Minimum Entropy Light and Shading Approximation

2006 ◽  
Author(s):  
A. H. Bhalerao
Keyword(s):  
Minimum Entropy

Weighted Minimum Entropy Autofocus Algorithm for ISAR Imaging

2011 ◽  
Vol 33 (8) ◽  
pp. 1809-1815
Author(s):  
Gang Xu ◽  
Lei Yang ◽  
Lei Zhang ◽  
Ya-chao Li ◽  
Meng-dao Xing
Keyword(s):  
Minimum Entropy ◽  
Isar Imaging ◽  
Autofocus Algorithm

scholarly journals Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

2021 ◽  
Vol 11 (14) ◽  
pp. 6590
Author(s):  
Krittakom Srijiranon ◽  
Narissara Eiamkanitchat
Keyword(s):  
Particulate Matter ◽  
Fuzzy Model ◽  
Original Data ◽  
Northern Region ◽  
National Standard ◽  
Minimum Entropy ◽  
Fuzzy Transformation ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Entropy Principle

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

scholarly journals An enhanced binarization framework for degraded historical document images

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wei Xiong ◽  
Lei Zhou ◽  
Ling Yue ◽  
Lirong Li ◽  
Song Wang
Keyword(s):  
Document Image ◽  
Morphological Operations ◽  
Document Images ◽  
Minimum Entropy ◽  
Stroke Width ◽  
Background Estimation ◽  
Structuring Element ◽  
Document Image Binarization ◽  
Benchmark Datasets ◽  
Stroke Width Transform

AbstractBinarization plays an important role in document analysis and recognition (DAR) systems. In this paper, we present our winning algorithm in ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018), which is based on background estimation and energy minimization. First, we adopt mathematical morphological operations to estimate and compensate the document background. It uses a disk-shaped structuring element, whose radius is computed by the minimum entropy-based stroke width transform (SWT). Second, we perform Laplacian energy-based segmentation on the compensated document images. Finally, we implement post-processing to preserve text stroke connectivity and eliminate isolated noise. Experimental results indicate that the proposed method outperforms other state-of-the-art techniques on several public available benchmark datasets.

Maximization of the Signal Impulsiveness Combining Envelope Technique with Minimum Entropy Deconvolution

2013 ◽  
Vol 392 ◽  
pp. 725-729 ◽  
Author(s):  
Rafael José Gomes de Oliveira ◽  
Mauro Hugo Mathias
Keyword(s):  
Test Bench ◽  
Minimum Entropy ◽  
Combined Application ◽  
Recent Developments ◽  
Gear Trains ◽  
The Subject ◽  
Using Data ◽  
High Frequency Resonance ◽  
Combined Technique ◽  
Experimental Test Bench

The application of the HFRT (High-Frequency Resonance Technique), a demodulation based technique, is a technique for evaluation the condition of bearings and other components in rotating machinery. Another technique MED (Minimum Entropy Deconvolution) has been the subject of recent developments for application in condition monitoring of gear trains and roller bearings. This article demonstrates the effectiveness of the combined application of the MED technique with HFRT in order to enhance the capacity of HFRT to identify the characteristic fault frequencies of damaged bearings by increasing the signal impulsivity. All tests were done using data collected from an experimental test bench in laboratory. The Kurtosis value is used as an indicator of effectiveness of the combined technique and the results shown an increase of five times the original kurtosis value with the application of MED filter together with the HFRT.

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