scholarly journals Leaf Diseases Classification on Peanut Leaves Based on Texture and Colour Features

2021 ◽  
Vol 10 (6) ◽  
pp. 149-155
Author(s):  
Brahma Ratih Rahayu F. ◽  
Panca Mudjirahardjo ◽  
Muhammad Aziz Muslim
Keyword(s):  
Technology Development ◽  
Texture Features ◽  
Plant Diseases ◽  
Food Crop ◽  
Normal Leaf ◽  
Colour Space ◽  
Colour Features ◽  
Hsv Colour Space ◽  
Multiclass Svm ◽  
Development Application

Peanuts are a food crop commodity that Indonesians widely consume as a vegetable fat and protein source. However, the quality and quantity of peanut productivity may decline, one of which is due to plant diseases. Efforts that can be made to maintain peanut productivity are the application of technology to detect peanut plant diseases early; thus, disease control can be carried out earlier. This study presents a technology development application, particularly digital image processing, to identify disease features of infected peanut leaves based on GLCM texture features and colour features in the HSV colour space and classified using the SVM method. The development of the SVM method that is applied is the Multiclass SVM with the DAGSVM strategy, which can classify more than two classes. Based on the experimental results, it confirms that the combination of HSV colour features and GLCM texture features with an angular orientation of 0 degrees and classified by the Multiclass SVM method with polynomial kernels produces the highest accuracy, i.e. 99.1667% for leaf spot class, 97.5% for leaf rust class, 98.8333% for eyespot class, 100% for normal leaf class and 100% for other leaf class.

scholarly journals Features for detecting smoke in laparoscopic videos

2017 ◽  
Vol 3 (2) ◽  
pp. 521-524
Author(s):  
Nour Aldeen Jalal ◽  
Tamer Abdulbaki Alshirbaji ◽  
Lars Mündermann ◽  
Knut Möller
Keyword(s):  
Laparoscopic Surgery ◽  
Texture Features ◽  
Surveillance Systems ◽  
Smoke Detection ◽  
Colour Space ◽  
Potential Applications ◽  
Hsv Colour Space ◽  
Occurrence Matrix ◽  
Laparoscopic Videos ◽  
Smoke Removal

AbstractVideo-based smoke detection in laparoscopic surgery has different potential applications, such as the automatic addressing of surgical events associated with the electrocauterization task and the development of automatic smoke removal. In the literature, video-based smoke detection has been studied widely for fire surveillance systems. Nevertheless, the proposed methods are insufficient for smoke detection in laparoscopic videos because they often depend on assumptions which rarely hold in laparoscopic surgery such as static camera. In this paper, ten visual features based on motion, texture and colour of smoke are proposed and evaluated for smoke detection in laparoscopic videos. These features are RGB channels, energy-based feature, texture features based on gray level co-occurrence matrix (GLCM), HSV colour space feature, features based on the detection of moving regions using optical flow and the smoke colour in HSV colour space. These features were tested on four laparoscopic cholecystectomy videos. Experimental observations show that each feature can provide valuable information in performing the smoke detection task. However, each feature has weaknesses to detect the presence of smoke in some cases. By combining all proposed features smoke with high and even low density can be identified robustly and the classification accuracy increases significantly.

scholarly journals Research on the Application of Artificial Intelligence Technology in Computer Graphics Processing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042037
Author(s):  
Xia Yang
Keyword(s):  
Artificial Intelligence ◽  
Threshold Value ◽  
Shooting Methods ◽  
Colour Space ◽  
Space Transformation ◽  
Artificial Intelligence Technology ◽  
Colour Saturation ◽  
Hsv Colour Space ◽  
Graphics Processing ◽  
Bright Area

Abstract In structured light geometric reconstruction, due to the complexity of shooting methods and scene lighting conditions, the resulting images may be lack of image details due to uneven light. For this reason, the article proposes a Retinex algorithm with colour restoration and colour saturation correction strategy based on HSV colour space transformation based on artificial intelligence technology. Then distinguish whether it is a bright area according to the threshold value, and modify the insufficient transmittance estimation of the bright area. Finally, the intensity component and saturation value are restored in the HIS colour space, and the histogram is used to stretch the intensity component.

scholarly journals Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features

Cancers ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1937 ◽  
Author(s):  
Subrata Bhattacharjee ◽  
Cho-Hee Kim ◽  
Hyeon-Gyun Park ◽  
Deekshitha Prakash ◽  
Nuwan Madusanka ◽  
...  
Keyword(s):  
Texture Features ◽  
Haar Wavelet ◽  
Histological Sections ◽  
Colour Features ◽  
R Programming ◽  
Grade 3 ◽  
Occurrence Matrix ◽  
Mlp Classifier ◽  
Prostate Carcinomas

Microscopic biopsy images are coloured in nature because pathologists use the haematoxylin and eosin chemical colour dyes for biopsy examinations. In this study, biopsy images are used for histological grading and the analysis of benign and malignant prostate tissues. The following PCa grades are analysed in the present study: benign, grade 3, grade 4, and grade 5. Biopsy imaging has become increasingly important for the clinical assessment of PCa. In order to analyse and classify the histological grades of prostate carcinomas, pixel-based colour moment descriptor (PCMD) and gray-level co-occurrence matrix (GLCM) methods were used to extract the most significant features for multilayer perceptron (MLP) neural network classification. Haar wavelet transformation was carried out to extract GLCM texture features, and colour features were extracted from RGB (red/green/blue) colour images of prostate tissues. The MANOVA statistical test was performed to select significant features based on F-values and P-values using the R programming language. We obtained an average highest accuracy of 92.7% using level-1 wavelet texture and colour features. The MLP classifier performed well, and our study shows promising results based on multi-feature classification of histological sections of prostate carcinomas.

Recognition and Classification of Food Grains, Fruits and Flowers Using Machine Vision

2009 ◽  
Vol 5 (4) ◽  
Author(s):  
Dayanand G Savakar ◽  
Basavaraj S Anami
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Texture Features ◽  
Morphological Features ◽  
Machine Vision System ◽  
Food Grains ◽  
Colour Features ◽  
Classification Of Images ◽  
Color Texture

In this paper, we have presented different methodologies devised for recognition and classification of images of agricultural/horticultural produce. A classifier based on BPNN is developed which uses the color, texture and morphological features to recognize and classify the different agricultural/horticultural produce. Even though these features have given different accuracies in isolation for varieties of food grains, mangoes and jasmine flowers, the combination of features proved to be very effective. The average recognition and classification accuracies using colour features are 87.5%, 78.4% and 75.7% for food grains, mango and jasmine flowers, respectively, and the average accuracies have increased to 90.8%, 80.2% and 85.8% for food grains, mangoes and jasmine flowers ,respectively, using texture features. The average accuracies have increased to 94.1%, 84.0% and 90.1% for food grains, mangoes and jasmine flowers, respectively. The results are encouraging and promise a good machine vision system in the area of recognition and classification of agricultural/horticultural produce.

scholarly journals The use of an extended set of key texture features Haralick in the diagnosis of plant diseases on leaf images

2019 ◽  
Vol 25 ◽  
pp. 122-127
Author(s):  
V. S. Tutygin ◽  
Х. М. А. Al-Vindi Basim ◽  
D. O. Leliuhin
Keyword(s):  
Texture Features ◽  
Plant Diseases

Using Fuzzy Segmentation for Colour Image Enhancement of Computed Tomography Perfusion Images

2011 ◽  
pp. 253-262
Author(s):  
Martin Tabakov
Keyword(s):  
Computed Tomography ◽  
Image Enhancement ◽  
Brain Perfusion ◽  
Colour Space ◽  
Computed Tomography Perfusion ◽  
Fuzzy Similarity ◽  
Colour Image ◽  
Fuzzy Similarity Relation ◽  
Hsv Colour Space ◽  
Fuzzy Segmentation

This chapter presents a methodology for an image enhancement process of computed tomography perfusion images by means of partition generated with appropriately defined fuzzy relation. The proposed image processing is used to improve the radiological analysis of the brain perfusion. Colour image segmentation is a process of dividing the pixels of an image in several homogenously- coloured and topologically connected groups, called regions. As the concept of homogeneity in a colour space is imprecise, a measure of dependency between the elements of such a space is introduced. The proposed measure is based on a pixel metric defined in the HSV colour space. By this measure a fuzzy similarity relation is defined, which next is used to introduce a clustering method that generates a partition, and so a segmentation. The achieved segmentation results are used to enhance the considered computed tomography perfusion images with the purpose of improving the corresponding radiological recognition.

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