A DCT-based spatially adaptive post-processing technique to reduce the blocking artifacts in transform coded images

2000 ◽  
Vol 10 (1) ◽  
pp. 36-41 ◽  
Author(s):  
Hoon Paek ◽  
Rin-Chul Kim ◽  
Sang-Uk Lee
Keyword(s):  
Processing Technique ◽  
Blocking Artifacts ◽  
Post Processing ◽  
Spatially Adaptive ◽  
Coded Images

scholarly journals A post-processing technique for stabilizing the discontinuous pressure projection operator in marginally-resolved incompressible inviscid flow

2016 ◽  
Vol 139 ◽  
pp. 120-129 ◽  
Author(s):  
Sumedh M. Joshi ◽  
Peter J. Diamessis ◽  
Derek T. Steinmoeller ◽  
Marek Stastna ◽  
Greg N. Thomsen
Keyword(s):  
Projection Operator ◽  
Inviscid Flow ◽  
Processing Technique ◽  
Post Processing ◽  
Pressure Projection ◽  
Discontinuous Pressure

scholarly journals Video capture and post-processing technique for approximating 3D projectile trajectory

2015 ◽  
Vol 8 (3-4) ◽  
pp. 124-129 ◽  
Author(s):  
Chase M. Pfeifer ◽  
Judith M. Burnfield ◽  
Guilherme M. Cesar ◽  
Max H. Twedt ◽  
Jeff A. Hawks
Keyword(s):  
Processing Technique ◽  
Video Capture ◽  
Post Processing

scholarly journals ScoreNet: A neural network-based post-processing model for identifying epileptic seizure onset and offset in EEGs

2020 ◽  
Author(s):  
Poomipat Boonyakitanont ◽  
Apiwat Lek-uthai ◽  
Jitkomut Songsiri
Keyword(s):  
Neural Network ◽  
Epileptic Seizure ◽  
False Positive Rate ◽  
Imbalanced Data ◽  
Detection Algorithm ◽  
Processing Technique ◽  
Post Processing ◽  
Scalp Eeg ◽  
Positive Rate ◽  
Eeg Recordings

AbstractThis article aims to design an automatic detection algorithm of epileptic seizure onsets and offsets in scalp EEGs. A proposed scheme consists of two sequential steps: the detection of seizure episodes, and the determination of seizure onsets and offsets in long EEG recordings. We introduce a neural network-based model called ScoreNet as a post-processing technique to determine the seizure onsets and offsets in EEGs. A cost function called a log-dice loss that has an analogous meaning to F1 is proposed to handle an imbalanced data problem. In combination with several classifiers including random forest, CNN, and logistic regression, the ScoreNet is then verified on the CHB-MIT Scalp EEG database. As a result, in seizure detection, the ScoreNet can significantly improve F1 to 70.15% and can considerably reduce false positive rate per hour to 0.05 on average. In addition, we propose detection delay metric, an effective latency index as a summation of the exponential of delays, that includes undetected events into account. The index can provide a better insight into onset and offset detection than conventional time-based metrics.

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