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.

Object Recognition with a Limited Database Using Shape Space Theory

2013 ◽  
pp. 181-200
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.

Curve Parameterization and Curvature via Method of Hurwitz-Radon Matrices

2011 ◽  
Vol 16 (1-2) ◽  
pp. 49-56 ◽  
Author(s):  
Dariusz Jakóbczak
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Object Recognition ◽  
Explicit Form ◽  
Local Maximum ◽  
Parametric Representation ◽  
Feature Points ◽  
Implicit Representation ◽  
Computer Vision Applications ◽  
Interpolation Nodes

Curve Parameterization and Curvature via Method of Hurwitz-Radon MatricesParametric representation of the curve is more appropriate in computer vision applications then explicit formy=f(x)or implicit representationf(x, y) = 0. Proposed method of Hurwitz-Radon Matrices (MHR) can be used in parameterization and interpolation of curves in the plane. Suitable parameterization leads to curvature calculations. Points with local maximum curvature are treated as feature points in object recognition and image analysis. This paper contains the way of curve parameterization and computing the curvature in the range of two successive interpolation nodes via MHR method. Proposed method is based on a family of Hurwitz-Radon (HR) matrices. The matrices are skew-symmetric and possess columns composed of orthogonal vectors. The operator of Hurwitz-Radon (OHR), built from these matrices, is described. It is shown how to create the orthogonal OHR and how to use it in a process of curve parameterization and curvature calculation.

Accelerated HOG+SVM for object recognition

2021 ◽  
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
International Symposium ◽  
Fast Track ◽  
Industrial Applications ◽  
Electronic Imaging ◽  
Intelligent Robotics

Fast track article for IS&T International Symposium on Electronic Imaging 2021: Intelligent Robotics and Industrial Applications using Computer Vision 2021 proceedings.

scholarly journals Visual Behavior Based Bio-Inspired Polarization Techniques in Computer Vision and Robotics

2012 ◽  
pp. 243-272 ◽  
Author(s):  
Abd El Rahman Shabayek ◽  
Olivier Morel ◽  
David Fofi
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Visual Perception ◽  
Polarization Vision ◽  
Visual Behavior ◽  
Biologically Inspired ◽  
Long Time ◽  
Comprehensive Survey ◽  
High Level ◽  
And Robotics

For long time, it was thought that the sensing of polarization by animals is invariably related to their behavior, such as navigation and orientation. Recently, it was found that polarization can be part of a high-level visual perception, permitting a wide area of vision applications. Polarization vision can be used for most tasks of color vision including object recognition, contrast enhancement, camouflage breaking, and signal detection and discrimination. The polarization based visual behavior found in the animal kingdom is briefly covered. Then, the authors go in depth with the bio-inspired applications based on polarization in computer vision and robotics. The aim is to have a comprehensive survey highlighting the key principles of polarization based techniques and how they are biologically inspired.

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.

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