by the "Lincoln" illusion, in which blurring of objects digitized into large blocks paradoxically increases their recognizability. Such blurring led to significant enhancement of LO activation. Despite the preferential activation to objects, LO did not seem to be involved in the final, "semantic," stages of the recognition process. Thus, objects varying widely in their recognizability (e.g., famous faces, common objects, and unfamiliar three-dimensional abstract sculptures) activated it to a similar degree. These results are thus evidence for an intermediate link in the chain of processing stages leading to object recognition in human visual cortex.

Traces, Patterns, Texture: In Search of Aesthetic Teaching/Learning Encounters

This inquiry reveals the crucial guidance of teachers toward surveying the capacity and needs of students, the formation of ideas, acting upon ideas, fostering connections, seeing potential, making judgments, and arranging conditions. Each aesthetic trace causes me to wonder how teachers learn to create experiences that foster student participation in the world aesthetically. The following considerations surface: • Given the emphasis in schools on outcomes and results, how do we encourage teachers to focus on acts of mind instead of end products in their work with students? • Given the orientations toward technical rationality, to fixed sequence, how do we help teachers experience fluid, purposeful learning adventures with students in which the imagi¬nation is given room to play? • Given the tendency to conceive of planning in teaching as the deciding of everything in advance, how do we help teachers and students become attuned to making good judgments derived from within learning experiences? • How do we help teachers build dialogical multivoiced conversations instead of monolithic curriculum? • What do we do to recover the pleasure dwelling in subject matter? How do we get teachers and students to engage thoughtfully in meaningful learning as opposed to covering curriculum7 • A capacity to attend sensitively, to perceive the complexity of relationships coming together in any teaching/learning experience seems critical. How do we help teachers and students attend to the unity of a learning experience and the play of meanings that arises from such undergoing and doing? The traces, patterns, and texture evidenced locate tremendous hope and wondrous possibilities alive within aesthetic teaching/learning encounters. It is such aliveness I encountered in the grade 4 art classroom that opened this account and continues to compel my attention. Possibilities for teaching, learning, and teacher education emerge. I am convinced they are most worthy of continued pursuit.

Grid-texture mechanisms in human vision:contrast detection of regular sparse micro-patterns requires specialist templates

Previous work has shown that human vision performs spatial integration of luminance contrast energy, where signals are squared and summed (with internal noise) over area at detection threshold. We tested that model here in an experiment using arrays of micro-pattern textures that varied in overall stimulus area and sparseness of their target elements, where the contrast of each element was normalised for sensitivity across the visual field. We found a power-law improvement in performance with stimulus area, and a decrease in sensitivity with sparseness. While the contrast integrator model performed well when target elements constituted 50–100% of the target area (replicating previous results), observers outperformed the model when texture elements were sparser than this. This result required the inclusion of further templates in our model, selective for grids of various regular texture densities. By assuming a MAX operation across these noisy mechanisms the model also accounted for the increase in the slope of the psychometric function that occurred as texture density decreased. Thus, for the first time, mechanisms that are selective for texture density have been revealed at contrast detection threshold. We suggest that these mechanisms have a role to play in the perception of visual textures.

Hitting a moving target: Perception and action in the timing of rapid interceptions

Different interceptive tasks and modes of interception (hitting or capturing) do not necessarily involve similar control processes. Control based on preprogramming of movement parameters is possible for actions with brief movement times but is now widely rejected; continuous perceptuomotor control models are preferred for all types of interception. The rejection of preprogrammed control and acceptance of continuous control is evaluated for the timing of rapidly executed, manual hitting actions. It is shown that a preprogrammed control model is capable of providing a convincing account of observed behavior patterns that avoids many of the arguments that have been raised against it. Prominent continuous perceptual control models are analyzed within a common framework and are shown to be interpretable as feedback control strategies. Although these models can explain observations of on-line adjustments to movement, they offer only post hoc explanations for observed behavior patterns in hitting tasks and are not directly supported by data. It is proposed that rapid manual hitting tasks make up a class of interceptions for which a preprogrammed strategy is adopted-a strategy that minimizes the role of visual feedback. Such a strategy is effective when the task demands a high degree of temporal accuracy.