Proceedings ArticleDOI
Novel PCA-based color-to-gray image conversion
Ja-Won Seo,Seong-Dae Kim +1 more
- pp 2279-2283
TLDR
Experimental results demonstrate that the proposed ELSSP (Eigenvalue-weighted Linear Sum of Subspace Projections) method is superior to the state-of-the-art methods in terms of both conversion speed and image quality.Abstract:
In this paper, we present a novel color-to-gray image conversion method which preserves both color and texture discriminabilities effectively. Unlike previous approaches, the proposed method does not require any user-specific parameters for conversion. Moreover, the computational complexity is low enough to be applied to real-time applications. These breakthroughs are achieved by applying the ELSSP (Eigenvalue-weighted Linear Sum of Subspace Projections) method, which is proposed in this paper for the color-to-gray image conversion. Experimental results demonstrate that the proposed method is superior to the state-of-the-art methods in terms of both conversion speed and image quality.read more
Citations
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Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter
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Fréchet PDF based Matched Filter Approach for Retinal Blood Vessels Segmentation.
TL;DR: A novel matched filter approach based on Fréchet probability distribution function that outperforms over latest and prominent works reported in the literature is proposed and can be observed that the cause of improved performance is due to better matching between vessel profile and FrÉchet template.
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Machine Learning-Based Classification of Good and Rotten Apple
TL;DR: This paper focuses on the classification of rotten and good apple and proposes a proposed approach by using SVM classifier, which is found better with respect to the other classifiers.
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A Novel Framework for Optimal RGB to Grayscale Image Conversion
Yi Wan,Qisong Xie +1 more
TL;DR: This paper proposes an entropy-based optimization framework to choose the optimal line direction so that all the pixel color vectors in an image have the most spread-out projections, thus increasing the grayscale image contrast.
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Mushroom Classification Using Feature-Based Machine Learning Approach.
TL;DR: This paper focuses on developing a method for classification of mushroom using its texture feature, which is based on the machine learning approach and the performance of the proposed approach is found better with respect to the other classifiers like KNN, Logistic Regression, Linear Discriminant, Decision Tree, and Ensemble classifiers.
References
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Reference EntryDOI
Principal Component Analysis
TL;DR: Principal component analysis (PCA) as discussed by the authors replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables.
Journal ArticleDOI
Color2Gray: salience-preserving color removal
TL;DR: The Color2Gray results offer viewers salient information missing from previous grayscale image creation methods.
Journal ArticleDOI
Decolorize: Fast, contrast enhancing, color to grayscale conversion
Mark Grundland,Neil A. Dodgson +1 more
TL;DR: This work presents a new contrast enhancing color to grayscale conversion algorithm which works in real-time and has the advantages of continuous mapping, global consistency, andgrayscale preservation, as well as predictable luminance, saturation, and hue ordering properties.
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