scholarly journals A Local Shape Analysis Based on Separation Logic

2006 ◽  
pp. 287-302 ◽  
Author(s):  
Dino Distefano ◽  
Peter W. O’Hearn ◽  
Hongseok Yang
Keyword(s):  
Shape Analysis ◽  
Separation Logic ◽  
Local Shape

Automated Segmentation Refinement of Small Lung Nodules in CT Scans by Local Shape Analysis

2011 ◽  
Vol 58 (12) ◽  
pp. 3418-3428 ◽  
Author(s):  
S. Diciotti ◽  
S. Lombardo ◽  
M. Falchini ◽  
G. Picozzi ◽  
M. Mascalchi
Keyword(s):  
Shape Analysis ◽  
Ct Scans ◽  
Lung Nodules ◽  
Automated Segmentation ◽  
Local Shape

Face spoofing detection from single images using texture and local shape analysis

2012 ◽  
Vol 1 (1) ◽  
pp. 3 ◽  
Author(s):  
J. Määttä ◽  
A. Hadid ◽  
M. Pietikäinen
Keyword(s):  
Shape Analysis ◽  
Local Shape ◽  
Spoofing Detection ◽  
Face Spoofing

scholarly journals Local Shape Analysis using MANCOVA

2009 ◽  
Author(s):  
Beatriz Paniagua ◽  
Martin Styner ◽  
Marc Macenko ◽  
Dimitrios Pantazis ◽  
Marc Niethammer
Keyword(s):  
Statistical Analysis ◽  
Shape Analysis ◽  
Anatomical Structures ◽  
Local Shape ◽  
Independent Variables ◽  
Different Types ◽  
Statistical Shape ◽  
Localized Analysis ◽  
Associated Variables ◽  
Unified Method

Gross shape measures such as volume have been widely used in statistical analysis of anatomical structures. Statistical shape analysis methods have emerged within the last decade to allow for a localized analysis of shape. Most shape analysis frameworks are though lacking a good statistical underpinning, as they commonly do not allow for the inclusion of independent variables such as age, gender or clinical scores. This work presents a unified method for local shape analysis that can accomodate different number of variates and contrasts. It also allows to include any number of associated variables in the statistical analysis of the data. Several cases of study are given to clarify the explanation of the different types of data that can be analyzed and the parameters that can be used to tune the program shapeAnalysisMANCOVA. This tool has been designed to interact seamlessly with the existing UNC SPHARM-PDM based shape analysis toolbox.

scholarly journals Local Shape Analysis of the Hippocampus using Hierarchical Level-of-Detail Representations

2004 ◽  
Vol 11A (7) ◽  
pp. 555-562
Author(s):  
Jeong-Sik Kim ◽  
Soo-Mi Choi ◽  
Yoo-Ju Choi ◽  
Myoung-Hee Kim
Keyword(s):  
Shape Analysis ◽  
Level Of Detail ◽  
Hierarchical Level ◽  
Local Shape

Local Shape Analysis for Overlaid Data Structures

2013 ◽  
pp. 150-171 ◽  
Author(s):  
Cezara Drăgoi ◽  
Constantin Enea ◽  
Mihaela Sighireanu
Keyword(s):  
Data Structures ◽  
Shape Analysis ◽  
Local Shape

scholarly journals Discovering Specifications for Unknown Procedures - Work in Progress

10.29007/wkvm ◽  
2018 ◽  
Author(s):  
Florian Craciun ◽  
Chenguang Luo ◽  
Guanhua He ◽  
Shengchao Qin ◽  
Wei-Ngan Chin
Keyword(s):  
Shape Analysis ◽  
Separation Logic ◽  
Automated Verification ◽  
Abductive Inference ◽  
Program Understanding ◽  
Inference Mechanism ◽  
Work In Progress ◽  
Partial Specification ◽  
Complete Specification

We study automated verification of pointer safety for heap-manipulating imperative programs with unknown procedure calls or code pointers. Given the specification of a procedure whose body contains calls to an unknown procedure, we try to infer the possible specifications for the unknown procedure from its calling contexts. We employ a forward shape analysis with separation logic and an abductive inference mechanism to synthesize both pre- and postconditions for the unknown procedure. The inferred specification is a partial specification of the unknown procedure. Therefore it is subject to a later verification when the code or the complete specification for the unknown procedure are available. Our inferred specifications can also be used for program understanding.

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