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About 10,000 papers for “shape analysis”

Shape Analysis

Bor-Yuh Evan Chang, Cezara Drăgoi, Roman Manevich et al. · 2020

Cited by 0

Shape Analysis

Bor-Yuh Evan Chang, Cezara Drăgoi, Roman Manevich et al. · Foundations and Trends® in Programming Languages · 2020

The computation of semantic information about the behavior of pointer-manipulating programs has been a long standing issue, attacked with diverse and numerous techniques and tools for over 50 years. As usual in automatic verification of infinite-state programs, properties of interest are not computable. Thus, static analyses can only be conservative, leading different analyses to make different tradeoffs between the intricacies of the properties they detect, the precision of their inference procedure and analysis, and the scalability of the analysis. In this context, shape analyses focus on inferring highly complex properties of heap-manipulating programs. These programs utilize data structures which are implemented using an unbounded number of dynamically- (heap-) allocated memory cells interconnected via mutable pointer-links. Because shape analyses have to reason about data structures whose size is not bounded by a fixed, known value, they cannot track explicitly the particular properties of every concrete memory cell which the program uses, as done, e.g., by analysis of variable-manipulating non-recursive programs. Instead, shape analyses summarize memory regions by letting one piece of abstract information, called summary predicate, describe several concrete cells. The need to cope with data structures of unbounded sizes is a challenge shape analyses share with static analyzers of array-manipulating programs. However, while the size of an array may change in different executions, its layout (i.e., its dimensions and the way its contents are spread over the memory) is fixed. In contrast, the layout of a pointer-linked data structure, colloquially referred to as its shape, may evolve dynamically during the program execution and a memory cell can be part of different data structures at different points in time. As a result, shape analyses need to let the denotation of summary predicates in terms of the constituents and layouts of the memory regions which they represent evolve during the analysis as well. In this survey, we consider that shape analyses are characterized and defined by the presence of summary predicates describing a set of concrete memory cells that varies during the course of the analysis. We use this characterization as a means for distinguishing shape analyses as a particular class of pointer analyses. We show that many “standard” pointer analyses do not fit the aforementioned description, while many analyses relying on very different mathematical foundations, e.g., shape graphs, three-valued logic, and separation logic, do. The ambition of this survey is to provide a comprehensive introduction to the field of shape analysis, and to present the foundation of this topic, in a single document that is accessible to readers who are not familiar with it. To do so, we characterize the essence of shape analysis compared to more classical pointer analyses. We supply the intuition underlying the abstractions commonly used in shape analysis and the algorithms that allow to statically compute intricate semantic properties. Then, we cover the main families of shape analysis abstraction and algorithms, highlight the similarities between them, and also characterize the main differences between the most common approaches. Last, we review a few other static analysis works (such as array abstractions, dictionary abstractions and interprocedural analyses) that were influenced by the ideas of shape analysis, so as to demonstrate the impact of the field.

Cited by 7Open access

Shape analysis.

Justin A. Ledogar (4759989), Theodora H. Y. Luk (4759992), Jonathan M. G. Perry (3298821) et al. · Figshare · 2018

<p>(<b>A</b>) Cranium of <i>Pithecia pithecia</i> (MCZ 30719) in anterior, lateral, and inferior view showing fixed landmarks (red) and sliding semi-landmarks (black) used in the shape analysis. Plots show results for the PCA of cranial shape in (<b>B</b>) <i>Callicebus</i>, (<b>C</b>) <i>Pithecia</i>, (<b>D</b>) <i>Chiropotes</i>, and (<b>E</b>) <i>Cacajao</i>. For each plot, crania at the positive and negative ends of the PC1 and PC2 axes describe shape change along these axes, warped onto the specimen closest to the group centroid (i.e., shortest distance from group centroid). The specimens with the most positive and most negative principal component scores along PC1 were selected for use in FEA. In <i>Callicebus</i>, PC1 mainly captures variation in neurocranial height and length and facial length, with longer and lower crania with slightly longer faces toward negative scores. PC2 explains some differences in facial width and orbit, with wider faces toward minimum scores. In <i>Pithecia</i>, PC1 describes differences in facial and neurocranial width, with wider crania toward positive scores. PC2 reflects aspects of neurocranial and facial variation, with the neurocranium extended superoinferiorly and face extended inferoanteriorly toward positive scores. In <i>Chiropotes</i>, PC1 captures some differences in neurocranial shape and facial projection, with longer crania and faces toward positive scores. Faces are slightly wider toward positive scores along PC2. <i>Cacajao</i> is similar to <i>Chiropotes</i>, except that longer crania and face are found along the negative end of PC1.</p>

Cited by 0Open access

Shape Analysis

Yulin Xue, Zhuang Liu, Ye Li · Scientific computation · 2025

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Shape Analysis

Erkut Erdem · 2012

• Shape is the primary source of visual information – Objects can be immediately recognized and classified

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Shape analysis

Chang, Bor-Yuh Evan, ăgoi, Cezara, Manevich, Roman et al. · CERN Document Server (European Organization for Nuclear Research) · 2020

Researchers and students new to the concept of shape analysis will find this a one-stop shop of information that will quickly get them up to speed on all aspects of the topic. With a comprehensive set of references, this accessible survey will enable the reader to adopt the techniques quickly in their own further research.

Cited by 0

SHAPE ANALYSIS

2008

The problem of shape analysis for long time has been given constant attention in computer vision. As a result, the field has been intensively developed over the past decades both in theoretical and applied domains. The shape of objects is a difficult concept. A shape parameter is a set function value of which does not depend on geometrical transformations such as translation, rotation, size changes and reflection. Currently three main directions in shape analysis can be observed: functional approach, set theory approach and point description. Each of them is based on certain mathematical ideas, that is, how the objects of which the shape is analyzed, are treated in mathematical terms. Here only two-dimensional objects or twodimensional images of three-dimensional objects, which can be their projections or sections, are considered. It is assumed that the two-dimensional information is sufficient for a reasonable characterization. Further, it is always assumed that the objects are sets in the sense of mathematical set theory and that the sets are compact and closed in the topological sense. Such sets are called figures. Processing of figures in sense of set theory leads to the set theoretic approach of shape analysis. Two different topics fractal theory and simple geometrical rations, are discussed in this chapter. An important method for characterizing figures is the representation of their contours by functions. Such representation is possible in many ways and it results in the functional approach of shape analysis. Having such contour functions many results of functional analysis and differential geometry can be applied to study the shape. An extensive overview of this approach is given in this chapter. The third direction in shape analysis (which is not discussed here) is related to the replacement of figures by a few points which usually lie on the contour and are defined by some geometrical properties or have a certain physical meaning. Such 'point fields ' can be analyzed, for example, by statistical methods.

Cited by 0

Shape Analysis

Reinhard Wilhelm, Mooly Sagiv, Thomas Reps · 2000

A shape-analysis algorithm statically analyzes a program to determine information about the heap-allocated data structures that the program manipulates. The results can be used to understand or verify programs. They also contain information valuable for debugging, compile-time garbage collection, instruction scheduling, and parallelization.

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Shape analysis in shape space

Taimouri, Vahid · DigitalCommons - WayneState (Wayne State University) · 2011

This study aims to classify different deformations based on the shape space concept. A shape space is a quotient space in which each point corresponds to a class of shapes. The shapes of each class are transformed to each other by a transformation group preserving a geometrical property in which we are interested. Therefore, each deformation is a curve on the high dimensional shape space manifold, and one can classify the deformations by comparison of their corresponding deformation curves in shape space. Towards this end, two classification methods are proposed. In the first method, a quasi conformal shape space is constructed based on a novel quasi-conformal metric, which preserves the curvature changes at each vertex during the deformation. Besides, a classification framework is introduced for deformation classification. The results on synthetic and real datasets show the effectiveness of the metric to estimate the intrinsic geometry of the shape space manifold, and its ability to classify and interpolate different deformations. In the second method, we introduce the medial surface shape space which classifies the deformations based on the medial surface and thickness of the shape. This shape space is based on the log map and uses two novel measures, average of the normal vectors and mean of the positions, to determine the distance between each pair of shapes on shape space. We applied these methods to classify the left ventricle deformations. The experimental results shows that the first method can remarkably classify the normal and abnormal subjects but this method cannot spot the location of the abnormality. In contrast, the second method can discriminate healthy subjects from patients with cardiomyopathy, and also can spot the abnormality on the left ventricle, which makes it a valuable assistant tool for diagnostic purposes.

Cited by 0Open access

2D-Shape Analysis Using Shape Invariants

Tomáš Stejskal · Applied Mechanics and Materials · 2014

High efficiency detection of two-dimensional objects is achieved by an appropriate choice of object invariants. The aim is to show an example of the construction of an algorithm for rapid identification also for highly complex objects. The program structure works in a similar way as animal systems in nature. Differentiating runs from whole to details. They are used to shape invariants. The program algorithm is specifically used a surfaces invariant, which represents a whole. Then was used a boundary length invariant around the object. Finally, the chord distribution code was used, which represent a detail of object recognition. The actual computational algorithms are not software-intensive and easy to debug. System uses the redundancy of uncertain information about the shape. In principle, chosen a certain balance between the confidence level of recognition and repetition of shape recognition by various methods.

Cited by 1

Cell shape analysis

Jacob Kerssemakers, Miloš Tišma · Zenodo (CERN European Organization for Nuclear Research) · 2024

Cell shape analysis --------------------------------------------- This package contains custom-written code associated with the paper "Direct observation of a crescent-shape chromosome in Bacillus subtilis" By Tišma et al. 2023 See README's in subdirectories for specific content description Notes: ---------------------------------------------- ## general * code for extended cell analysis as used in the Cees Dekker Lab, 2017-onwards ## associated publications 1. [current submission] 'Direct observation of a crescent-shape chromosome in Bacillus subtilis' Authors Miloš Tišma, Florian Patrick Bock, Jacob Kerssemakers, Aleksandre Japaridze, Stephan Gruber, Cees Dekker, submitted (2023). 2. MukBEF-dependent chromosomal organization in widened Escherichia coli Authors Aleksandre Japaridze, Raman van Wee, Christos Gogou, Jacob WJ Kerssemakers, Cees Dekker; (2022) 3. Direct observation of independently moving replisomes in Escherichia coli Authors Aleksandre Japaridze, Christos Gogou, Jacob WJ Kerssemakers, Huyen My Nguyen, Cees Dekker, Nat.Com (2020) 4. Direct imaging of the circular chromosome in a live bacterium Authors Fabai Wu, Aleksandre Japaridze, Xuan Zheng, Jakub Wiktor, Jacob WJ Kerssemakers, Cees Dekker, Nat.Com (2019) ## package description This package collects code to analyze multi-channel microscope images of cells. Patterns of interest may be extended, such as a fully labeled chromosome, or compact, as one or multiple labeled spots. The code is used to select and screen individual cells and to quantify locations of above labels. ## package contents * custom-written source code [Matlab] * test data to demonstrate the software code ## system requirements and used software * Windows 10, 64bits * Matlab Version 2021 [custom source code] * Oufti Version .... * Fiji(ImageJ) Version 1.52a * Dip_image analysis software Version 2.9 ## installation guide * please follow the installation instructions provided by above package distributors * installation of all platforms should take less than a day on a standard PC ## demo and instructions for use * the code is divided in three main subsections (that are to be used in this order) and one extra: * 'crop': works on microsocopic data, outputs data organized per cells * 'donut': in-depth analysis on chromosome patterns and other labels ('spots') * 'repli': follow-up analysis on spots * 'topical subjects': shorter code platforms for various analyses * see 'README.md' in each subdirectory for a detailed step-by-step descriptions, in order of appearance or importance in the analysis pipeline * see 'README_code_annotations.md' in each subdirectory for a full collection of all custom function descriptions * demo data for two cells is provided to illustrate workings and settings. Running this demo data typically only takes a few minutes * for new data, follow the setup as described via the demo data ## contributions ---------------------------------------------- * F. Wu and X. Zheng originally wrote and assembled the original code package * J.Kerssemakers organized, re-edited and expanded the 'crop' code package * J.Kerssemakers wrote the 'donut' and 'repli' code packages * A. Japaridze, Raman van Wee, Christos Gogou and M.Tisma contributed to analysis design * J.Kerssemakers and M.Tisma contributed to 'topical subjects' ## disclaimer --------------------------------------------- This code was custom written and shared to illustrate used algorithms, analysis pathways etc. in relation to published results. The code was regularly used and tested. However, small bugs, not relevant for the data analysis as present in publications may still be around. Interested users are expected to have a sufficient knowledge of Matlab code to understand and adapt the code to their own wishes.

Cited by 0Open access

Shape-Based Analysis

Krishna Chaitanya Gurijala · Academic Commons (Stony Brook University) · 2014

Shape analysis plays a critical role in many fields, especially in medical analysis. There has been substantial research performed for shape analysis in manifolds. On the contrary, shape-based analysis has not received much attention for volumetric data. It is not feasible to directly extend the successful manifold shape analysis methods, such as heat diffusion, to volumes due to the huge computational cost. The work presented herein seeks to address this problem by presenting two approaches for shape analysis in volumes that not only capture the shape information efficiently but also reduce the computational time drastically. The first approach is a cumulative approach and is called the Cumulative Heat Diffusion, where the heat diffusion is carried out by simultaneously considering all the voxels as sources. The cumulative heat diffusion is monitored by a novel operator called the Volume Gradient Operator, which is a combination of the well-known Laplace-Beltrami operator and a data-driven operator. The cumulative heat diffusion is computed by considering all the voxels and hence is inherently dependent on the resolution of the data. Therefore, we propose a second approach which is a stochastic approach for shape analysis. In this approach the diffusion process is carried out by using tiny massless particles termed shapetons. An appropriate distance value is chosen as new definition of time step. The shapetons are diffused in a Monte Carlo fashion across the voxels until the pre-defined distance value (serves as single time step) is reached. The direction of propagation for the shapetons is determined by the volume gradient operator. The shapeton diffusion is a novel diffusion approach and is independent of the resolution of the data. These approaches robustly extract features and objects based on shape. Both shape analysis approaches are used in several medical applications such as segmentation, feature extraction, registration, transfer function design and tumor detection. This work majorly focuses on the diagnosis of colon cancer. Virtual colonoscopy is a viable non-invasive screening method, whereby a radiologist can explore a colon surface to locate and remove the precancerous polyps (protrusions/ bumps on the colon wall). To facilitate an efficient colon exploration, a robust and shape-preserving colon flattening algorithm is presented using the heat diffusion metric which is insensitive to topological noise. The flattened colon surface provides effective colon exploration, navigation, polyp visualization, detection, and verification. In addition, the flattened colon surface is used to consistently register the supine and prone colon surfaces. Anatomical landmarks such as the taeniae coli, flexures and the surface feature points are used in the colon registration pipeline and this work presents techniques using heat diffusion to automatically identify them. Shape analysis in graphs is vital to represent and visualize relationships between data items. Graph embedding methods play an important role in visualizing the data items and their relationships by providing an automatic and clutter free layout. A novel graph embedding approach is presented that will compute the global characteristics of a graph, such as hyperbolic or parabolic type, and also the Ricci curvature in the local neighborhood, which can analyze the structure of the graph. The method has three stages. In the first stage, the graph is embedded on a topological surface. In the second stage it is embedded on a Riemann surface by computing the Ricci flow and finally in the third stage it is embedded onto a surface in three dimensional Euclidean space. The approach is general, practical, and theoretically rigorous. | 168 pages

Cited by 0

Load Shape Analysis.

Ellis, Abraham; Lave, Matthew Samuel · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2020

Abstract not provided.

Cited by 0Open access

SHAPE dataset analysis.

Luke Trinity (6792770), Ian Wark (5066483), Lance Lansing (14691756) et al. · Figshare · 2023

<p>A: Comparison of −1 PRF sequence SHAPE reactivity dataset reported by Manfredonia et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010922#pcbi.1010922.ref013" target="_blank">13</a>], Huston et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010922#pcbi.1010922.ref012" target="_blank">12</a>], Yang et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010922#pcbi.1010922.ref046" target="_blank">46</a>], and Zhang et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010922#pcbi.1010922.ref015" target="_blank">15</a>]. Reactivity at or below 0.3 is considered to be low or non-reactive indicating the base is paired. B: ShapeKnots’ predictions using bootstrapped SHAPE values were obtained 10, 000 times and averaged for each index. The mean and variance of the 10, 000 predictions for each respective SHAPE dataset are shown here. The y-axis indicates the frequency each nucleotide is predicted as paired. Mean value of 1.0 at a specific position conveys that the nucleotide was predicted as paired for all bootstrapped values, and mean value of 0.0 indicates that the nucleotide was predicted as unpaired for all bootstrapped data. C: Bootstrap procedure was repeated with Shapify used for prediction. Red arrows mark differences from ShapeKnots predictions.</p>

Cited by 0Open access