Fuzzy Lowpass Filtering

2005 ◽  
pp. 378-385
Author(s):  
Yasar Becerikli ◽  
M. Mucteba Tutuncu ◽  
H. Engin Demiray
Keyword(s):  
Lowpass Filtering

Lowpass Filtering Capabilities of Heavily Doped Actin Filaments

2017 ◽  
Vol 6 (2) ◽  
pp. 127-132
Author(s):  
Tapatosh Sadhu ◽  
Debashis De
Keyword(s):  
Actin Filaments ◽  
Heavily Doped ◽  
Lowpass Filtering

Closed-form solutions for lowpass filtering of 3D digital geometry

2007 ◽  
Vol 43 (9) ◽  
pp. 503
Author(s):  
I.S. Lim ◽  
D. Hughes ◽  
N.W. John
Keyword(s):  
Closed Form ◽  
Digital Geometry ◽  
Closed Form Solutions ◽  
Lowpass Filtering

Effect of envelope lowpass filtering on consonant and melody recognition

2002 ◽  
Vol 112 (5) ◽  
pp. 2245-2245 ◽  
Author(s):  
Arthur P. Lobo ◽  
Felipe Toledos ◽  
Philip C. Loizou ◽  
Michael F. Dorman
Keyword(s):  
Melody Recognition ◽  
Lowpass Filtering

Acquisition of the Palm Print and Research on Pretreating Simulation

2013 ◽  
Vol 380-384 ◽  
pp. 3551-3555
Author(s):  
Tian Fang Cai
Keyword(s):  
Noise Level ◽  
Histogram Equalization ◽  
Corner Detection ◽  
Edge Extraction ◽  
Characteristic Extraction ◽  
Palm Print ◽  
Lowpass Filtering

The paper does the pretreating operation of palm print, the process of which includes lowpass filtering, histogram equalization, binarization processing, edge extraction, corner detection, rotating location and extraction of subgraph ROI. In images done with pretreating, translation and rotation phenomenon of noise level has almost been removed, which benefits the characteristic extraction and identification of palm print. And we finally demonstrate by experiments.

Image Denoising via 2-D FIR Filtering Approach

2013 ◽  
pp. 184-198
Author(s):  
Jingyu Hua ◽  
Wankun Kuang
Keyword(s):  
Computer Simulation ◽  
Image Denoising ◽  
Bilateral Filtering ◽  
Learning Method ◽  
Swarm Optimization ◽  
Filtering Method ◽  
Fir Filtering ◽  
Mean Filter ◽  
Filtering Approach ◽  
Lowpass Filtering

Image denoising has received much concern for decades. One of the simplest methods for image denoising is the 2-D FIR lowpass filtering approach. Firstly, the authors make a comparative study of the conventional lowpass filtering approach, including the classical mean filter and three 2-D FIR LowPass Filters (LPF) designed by McClellan transform. Then an improved method based on learning method is presented, where pixels are filtered by five edge-oriented filters, respectively, facilitated to their edge details. Differential Evolution Particle Swarm Optimization (DEPSO) algorithm is exploited to refine those filters. Computer simulation demonstrates that the proposed method can be superior to the conventional filtering method, as well as the modern Bilateral Filtering (BF) and the Stochastic Denoising (SD) method.

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