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Mathematics and statistics

Image & signal processing

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Displaying 226 - 250 of 320

Big Data Issues in Quantitative Imaging

August 29, 2012
Author(s)
Mary C. Brady, Alden A. Dima, Charles D. Fenimore, James J. Filliben, John Lu, Adele Peskin, Mala Ramaiah, Ganesh Saiprasad, Ram D. Sriram

An Exploration of the Operational Ramifications of Lossless Compression of 1000 ppi Fingerprint Imagery

August 6, 2012
Author(s)
Shahram Orandi, John M. Libert, John D. Grantham, Kenneth Ko, Stephen S. Wood, Jin Chu Wu, Lindsay M. Petersen, Bruce Bandini
This paper presents the findings of a study initially conducted to measure the operational impact of JPEG 2000 lossy compression on 1000 ppi fingerprint imagery at various levels of compression, but later expanded to include lossless compression. Lossless

Not Your Mother's Web3D: Integrating X3DOM, jQuery and HTML5

August 4, 2012
Author(s)
Sanford P. Ressler
This tutorial, for intermediate users, is suitable for educators, scientists and artists, with some 3D and programming experience, (all attendees). The benefits of declarative 3D markup, as realized by X3DOM will be emphasized in the context of integration

Feature-preserved 3D Canonical Form

July 28, 2012
Author(s)
Afzal A. Godil, Zhouhui Lian
Abstract Measuring the dissimilarity between non-rigid objects is a challenging problem in 3D shape retrieval. One potential solution is to construct the models’ 3D canonical forms (i.e., isometry-invariant representations in 3D Euclidean domain) on which

A New Convexity Measurement for 3D Meshes

June 16, 2012
Author(s)
Afzal A. Godil, Zhouhui Lian
This paper presents a novel convexity measurement for 3D meshes. The new convexity measure is calculated by minimizing the ratio of the summed area of valid regions in a mesh’s six views, which are projected on faces of the bounding box whose edges are

Grand Challenges of Measurement Science with Big Data

June 14, 2012
Author(s)
Peter Bajcsy
This presentation discusses Grand Challenges of Measurement Science with Big Data. It illustrates the difference between Measurement Science with Little Data and Big Data in the domain of cell biology and microscopy image observations.

SHREC12 Track: Generic 3D Shape Retrieval

May 13, 2012
Author(s)
Bo Li, Afzal A. Godil
Generic 3D shape retrieval is a fundamental research area in the field of content-based 3D model retrieval. The aim of this track is to measure and compare the performance of generic 3D shape retrieval methods implemented by different participants over the

SHREC12 Track: Sketch-Based 3D Shape Retrieval

May 13, 2012
Author(s)
Bo Li, Afzal A. Godil
Sketch-based 3D shape retrieval has become an important research topic in content-based 3D object retrieval. The aim of this track is to measure and compare the performance of sketch-based 3D shape retrieval methods implemented by different participants

An Evaluation of Local Shape Descriptors for 3D Shape Retrieval

March 8, 2012
Author(s)
Sarah Y. Tang
As the usage of 3D models increases, so does the importance of developing accurate 3D shape retrieval algorithms. Most prominently, shape descriptors are used to describe the geometric and topological properties of objects and compared to determine two

Objective Evaluation of Imager Performance

December 1, 2011
Author(s)
Francine K. Amon, Dennis D. Leber, Nicholas Paulter
We describe a method by which the evaluation and characterization of the performance of an imager can be done objectively and scientifically, that is, without routine operator interpretation. Although this method is demonstrated herein for passive long

Integrating jQuery with the 3D Descriptive Markup of X3DOM

October 31, 2011
Author(s)
Sanford P. Ressler
This paper describes a number of techniques using jQuery to take advantage of the 3D descriptive markup implemented by X3DOM. X3DOM is a project of the Web3D Consortium, primarily being implemented by staff at Fraunhoffer IGD to provide 3D descriptive

SHREC11 Track: Generic Shape Retrieval

April 14, 2011
Author(s)
Helin Dutagaci, Afzal A. Godil
In this paper we present the results of the 3D Shape Retrieval Contest 2011 (SHREC’11) track on generic shape retrieval. The aim of this track is to evaluate the performance of 3D shape retrieval algorithms that can operate on arbitrary 3D models. The

Evaluation of 3D Interest Point Detection Techniques

April 7, 2011
Author(s)
Helin Dutagaci, Chun Pan (Benny) Cheung, Afzal A. Godil
In this paper, we compare the results of five 3D interest point detection techniques to the interest points marked by human subjects. This comparison is used to quantitatively evaluate the interest point detection algorithms. We asked human subjects to

Salient Local 3D Features for 3D Shape Retrieval

April 7, 2011
Author(s)
Afzal A. Godil, Asim Wagan
In this paper we describe a new formulation for the 3D salient local features based on the voxel grid inspired by the Scale Invariant Feature Transform (SIFT). We use it to identify the salient keypoints (invariant points) on a 3D voxelized model and

Efficient Motion Vector Interpolation for Error Concealment of H.264/AVC

March 1, 2011
Author(s)
Hamid Gharavi
In video communications the compressed video stream is very likely to be corrupted by channel errors. In order to combat these channel errors, many methods have been proposed. In this paper, a new method for motion vectors (MVs) interpolation with the help

A Benchmark for Best View Selection of 3D Objects

December 23, 2010
Author(s)
Helin Dutagaci, Chun Pan (Benny) Cheung, Afzal A. Godil
The best view selection corresponds to the task of automatically selecting the most representative view of a 3D model. In this paper, we describe a benchmark for evaluation of best view selection algorithms. The benchmark consists of the preferred views of

Benchmarks, Performance Evaluation and Contests for 3D Shape Retrieval

December 20, 2010
Author(s)
Afzal A. Godil, Zhouhui Lian, Helin Dutagaci
Benchmarking of 3D Shape retrieval allows developers and researchers to compare the strengths of different algorithms on a standard dataset. Here we discuss the different steps involved in developing a benchmark and some of the issues involved. We then
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