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In background subtraction, cast shadows induce silhouette distortions and object fusions hindering performance of high level algorithms in scene monitoring. We introduce a nonparametric framework to model surface behavior when shadows are cast on them. Based on physical properties of light sources and surfaces, we identify a direction in RGB space on which background surface values under cast shadows...
In this paper, an analysis of locally linear embedding (LLE) in the context of clustering is developed. As LLE conserves the local affine coordinates of points, shape protrusions as high-curvature regions of the surface are preserved. Also, LLEpsilas covariance constraint acts as a force stretching those protrusions and making them wider separated and lower dimensional. A novel scheme for unsupervised...
This paper describes a coarse-to-fine learning based image registration algorithm which has particular advantages in dealing with multi-modality images. Many existing image registration algorithms use a few designed terms or mutual information to measure the similarity between image pairs. Instead, we push the learning aspect by selecting and fusing a large number of features for measuring the similarity...
This paper presents a new method to enforce inverse consistency in nonrigid image registration and matching. Conventional approaches assume diffeomorphic transformation, implicitly or explicitly. However, the inherent smoothness constraint discourages discontinuity consideration. We propose a post-processing algorithm that integrates the input forward and backward fields, which are output by existing...
Automated cell tracking using in vivo imagery is difficult, in general, due to the noise inherent in the imaging process, occlusions, varied cell appearance over time, motion of other tissue (distractors), and cells traveling in and out of the image plane. For certain types of cells these problems are exacerbated due to erratic motion patterns. In this paper, we introduce the Radial Flow Transform,...
We propose a novel approach for activity analysis in multiple synchronized but uncalibrated static camera views. We assume that the topology of camera views is unknown and quite arbitrary, the fields of views covered by these cameras may have no overlap or any amount of overlap, and objects may move on different ground planes. Using low-level cues, objects are tracked in each of the camera views independently,...
In this paper, we address the problem of classifying image sets, each of which contains images belonging to the same class but covering large variations in, for instance, viewpoint and illumination. We innovatively formulate the problem as the computation of Manifold-Manifold Distance (MMD), i.e., calculating the distance between nonlinear manifolds each representing one image set. To compute MMD,...
We present a novel method for learning human motion models from unsegmented videos. We propose a unified framework that encodes spatio-temporal relationships between descriptive motion parts and the appearance of individual poses. Sparse sets of spatial and spatio-temporal features are used. The method automatically learns static pose models and spatio-temporal motion parts. Neither motion cycles...
Most image annotation systems consider a single photo at a time and label photos individually. In this work, we focus on collections of personal photos and explore the associated GPS and time information for semantic annotation. First, we employ a constrained clustering method to partition a photo collection into event-based sub-collections, considering that the GPS records may be partly missing (a...
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...
Linear discriminant analysis (LDA) might be the most widely used linear feature extraction method in pattern recognition. Based on the analysis on the several limitations of traditional LDA, this paper makes an effort to propose a new computational paradigm named optimal discriminatory projection pursuit (ODPP), which is totally different from the traditional LDA and its variants. Only two simple...
The following topics were dealt with: statistical methods and learning in image processing; image segmentation; computer vision; motion analysis for structure, shape, and pose; motion and tracking; object detection, categorization, and recognition; video analysis; image retrieval; and video retrieval.
We propose a principled approach to summarization of visual data (images or video) based on optimization of a well-defined similarity measure. The problem we consider is re-targeting (or summarization) of image/video data into smaller sizes. A good ldquovisual summaryrdquo should satisfy two properties: (1) it should contain as much as possible visual information from the input data; (2) it should...
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,...
In this paper we want to start the discussion on whether image based 3D modelling techniques can possibly be used to replace LIDAR systems for outdoor 3D data acquisition. Two main issues have to be addressed in this context: (i) camera calibration (internal and external) and (ii) dense multi-view stereo. To investigate both, we have acquired test data from outdoor scenes both with LIDAR and cameras...
In this paper, we study how to build a vision-based system for global localization with accuracies within 10 cm. for robots and humans operating both indoors and outdoors over wide areas covering many square kilometers. In particular, we study the parameters of building a landmark database rapidly and utilizing that database online for real-time accurate global localization. Although the accuracy...
Practically all existing approaches to structure and motion computation use only positive image correspondences to verify the camera pose hypotheses. Incorrect epipolar geometries are solely detected by identifying outliers among the found correspondences. Ambiguous patterns in the images are often incorrectly handled by these standard methods. In this work we propose two approaches to overcome such...
This paper presents an approach to reconstruct non-stationary, articulated objects from silhouettes obtained with a monocular video sequence. We introduce the concept of motion blurred scene occupancies, a direct analogy of motion blurred images but in a 3D object scene occupancy space resulting from the motion/deformation of the object. Our approach starts with an image based fusion step that combines...
In this paper, we present a new approach for image labeling based on the recently introduced graph-shifts algorithm. Graph-shifts is an energy minimization algorithm that does labeling by dynamically manipulating, or shifting, the parent-child relationships in a hierarchical decomposition of the image. Each shift optimally reduces the energy by indirectly causing a change to the labeling; graph-shifts...
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