Graph Matching Techniques for Computer Vision

2012 ◽  
pp. 1-41 ◽  
Author(s):  
Mario Vento ◽  
Pasquale Foggia
Keyword(s):  
Computer Vision ◽  
Graph Matching ◽  
Reference Model ◽  
Computer Vision Applications ◽  
Matching Techniques

Many computer vision applications require a comparison between two objects, or between an object and a reference model. When the objects or the scenes are represented by graphs, this comparison can be performed using some form of graph matching. The aim of this chapter is to introduce the main graph matching techniques that have been used for computer vision, and to relate each application with the techniques that are most suited to it.

Graph Matching Techniques for Computer Vision

2013 ◽  
pp. 381-421 ◽  
Author(s):  
Mario Vento ◽  
Pasquale Foggia
Keyword(s):  
Computer Vision ◽  
Graph Matching ◽  
Reference Model ◽  
Computer Vision Applications ◽  
Matching Techniques

Many computer vision applications require a comparison between two objects, or between an object and a reference model. When the objects or the scenes are represented by graphs, this comparison can be performed using some form of graph matching. The aim of this chapter is to introduce the main graph matching techniques that have been used for computer vision, and to relate each application with the techniques that are most suited to it.

scholarly journals A comparison of semiglobal and local dense matching algorithms for surface reconstruction

2014 ◽  
Vol XL-5 ◽  
pp. 187-194 ◽  
Author(s):  
E. Dall'Asta ◽  
R. Roncella
Keyword(s):  
Computer Vision ◽  
Stereo Matching ◽  
Least Square ◽  
Stereo Image ◽  
Dense Matching ◽  
Consistency Constraints ◽  
Global Matching ◽  
Matching Cost ◽  
Software Codes ◽  
Matching Techniques

Encouraged by the growing interest in automatic 3D image-based reconstruction, the development and improvement of robust stereo matching techniques is one of the most investigated research topic of the last years in photogrammetry and computer vision.<br><br> The paper is focused on the comparison of some stereo matching algorithms (local and global) which are very popular both in photogrammetry and computer vision. In particular, the Semi-Global Matching (SGM), which realizes a pixel-wise matching and relies on the application of consistency constraints during the matching cost aggregation, will be discussed.<br><br> The results of some tests performed on real and simulated stereo image datasets, evaluating in particular the accuracy of the obtained digital surface models, will be presented. Several algorithms and different implementation are considered in the comparison, using freeware software codes like MICMAC and OpenCV, commercial software (e.g. Agisoft PhotoScan) and proprietary codes implementing Least Square e Semi-Global Matching algorithms. The comparisons will also consider the completeness and the level of detail within fine structures, and the reliability and repeatability of the obtainable data.

An efficient hexagonal image framework using pseudo hexagonal pixel for computer vision applications

2021 ◽  
pp. 1-14
Author(s):  
Prathibha Varghese ◽  
G. Arockia Selva Saroja
Keyword(s):  
Computer Vision ◽  
Heuristic Algorithms ◽  
Ganglion Cells ◽  
Signal To Noise Ratio ◽  
Hexagonal Lattice ◽  
Display Device ◽  
Angular Correction ◽  
Nature Inspired Computing ◽  
Computer Vision Applications ◽  
Simulated Images

Nature-inspired computing has been a real source of motivation for the development of many meta-heuristic algorithms. The biological optic system can be patterned as a cascade of sub-filters from the photoreceptors over the ganglion cells in the fovea to some simple cells in the visual cortex. This spark has inspired many researchers to examine the biological retina in order to learn more about information processing capabilities. The photoreceptor cones and rods in the human fovea resemble hexagon more than a rectangular structure. However, the hexagonal meshes provide higher packing density, consistent neighborhood connectivity, and better angular correction compared to the rectilinear square mesh. In this paper, a novel 2-D interpolation hexagonal lattice conversion algorithm has been proposed to develop an efficient hexagonal mesh framework for computer vision applications. The proposed algorithm comprises effective pseudo-hexagonal structures which guarantee to keep align with our human visual system. It provides the hexagonal simulated images to visually verify without using any hexagonal capture or display device. The simulation results manifest that the proposed algorithm achieves a higher Peak Signal-to-Noise Ratio of 98.45 and offers a high-resolution image with a lesser mean square error of 0.59.

Object Recognition with a Limited Database Using Shape Space Theory

2012 ◽  
pp. 128-147
Author(s):  
Yuexing Han ◽  
Bing Wang ◽  
Hideki Koike ◽  
Masanori Idesawa
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Shape Space ◽  
Image Understanding ◽  
Data Models ◽  
Hard Work ◽  
Space Theory ◽  
Single Object ◽  
Intermediate Data ◽  
Computer Vision Applications

One of the main goals of image understanding and computer vision applications is to recognize an object from various images. Object recognition has been deeply developed for the last three decades, and a lot of approaches have been proposed. Generally, these methods of object recognition can successfully achieve their goal by relying on a large quantity of data. However, if the observed objects are shown to diverse configurations, it is difficult to recognize them with a limited database. One has to prepare enough data to exactly recognize one object with multi-configurations, and it is hard work to collect enough data only for a single object. In this chapter, the authors will introduce an approach to recognize objects with multi-configurations using the shape space theory. Firstly, two sets of landmarks are obtained from two objects in two-dimensional images. Secondly, the landmarks represented as two points are projected into a pre-shape space. Then, a series of new intermediate data can be obtained from data models in the pre-shape space. Finally, object recognition can be achieved in the shape space with the shape space theory.

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