Histogram method of image binarization based on fuzzy pixel representation

2017 ◽  
Author(s):  
Egor Pugin ◽  
Arkady Zhiznyakov
Keyword(s):  
Image Binarization ◽  
Histogram Method

scholarly journals Sugeno integral generalization applied to improve adaptive image binarization

2021 ◽  
Vol 68 ◽  
pp. 37-45
Author(s):  
Francesco Bardozzo ◽  
Borja De La Osa ◽  
Ľubomíra Horanská ◽  
Javier Fumanal-Idocin ◽  
Mattia delli Priscoli ◽  
...  
Keyword(s):  
Image Binarization ◽  
Sugeno Integral

Classification and Recognition of Plant Leaf Based on Neural Networks

2011 ◽  
Vol 464 ◽  
pp. 38-42 ◽  
Author(s):  
Ping Ye ◽  
Gui Rong Weng
Keyword(s):  
Neural Networks ◽  
Fractal Dimension ◽  
Recognition Rate ◽  
Image Binarization ◽  
Characteristic Parameters ◽  
Moment Invariant ◽  
Plant Leaf ◽  
The Neural Networks ◽  
Novel Method ◽  
The Moment

This paper proposed a novel method for leaf classification and recognition. In the method, the moment invariant and fractal dimension were regarded as the characteristic parameters of the plant leaf. In order to extract the representative characteristic parameters, pretreatment of the leaf images, including RGB-gray converting, image binarization and leafstalk removing. The extracted leaf characteristic parameters were further utilized as training sets to train the neural networks. The proposed method was proved effectively to reach a recognition rate about 92% for most of the testing leaf samples

The triple histogram method for waste classification

2020 ◽  
Author(s):  
Janusz Bobulski ◽  
Mariusz Kubanek
Keyword(s):  
Histogram Method ◽  
Waste Classification

scholarly journals Unsupervised Neural Domain Adaptation for Document Image Binarization

2021 ◽  
pp. 108099
Author(s):  
Francisco J. Castellanos ◽  
Antonio-Javier Gallego ◽  
Jorge Calvo-Zaragoza
Keyword(s):  
Domain Adaptation ◽  
Document Image ◽  
Image Binarization ◽  
Document Image Binarization

scholarly journals Calcium Identification and Scoring Based on Echocardiography. An Exploratory Study on Aortic Valve Stenosis

2021 ◽  
Vol 11 (7) ◽  
pp. 598
Author(s):  
Luis B. Elvas ◽  
Ana G. Almeida ◽  
Luís Rosario ◽  
Miguel Sales Dias ◽  
João C. Ferreira
Keyword(s):  
Aortic Valve ◽  
Ct Scan ◽  
Pearson Correlation ◽  
Region Of Interest ◽  
Expert Assessment ◽  
Image Binarization ◽  
Real Patient ◽  
Margin Of Error ◽  
Pixel Value ◽  
Echocardiographic Images

Currently, an echocardiography expert is needed to identify calcium in the aortic valve, and a cardiac CT-Scan image is needed for calcium quantification. When performing a CT-scan, the patient is subject to radiation, and therefore the number of CT-scans that can be performed should be limited, restricting the patient’s monitoring. Computer Vision (CV) has opened new opportunities for improved efficiency when extracting knowledge from an image. Applying CV techniques on echocardiography imaging may reduce the medical workload for identifying the calcium and quantifying it, helping doctors to maintain a better tracking of their patients. In our approach, a simple technique to identify and extract the calcium pixel count from echocardiography imaging, was developed by using CV. Based on anonymized real patient echocardiographic images, this approach enables semi-automatic calcium identification. As the brightness of echocardiography images (with the highest intensity corresponding to calcium) vary depending on the acquisition settings, echocardiographic adaptive image binarization has been performed. Given that blood maintains the same intensity on echocardiographic images—being always the darker region—blood areas in the image were used to create an adaptive threshold for binarization. After binarization, the region of interest (ROI) with calcium, was interactively selected by an echocardiography expert and extracted, allowing us to compute a calcium pixel count, corresponding to the spatial amount of calcium. The results obtained from these experiments are encouraging. With this technique, from echocardiographic images collected for the same patient with different acquisition settings and different brightness, obtaining a calcium pixel count, where pixel values show an absolute pixel value margin of error of 3 (on a scale from 0 to 255), achieving a Pearson Correlation of 0.92 indicating a strong correlation with the human expert assessment of calcium area for the same images.

On Perceptually Consistent Image Binarization

2013 ◽  
Vol 20 (1) ◽  
pp. 3-6 ◽  
Author(s):  
Zhongjie Zhu ◽  
Yuer Wang ◽  
Gangyi Jiang
Keyword(s):  
Image Binarization
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