Images form a rich information source, which remains underutilized in biomedical document classification. We present here work that uses both image- and text-based features in order to identify articles of interest, in this case, pertaining to cis-regulatory modules in the context of gene-networks. Extending on our new idea, which we have recently introduced, of using OCR-based features to identify DNA contents in images, we combine image and text based classifiers to categorize documents as relevant or irrelevant to cis-regulatory modules. Using a set of hundreds of articles, marked by experts as relevant or irrelevant to cis-regulatory modules, we train/test image and text based classifiers, as well as classifiers integrating both. Our results indicate that the latter show the best performance with Recall, F-measure and Utility measures all above 0.9, demonstrating the significance of incorporating image data, and specifically OCR-based features, into the document categorization process. Moreover, the use of character distribution properties to represent images is directly relevant to other biomedical images containing text (e.g. RNA, proteins). Diagrams and other images containing text are also prevalent outside the biomedical domain, hence the work stands to be applicable and beneficial in other application areas.

Textureless regions, though error prone in stereo, may contain shading information that may be exploited. Shape from shading (SFS) results relate to world coordinates by arbitrary scaling factors which are difficult to estimate. We propose a method for estimating dense disparities from sparse correspondences using SFS cues. We show that SFS can impose constraints on the gradient of disparity in textureless regions with constant albedo. Gradient Constrained Interpolation (GCI), which solves the estimation problem in one dimension, is presented. We efficiently generate paths between correspondences that cover the image and then use GCI to fill the pixels in between. Results are presented on real and synthetic images, and provide quantitative evaluations to show that the method outperforms baseline methods.

Marine radars have been employed to gather data in applications that require near-continuous monitoring and tracking of objects over a wide area from a single viewpoint, independent of weather and light conditions. However, little attention has been paid toward utilizing such systems for the study of long-term phenomena and detecting anomalous environmental events or hazards that may occur infrequently but have potentially significant impacts on coastal populations. In this paper, we concentrate on tracking features in seasonally ice-covered Arctic coastal ocean environments. We have developed tools for automated analysis of ground-based radar images of landfast ice and moving sea ice to extract ice-floe trajectories and velocity fields, delineate the boundary of stable landfast ice, detect events relevant to coastal populations and identify surface vessels. We employ dense and feature-based optical flow approaches to compute motion fields from the images, active contours for delineation of stable landfast ice, and Hidden Markov Models for machine learning based event detection. We present results from the analysis of sample images jointly with a quantitative evaluation of algorithm performance relative to operator-based assessments.

An ideal approach to the problem of pose-invariant face recognition would handle continuous pose variations, would not be database specific, and would achieve high accuracy without any manual intervention. Most of the existing approaches fail to match one or more of these goals. In this paper, we present a fully automatic system for pose-invariant face recognition that not only meets these requirements but also outperforms other comparable methods. We propose a 3D pose normalization method that is completely automatic and leverages the accurate 2D facial feature points found by the system. The current system can handle 3D pose variation up to ±45° in yaw and ±30° in pitch angles. Recognition experiments were conducted on the USF 3D, Multi-PIE, CMU-PIE, FERET, and FacePix databases. Our system not only shows excellent generalization by achieving high accuracy on all 5 databases but also outperforms other methods convincingly.

Autonomous systems which learn and utilize a limited visual vocabulary have wide spread applications. Enabling such systems to segment a set of cluttered scenes into objects is a challenging vision problem owing to the non-homogeneous texture of objects and the random configurations of multiple objects in each scene. We present a solution to the following question: given a collection of images where each object appears in one or more images and multiple objects occur in each image, how best can we extract the boundaries of the different objects? The algorithm is presented with a set of stereo images, with one stereo pair per scene. The novelty of our work is the use of both color/texture and structure to refine previously determined object boundaries to achieve segmentation consistent with each of the input scenes presented. The algorithm populates an object library, which consists of a 3D model per object. Since an object is characterized both by texture and structure, for most purposes this representation is both complete and concise.

Computer vision techniques have been applied for rapid and accurate structure recovery in many fields. Most methods perform poorly in areas containing little or no texture and in presence of repetitive patterns. We present a portable, cost-effective pattern projector system powered by the flash of a camera, to aid the reconstruction of such areas. No calibration is required between the camera-projector, projector-scene or pattern. We demonstrate the effectiveness of our system on various representative surfaces like metal, clay, porcelain and natural fibres with different inherent colors/textures. A pipeline is presented to automatically generate textured, true scale metric models, which can be used for quantitative studies or visualization. The practability of our system is explored in the specific area of digital archiving of historically significant objects.

Stereo algorithms for structure reconstruction demand accurate disparities with low mismatch errors and false positives. Mismatch errors in large textureless regions force most accurate algorithms to be sparse, with disparities known only in textured regions. We propose a novel method which uses characteristics of the multi-valued disparity to segregate image regions into unambiguous, occluded but textured and regions with low color variation. The disparity in the unambiguous region is calculated using stable matching with local disparity filtering. The disparity is interpolated into other regions by diffusion using unstructured triangulation and method of finite elements for rapid convergence. The boundary conditions for each of the region are appropriately modified so that accurate discontinuities in the disparity are preserved. A comparison of our method with some existing methods through experiments reveal that this algorithm indeed performs significantly better in producing dense accurate disparities.

To allocate audio conference servers (CS) [R. V. Prasad et al., June 2003] for virtual conferencing over IP, we leverage session initiation protocol (SIP) for signaling. We address here the problem of facilitating seamless conference amongst participants using CSs. This demands a proper allocation of CSs to clients to maximize the number of participants served and at a reduced cost. Seeking a more realistic approach, we avoid over-simplifying assumptions; hence the problem becomes relatively harder. We present three heuristic algorithms for these NP-hard problems and bring about the effectiveness of their performance.