Mr. David M. Rouse Profile

David M. Rouse

at Cornell Univ

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Publications (6)

Proceedings Article | 21 May 2015 Paper

Feature-aided multiple hypothesis tracking using topological and statistical behavior classifiers

David Rouse, Adam Watkins, David Porter, John Harer, Paul Bendich, Nate Strawn, Elizabeth Munch, Jonathan DeSena, Jesse Clarke, Jeff Gilbert, Sang Chin, Andrew Newman

Proceedings Volume 9474, 94740L (2015) https://doi.org/10.1117/12.2179555

KEYWORDS: Kinematics, Sensors, Data analysis, Target detection, Target recognition, Electro optical sensors, Machine learning, Data modeling, Electro optical modeling, Error analysis

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Proceedings Article | 5 March 2011 Paper

Measuring contour degradation in natural image utility assessment: methods and analysis

Guilherme Pinto, David Rouse, Sheila Hemami

Proceedings Volume 7865, 78650U (2011) https://doi.org/10.1117/12.877102

KEYWORDS: Image quality, Image analysis, Electronic imaging, Distance measurement, Human vision and color perception, Current controlled current source, Binary data, Databases, Performance modeling

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Proceedings Article | 18 February 2010 Paper

Tradeoffs in subjective testing methods for image and video quality assessment

David Rouse, Romuald Pépion, Patrick Le Callet, Sheila Hemami

Proceedings Volume 7527, 75270F (2010) https://doi.org/10.1117/12.845389

KEYWORDS: Video, Databases, Video processing, Image quality, Testing and analysis, Image processing, Molybdenum, Image compression, Reliability, Video coding

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Proceedings Article | 10 February 2009 Paper

Image utility assessment and a relationship with image quality assessment

David Rouse, Romuald Pepion, Sheila Hemami, Patrick Le Callet

Proceedings Volume 7240, 724010 (2009) https://doi.org/10.1117/12.811664

KEYWORDS: Image quality, Visualization, Information visualization, Image analysis, Spatial frequencies, Wavelets, Molybdenum, Algorithm development, Signal to noise ratio, Error analysis

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Present quality assessment (QA) algorithms aim to generate scores for natural images consistent with subjective scores for the quality assessment task. For the quality assessment task, human observers evaluate a natural image based on its perceptual resemblance to a reference. Natural images communicate useful information to humans, and this paper investigates the utility assessment task, where human observers evaluate the usefulness of a natural image as a surrogate for a reference. Current QA algorithms implicitly assess utility insofar as an image that exhibits strong perceptual resemblance to a reference is also of high utility. However, a perceived quality score is not a proxy for a perceived utility score: a decrease in perceived quality may not affect the perceived utility. Two experiments are conducted to investigate the relationship between the quality assessment and utility assessment tasks. The results from these experiments provide evidence that any algorithm optimized to predict perceived quality scores cannot immediately predict perceived utility scores. Several QA algorithms are evaluated in terms of their ability to predict subjective scores for the quality and utility assessment tasks. Among the QA algorithms evaluated, the visual information fidelity (VIF) criterion, which is frequently reported to provide the highest correlation with perceived quality, predicted both perceived quality and utility scores reasonably. The consistent performance of VIF for both the tasks raised suspicions in light of the evidence from the psychophysical experiments. A thorough analysis of VIF revealed that it artificially emphasizes evaluations at finer image scales (i.e., higher spatial frequencies) over those at coarser image scales (i.e., lower spatial frequencies). A modified implementation of VIF, denoted VIF*, is presented that provides statistically significant improvement over VIF for the quality assessment task and statistically worse performance for the utility assessment task. A novel utility assessment algorithm, referred to as the natural image contour evaluation (NICE), is introduced that conducts a comparison of the contours of a test image to those of a reference image across multiple image scales to score the test image. NICE demonstrates a viable departure from traditional QA algorithms that incorporate energy-based approaches and is capable of predicting perceived utility scores.

Proceedings Article | 14 February 2008 Paper

Analyzing the role of visual structure in the recognition of natural image content with multi-scale SSIM

David Rouse, Sheila Hemami

Proceedings Volume 6806, 680615 (2008) https://doi.org/10.1117/12.768060

KEYWORDS: Visualization, Image quality, Information visualization, Detection and tracking algorithms, Image analysis, Distortion, Image processing, Visual analytics, Signal processing, Visual process modeling

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Natural images are meaningful to humans - the physical world exhibits statistical regularities that permit the human visual system (HVS) to infer useful interpretations. These regularities communicate the visual structure of the physical world and govern the statistics of images (image structure). A signal processing framework is sought to analyze image characteristics for a relationship with human interpretation. This work investigates the first step toward an objective visual information evaluation: predicting the recognition threshold of different image representations. Given a image sequence, whose images begin as unrecognizable and are gradually refined to include more information according to some measure, the recognition threshold corresponds to first the image in the sequence in which an observer accurately identifies the content. Sequences are produced using two types of image representations: signal-based and visual structure preserving. Signal-based representations add information as dictated by conventional mathematical characterizations of images based on models of low-level HVS processing and use basis functions as the basic image components. Visual structure preserving representations add information to images attributed to visual structure and attempt to mimic higher-level HVS processing by considering the scene's objects as the basic image components. An experiment is conducted to identify the recognition threshold image. Several full-reference perceptual quality assessment algorithms are evaluated in terms of their ability to predict the recognition threshold of different image representations. The cross-correlation component of a modified version of the multi-scale structural similarity (MS-SSIM) metric, denoted MS-SSIM*, exhibits a better overall correlation with the signal-based and visual structure preserving representations' average recognition thresholds than the standard MS-SSIM cross-correlation component. These findings underscore the significance of visual structure in recognition and advocate a multi-scale image structure analysis for a rudimentary evaluation of visual information.

Proceedings Article | 12 February 2007 Paper

Quantifying the use of structure in cognitive tasks

David Rouse, Sheila Hemami

Proceedings Volume 6492, 64921O (2007) https://doi.org/10.1117/12.707539

KEYWORDS: Visualization, Signal processing, Spatial frequencies, Information visualization, Image visualization, Image processing, Image compression, Sensors, Visual process modeling, Discrete wavelet transforms

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Showing 5 of 6 publications

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