Rama Chellappa - US grants

Two problems in the areas of image sequence analysis and multidimensional signal processing are studied. In the former, the problem to be considered is that of a rigid body undergoing unknown rotational and translational motion. Algorithms are developed for estimation of object motion parameters from a sequence of 20-30 noisy frames. In the latter problem, algorithms for a two-dimensional deconvolution problem with applications to image restoration are developed.

This research is concerned with the problem of estimating parameters in systems which have noncausal impulse responses. Such systems arise in a number of signal processing applications such as seismic processing. The approach is to derive state variable models for one-dimensional noncausal systems and to develop maximum-likelihood solutions to the problem of estimating the unknown parameters in these models.

The problem addressed by this project is the inference of 3- D attributes of a moving object from a sequence of noisy images of the object. The 3-D attributes of interest are the translational motion and position, to a global scale factor, the rotational motion and position, and the object structure, again to a global scale factor. This research, high image noise levels are allowed, perhaps as much as 20% of the object image size. Such noise levels can occur even in high resolution imagery, whenever the image of the object is small relative to the sensor field of view. The approach taken in this proposal is to model the 3-D rigid body motion using the principles of kinematics. The kinematic equations propagationg translation and rotation are written in the form of a state space model. The noisy feature points or lines form the measurement model. Given that point or line correspondences over a long sequence of frames are available (or established) our goal to develop recursive and batch techniques for the estimation of 3-D motion and structure parameters. Performance measures such as the theoretically attainable lower bounds for the estimated parameters will be derived. Extensions to the two camera problem will also be attempted and algorithms developed will be tested on both synthetic and real image sequences.

There has been a resurgence of interest in Markov Random Field models for analysis of digital signals, image structures, and neural-network learning and pattern matching. This workshop will bring together active researchers to discuss emerging themes and limitations. Proceedings of the workshop will be widely disseminated in the relevant research communities.

This is a project to build a mesh multiprocessor, in which memory busses run along the rows and columns. A dual-ported memory at each grid point in the square mesh is connected to its row bus and its column bus. There is one processor for each element of the main diagonal, which can be connected either to its row or its column. The multiprocessor is to be used in image processing, and may also have some application to the matrix computations used for simulating neural networks.

This award is for the purchase of a multiple degree of freedom, high precision, light weight stereo camera head. The same equipment is being purchased at three other institutions in a shared research effort. Research topics include gaze control and target tracking, stereo and motion analysis, landmark-based navigation, automatic acquisition of object and environmental models, hand-eye coordination, dextrous manipulation with multi-fingered hands, real-time perception and manipulation. System software, algorithms and subsystems will be shared across the institutions. The University of Maryland will be purchasing equipment to support a multi-institutional shared research effort in active vision for robot navigation and manipulation. Other members of the consortium are the University of Rochester, University of Pennsylvania and the University of Massachusetts. Each institution will acquire the same state- of-the-art binocular camera head of high precision and high speed, and will share in the development of systems software and application software.

9318183 Davis The Grand Challenge Application Groups competition provides one mechanism for the support of multidisciplinary teams of scientists and engineers to meet the goals of the High Performance Computing and Communications (HPCC) Initiative in Fiscal Year 1993. The ideal proposal provided not only the opportunity to achieve significant progress on (1) a fundamental problem in science or engineering whose solution could be advanced by applying high performance computing techniques and resources, or (2) enabling technologies which facilitate those advances but also significant interactions between scientific and computational activities, usually involving mathematical, computer or computational scientist, that would have impact in high performance computational activities beyond the specific scientific or engineering problem area(s) or discipline being studied. The investigators will study the application of high performance parallel computing to a class of scientifically important and computationally demanding problems in remote sensing- land cover dynamics problems including generating improved- fine spatial resolution data for the global carbon cycle, hydrological modeling and global ecological responses to climate changes and human activity. The research is collaborative, including scientist from the University of Maryland, University of Indiana, University of news Hampshire and NASA's Goddard Space Center. The award will combine research on -new analysis procedures for remotely sensed data -the integration of multispectral, multiresolution and multitemporal image data sets into a unified global data structure based on hierarchical data structures (i.e., quadtrees) -the utilization of these hierarchical, parallel data structures for the representation of spatial data (maps and products developed from image analysis) and the development of a spatial data base system with a sophisticate d query language that scientist can use to control the application of biophysical models to global data sets -run-time support for constructing scalable and parallel solutions to problems involving the manipulation of irregular data structures such as quadtrees -parallel l/O,especially novel methods for mapping large arrays and quadtrees onto parallel disks and disk systems, and for accessing them using low overhead bulk transfers The development work will be conducted on a 32 processor Connection Machine CM5, installed at the University of Maryland, and on an IBM SP1 which we propose to obtain as part of the program. This award is being supported by the Advanced Projects Research Agency as well as NSF programs in geological, biological, and computer sciences.

In this two-year effort, we propose to address some of the basic research issues that arise in the interpretation of video streams, simultaneously collected by a set of indoor or outdoor cameras.
Specifically, we are interested in inferring movements and activities of one or more humans using
distributed cameras. We propose to develop novel methods for detecting and tracking humans using 3D models
for body parts, and quasi-invariant recognition of activities humans are engaged in. We will make use of the recent advances made in the computational aspects of estimating posterior probability density functions using
Monte Carlo Markov Chain techniques to infer human descriptions and their activities.

We propose to develop novel techniques and methodologies for distributed smart camera networks through an integrated exploration of distributed algorithms, embedded architectures, and software synthesis techniques. We will develop new architectures and tools that are designed to tackle modern video processing algorithms. The algorithms will leverage distributed architectures for efficient implementations. We will investigate a series of complex smart camera algorithms drawn from surveillance and biometrics applications. Specifically, we will be investigating efficient implementations of algorithms for self-calibration of distributed cameras, view-synthesis using image-based visual hulls, and human identification using face and gait.

Networks of distributed cameras are an emerging enabling technology for a broad range of important information technology applications, including surveillance, biometrics, and smart conference rooms. By having acces to scenes from multiple directions, such networks have the potential for complete views than single-camera systems. This contributes to increased robustness and generality of algorithms. However, this distributed nature, coupled with the inherent challenges associated with real-time video processing, greatly complicates the devlopment of effective algorithms, architectures and software.
An integrated research program inclduing video, design tools, and embedded system architecture is required to understand how new generation of
smart camera hardware can be best utilized.

The PIs propose to establish a five-year ITR program that will lead the development of the next generation distributed video sensing systems for understanding human movements. Novel models of human movement and structure will be used for modeling the movements of singe-joint and whole bodies with applications to animation, biomotion, and gait analysis for diagnosing and treating movement-related disorders. The interdisciplinary team includes leading researchers from three core institutions - the University of Maryland (lead institution), Stanford University and New York University. The researchers cover a broad spectrum of interests, including biomechanics, computer science and engineering, electrical engineering, and kinesiology. The proposed research efforts will enable novel approaches for realistic animation and the detection of subtle variations in movement, leading to better diagnostic tools and personalized programs for rehabilitation of movement disorders. Strong educational and industrial outreach programs will also enhance our research program.

Researchers at the University of Maryland plan to build a high-performance computing and visualization cluster taking advantage of synergies afforded by coupling central processing units (CPUs), graphics processing units (GPUs), displays, and storage. The infrastructure will be used to support a broad program of computing research that will revolve around understanding, augmenting, and leveraging the power of heterogeneous vector computing enabled by GPU co-processors. The driving force here is the availability of cheap, powerful, and programmable graphics processing units (GPUs) through their commercialization in interactive 3D graphics applications, including interactive games. The CPU-GPU coupled cluster will enable the pursuit of several new research directions in computing, as well as enable a better understanding and fast solutions to several existing interdisciplinary problems through a visualization-assisted computational steering environment. In addition, it will foster research to cast several problems into a better spot on the price-performance curve.

Intellectual Impact: The proposed research that will use this cluster falls into several broad interdisciplinary computing areas. The researchers plan to explore visualization of large datasets and algorithms for parallel rendering. In high-performance scientific computing we plan to develop and analyze efficient algorithms for use with complex systems when uncertainty is included in models. The researchers plan to use the cluster for several applications in computational biology, including computational modeling and visualization of proteins, conformational steering in protein structure prediction, folding, and drug design, large-scale phylogeny visualization, and sequence alignment.

The researchers also plan to use the cluster for applications in real-time computer vision, real-time 3D virtual audio, and for efficient compilation of signal processing algorithms.

Broader Impact: An important aspect of this research is to ensure a high impact of the cluster towards educational and outreach goals. The investigators plan to enrich their current coursework with research results obtained on the cluster. The coupled cluster with a large-area high-resolution display screen will serve as a valuable resource to present, interactively explore, evaluate, and validate the ongoing research in visualization, vision, scientific computing, human-computer interfaces, and computational biology with active participation of graduate as well as undergraduate students.

Title: February Fourier Talks 2012

This award provides support for the seventh meeting in the February Fourier
Talks (FFT) series. The 2012 FFT will be held February 16-17, 2012, at the
University of Maryland, College Park. The FFT is a high-level forum for
harmonic analysts to bring their work to scientists from industry and
government agencies. In addition it allows experts in applied and pure
harmonic analysis to get familiar with the latest problems in need of
mathematical formulation and solution. Finally, it introduces young
mathematicians and scientists to applied and pure harmonic analysis. More
information, including a list of speakers and abstracts, registration
information, and an archive of past conferences, can be found at the
conference webpage: www.fft2012.org.

The February Fourier Talks directly encourage dialogue and collaboration
between mathematicians and scientists working in industry and government.
The structure of the conference consists of three main lectures and sixteen
30 minute invited talks. The conference encourages and financially supports
participation by students, recent Ph.D. recipients, and members of groups
underrepresented in mathematics.

Title: February Fourier Talks 2013:
This award provides support for the eighth meeting in the February Fourier Talks (FFT) series and a satellite Workshop on "Phaseless Reconstruction." The 2013 FFT will be held February 21-22, 2013, at the University of Maryland, College Park, and will be followed by the Workshop on Phaseless Reconstruction to be held February 23 - 25, 2013. The FFT is a high-level forum for harmonic analysts to bring their work to scientists from industry and government agencies. In addition, it allows experts in applied and pure harmonic analysis to become familiar with the latest problems in need of mathematical formulation and solution. Finally, it introduces young mathematicians and scientists to applied and pure harmonic analysis. More information, including a list of speakers and abstracts, registration information, and an archive of past conferences, can be found at the conference webpage: www.fft2013.org. The Workshop on Phaseless Reconstruction will bring together researchers from harmonic analysis, quantum information theory, and electrical engineering communities to discuss new development on efficient signal reconstruction from phaseless measurements.

The February Fourier Talks directly encourage dialogue and collaboration between mathematicians and scientists working in industry and government. The structure of the conference consists of three main lectures and sixteen 30 minute invited talks. The conference encourages and financially supports participation by students, recent Ph.D. recipients, and members of groups underrepresented in mathematics. The workshop on Phaseless Reconstruction is structured in a similar manner to the FFT, and allows ample discussion time among the participants. More information, including a list of speakers and abstracts and registration information is available at the workshop webpage: http://www.norbertwiener.umd.edu/FFT/2013/phaseless.html.

This award provides support for the ninth meeting in the February Fourier Talks (FFT) series. The 2014 FFT will be held at the University of Maryland, College Park. The FFT is a high-level forum for harmonic analysts to bring their work to scientists from industry and government agencies. In addition, it allows experts in applied and pure harmonic analysis to become familiar with the latest problems in need of mathematical formulation and solution. Finally, it introduces young mathematicians and scientists to applied and pure harmonic analysis. More information, including a list of speakers and abstracts, registration information, and an archive of past conferences, can be found at the conference webpage: www.fft2014.org.

The February Fourier Talks directly encourage dialogue and collaboration between mathematicians and scientists working in industry and government. The structure of the conference consists of three main lectures, 30 minute invited talks, and a poster session that showcases the work of students (both undergraduate and graduate) as well as postdocs. The conference encourages and financially supports participation by students, recent Ph.D. recipients, and members of groups underrepresented in mathematics.

The bombing attacks at the Boston Marathon in April 2013 presented the law enforcement community with significant challenges in terms of the volume and variety of video and still images acquired in the course of the investigation. Tens of thousands of individual media files in multiple formats were submitted from a variety of sources. These sources included broadcast television feeds, private Close-Circuit Television (CCTV) systems, mobile device photographs and videos recovered from the scene, as well as photographs and videos submitted by the public. Teams of analysts reviewed this evidence using mostly manual processes to determine the sequence of events before and after the bombing, ultimately leading to a quick resolution of the case. In the aftermath, it has become evident that the proliferation of video and image recording devices in fixed and mobile devices make it inevitable that a similar situation will occur in future events. As a result, it is incumbent upon the law enforcement community and the U.S. Government at large to further explore the use of automated approaches, available today or in the coming years, to better organize and analyze such large volumes of multimedia data. The findings of this workshop will help define the future research agenda.

The problem of searching for actionable intelligence information from unconstrained images and videos is an unsolved problem. Solving this involves addressing many sub-problems such as video summarization, shot detection/scene change detection, geo-tagging, robust face recognition, human action recognition, semantic description, image recognition and designing human in the loop systems. In addition, issues such as data collection and performance evaluation have to be addressed. Given that several hundreds of videos and a large collection of still images may be available for analysis, there is a great need to develop robust computer vision techniques. While many existing computer vision algorithms perform reasonable well in constrained acquisition conditions, their performance when unconstrained images and videos are given, is less than satisfactory. This workshop precisely addresses the challenges that arise in analyzing a large collection of unstructured image/video collection. This workshop explores the state of the art in algorithms being developed in academia that can support forensic analysis and identification in large volumes of images and videos (e.g., multimedia). The workshop informs long- and near-term research and development efforts aimed at optimally addressing this situation in the future. The workshop identifies those video and image analysis problems which are: (1) Considered solved (i.e., ready to deploy in specific operational scenarios); (2) Nearly solved (i.e., could lead to solutions with one to three years of development); and (3) Over-the-Horizon problems (i.e., those challenges requiring concerted effort over the next 3-5 years and beyond).

The overarching goal of this application is to build an Artificial Intelligence (AI) and Technology Collaboratory (AITC) ecosystem that will serve as a national resource to promote the development and implementation of novel AI and technology approaches to improve care and health outcomes for older Americans. The specific aims are: 1) To engage AI and geriatric/gerontology investigators from across the country and to identify, validate, test, and develop new AI and technologies relevant to improving the health and wellbeing of older adults through crucial pilot study mechanisms; 2) To serve as a national resource center that stimulates and leads the development and implementation of effective novel AI and technology approaches and products that will promote the health, wellbeing and independence of all older Americans; 3) To support the engagement of stakeholders in AI research; 4) To build an ecosystem of overlapping innovation and business, academic, and communities- of-practice networks ; and 5) To provide highest quality expertise, support, and infrastructure needed to disseminate technical and policy guidelines and best practices for effectively incorporating AI approaches and technology for older Americans, in partnership with private industry, angel investors, venture capital firms, and healthcare systems. This AITC is directed by a multi-PI interdisciplinary team led by two world-class experienced investigators who have long worked successfully in the fields of AI and technology development areas partnered with investigators who have long and successfully worked at the translational interface that connects real-world medical, cognitive, and functional declines that impact older adults with medical and technological solutions. Each of these investigators has a complementary skill set and a long track records of organizing transdisciplinary teams and consortiums of investigators around core themes. This interdisciplinary, accomplished, and highly visible leadership team will work together to develop vision for the next generation of AI in aging science and to build a scientifically and culturally diverse community of AI scholars and trainees around Aging. To achieve our goals, we designed the JHU AITC to have robust scientific and technological expertise that are described in eight core components. This infrastructure will support the implementation of stakeholder input and the identification of relevant technologies and investigators locally and nationally through a vetting and feasibility testing process of both technology and data processes. It will include a pilot testing phase and related oversight process. We have also established a key partnership with the Iowa office of Rural Health and Veterans Rural Health Resource Centers Leadership and with organizations within Johns Hopkins University that focus on improvements in the health and well-being of older adults in underserved urban communities. Connections with key academic, industry partners have also been established to accelerate the development of relevant technologies into products. This team is dedicated to developing the next AI scientific advances and disseminating resulting strategies into practice and policy that will maximize health, well-being, and independence for older adults.