Hany Farid - US grants

The objective of this award is to build an education and a research program in digital image processing. The development of digital technology is facilitating advances in virtually every area of science. Further advances will surely accrue as the technology improves.

The objective of this award is to build an education and a research program in digital image processing. The development of digital technology is facilitating advances in virtually every area of science. Further advances will surely accrue as the technology improves.

The rapid development of digital technology is due to work that spans several disciplines. As a result, students interested in this area must cobble together courses from a range of departments. Even if students are able to take all of the necessary courses, the material is certainly not presented to them within a cohesive framework. One objective of this award is the creation of a curriculum for the study of digital image processing. This curriculum will draw from topics in computer science, electrical engineering, mathematics, optics, and psychology.

The second objective of this award is the creation of a research group dedicated to topics in digital image processing and computer vision. My group will undoubtedly be involved in a variety of research projects, but the emphasis will be on studying the mixing process that typifies the formation of digital images. This research will have a dual focus: (1) the first focus will be in separating the various components that contribute to the appearance of an image (e.g., lighting, reflectance, and shape); (2) the second focus will be in understanding how these image components are mixed to form natural images, and in characterizing the statistics of natural images.

The rapid development of digital technology is due to work that spans several disciplines. As a result, students interested in this area must cobble together courses from a range of departments. Even if students are able to take all of the necessary courses, the material is certainly not presented to them within a cohesive framework. One objective of this award is the creation of a curriculum for the study of digital image processing. This curriculum will draw from topics in computer science, electrical engineering, mathematics, optics, and psychology.

The second objective of this award is the creation of a research group dedicated to topics in digital image processing and computer vision. My group will undoubtedly be involved in a variety of research projects, but the emphasis will be on studying the mixing process that typifies the formation of digital images. This research will have a dual focus: (1) the first focus will be in separating the various components that contribute to the appearance of an image (e.g., lighting, reflectance, and shape); (2) the second focus will be in understanding how these image components are mixed to form natural images, and in characterizing the statistics of natural images.

Understanding the evolution of mating decisions is crucial to understanding the mechanisms that have created new species by sexual selection. The studies proposed in this grant will use confocal microscopy to quantify the docking patterns of damselfly male and female structures in mate choice, behavioral experiments to determine the persistence of mating preferences when males of the same species are held at different frequencies, and determine the degree to which natural hybridization occurs in nature.

Ultimately new species come into existence because females develop mating preferences that include some males and exclude others - this is the definition of the Biological Species Concept. These studies test fundamental assumptions about the properties of breeding systems that shape the evolution of these mating decisions and the importance of species interactions to those responses. These studies also give fundamental insights into how females discriminate among males to enforce the genetic integrity of species and thus the relationship of these mechanisms to speciation. Moreover, taken with past phylogenetic and ecological studies, these experiments will give a comprehensive picture of the adaptation and diversification of the Enallagma damselflies over the past 10-15 million years. The broader impacts of the proposed activity are to engage undergraduates in all components of the scientific research enterprise, and to contribute to the scientific curriculum of local K-12 schools.

We propose to build a Digital Imaging Laboratory that will support research, teaching and cross-disciplinary collaboration at the boundary of Art, Engineering, Law and Science. Four primariy research projects will be supported by this Laboratory: (1) Distinguishing between real and computer generated images and video; (2) Building compact and intuitive representations of human motion; (3) Art authentication; and (4) Lighting art. The Laboratory will also be made available to students enrolled in the newly created Digital Arts minor. And, faculty and students across the Dartmouth campus will be able to take advantage of the Digital Imaging Laboratory. Progress reports for this project will be regularly updated at www.cs.dartmouth.edu/farid.

A grant has been awarded to Drs. Douglas Bolger and Hany Farid of Dartmouth College to develop tools for the individual recognition of animals. The ability to recognize and follow individual animals over space and time is perhaps the most important tool of animal population biology. Recognizing individuals allows researchers to estimate population size and birth and death rates, and quantify social behavior. These parameters form the basis of most pure and applied population biology. Traditionally, this recognition has been accomplished by capturing animals and placing visible and unique marks on them. These methods are known as mark-recapture or mark-resight. The primary limitations on the use of traditional marking techniques are animal welfare, cost and difficulty. One promising non-invasive technique is the use of photographic ?mark? and resight methods. For animals with unique markings, individuals can be photographed (marked) and the images stored in a database. Animals photographed later can then be compared to the image database to determine if that individual had been seen before (a resight) or if it is new to the study. This method has been used manually for the study of relatively small populations such as those of whales. For use in large populations this image matching process needs to be computer-assisted to be feasible.

Our project is a collaboration between biology and computer science to develop and test an open-source application for individual recognition of animals. This application will include the following modules (1) an image database for the storing and accessing of individual images; (2) several choices of pattern extraction techniques; and (3) several choices of pattern matching algorithm. This system will process digital photographs of individual animals, efficiently extract the essential pattern information, store this information in a database, and efficiently search the existing database for matching images. The feature detectors to be implemented include the Harris detector, steerable filters, and scale invariant feature transform (SIFT). Pattern recognition algorithms will include nearest neighbor, principal components analysis, linear discriminant analysis, and K-means. The system will be tested against our existing mark-resight photographic database of 8,250 wildebeest images from the Tarangire ecosystem in northern Tanzania. Furthermore, we will capture images of two other uniquely patterned ungulates from the same ecosystem: zebra and giraffe. We will use these data and analyses to estimate population size, survival and recruitment for wildebeest. For giraffe and zebra we will be able to estimate population size. In addition to these three African ungulate populations we will also test this system against two collaborator image databases of spotted salamanders and whale sharks. System performance will be evaluated on the basis of the misidentification error rates that it produces for these test databases, as well as the general adaptability of the system for use with different species.

The tools will be developed using common open source applications and will be made available to other researchers through Dartmouth College?s website. In recent years there have been tremendous advances in analytical methods for mark-resight data. These new analytical techniques allow for more accurate parameter estimation and give researchers the ability to rigorously test complex hypotheses. However, the application of these methods has been limited by the relatively small number of populations that have sufficient mark-resight data available. The availability of the tools we create should lead to an increase in at least one to two orders of magnitude in the number of populations that can be monitored and parameterized using photographic mark-resight methodology. This should in turn lead to more informed management and conservation of these animal populations.

GridIron is a computing grid resource for the Dartmouth computer science department, enabling cutting-edge research and training in a wide range of projects involving analysis of large quantities of data and search over large, complex spaces. In computational structural biology, researchers are developing and applying methods to search protein conformation spaces, to systematically decompose the protein structure universe, and to model and design protein-protein interactions for specificity. In computer vision, researchers are developing and applying methods to authenticate digital images and to perform large-scale image search. Other significant areas of investigation include large-scale smartphone sensing, latency mitigation, and malware detection. The grid is also an asset for Digital Arts projects and research, including information visualization for large data sets and real-time rendering for motion capture. Finally, the grid supports a number of education and outreach activities, including non-major, undergraduate, and graduate courses and a summer camp for high school students. It provides invaluable practical experience in the use of large-scale computation in solving difficult challenges in the analysis of massive data sets.