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Given a finite number of data points sampled from a low-dimensional manifold embedded in a high dimensional space together with the parameter vectors for a subset of the data points, we need to determine the parameter vectors for the rest of the data points. This problem is known as semi-supervised manifold learning, and in this paper we propose methods to handle this problem by solving certain eigenvalue-problems...
In this paper, we propose a novel robust retrieval and classification system for video and motion events based on null space representation. In order to analyze the robustness of the system, the perturbed null operators have been derived with the first order perturbation theory. Subsequently, the sensitivity of the null operators is discussed in terms of the error ratio and the SNR respectively. Meanwhile,...
This paper deals with shading and AAMs. Shading is created by lighting change. It can be of two types: self- shading and external shading. The effect of self-shading can be explicitly learned and handled by AAMs. This is not however possible for external shading, which is usually dealt with by robustifying the cost function. We take a different approach: we measure the fitting cost in a so-called...
A new algorithm is proposed for background subtraction in highly dynamic scenes. Background subtraction is equated to the dual problem of saliency detection: background points are those considered not salient by suitable comparison of object and background appearance and dynamics. Drawing inspiration from biological vision, saliency is defined locally, using center-surround computations that measure...
Linear inverse problems in computer vision, including motion estimation, shape fitting and image reconstruction, give rise to parameter estimation problems with highly correlated errors in variables. Established total least squares methods estimate the most likely corrections Acirc and bcirc to a given data matrix [A, b] perturbed by additive Gaussian noise, such that there exists a solution y with...
Estimating the color of a scene illuminant often plays a central role in computational color constancy. While this problem has received significant attention, the methods that exist do not maximally leverage spatial dependencies between pixels. Indeed, most methods treat the observed color (or its spatial derivative) at each pixel independently of its neighbors. We propose an alternative approach...
The simultaneous segmentation of multiple objects is an important problem in many imaging and computer vision applications. Various extensions of level set segmentation techniques to multiple objects have been proposed; however, no one method maintains object relationships, preserves topology, is computationally efficient, and provides an object-dependent internal and external force capability. In...
Learning a discriminant becomes substantially more difficult when the datasets are high-dimensional and the available samples are few. This is often the case in computer vision and medical diagnosis applications. A novel conic section classifier (CSC) was recently introduced in the literature to handle such datasets, wherein each class was represented by a conic section parameterized by its focus,...
Human faces are neither exactly Lambertian nor entirely convex and hence most models in literature which make the Lambertian assumption, fall short when dealing with specularities and cast shadows. In this paper, we present a novel anti-symmetric tensor spline (a spline for tensor-valued functions) based method for the estimation of the Apparent BRDF (ABRDF) field for human faces that seamlessly accounts...
A moving plane observed by a fixed camera induces a fundamental matrix F across multiple frames, where the ratios among the elements in the upper left 2times2 submatrix are herein referred to as the Fundamental Ratios. We show that fundamental ratios are invariant to camera parameters, and hence can be used to identify similar plane motions from varying viewpoints. For action recognition, we decompose...
A novel approach to scene categorization is proposed. Similar to previous works of [11, 15, 3, 12], we introduce an intermediate space, based on a low dimensional semantic ldquothemerdquo image representation. However, instead of learning the themes in an unsupervised manner, they are learned with weak supervision, from casual image annotations. Each theme induces a probability density on the space...
An efficient and robust framework for two-view multiple structure and motion segmentation is proposed. To handle this otherwise recursive problem, hypotheses for the models are generated by local sampling. Once these hypotheses are available, a model selection problem is formulated which takes into account the hypotheses likelihoods and model complexity. An explicit model for outliers is also added...
This paper presents a general method for segmenting a vector valued sequence into an unknown number of subsequences where all data points from a subsequence can be represented with the same affine parametric model. The idea is to cluster the data into the minimum number of such subsequences which, as we show, can be cast as a sparse signal recovery problem by exploiting the temporal correlation between...
This paper introduces the minimal local reconstruction error (MLRE) as a similarity measure and presents a MLRE-based classier. From the geometric meaning of the minimal local reconstruction error, we derive that the MLRE-based classifier is a generalization of the conventional nearest neighbor classier and the nearest neighbor line and plane classifiers. We further apply the MLRE measure to characterize...
3D object design has many applications including flexible 3D sketch input in CAD, computer game, webpage content design, image based object modeling, and 3D object retrieval. Most current 3D object design tools work on a 2D drawing plane such as computer screen or tablet, which is often inflexible with one dimension lost. On the other hand, virtual reality based methods have the drawbacks that there...
Recovering the three-dimensional (3D) object shape remains an unresolved area of research on the cross-section of computer vision, photogrammetry and bioinformatics. Although various techniques have been developed, the computational complexity and the constraints introduced to overcome the problems have limited their applicability in the real world scenarios. In this paper, we propose a method that...
In this work, we introduce a novel implicit representation of shape which is based on assigning to each pixel a probability that this pixel is inside the shape. This probabilistic representation of shape resolves two important drawbacks of alternative implicit shape representations such as the level set method: Firstly, the space of shapes is convex in the sense that arbitrary convex combinations...
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