Finger-Knuckle-Print recognition using BLPOC-based local block matching

2011 ◽  
Author(s):  
Shoichiro Aoyama ◽  
Koichi Ito ◽  
Takafumi Aoki
Keyword(s):  
Block Matching ◽  
Local Block

A finger-knuckle-print recognition algorithm using phase-based local block matching

2014 ◽  
Vol 268 ◽  
pp. 53-64 ◽  
Author(s):  
Shoichiro Aoyama ◽  
Koichi Ito ◽  
Takafumi Aoki
Keyword(s):  
Recognition Algorithm ◽  
Block Matching ◽  
Local Block

Verified Robust Optical Flow Guided Local Block-Matching and 4D Filtering

2015 ◽  
Vol 4 (2) ◽  
Author(s):  
Xueqing Liu ◽  
Xinghao Jiang ◽  
Tanfeng Sun ◽  
Ajing Xu
Keyword(s):  
Optical Flow ◽  
Block Matching ◽  
Local Block

scholarly journals Spectral-Smoothness and Non-Local Self-Similarity Regularized Subspace Low-Rank Learning Method for Hyperspectral Mixed Denoising

2021 ◽  
Vol 13 (16) ◽  
pp. 3196
Author(s):  
Wei Liu ◽  
Chengxun He ◽  
Le Sun
Keyword(s):  
Coefficient Matrix ◽  
Noise Removal ◽  
Block Matching ◽  
Low Rank ◽  
Self Similarity ◽  
Basis Matrix ◽  
Block Matrices ◽  
Non Local ◽  
Mixed Noise ◽  
Local Block

During the imaging process, hyperspectral image (HSI) is inevitably affected by various noises, such as Gaussian noise, impulse noise, stripes or deadlines. As one of the pre-processing steps, the removal of mixed noise for HSI has a vital impact on subsequent applications, and it is also one of the most challenging tasks. In this paper, a novel spectral-smoothness and non-local self-similarity regularized subspace low-rank learning (termed SNSSLrL) method was proposed for the mixed noise removal of HSI. First, under the subspace decomposition framework, the original HSI is decomposed into the linear representation of two low-dimensional matrices, namely the subspace basis matrix and the coefficient matrix. To further exploit the essential characteristics of HSI, on the one hand, the basis matrix is modeled as spectral smoothing, which constrains each column vector of the basis matrix to be a locally continuous spectrum, so that the subspace formed by its column vectors has continuous properties. On the other hand, the coefficient matrix is divided into several non-local block matrices according to the pixel coordinates of the original HSI data, and block-matching and 4D filtering (BM4D) is employed to reconstruct these self-similar non-local block matrices. Finally, the formulated model with all convex items is solved efficiently by the alternating direction method of multipliers (ADMM). Extensive experiments on two simulated datasets and one real dataset verify that the proposed SNSSLrL method has greater advantages than the latest state-of-the-art methods.

scholarly journals Fast Tracking of the Left Ventricle Using Global Anatomical Affine Optical Flow and Local Recursive Block Matching

2014 ◽  
Author(s):  
Daniel Barbosa ◽  
Denis Friboulet ◽  
Jan D'hooge ◽  
Olivier Bernard
Keyword(s):  
Left Ventricle ◽  
Optical Flow ◽  
Automatic Segmentation ◽  
Block Matching ◽  
Temporal Consistency ◽  
Matching Problem ◽  
Fast Tracking ◽  
4D Ultrasound ◽  
Novel Method ◽  
Local Block

We present a novel method for segmentation and tracking of the left ventricle (LV) in 4D ultrasound sequences using a combination of automatic segmentation at the end-diastolic frame and tracking using both a global optical flow-based tracker and local block matching. The core novelty of the proposed algorithm relies on the recursive formulation of the block-matching problem, which introduces temporal consistency on the patterns being tracked. The proposed method offers a competitive solution, with average segmentation errors of 2.29 and 2.26mm in the training (#=15) and testing (#=15) datasets respectively.

Local block relaxation method for the solution of equations of gasdynamics

AIAA Journal ◽  
2000 ◽  
Vol 38 ◽  
pp. 1377-1384
Author(s):  
Carlo de Nicola ◽  
Renato Tognaccini ◽  
Vittorio Puoti
Keyword(s):  
Relaxation Method ◽  
Solution Of Equations ◽  
Block Relaxation ◽  
Local Block
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