Sameer Shirdhonkar |
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I am a PhD candidate in the
Computer Science Department at the
University of Maryland. My
adviser is Prof.
David Jacobs. I am affiliated with the Center for Automation
Research, a laboratory under UMIACS. Previously, I was an undergrad at Indian Institute of Technology,
Bombay in the Electrical Engineering
department.
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Publications:
Peer reviewed papers:
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Sameer Shirdhonkar and David Jacobs, Approximate earth mover’s distance in linear time, IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, USA, 2008. Poster (pdf) Abstract |
| The earth
mover’s distance (EMD) is an important perceptually
meaningful metric for comparing histograms, but it suffers
from high (O(N3 log N)) computational complexity.
We present a novel linear time algorithm for approximating the
EMD for low dimensional histograms using the sum of absolute
values of the weighted wavelet coefficients of the difference
histogram. EMD computation is a special case of the
Kantorovich-Rubinstein transshipment problem, and we exploit
the Hölder continuity constraint in its dual form to
convert it into a simple optimization problem with an explicit
solution in the wavelet domain. We prove that the resulting
wavelet EMD metric is equivalent to EMD, i.e. the ratio of the
two is bounded. We also provide estimates for the bounds. The weighted wavelet transform can be computed in time linear in the number of histogram bins, while the comparison is about as fast as for normal Euclidean distance or Χ2 statistic. We experimentally show that wavelet EMD is a good approximation to EMD, has similar performance, but requires much less computation. | |
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Peter N. Belhumeur, Daozheng Chen, Steven Feiner, David Jacobs, W. John Kress, Haibin Ling, Ida Lopez, Ravi Ramamoorthi, Sameer Shirdhonkar, Sean White and Ling Zhang, Searching the World's Herbaria: A System of Visual Identification of Plant Species, European Conference on Computer Vision, Marseille, France, 2008. Abstract |
| We describe working computer vision system that aids in the identification of plant species. A user photographs an isolated leaf on a blank back ground, and the system extracts the leaf shape and matches it to the shape of leaves of known species. In a few seconds, the system displays the top matching species, along with textual descriptions and additional images. This system is currently in use by botanists at the Smithsonian Institution National Museum of Natural History. The primary contributions of this paper are: a description of a working computer vision system and its user interface for an important new application area; the introduction of three new datasets containing thousands of single leaf images, each labeled by species and verified by botanists at the United States National Herbarium; recognition results on two of the three leaf datasets; and descriptions throughout of practical lessons learned in constructing this system. | |
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Gaurav Agarwal, Peter Belhumeur, Steven Feiner, David Jacobs, W. John Kress,Ravi Ramamoorthi, Norman A. Bourg, Nandan Dixit, Haibin Ling, Dhruv Mahajan, Rusty Russell, Sameer Shirdhonkar, Kalyan Sunkavalli and Sean White First Steps Toward an Electronic Field Guide for Plants , Taxon, 55(3): 597-610, August, 2006. Abstract |
| We describe an ongoing project to digitize information about plant specimens and make it available to botanists in the field. This first requires digital images and models, and then effective retrieval and mobile computing mechanisms for accessing this information. We have almost completed a digital archive of the collection of type specimens at the Smithsonian Institution Department of Botany. Using these and additional images, we have also constructed prototype electronic field guides for the flora of Plummers Island. Our guides use a novel computer vision algorithm to compute leaf similarity. This algorithm is integrated into image browsers that assist a user in navigating a large collection of images to identify the species of a new specimen. For example, our systems allow a user to photograph a leaf and use this image to retrieve a set of leaves with similar shapes. We measured the effectiveness of one of these systems with recognition experiments on a large dataset of images, and with user studies of the complete retrieval system. In addition, we describe future directions for acquiring models of more complex, 3D specimens, and for using new methods in wearable computing to interact with data in the 3D environment in which it is acquired. | |
| Project website: Electronic Field Guide | |
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Sameer Shirdhonkar and David Jacobs, Non-negative Lighting and Specular Object Recognition, IEEE International Conference on Computer Vision, Beijing, China, 2005. Poster (pdf). Abstract |
| Recognition of specular objects is particularly difficult because their appearance is much more sensitive to lighting changes than that of Lambertian objects. We consider an approach in which we use a 3D model to deduce the lighting that best matches the model to the image. In this case, an important constraint is that incident lighting should be non-negative everywhere. In this paper, we propose a new method to enforce this constraint and explore its usefulness in specular object recognition, using the spherical harmonic representation of lighting. The method follows from a novel extension of Szego’s eigenvalue distribution theorem to spherical harmonics, and uses semidefinite programming to perform a constrained optimization. The new method is faster as well as more accurate than previous methods. Experiments on both synthetic and real data indicate that the constraint can improve recognition of specular objects by better separating the correct and incorrect models. | |