Incremental pairwise discriminant analysis based visual tracking

The distinguishment between the object appearance and the background is the useful cues available for visual tracking in which the discriminant analysis is widely applied However due to the diversity of the background observation there are not adequate negative samples from the background which usually lead the discriminant method to tracking failure Thus a natural solution is to construct an object-background pair constrained by the spatial structure which could not only reduce the neg-sample number but also make full use of the background information surrounding the object However this Idea is threatened by the variant of both the object appearance and the spatial-constrained background observation especially when the background shifts as the moving of the object Thus an Incremental pairwise discriminant subspace is constructed in this paper to delineate the variant of the distinguishment In order to maintain the correct the ability of correctly describing the subspace we enforce two novel constraints for the optimal adaptation (1) pairwise data discriminant constraint and (2) subspace smoothness The experimental results demonstrate that the proposed approach can alleviate adaptation drift and achieve better visual tracking results for a large variety of nonstationary scenes (C) 2010 Elsevier B V All rights reserved

A Video Quality Assessment Metric Based on Human Visual System

It is important for practical application to design an effective and efficient metric for video quality. The most reliable way is by subjective evaluation. Thus, to design an objective metric by simulating human visual system (HVS) is quite reasonable and available. In this paper, the video quality assessment metric based on visual perception is proposed. Three-dimensional wavelet is utilized to decompose video and then extract features to mimic the multichannel structure of HVS. Spatio-temporal contrast sensitivity function (S-T CSF) is employed to weight coefficient obtained by three-dimensional wavelet to simulate nonlinearity feature of the human eyes. Perceptual threshold is exploited to obtain visual sensitive coefficients after S-T CSF filtered. Visual sensitive coefficients are normalized representation and then visual sensitive errors are calculated between reference and distorted video. Finally, temporal perceptual mechanism is applied to count values of video quality for reducing computational cost. Experimental results prove the proposed method outperforms the most existing methods and is comparable to LHS and PVQM.

Color to Gray: Visual Cue Preservation

Both commercial and scientific applications often need to transform color images into gray-scale images, e. g., to reduce the publication cost in printing color images or to help color blind people see visual cues of color images. However, conventional color to gray algorithms are not ready for practical applications because they encounter the following problems: 1) Visual cues are not well defined so it is unclear how to preserve important cues in the transformed gray-scale images; 2) some algorithms have extremely high time cost for computation; and 3) some require human-computer interactions to have a reasonable transformation. To solve or at least reduce these problems, we propose a new algorithm based on a probabilistic graphical model with the assumption that the image is defined over a Markov random field. Thus, color to gray procedure can be regarded as a labeling process to preserve the newly well-defined visual cues of a color image in the transformed gray-scale image. Visual cues are measurements that can be extracted from a color image by a perceiver. They indicate the state of some properties of the image that the perceiver is interested in perceiving. Different people may perceive different cues from the same color image and three cues are defined in this paper, namely, color spatial consistency, image structure information, and color channel perception priority. We cast color to gray as a visual cue preservation procedure based on a probabilistic graphical model and optimize the model based on an integral minimization problem. We apply the new algorithm to both natural color images and artificial pictures, and demonstrate that the proposed approach outperforms representative conventional algorithms in terms of effectiveness and efficiency. In addition, it requires no human-computer interactions.

Visual-Context Boosting for Eye Detection

Eye detection plays an important role in many practical applications. This paper presents a novel two-step scheme for eye detection. The first step models an eye by a newly defined visual-context pattern (VCP), and the second step applies semisupervised boosting for precise detection. VCP describes both the space and appearance relations between an eye region (region of eye) and a reference region (region of reference). The context feature of a VCP is extracted by using the integral image. Aiming to reduce the human labeling efforts, we apply semisupervised boosting, which integrates the context feature and the Haar-like features for precise eye detection. Experimental results on several standard face data sets demonstrate that the proposed approach is effective, robust, and efficient. We finally show that this approach is ready for practical applications.

Hydrogen-Bonding Recognition-Induced Color Change of Gold Nanoparticles for Visual Detection of Melamine in Raw Milk and Infant Formula

The color change induced by triple hydrogen-bonding recognition between melamine and a cyanuric acid derivative grafted on the surface of gold nanoparticles can be used for reliable detection of melamine. Since such a color change can be readily seen by the naked eye, the method enables on-site and real-time detection of melamine in raw milk and infant formula even at a concentration as low as 2.5 ppb without the aid of any advanced instruments.

非言语学习不良青少年空间关系推理及其影响因素研究

It has reported that individuals with nonverbal learning disabilities (NLD) have deficits in visual-spatial organization and strengths in rote language abilities. At present, there are few studies on higher order cognitive abilities of adolescents with NLD, such as the reasoning about spatial relations. The study sampled three groups: a normal group (a control group, C), a nonverbal learning disabilities group (NLD), and a verbal learning disabilities group (VLD). The aim of this study was to examine spatial and nonspatial relation reasoning abilities in adolescents with NLD under figure and word conditions, and assessed the relative involvement of different working memory components in four types of reasoning tasks: reasoning about figure-spatial, figure-nonspatial, verbal-spatial, and verbal-nonspatial relations. Using the double-tasks methodology, visual, spatial, central-executive, and phonological loads were realized. We tried to find how working memory components impact on adolescents with NLD spatial and nonspatial reasoning. The main results of present research are as follows. (1) The NLD group didn’t differ from normal group on reasoning about figure-nonspatial relations. The NLD group scored lower than the C group in spatial problems. So, adolescents with NLD showed a dissociation between spatial and non-spatial relation reasoning. They scored higher in non-spatial problems than in spatial ones. Adolescents with VLD developed well in reasoning about figure-nonspatial relations, but showed deficits in other three tasks. (2) For each reasoning task, the difficult of four types of reasoning problem had different changing trend. For figure and verbal spatial problems, mental model approach can interpret performance of the four problems well. For verbal nonspatial problems, a logical rule approach can interpret performance of the four problems well. (3) Adolescents with NLD did not differ from adolescents with VLD and normal adolescents in phonological, central-executive, and visual dual tasks. But the NLD group had lower performance than the other two groups in spatial dual task. The results showed a dissociation between visual and spatial working memory in NLD group. The VLD group only experienced deficits in central-executive subsystem. (4) The studies found that spatial reasoning mainly loaded spatial working memory, whist the involvement of spatial resources in nonspatial reasoning was little. Visual working memory mainly involved in reasoning about spatial and figure-nonspatial relations, especially in figure-nonspatial problems, and had few impacts on verbal-nonspatial reasoning. Central executive system was involved in all reasoning tasks. The role of phonological loop in the reasoning tasks required further explored. (5) According to the findings, we concluded that the deficits in spatial working memory resulted in poor spatial reasoning abilities for teenagers with NLD, whist because of the limited central executive capability, teenagers with VLD showed poor reasoning abilities. (6) The three groups can used multiple strategies during the reasoning process. They didn’t differ from each other in reasoning strategies. They all used mental model strategy to solve figure and verbal spatial problems, and used logic rule strategy to solve verbal nonspatial problems.

汉语发展性阅读障碍儿童的视觉大细胞通路特点

Developmental dyslexia (DD) is a common kind of learning disorder, which affects 5-18% of people. It seems important to explore the deficit in visual magnocellular pathway in Chinese developmental dyslexia, for many researches demonstrated that one of the core deficits of Chinese developmental dyslexia was orthographic deficit which was associated with the deficit in visual magnocellular pathway. Two studies were done to detect the differences among Chinese developmental dyslexics, average readers of the same chronological age (CA controls) and average readers of the same reading level (RL controls) in reaction time, accuracy and visual mismatch negativity (vMMN) elicited by the moving gratings responded by visual magnocellular pathway. There were two grating-conditions which were low contrast/low spatial frequency condition and high contrast/high spatial frequency condition respectively. In ERP study, a modified “cross-modal delayed response” paradigm was used to elicit the vMMN. The results showed that the developmental dyslexics responded slower than CA controls, had more errors than RL controls, and had smaller amplitudes of vMMNs than the two controls in visual magnocellular pathway condition, but not in control condition. That is to say, Chinese developmental dyslexics had deficits in visual magnocellular pathway.

Qualidade visual de cenourete e catetinho em função das operações de acabamento e centrifugação.

2007

Curved Inertia Frames: Visual Attention and Perceptual Organization Using Convexity and Symmetry

In this paper we present an approach to perceptual organization and attention based on Curved Inertia Frames (C.I.F.), a novel definition of "curved axis of inertia'' tolerant to noisy and spurious data. The definition is useful because it can find frames that correspond to large, smooth, convex, symmetric and central parts. It is novel because it is global and can detect curved axes. We discuss briefly the relation to human perception, the recognition of non-rigid objects, shape description, and extensions to finding "features", inside/outside relations, and long- smooth ridges in arbitrary surfaces.

Fast Perceptual Learning in Visual Hyperacuity

In many different spatial discrimination tasks, such as in determining the sign of the offset in a vernier stimulus, the human visual system exhibits hyperacuity-level performance by evaluating spatial relations with the precision of a fraction of a photoreceptor"s diameter. We propose that this impressive performance depends in part on a fast learning process that uses relatively few examples and occurs at an early processing stage in the visual pathway. We show that this hypothesis is plausible by demonstrating that it is possible to synthesize, from a small number of examples of a given task, a simple (HyperBF) network that attains the required performance level. We then verify with psychophysical experiments some of the key predictions of our conjecture. In particular, we show that fast timulus-specific learning indeed takes place in the human visual system and that this learning does not transfer between two slightly different hyperacuity tasks.

Visual Tracking

A typical robot vision scenario might involve a vehicle moving with an unknown 3D motion (translation and rotation) while taking intensity images of an arbitrary environment. This paper describes the theory and implementation issues of tracking any desired point in the environment. This method is performed completely in software without any need to mechanically move the camera relative to the vehicle. This tracking technique is simple an inexpensive. Furthermore, it does not use either optical flow or feature correspondence. Instead, the spatio-temporal gradients of the input intensity images are used directly. The experimental results presented support the idea of tracking in software. The final result is a sequence of tracked images where the desired point is kept stationary in the images independent of the nature of the relative motion. Finally, the quality of these tracked images are examined using spatio-temporal gradient maps.

Virtual Visual Hulls: Example-Based 3D Shape Estimation from a Single Silhouette

Recovering a volumetric model of a person, car, or other object of interest from a single snapshot would be useful for many computer graphics applications. 3D model estimation in general is hard, and currently requires active sensors, multiple views, or integration over time. For a known object class, however, 3D shape can be successfully inferred from a single snapshot. We present a method for generating a ``virtual visual hull''-- an estimate of the 3D shape of an object from a known class, given a single silhouette observed from an unknown viewpoint. For a given class, a large database of multi-view silhouette examples from calibrated, though possibly varied, camera rigs are collected. To infer a novel single view input silhouette's virtual visual hull, we search for 3D shapes in the database which are most consistent with the observed contour. The input is matched to component single views of the multi-view training examples. A set of viewpoint-aligned virtual views are generated from the visual hulls corresponding to these examples. The 3D shape estimate for the input is then found by interpolating between the contours of these aligned views. When the underlying shape is ambiguous given a single view silhouette, we produce multiple visual hull hypotheses; if a sequence of input images is available, a dynamic programming approach is applied to find the maximum likelihood path through the feasible hypotheses over time. We show results of our algorithm on real and synthetic images of people.

Sharing visual features for multiclass and multiview object detection

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (run-time) computational complexity, and the (training-time) sample complexity, scales linearly with the number of classes to be detected. It seems unlikely that such an approach will scale up to allow recognition of hundreds or thousands of objects. We present a multi-class boosting procedure (joint boosting) that reduces the computational and sample complexity, by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required, and therefore the computational cost, is observed to scale approximately logarithmically with the number of classes. The features selected jointly are closer to edges and generic features typical of many natural structures instead of finding specific object parts. Those generic features generalize better and reduce considerably the computational cost of an algorithm for multi-class object detection.

The Role of Fixation and Visual Attention in Object Recognition

This research project is a study of the role of fixation and visual attention in object recognition. In this project, we build an active vision system which can recognize a target object in a cluttered scene efficiently and reliably. Our system integrates visual cues like color and stereo to perform figure/ground separation, yielding candidate regions on which to focus attention. Within each image region, we use stereo to extract features that lie within a narrow disparity range about the fixation position. These selected features are then used as input to an alignment-style recognition system. We show that visual attention and fixation significantly reduce the complexity and the false identifications in model-based recognition using Alignment methods. We also demonstrate that stereo can be used effectively as a figure/ground separator without the need for accurate camera calibration.