Research

Model-Based Recovery of Fluid-Flow Parameters from Video
The flow of a liquid film over a rapidly rotating horizontal disk has numerous industrial and engineering applications, ranging from the spin-coating of silicon wafers to the atomization of liquids. It also has many applications in medical fields (for example, blood oxygenation). The spinning disk reactor exploits the benefits of centrifugal force, which produces thin, highly sheared films due to radial acceleration. The hydrodynamics of the film results in excellent fluid mixing and high heat or mass transfer rates. Different wave regimes of fluid flow have a strong influence on those processes, so it is important to control the formation of waves.
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Detection of Thin Lines using Low-Quality Video from Low-Altitude Aircraft in Urban Settings
A novel thin line detection algorithm for use in low-altitude aerial vehicles is presented. This algorithm is able to detect thin obstacles such as cables, power lines, and wires. The system is intended to be used during urban search and rescue operations, capable of dealing with low-quality images, robust to image clutter, bad weather, and sensor artifacts. The detection process uses motion estimation at the pixel level, combined with edge detection, followed by a windowed Hough transform. The evidence of lines is tracked over time in the resulting parameter spaces using a dynamic line movement model. The algorithm’s receiver operating characteristic curve (ROC) is shown, based on a multi-site dataset with 86 videos with 10160 wires spanning in 5576 frames.

J. Candamo, R. Kasturi, D. Goldgof, S. Sarkar, "Detection of Thin Lines using Low-Quality Video from Low-Altitude Aircraft in Urban Settings," IEEE Transactions on Aerospace and Electronic Systems, vol.45, no.3, pp.937-949, July 2009
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Advanced System for visual Maritime Security
This work presents an advanced system for maritime security. The system is autonomous and is designed to remain in the ocean for extended periods up to two months. It is based on the Bottom Stationing Ocean Profiler (BSOP), an un-tethered, autonomous platform that stations itself on the sea floor and ascends to the surface at specific time intervals or, potentially, when triggered by certain events such as recognizable acoustic signals, collected and analyzed on board. The surface operations of the system include optical data acquisition, image data analysis, communication with the ground station, and retrieval based functionality. The system is designed to take video and imagery of the surrounding ocean surface and analyze it for the presence of ships, thus, potentially enabling automatic detection and tracking of marine vehicles as they transit in the vicinity of the platform. The system transmits the data to the ground control via bi-directional RF satellite link and can have its mission parameters reprogrammed during the deployment. The described unit is low cost, easy to deploy and recover, and does not reveal itself to the potential targets.
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Dealing with high noise in 3D reconstructive geometry (2007-Current)
This research is to deal with situations when we want very high accuracy of reconstruction from data that might have upto 95% noise. Our research extends from two view geometry to multi-view geometry in computer vision and is general enough to be applied in any data-mining and machine learning application. Even randomized approximation algorithm like RANSAC for NP hard matching problem would require billions of iterations to meet 99% accuracy in such heavy noise and so cannot be brought into practice. Our task is to accomplish this in managable iterations or time so that reliable 3D reconstructive geometry can be made practical over devices as common as mobile cameras.

Vision Problems in Automated Recognition of Sign Language (2003-current)
Sign languages are complex, abstract linguistic systems, with their own grammars. We are concerned with building automated algorithms that can take sign language video of and recognize the signs performed. This would be useful in facilitating the communication between Deaf and hearing persons. The goal is to go beyond the recognition of isolated signs or continuous signs in short sentences based on video, without the use of special equipment such as data gloves or magnetic markers. The focus was on the design of scalable formalisms for representation, model learning, and matching methods that are robust to image segmentation errors.
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Perceptual Organization of Image Features and Audio-Video Events
Cross-modal effects of enhanced perception exist in humans. There is also emerging experimental evidence about the neural basis of cross-modal perceptual organization. From an (Gibsonian) ecological point of view, it is reasonable to postulate that multi-sensory stimulation that co-varies in place and time originates from a single event or object. All of these point to the importance of cross-modal effects in the perception of the world by natural systems, which suggests that such principles and processes could be important in artificial systems. Of course, the representations and implementations of these ideas need not be constrained by those in the natural systems. The goal of the proposed research is to suggest representations, find computational models, and hypothesize semantic-level interpretations of audio-video events, by exploiting the perceptual organization of sounds and images, constrained by prior knowledge, as captured the ontology of the application domain. Perceptual organization of events will be used to bridge the semantic gap between the features extracted from the raw signal and the high-level, domain-dependent, semantics.
Biometrics: Far, Outdoors, Security, and Privacy Issues (2001-—current)

The ability of being to identify humans from a distance in a passive manner has obvious applications in surveillance and threat assessment. However, there are other possible innovative uses, such as in smart rooms, designing environmentally aware electronic devices, and next generation computer games. In this general context, we are (i) researching modalities to recognize persons from a distance using image and video data, and (ii) looking into privacy and security related issues.
In particular, we have found a novel way to reconstruct biometric templates from scores. This exposes a serious vulnerability in biometric systems
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Computing with Nanodevices and Computer Vision
Field coupled computing is a radically different paradigm where electrical (QCA), magnetic (nano-magnets) or spin coupling among nano-devices are utilized for computation. We are exploring how traditional logic-based computing can be accomplished using such coupled devices. In addition, we are looking at an unconventional front in computing, which we call magnetic field-based computing (MFC), that harnesses the energy minimization aspects of a collection of nanomagnets to directly solve quadratic energy minimization problems, such as those arising in computationally intensive computer vision tasks.