2674 Slices
Medium 9781601322548

3D Active Shape Models Integrating Robust Edge Identification and Statistical Shape Models

Hamid R. Arabnia, Leonidas Deligiannidis, Joan Lu, Fernando G. Tinetti, Jane You, George Jandieri, Gerald Schaefer, Ashu M. G. Solo, Vladimir Volkov CSREA Press PDF

Int'l Conf. IP, Comp. Vision, and Pattern Recognition | IPCV'13 |

3D Active Shape Models Integrating Robust Edge Identification and Statistical Shape Models

Brent C. Munsell1 , Martin Styner2,3 , Heather Hazlett3 , and Song Wang4

1 Department of Mathematics and Computer Science, Claflin University, Orangeburg, SC, USA

2 Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA

3 Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA

4 Department of Computer Science, University of South Carolina, Columbia, SC, USA

Abstract— Based on the Point Distribution Model (PDM),

Active Shape Model (ASM) is an iterative algorithm used to detect structures of interest from images. However, current

ASM methods are sensitive to image noise that may trap the ASM to false edges and/or lead to a structure not within the shape space defined by the PDM. Such problems are particularly serious when segmenting 3D anatomical surface structures from 3D medical images. In this paper we propose two strategies to improve the performance of 3D ASM: (a) developing a robust edge-identification algorithm to reduce the risk of detecting false edges, and (b) integrating the edge-fitting error and statistical shape model defined by a PDM into a unified cost function. We apply the proposed ASM to the challenging tasks of detecting the left hippocampus and caudate surfaces from an subset of 3D pediatric MR images and compare its performance with a recently reported atlas-based method.

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Medium 9781601323170

Session Learning Models, Methodologies, Tools and Case Studies

Hamid R. Arabnia Azita Bahrami, Leonidas Deligiannidis, George Jandieri, Ashu M. G. Solo, and Fernando G. Tinetti CSREA Press PDF

Int'l Conf. Frontiers in Education: CS and CE | FECS'14 |163�Automated Feedback for Personalized LearningDavid J. Coe, Ronald Bowman, and Jason WinninghamDepartment of Electrical and Computer EngineeringThe University of Alabama in Huntsville, Huntsville, Alabama, USAAbstract - Presented here is an automated grading framework for text interface data structures programming assignments. This framework provides rapid feedback to students, consistency in marking of assignments, and requires minimal time to set up and use. A test driver processes test commands read from input files allowing the framework to support systematic, thorough functional and structural testing of student submissions. The framework generates individualized grade reports summarizing test results and a .csv file that summarizes student grades to speed entry of the grades into our Learning ManagementSystem. The automated grading framework has been enhanced to include screening for memory leaks, a common error for students learning to implement container classes in C++. A grade preview mechanism has been derived from the framework to give students personalized feedback on specific defects in their code prior to the final submission deadline, allowing students to prioritize debugging efforts on the most critical functionality.

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Medium 9781601322364

3D Lattice Monte Carlo Simulations on FPGAs

Hamid R. Arabnia, Leonidas Deligiannidis, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF


Int'l Conf. Computer Design | CDES'13 |

3D Lattice Monte Carlo Simulations on FPGAs

A. Gilman1 , A. Leist2 and K.A. Hawick1

1 Institute of Natural and Mathematical Sciences,

2 School of Engineering and Advanced Technology,

Massey University, North Shore 102-904, Auckland, New Zealand email: { a.gilman, a.leist, k.a.hawick }@massey.ac.nz

Tel: +64 9 414 0800 Fax: +64 9 441 8181

Abstract— Field Programmable Gate Arrays (FPGAs) offer significant performance advantages over general purpose compute architectures for certain scientific problems, including lattice-based Monte Carlo simulations of complex systems models. We report on a custom logic design for the 3D-lattice Ising model that keeps the entire system state in on-chip memory to achieve very high throughput rates. The pipelined architecture, which is implemented in Verilog, is able to process an entire row of cells per clock cycle. When processing a system of 2563 spins on a Xilinx Virtex-7 device, about 3000 full system sweeps can be performed per second. We discuss implementation issues and solutions that apply in similar ways to a variety of nearest neighbour, lattice-based Monte

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Medium 9781601323200

Session - XIV Technical Session on Applications of Advanced AI Techniques to Information Management for Solving Company-Related Problems

Medium 9781601322470

Simulation Results for a Cache Management System used in a Deductive Database

Hamid R. Arabnia, David de la Fuente Elena B. Kozerenko, Peter M. LaMonica Raymond A. Liuzzi, Todd Waskiewicz, George Jandieri, Ashu M. G. Solo, Ivan Nunes da Silva, Fernando G. Tinetti, and Fadi Thabtah CSREA Press PDF


Int'l Conf. Artificial Intelligence | ICAI'13 |

Simulation Results for a Cache Management System used in a Deductive Database


Larry Williams1, Martin Maskarinec1, and Kathleen Neumann1

School of Computer Sciences, Western Illinois University, Macomb, IL

Abstract - This paper will present the effectiveness of a cache management system which was previously developed for a Deductive Database. In order to do this, a simulation of user inputted queries was developed and then executed on two different input sets. This paper presents the results of these simulation runs and a comparison between the results of our caching algorithm as compared to two other, standard approaches.

Keywords: Intelligent Database, Deductive Database, Cache


1. Introduction

A deductive rule uses predicates to represent knowledge that may be derived from known facts. For example, we may write “P(X,Z) :- F1(X,Y), F2(Y,Z)”. This rule indicates that the predicate “P” is dependent on the facts “F1” and “F2”.

These predicates and facts may have arguments; the variables X, Y, and Z indicate how the arguments of the resulting predicate P are derived from the predicates of F1 and F2 and any other constraints (in this example, the second argument of F1 must match the first argument of

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