Face Recognition Based on the Fusion of Bit-plane and Binary Image Compression Techniques

2018 ◽  
Author(s):  
Sunil S Harakannanavar ◽  
C R Prashanth ◽  
K B Raja ◽  
Chetan T Madiwalar
Keyword(s):  
Face Recognition ◽  
Image Compression ◽  
Binary Image ◽  
Bit Plane

Study of binary image compression using universal coding

1991 ◽  
Author(s):  
Yasuhiko Nakano ◽  
Hirotaka Chiba ◽  
Yoshiyuki Okada ◽  
Shigeru Yoshida ◽  
Masahiro Mori
Keyword(s):  
Image Compression ◽  
Binary Image ◽  
Universal Coding

Image compression using bit-plane coding of wavelet coefficients

1996 ◽  
Vol 32 (19) ◽  
pp. 1773 ◽  
Author(s):  
K. Nguyen-Phi ◽  
H. Weinrichter
Keyword(s):  
Image Compression ◽  
Wavelet Coefficients ◽  
Bit Plane Coding ◽  
Bit Plane

A Hybrid Lossless-Lossy Binary Image Compression Scheme

2013 ◽  
Vol 3 (4) ◽  
pp. 37-50
Author(s):  
Saif alZahir ◽  
Syed M. Naqvi
Keyword(s):  
Image Compression ◽  
Mathematical Models ◽  
Regression Models ◽  
Input Image ◽  
Binary Image ◽  
Large Set ◽  
Image Simulation ◽  
Compression Scheme ◽  
Binary Images ◽  
Visual Distortion

In this paper, the authors present a binary image compression scheme that can be used either for lossless or lossy compression requirements. This scheme contains five new contributions. The lossless component of the scheme partitions the input image into a number of non-overlapping rectangles using a new line-by-line method. The upper-left and the lower-right vertices of each rectangle are identified and the coordinates of which are efficiently encoded using three methods of representation and compression. The lossy component, on the other hand, provides higher compression through two techniques. 1) It reduces the number of rectangles from the input image using our mathematical regression models. These mathematical models guarantees image quality so that rectangular reduction should not produce visual distortion in the image. The mathematical models have been obtained through subjective tests and regression analysis on a large set of binary images. 2) Further compression gain is achieved through discarding isolated pixels and 1-pixel rectangles from the image. Simulation results show that the proposed schemes provide significant improvements over previously published work for both the lossy and the lossless components.

Bayesian Robust Coding Face Recognition Algorithm Based on New Dictionary

2014 ◽  
Vol 644-650 ◽  
pp. 4080-4083
Author(s):  
Ye Cai Guo ◽  
Ling Hua Zhang
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Binary Image ◽  
Test Image ◽  
Face Database ◽  
Robust Coding ◽  
The Face ◽  
Isolated Points ◽  
Rate Comparison

In order to overcome the defects that the face recognition rate can be greatly reduced in the existing uncontrolled environments, Bayesian robust coding for face recognition based on new dictionary was proposed. In this proposed algorithm, firstly a binary image is gained by gray threshold transformation and a more clear image without some isolated points can be obtained via smoothing, secondly a new dictionary can be reconstructed via fusing the binary image with the original training dictionary, finally the test image can be classified as the existing class via Bayesian robust coding. The experimental results based on AR face database show that the proposed algorithm has higher face recognition rate comparison with RRC and RSC algorithm.

Edge Detection Using Block Truncation Coding

2003 ◽  
Vol 17 (06) ◽  
pp. 951-966 ◽  
Author(s):  
Yu-Chen Hu ◽  
Chin-Chen Chang
Keyword(s):  
Computational Complexity ◽  
Image Compression ◽  
Edge Detection ◽  
Digital Image ◽  
Block Type ◽  
Detection Scheme ◽  
Block Truncation Coding ◽  
Bit Plane ◽  
Digital Image Compression ◽  
Simple Block

In this paper, a new edge detection scheme based on block truncation coding (BTC) is proposed. As we know, the BTC is a simple and fast scheme for digital image compression. To detect an edge boundary using the BTC scheme, the bit plane information of each BTC-compressed block is exploited, and a simple block type classifier is introduced. The experimental results show that the proposed scheme clearly detects the edge boundaries of digital images while requiring very little computational complexity. Meanwhile, the edge detection process can be incorporated into all BTC variant schemes. In other words, the newly proposed scheme provides a good approach for the detection of edge boundaries using block truncation coding.

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