Lossy Compression of Noisy Images Using Autoencoders for Computer Vision Applications

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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Graph Matching Techniques for Computer Vision

Image Processing ◽

10.4018/978-1-4666-3994-2.ch021 ◽

2013 ◽

pp. 381-421 ◽

Cited By ~ 1

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.

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Graph Matching Techniques for Computer Vision

Graph-Based Methods in Computer Vision ◽

10.4018/978-1-4666-1891-6.ch001 ◽

2012 ◽

pp. 1-41 ◽

Cited By ~ 1

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.

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Object Recognition with a Limited Database Using Shape Space Theory

Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-61350-429-1.ch008 ◽

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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