Multiple feature-based contrast enhancement of ROI of backlit images

2021 ◽  
Vol 33 (1) ◽  
Author(s):  
Gaurav Yadav ◽  
Dilip Kumar Yadav
Keyword(s):  
Contrast Enhancement ◽  
Multiple Feature ◽  
Feature Based

Hyperspectral Image Classification via Multiple-Feature-Based Improved Sparse Representation

2019 ◽  
Vol 39 (5) ◽  
pp. 0528004
Author(s):  
李非燕 Li Feiyan ◽  
霍宏涛 Huo Hongtao ◽  
李静 Li Jing ◽  
白杰 Bai Jie
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Multiple Feature ◽  
Feature Based

scholarly journals Extended multiple feature-based classifications for adaptive loop filtering

2019 ◽  
Vol 8 ◽  
Author(s):  
Johannes Erfurt ◽  
Wang-Q Lim ◽  
Heiko Schwarz ◽  
Detlev Marpe ◽  
Thomas Wiegand
Keyword(s):  
Video Compression ◽  
Main Idea ◽  
Loop Filter ◽  
Coding Efficiency ◽  
Multiple Feature ◽  
Wiener Filters ◽  
Feature Based ◽  
Block Based ◽  
Adaptive Loop Filter ◽  
Offset Filtering

Abstract Recent progress in video compression is seemingly reaching its limits making it very hard to improve coding efficiency significantly further. The adaptive loop filter (ALF) has been a topic of interest for many years. ALF reaches high coding gains and has motivated many researchers over the past years to further improve the state-of-the-art algorithms. The main idea of ALF is to apply a classification to partition the set of all sample locations into multiple classes. After that, Wiener filters are calculated and applied for each class. Therefore, the performance of ALF essentially relies on how its classification behaves. In this paper, we extensively analyze multiple feature-based classifications for ALF (MCALF) and extend the original MCALF by incorporating sample adaptive offset filtering. Furthermore, we derive new block-based classifications which can be applied in MCALF to reduce its complexity. Experimental results show that our extended MCALF can further improve compression efficiency compared to the original MCALF algorithm.

scholarly journals Handwritten Script Recognition using DCT, Gabor Filter and Wavelet Features at Line Level

2011 ◽  
pp. 85-90
Author(s):  
Dr. G.G. Rajput ◽  
Anita H.B.
Keyword(s):  
Recognition Accuracy ◽  
Gabor Filter ◽  
Automatic Identification ◽  
Document Images ◽  
Handwritten Documents ◽  
Multiple Feature ◽  
Wavelet Features ◽  
Feature Based ◽  
Nearest Neighbour Classifier ◽  
Line Level

In a country like India where more number of scripts are in use, automatic identification of printed and handwritten script facilitates many important applications including sorting of document images and searching online archives of document images. In this paper, a multiple feature based approach is presented to identify the script type of the collection of handwritten documents. Eight popular Indian scripts are considered here. Features are extracted using Gabor filters, Discrete Cosine Transform, and Wavelets of Daubechies family. Experiments are performed to test the recognition accuracy of the proposed system at line level for bilingual scripts and later extended to trilingual scripts. We have obtained 100% recognition accuracy for bi-scripts at line level. The classification is done using k-nearest neighbour classifier.

Multiple Feature-based Classifications Adaptive Loop Filter

2018 ◽  
Author(s):  
Johannes Erfurt ◽  
Wang-Q Lim ◽  
Heiko Schwarz ◽  
Detlev Marpe ◽  
Thomas Wiegand
Keyword(s):  
Loop Filter ◽  
Multiple Feature ◽  
Feature Based ◽  
Adaptive Loop Filter

A multiple feature-based offline handwritten signature verification system

2019 ◽  
Vol 59 (3) ◽  
pp. 214
Author(s):  
Vivek Kumar Singh ◽  
Prateek Singh ◽  
Akriti Nigam ◽  
R.C. Tripathi
Keyword(s):  
Signature Verification ◽  
Verification System ◽  
Multiple Feature ◽  
Feature Based ◽  
Handwritten Signature

scholarly journals Complementary human detection and multiple feature based tracking using a stereo camera

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Gakuto Masuyama ◽  
Takehiro Kawashita ◽  
Kazunori Umeda
Keyword(s):  
Human Detection ◽  
Stereo Camera ◽  
Multiple Feature ◽  
Feature Based
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