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There is a large variety of trackers, which have been proposed in the literature during the last two decades with some mixed success. Object tracking in realistic scenarios is a difficult problem, therefore, it remains a most active area of research in computer vision. A good tracker should perform well in a large number of videos involving illumination changes, occlusion, clutter, camera motion, low contrast, specularities, and at least six more aspects. However, the performance of proposed trackers have been evaluated typically on less than ten videos, or on the special purpose datasets. In this paper, we aim to evaluate trackers systematically and experimentally on 315 video fragments covering above aspects. We selected a set of nineteen trackers to include a wide variety of algorithms often cited in literature, supplemented with trackers appearing in 2010 and 2011 for which the code was publicly available. We demonstrate that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing. We find that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score. The analysis under a large variety of circumstances provides objective insight into the strengths and weaknesses of trackers. [ABSTRACT FROM AUTHOR]

This paper conceives of tracking as the developing distinction of a foreground against the background. In this manner, fast changes in the object or background appearance can be dealt with. When modelling the target alone (and not its distinction from the background), changes of lighting or changes of viewpoint can invalidate the internal target model. As the main contribution, we propose a new model for the detection of the target using foreground/background texture discrimination. The background is represented as a set of texture patterns. During tracking, the algorithm maintains a set of discriminant functions each distinguishing one pattern in the object region from background patterns in the neighborhood of the object. The idea is to train the foreground/background discrimination dynamically, that is while the tracking develops. In our case, the discriminant functions are efficiently trained online using a differential version of Linear Discriminant Analysis (LDA). Object detection is performed by maximizing the sum of all discriminant functions. The method employs two complementary sources of information: it searches for the image region similar to the target object, and simultaneously it seeks to avoid background patterns seen before. The detection result is therefore less sensitive to sudden changes in the appearance of the object than in methods relying solely on similarity to the target. The experiments show robust performance under severe changes of viewpoint or abrupt changes of lighting. [ABSTRACT FROM AUTHOR]

We report a six-stimulus basis for stochastic texture perception. Fragmentation of the scene by a chaotic process causes the spatial scene statistics to conform to a Weibull-distribution. The parameters of the Weibull distribution characterize the spatial structure of uniform stochastic textures of many different origins completely. In this paper, we report the perceptual significance of the Weibull parameters. We demonstrate the parameters to be sensitive to orthogonal variations in the imaging conditions, specifically to the illumination conditions, camera magnification and resolving power, and the texture orientation. Apparently, the Weibull parameters form a six-stimulus basis for stochastic texture description. The results indicate that texture perception can be approached like the experimental science of colorimetry. [ABSTRACT FROM AUTHOR]

We propose a new image segmentation technique called strings. A string is a variational deformable model that is learned from a collection of example objects rather than built from a priori analytical or geometrical knowledge. As opposed to existing approaches, an object boundary is represented by a one-dimensional multivariate curve in functional space, a feature function, rather than by a point in vector space. In the learning phase, feature functions are defined by extraction of multiple shape and image features along continuous object boundaries in a given learning set. The feature functions are aligned, then subjected to functional principal components analysis and functional principal regression to summarize the feature space and to model its content, respectively. Also, a Mahalanobis distance model is constructed for evaluation of boundaries in terms of their feature functions, taking into account the natural variations seen in the learning set. In the segmentation phase, an object boundary in a new image is searched for with help of a curve. The curve gives rise to a feature function, a string, that is weighted by the regression model and evaluated by the Mahalanobis model. The curve is deformed in an iterative procedure to produce feature functions with minimal Mahalanobis distance. Strings have been compared with active shape models on 145 vertebra images, showing that strings produce better results when initialized close to the target boundary, and comparable results otherwise. [ABSTRACT FROM AUTHOR]

Focuses on the impact of the development in hardware, storage, connectivity and sensor technology to the introduction of large image repositories. Efficacy of man and machine interaction; Challenges of the computer vision in content-based retrieval; Citation of the essential components of image retrieval systems.

This paper studies automatic image classification by modeling soft assignment in the popular codebook model. The codebook model describes an image as a bag of discrete visual words selected from a vocabulary, where the frequency distributions of visual words in an image allow classification. One inherent component of the codebook model is the assignment of discrete visual words to continuous image features. Despite the clear mismatch of this hard assignment with the nature of continuous features, the approach has been successfully applied for some years. In this paper, we investigate four types of soft assignment of visual words to image features. We demonstrate that explicitly modeling visual word assignment ambiguity improves classification performance compared to the hard assignment of the traditional codebook model. The traditional codebook model is compared against our method for five well-known data sets: 15 natural scenes, Caltech-101, Caltech-256, and Pascal VOC 2007/2008. We demonstrate that large codebook vocabulary sizes completely deteriorate the performance of the traditional model, whereas the proposed model performs consistently. Moreover, we show that our method profits in high-dimensional feature spaces and reaps higher benefits when increasing the number of image categories. [ABSTRACT FROM AUTHOR]

Luminance-based features are widely used as low-level input for computer vision applications, even when color data is available. The extension of feature detection to the color domain prevents information loss due to isoluminance and allows us to exploit the photometric information. To fully exploit the extra information in the color data, the vector nature of color data has to be taken into account and a sound framework is needed to combine feature and photometric invariance theory. In this paper, we focus on the structure tensor, or color tensor, which adequately handles the vector nature of color images. Further, we combine the features based on the color tensor with photometric invariant derivatives to arrive at photometric invariant features. We circum- vent the drawback of unstable photometric invariants by deriving an uncertainty measure to accompany the photometric invariant derivatives. The uncertainty is incorporated in the color tensor, hereby allowing the computation of robust photometric invariant features. The combination of the photometric invariance theory and tensor-based features allows for detection of a variety of features such as photometric invariant edges, corners, optical flow, and curvature. The proposed features are tested for noise char- acteristics and robustness to photometric changes. Experiments show that the proposed features are robust to scene incidental events and that the proposed uncertainty measure improves the applicability of full invariants. [ABSTRACT FROM AUTHOR]

Abstract: In computer vision, measurement of image properties such as color or texture is essential. In this paper, we propose a solid framework for the local measurement of texture in color images. We give a physical basis for the integration of the well-known Gabor filters with the measurement of color. Our method implies that the color–texture is measured in the wavelength-Fourier domain. The measurement filter in this domain boils down to a 3D Gaussian, representing a Gabor–Gaussian in the spatial-color domain. In addition, the extended measurements invariant to shadow or shading can be derived directly from the proposed method. We apply a simple segmentation algorithm to illustrate the performance of our proposed color–texture measurement. The derived color–texture filter is demonstrated to be accurate in capturing texture statistics. In addition, the method is compatible with the measurement of gray-value texture. [Copyright &y& Elsevier]

We present the ALOI collection of 1,000 objects recorded under various imaging circumstances. In order to capture the sensory variation in object recordings, we systematically varied viewing angle, illumination angle, and illumination color for each object, and additionally captured wide-baseline stereo images. We recorded over a hundred images of each object, yielding a total of 110,250 images for the collection. These images are made publicly available for scientific research purposes. [ABSTRACT FROM AUTHOR]

Light source position (LSP) estimation is a difficult yet an important problem in computer vision. A common approach for estimating the LSP assumes Lambert’s law. However, in real-world scenes, Lambert’s law does not hold for all different types of surfaces. Instead of assuming all that surfaces follow Lambert’s law, our approach classifies image surface segments based on their photometric and geometric surface attributes (i.e. glossy, matte, curved, and so on) and assigns weights to image surface segments based on their suitability for LSP estimation. In addition, we propose the use of the estimated camera pose to globally constrain LSP for RGB-D video sequences. Experiments on Boom and a newly collected RGB-D video data sets show that the state-of-the-art methods are outperformed by the proposed method. The results demonstrate that weighting image surface segments based on their attributes outperform the state-of-the-art methods in which the image surface segments are considered to equally contribute. In particular, by using the proposed surface weighting, the angular error for LSP estimation is reduced from 12.6° to 8.2° and 24.6° to 4.8° for Boom and RGB-D video data sets, respectively. Moreover, using the camera pose to globally constrain LSP provides higher accuracy (4.8°) compared with using single frames (8.5°). [ABSTRACT FROM PUBLISHER]