12006498
Computing depth under ambient illumination using multi-shuttered light
Range imaging has become a critical component of many computer vision applications. The quality of the depth data is of critical importance, but so is the need for speed. Shuttered light-pulse (SLP) imaging uses active illumination hardware to provide high quality depth maps at video frame rates. Unfortunately, current analytical models for deriving depth from SLP imagers are specific to the number of shutters and have a number of deficiencies. As a result, depth estimation often suffers from bias due to object reflectivity, incorrect shutter settings, or strong ambient illumination such as that encountered outdoors. These limitations make SLP imaging unsuitable for many applications requiring stable depth readings. This paper introduces a method that is general to any number of shutters. Using three shutters, the new method produces invariant estimates under changes in ambient illumination, producing high quality depth maps in a wider range of situations.
2402326
Efficiently combining positions and normals for precise 3D geometry
Range scanning, manual 3D editing, and other modeling approaches can provide information about the geometry of surfaces in the form of either 3D positions (e.g., triangle meshes or range images) or orientations (normal maps or bump maps). We present an algorithm that combines these two kinds of estimates to produce a new surface that approximates both. Our formulation is linear, allowing it to operate efficiently on complex meshes commonly used in graphics. It also treats high-and low-frequency components separately, allowing it to optimally combine outputs from data sources such as stereo triangulation and photometric stereo, which have different error-vs.-frequency characteristics. We demonstrate the ability of our technique to both recover high-frequency details and avoid low-frequency bias, producing surfaces that are more widely applicable than position or orientation data alone.
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.
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.
2294366
The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces
We present an unsupervised algorithm for registering 3D surface scans of an object undergoing significant deformations. Our algorithm does not need markers, nor does it assume prior knowledge about object shape, the dynamics of its deformation, or scan alignment. The algorithm registers two meshes by optimizing a joint probabilistic model over all point-to-point correspondences between them. This model enforces preservation of local mesh geometry, as well as more global constraints that capture the preservation of geodesic distance between corresponding point pairs. The algorithm applies even when one of the meshes is an incomplete range scan; thus, it can be used to automatically fill in the remaining surfaces for this partial scan, even if those surfaces were previously only seen in a different configuration. We evaluate the algorithm on several real-world datasets, where we demonstrate good results in the presence of significant movement of articulated parts and non-rigid surface deformation. Finally, we show that the output of the algorithm can be used for compelling computer graphics tasks such as interpolation between two scans of a non-rigid object and automatic recovery of articulated object models.
7453479
Dienst: an architecture for distributed document libraries
As one of the five universities participating in the ARPA-sponsored Computer Science Technical Report project, we at Cornell have developed a digital library architecture called Dienst. Dienst is a protocol and implementation that provides Internet access to a distributed, decentralized multi-format document collection. The collection is managed by a set of interoperating Dienst servers distributed over the Internet. These servers provide three digital library services: repositories of multi-format documents; indexes into the document collection and search engines for these indexes; and user interfaces for browsing, searching, and accessing the collection.
15670831
An expressive three-mode principal components model of human action style
We present a three-mode expressive-feature model for representing and recognizing performance styles of human actions. A set of style variations for an action are initially arranged into a three-mode data representation (body pose, time, style) and factored into its three-mode principal components to reduce the data dimensionality. We next embed tunable weights on trajectories within the sub-space model to enable different context-based style estimations. We outline physical and perceptual parameterization methods for choosing style labels for the training data, from which we automatically learn the necessary expressive weights using a gradient descent procedure. Experiments are presented examining several motion-capture walking variations corresponding to carrying load, gender, and pace. Results demonstrate a greater flexibility of the expressive three-mode model, over standard squared-error style estimation, to adapt to different style matching criteria.
14448568
Sampling Representative Examples for Dimensionality Reduction and Recognition - Bootstrap Bumping LDA
We present a novel method for dimensionality reduction and recognition based on Linear Discriminant Analysis (LDA), which specifically deals with the Small Sample Size (SSS) problem in Computer Vision applications. Unlike the traditional methods, which impose specific assumptions to address the SSS problem, our approach introduces a variant of bootstrap bumping technique, which is a general framework in statistics for model search and inference. An intermediate linear representation is first hypothesized from each bootstrap sample. Then LDA is performed in the reduced subspace. Lastly, the final model is selected among all hypotheses for the best classification. Experiments on synthetic and real datasets demonstrate the advantages of our Bootstrap Bumping LDA (BB-LDA) approach over the traditional LDA based methods.
18551439
An adaptive focus-of-attention model for video surveillance and monitoring
In current video surveillance systems, commercial pan/tilt/zoom (PTZ) cameras typically provide naive (or no) automatic scanning functionality to move a camera across its complete viewable field. However, the lack of scene-specific information inherently handicaps these scanning algorithms. We address this issue by automatically building an adaptive, focus-of-attention, scene-specific model using standard PTZ camera hardware. The adaptive model is constructed by first detecting local human activity (i.e., any translating object with a specific temporal signature) at discrete locations across a PTZ camera’s entire viewable field. The temporal signature of translating objects is extracted using motion history images (MHIs) and an original, efficient algorithm based on an iterative candidacy-classification-reduction process to separate the target motion from noise. The target motion at each location is then quantified and employed in the construction of a global activity map for the camera. We additionally present four new camera scanning algorithms which exploit this activity map to maximize a PTZ camera’s opportunity of observing human activity within the camera’s overall field of view. We expect that these efficient and effective algorithms are implementable within current commercial camera systems.
1681184
Improved error reporting for software that uses black-box components
An error occurs when software cannot complete a requested action as a result of some problem with its input, configuration, or environment. A high-quality error report allows a user to understand and correct the problem. Unfortunately, the quality of error reports has been decreasing as software becomes more complex and layered. End-users take the cryptic error messages given to them by programsand struggle to fix their problems using search engines and support websites. Developers cannot improve their error messages when they receive an ambiguous or otherwise insufficient error indicator from a black-box software component.
 We introduce Clarify, a system that improves error reporting by classifying application behavior. Clarify uses minimally invasive monitoring to generate a behavior profile, which is a summary of the program's execution history. A machine learning classifier uses the behavior profile to classify the application's behavior, thereby enabling a more precise error report than the output of the application itself.
 We evaluate a prototype Clarify system on ambiguous error messages generated by large, modern applications like gcc, La-TeX, and the Linux kernel. For a performance cost of less than 1% on user applications and 4.7% on the Linux kernel, the proto type correctly disambiguates at least 85% of application behaviors that result in ambiguous error reports. This accuracy does not degrade significantly with more behaviors: a Clarify classifier for 81 La-TeX error messages is at most 2.5% less accurate than a classifier for 27 LaTeX error messages. Finally, we show that without any human effort to build a classifier, Clarify can provide nearest-neighbor software support, where users who experience a problem are told about 5 other users who might have had the same problem. On average 2.3 of the 5 users that Clarify identifies have experienced the same problem.