A set of microarray images were acquired by a sequence of biological experiments which were scanned via a high resolution scanner. For each spot corresponding to a gene, the ratio of Cy3 and Cy5 fluorescent signal int...
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In this paper, we propose a method to track multiple deformable objects in sequences (with a static camera) in and beyond the visible spectrum by combining Gabor filtering and clustering. In a first step, a set of Gab...
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In this paper, we propose a method to track multiple deformable objects in sequences (with a static camera) in and beyond the visible spectrum by combining Gabor filtering and clustering. In a first step, a set of Gabor filter banks is used to filter the difference image between two consecutive frames. Then, the moving areas are sampled by randomly positioning particles in high magnitude area of the filtered image. Finally, these points are clustered to obtain one class for each moving object. The novelty in our method is in using cluster information from the previous frame to classify new particles in the current frame. This makes our method robust to occlusions, objects entering and leaving the field of view, objects stopping and starting, and moving objects getting really close to each other.
Real-time 3D geometry and motion estimation has many important applications in areas such as robot navigation and dynamic image-based rendering. A novel algorithm is proposed in this paper for estimating 3D geometry a...
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The core of the traditional RANSAC algorithm and its more recent efficient counterparts is the hypothesis evaluation stage, with the focus on finding the best, outlier free hypothesis. Motivated by a non-parametric en...
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The core of the traditional RANSAC algorithm and its more recent efficient counterparts is the hypothesis evaluation stage, with the focus on finding the best, outlier free hypothesis. Motivated by a non-parametric ensemble techniques, we demonstrate that it proves advantageous to use the entire set of hypotheses generated in the sampling stage. We show that by studying the residual distribution of each data point with respect to the entire set of hypotheses, the problem of inlier/ outlier identification can be formulated as a classification problem. We present extensive simulations of the approach, which in the presence of a large percentage (> 50%) of outliers, provides a repeatable and, an order of magnitude more efficient method compared to the currently existing techniques. Results on widebaseline matching and fundamental matrix estimation are presented.
This paper presents a method for automatically detecting and recognising unusual events on stairs from video data. The motivation is to provide a tool for biomedical researchers to rapidly find and analyse the events ...
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Perceptual grouping of the complete boundaries of objects in natural images remains an unsolved problem in computervision. The computational complexity of the problem and difficulties capturing global constraints lim...
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Perceptual grouping of the complete boundaries of objects in natural images remains an unsolved problem in computervision. The computational complexity of the problem and difficulties capturing global constraints limit the performance of current algorithms. In this paper we develop a coarse-to-fine Bayesian algorithm which addresses these constraints. Candidate contours are extracted at a coarse scale and then used to generate spatial priors on the location of possible contours at finer scales. In this way, a rough estimate of the shape of an object is progressively refined. The coarse estimate provides robustness to texture and clutter while the refinement process allows for the extraction of detailed object contours. The grouping algorithm is probabilistic and uses multiple grouping cues derived from natural scene statistics. We present a quantitative evaluation of grouping performance on the Berkeley Segmentation Database, and show that the multi-scale approach outperforms several single-scale contour extraction algorithms.
Manifold learning has become an important tool to characterize high-dimensional data that vary nonlinearly due to a few parameters. Applications to the analysis of medical imagery and human motion patterns have been s...
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Manifold learning has become an important tool to characterize high-dimensional data that vary nonlinearly due to a few parameters. Applications to the analysis of medical imagery and human motion patterns have been successful despite the lack of effective tools to parameterize cyclic data sets. This paper offers an initial approach to this problem, and provides for a minimal parameterization of points that are drawn from cylindrical manifolds-data whose (unknown) generative model includes a cyclic and a non-cyclic parameter. Solving for this special case is important for a number of current, practical applications and provides a start toward a general approach to cyclic manifolds. We offer results on synthetic and real data sets and illustrate an application to de-noising cardiac ultrasound images.
RANSAC is the most widely used robust regression algorithm in computervision. However, RANSAC has a few drawbacks which make it difficult to use in a lot of applications. Some of these problems have been addressed th...
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RANSAC is the most widely used robust regression algorithm in computervision. However, RANSAC has a few drawbacks which make it difficult to use in a lot of applications. Some of these problems have been addressed through improved sampling algorithms or better cost functions, but an important problem still remains. The algorithms are not user independent, and require some knowledge of the scale of the inlier noise. The projection based M-estimator (pbM) offers a solution to this by reframing the regression problem in a projection pursuit framework. In this paper we derive the pbM algorithm for heteroscedastic data. Our algorithm is applied to various real problems and its performance is compared with RANSAC and MSAC. It is shown that pbM gives better results than RANSAC and MSAC in spite of being user independent.
Gabor features are a common choice for texture analysis. The particular set of Gabor filters used for extracting the features is usually designed for optimal signal representation. We propose here an alternative crite...
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Gabor features are a common choice for texture analysis. The particular set of Gabor filters used for extracting the features is usually designed for optimal signal representation. We propose here an alternative criterion for designing the filter set. We consider a set of filters and its response to pairs of harmonic signals. Two signals are considered separable if the corresponding two sets of vector responses are disjoint in at least one of the components. We look for the set of Gabor filters that maximizes the fraction of separable harmonic signal pairs. The resulting filters are significantly different from the traditional ones. We test these maximal harmonic discrimination (MHD) filters using two texture discrimination methods, and describe their advantages over traditional filters.
In an object recognition task where an image is represented as a constellation of image patches, often many patches correspond to the cluttered background. If such patches are used for object class recognition, they w...
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In an object recognition task where an image is represented as a constellation of image patches, often many patches correspond to the cluttered background. If such patches are used for object class recognition, they will adversely affect the recognition rate. In this paper, we present a two stage method for selecting image patches which characterize the target object class and are capable of discriminating between the positive images containing the target objects and the complementary negative images. The first stage selection is done using a novel combinatorial optimization formulation on a weighted multipartite graph representing similarities between images patches across different instances of the target object. The following stage is a statistical method for selecting those images patches from the positive images which, when used individually, have the power of discriminating between the positive and negative images in the evaluation data. The individual methods have a performance competitive with the state of the art methods on a popular benchmark data set and their sequential combination consistently outperforms the individual methods and most of the other known methods while approaching the best known results.
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