Dr. Amit Singhal Profile

Dr. Amit Singhal

Principal Research Scientist

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Publications (11)

SPIE Journal Paper | 1 January 2010

Inpainting quality assessment

Paul Ardis, Christopher Brown, Amit Singhal

JEI, Vol. 19, Issue 01, 011002, (January 2010) https://doi.org/10.1117/12.10.1117/1.3267088

KEYWORDS: Image quality, Digital imaging, Image filtering, Visualization, Image classification, Optical filters, Video, Visual process modeling, Image segmentation, Eye

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Proceedings Article | 19 January 2009 Paper

Depth-from-trajectories for uncalibrated multiview video

Paul Ardis, Amit Singhal, Christopher Brown

Proceedings Volume 7252, 725209 (2009) https://doi.org/10.1117/12.806816

KEYWORDS: Video, Cameras, Motion models, Matrices, Calibration, Optimization (mathematics), Performance modeling, Video processing, Machine vision, Computer vision technology

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Proceedings Article | 19 January 2009 Paper

Visual salience metrics for image inpainting

Paul Ardis, Amit Singhal

Proceedings Volume 7257, 72571W (2009) https://doi.org/10.1117/12.808942

KEYWORDS: Visualization, Visual process modeling, Photography, Solids, Digital photography, Machine vision, Computer vision technology, Video, Visual communications, Image processing

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SPIE Journal Paper | 1 January 2007 Open Access

The Essential Guide to Video Processing

Amit Singhal, Alan Bovik

JEI, Vol. 19, Issue 01, 019901, (January 2007) https://doi.org/10.1117/12.10.1117/1.3362015

KEYWORDS: Video, Video surveillance, Video processing, Video coding, Video compression, Motion estimation, Digital signal processing, Signal processing, Computer science, Network security

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Proceedings Article | 29 April 2005 Paper

Shape regularized active contour based on dynamic programming for anatomical structure segmentation

Tianli Yu, Jiebo Luo, Amit Singhal, Narendra Ahuja

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.594662

KEYWORDS: Image segmentation, Lung, Computer programming, Medical imaging, Principal component analysis, Radiography, Statistical analysis, Blood vessels, Chest, Algorithm development

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We present a method to incorporate nonlinear shape prior constraints into segmenting different anatomical structures in medical images. Kernel space density estimation (KSDE) is used to derive the nonlinear shape statistics and enable building a single model for a class of objects with nonlinearly varying shapes. The object contour is coerced by image-based energy into the correct shape sub-distribution (e.g., left or right lung), without the need for model selection. In contrast to an earlier algorithm that uses a local gradient-descent search (susceptible to local minima), we propose an algorithm that iterates between dynamic programming (DP) and shape regularization. DP is capable of finding an optimal contour in the search space that maximizes a cost function related to the difference between the interior and exterior of the object. To enforce the nonlinear shape prior, we propose two shape regularization methods, global and local regularization. Global regularization is applied after each DP search to move the entire shape vector in the shape space in a gradient descent fashion to the position of probable shapes learned from training. The regularized shape is used as the starting shape for the next iteration. Local regularization is accomplished through modifying the search space of the DP. The modified search space only allows a certain amount of deformation of the local shape from the starting shape. Both regularization methods ensure the consistency between the resulted shape with the training shapes, while still preserving DP’s ability to search over a large range and avoid local minima. Our algorithm was applied to two different segmentation tasks for radiographic images: lung field and clavicle segmentation. Both applications have shown that our method is effective and versatile in segmenting various anatomical structures under prior shape constraints; and it is robust to noise and local minima caused by clutter (e.g., blood vessels) and other similar structures (e.g., ribs). We believe that the proposed algorithm represents a major step in the paradigm shift to object segmentation under nonlinear shape constraints.

Proceedings Article | 29 April 2005 Paper

Inducing node specification in active shape models for accurate lung-field segmentation

Amit Singhal, Mark Bolin, Hui Luo, Wei Hao, Jiebo Luo

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.595454

KEYWORDS: Medical imaging, Image processing, Mechanics, Lung, Heart

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SPIE Journal Paper | 1 January 2004

Computer Vision and Fuzzy-Neural Systems

Amit Singhal

JEI, Vol. 13, Issue 01, (January 2004) https://doi.org/10.1117/12.10.1117/1.1640620

Proceedings Article | 17 June 2003 Paper

Psychophysical study of image orientation perception

Jiebo Luo, David Crandall, Amit Singhal, Robert Gray

Proceedings Volume 5007, (2003) https://doi.org/10.1117/12.473896

KEYWORDS: Image resolution, Zoom lenses, Detection and tracking algorithms, Object recognition, Clouds, Roads, Bridges, Algorithm development, Computing systems, Copper

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Proceedings Article | 7 May 2003 Paper

Hybrid approach to classifying sky regions in natural images

Amit Singhal, Jiebo Luo

Proceedings Volume 5022, (2003) https://doi.org/10.1117/12.476455

KEYWORDS: Sensors, Neural networks, Wavelets, Detection and tracking algorithms, Photography, Data modeling, Light scattering, Physics, RGB color model, Image classification

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Proceedings Article | 8 June 2001 Paper

Performance-scalable computational approach to main-subject detection in photographs

Jiebo Luo, Stephen Etz, Amit Singhal, Robert Gray

Proceedings Volume 4299, (2001) https://doi.org/10.1117/12.429521

KEYWORDS: Image segmentation, Photography, Sensors, Feature extraction, Skin, Detection and tracking algorithms, Binary data, Image understanding, Image processing algorithms and systems, Algorithm development

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Proceedings Article | 22 September 1997 Paper

Dynamic Bayes net approach to multimodal sensor fusion

Amit Singhal, Christopher Brown

Proceedings Volume 3209, (1997) https://doi.org/10.1117/12.287628

KEYWORDS: Sensors, Sensor fusion, Mobile robots, Sensor performance, Environmental sensing

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Showing 5 of 11 publications

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