Precise Statistical Approach for Leaf Segmentation
| Autor: | Adel Khelifi, Shams Shaker, Ayman El-Baz, Mohammed Ghazal, Ahmed Shalaby, Ali Mahmoud |
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| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Pixel Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Image segmentation 01 natural sciences Grayscale Support vector machine Region of interest 0202 electrical engineering electronic engineering information engineering RGB color model 020201 artificial intelligence & image processing Segmentation Artificial intelligence business 010606 plant biology & botany |
| Zdroj: | ICIP |
| Popis: | One thing that assists in automatic environmental monitoring is leaf segmentation. By segmenting a leaf, image-based leaf health assessment can be performed which is crucial in maintaining the effectiveness of the environmental balance. This paper presents a technique that serves an accurate framework for diseased leaf segmentation from Coloured imaged. In other words, this method works to use information generated from RGB images that we have stored in our data base to represent the current input image. To achieve such technique, four main steps were constructed: 1) Using contrast variations to characterize the region of interest (ROI) of a given leaf which enhances the accuracy of the segmentation using minimal time. 2) using linear combination of discrete Gaussians (LCDG) to represent the visual appearance of the input image and to assume the marginal probability distributions of the three regions of interest classes. 3) Using information generated from RGB images that we have stored in our data base to calculate the probabilities of the three classes on a pixel basis in step two. 4) Lastly, clarifying the labels with Gauss-Markov random field model (GGMRF) to maintain the continuity. After all these steps, the experimental validation promised high accuracy. |
| Databáze: | OpenAIRE |
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