29147523
SCAPE: shape completion and animation of people
We introduce the SCAPE method (Shape Completion and Animation for PEople)---a data-driven method for building a human shape model that spans variation in both subject shape and pose. The method is based on a representation that incorporates both articulated and non-rigid deformations. We learn a pose deformation model that derives the non-rigid surface deformation as a function of the pose of the articulated skeleton. We also learn a separate model of variation based on body shape. Our two models can be combined to produce 3D surface models with realistic muscle deformation for different people in different poses, when neither appear in the training set. We show how the model can be used for shape completion --- generating a complete surface mesh given a limited set of markers specifying the target shape. We present applications of shape completion to partial view completion and motion capture animation. In particular, our method is capable of constructing a high-quality animated surface model of a moving person, with realistic muscle deformation, using just a single static scan and a marker motion capture sequence of the person.
7180811
Calibrating pan-tilt cameras in wide-area surveillance networks
Pan-tilt cameras are often used as components of wide-area surveillance systems. It is necessary to calibrate these cameras in relation to one another in order to obtain a consistent representation of the entire space. Existing methods for calibrating pan-tilt cameras have assumed an idealized model of camera mechanics. In addition, most methods have been calibrated using only a small range of camera motion. We present a method for calibrating pan-tilt cameras that introduces a more complete model of camera motion. Pan and tilt rotations are modeled as occurring around arbitrary axes in space. In addition, the wide area surveillance system itself is used to build a large virtual calibration object, resulting in better calibration than would be possible with a single small calibration target. Finally, the proposed enhancements are validated experimentally, with comparisons showing the improvement provided over more traditional methods.
8652205
Copy detection mechanisms for digital documents
In a digital library system, documents are available in digital form and therefore are more easily copied and their copyrights are more easily violated. This is a very serious problem, as it discourages owners of valuable information from sharing it with authorized users. There are two main philosophies for addressing this problem: prevention and detection. The former actually makes unauthorized use of documents difficult or impossible while the latter makes it easier to discover such activity.In this paper we propose a system for registering documents and then detecting copies, either complete copies or partial copies. We describe algorithms for such detection, and metrics required for evaluating detection mechanisms (covering accuracy, efficiency, and security). We also describe a working prototype, called COPS, describe implementation issues, and present experimental results that suggest the proper settings for copy detection parameters.
1351850
Filling holes in complex surfaces using volumetric diffusion
We address the problem of building watertight 3D models from surfaces that contain holes - for example, sets of range scans that observe most but not all of a surface. We specifically address situations in which the holes are too geometrically and topologically complex to fill using triangulation algorithms. Our solution begins by constructing a signed distance function, the zero set of which defines the surface. Initially, this function is defined only in the vicinity of observed surfaces. We then apply a diffusion process to extend this function through the volume until its zero set bridges whatever holes may be present. If additional information is available, such as known-empty regions of space inferred from the lines of sight to a 3D scanner, it can be incorporated into the diffusion process. Our algorithm is simple to implement, is guaranteed to produce manifold non-interpenetrating surfaces, and is efficient to run on large datasets because computation is limited to areas near holes.
5870313
BRDF Invariant Stereo Using Light Transport Constancy
Nearly all existing methods for stereo reconstruction assume that scene reflectance is Lambertian and make use of brightness constancy as a matching invariant. We introduce a new invariant for stereo reconstruction called light transport constancy (LTC), which allows completely arbitrary scene reflectance (bidirectional reflectance distribution functions (BRDFs)). This invariant can be used to formulate a rank constraint on multiview stereo matching when the scene is observed by several lighting configurations in which only the lighting intensity varies. In addition, we show that this multiview constraint can be used with as few as two cameras and two lighting configurations. Unlike previous methods for BRDF invariant stereo, LTC does not require precisely configured or calibrated light sources or calibration objects in the scene. Importantly, the new constraint can be used to provide BRDF invariance to any existing stereo method whenever appropriate lighting variation is available.
16145112
Mosaics of scenes with moving objects
Image mosaics are useful for a variety of tasks in vision and computer graphics. A particularly convenient way to generate mosaics is by 'stitching' together many ordinary photographs. Existing algorithms focus on capturing static scenes. This paper presents a complete system for creating visually pleasing mosaics in the presence of moving objects. There are three primary contributions. The first component of our system is a registration method that remains unbiased by movement-the Mellin transform is extended to register images related by a projective transform. Second an efficient method for finding a globally consistent registration of all images is developed. By solving a linear system of equations, derived from many pairwise registration matrices, we find an optimal global registration. Lastly, a new method of compositing images is presented. Blurred areas due to moving objects are avoided by segmenting the mosaic into disjoint regions and sampling pixels in each region from a single source image.
10444578
A reliable-inference framework for recognition of human actions
We present an action recognition method based on the concept of reliable inference. Our approach is formulated in a probabilistic framework using posterior class ratios to verify the saliency of an input before committing to any action classification. The framework is evaluated in the context of walking, running, and standing at multiple viewpoints and compared to ML and MAP approaches. Results examining individual silhouette images with the framework demonstrate that these actions can be reliably discriminated while discounting confusing images.
17875435
Extraction of Person Silhouettes from Surveillance Imagery using MRFs
We present a method for the simultaneous detection and segmentation of objects from static images. We employ low-level contour features that enable us to learn the coarse object shape using a simple training phase requiring no manual segmentation. Based on the observation that most interesting objects (e.g., people) have regular and closed boundaries, we exploit relations between these features to extract midlevel cues, such as continuity and closure. For segmentation, we employ a Markov random field that combines these cues with information learned from training. The algorithm is evaluated for extracting person silhouettes from surveillance images, and quantitative results are presented
62531827
Robust detection of people in thermal imagery
We present a new contour analysis technique to detect people in thermal imagery. Background-subtraction is first used to identify local regions-of-interest. Gradient information within each region is then combined into a contour saliency map. To extract contour fragments, a watershed-based selection algorithm is used. A path-constrained A* search is employed to complete any broken contours, from which silhouettes are formed. Results using thermal video sequences demonstrate the capability of the approach to robustly detect people across a wider range of environmental conditions than is possible with standard approaches.
9994863
Motion segmentation and pose recognition with motion history gradients
Abstract. This paper presents a fast and simple method using a timed motion history image (tMHI) for representing motion from the gradients in successively layered silhouettes. This representation can be used to (a) determine the current pose of the object and (b) segment and measure the motions induced by the object in a video scene. These segmented regions are not “motion blobs”, but instead are motion regions that are naturally connected to parts of the moving object. This method may be used as a very general gesture recognition “toolbox”. We demonstrate the approach with recognition of waving and overhead clapping motions to control a music synthesis program.
10174533
Cost-Sensitive Decision Tree Learning for Forensic Classification
In some learning settings, the cost of acquiring features for classification must be paid up front, before the classifier is evaluated. In this paper, we introduce the forensic classification problem and present a new algorithm for building decision trees that maximizes classification accuracy while minimizing total feature costs. By expressing the ID3 decision tree algorithm in an information theoretic context, we derive our algorithm from a well-formulated problem objective. We evaluate our algorithm across several datasets and show that, for a given level of accuracy, our algorithm builds cheaper trees than existing methods. Finally, we apply our algorithm to a real-world system, Clarify. Clarify classifies unknown or unexpected program errors by collecting statistics during program runtime which are then used for decision tree classification after an error has occurred. We demonstrate that if the classifier used by the Clarify system is trained with our algorithm, the computational overhead (equivalently, total feature costs) can decrease by many orders of magnitude with only a slight (<1%) reduction in classification accuracy.