Image Processing Representation Using Binary Image; Grayscale, Color Image, and Histogram

Mohan, V.; Durga, R. Kanaka; Devathi, Swathi; Raju, K. Srujan

doi:10.1007/978-81-322-2526-3_37

Cited by 14 publications

(6 citation statements)

References 8 publications

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“…The black side is the weakest while the white has the strongest value [31]. The grey value corresponds with the brightness capacity [32]. The next step is to change the grayscale to a binary image [33].…”

Section: Methodsmentioning

confidence: 99%

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Setiawan¹,

Hadiyanto²,

Widodo³

2022

RIA

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Shrimp is a marine culture found globally due to the ability of its yields to boost a country's economy. It is imperative to monitor its size to determine the condition of the shrimp underwater with complex noise using a non-invasive method. Therefore, this study aims to develop a new method for measuring the body weight of shrimp using morphometric features based on underwater image analysis and a machine learning approach. The method used consists of several steps, data collection using an underwater camera, image analysis using image grayscale, image binary, edge detection, region of interest detection, shrimp image morphometric features extraction, camera calibration using Triangle Similarity (TS), and Correction Factor (CF), calculation of morphometric features value, create machine learning model, training data, and testing data for estimation of underwater shrimp body weight. After testing the model, get the best accuracy value is RMSE = 0.05, MAE = 0.04, and R2 = 0.96 from the MLR method. In conclusion, the results showed that the hybrid method TS-CF-MLR is the best method for measuring underwater shrimp body weight estimation with the lowest error rate and highest coefficient of determination.

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Section: Methodsmentioning

confidence: 99%

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Setiawan¹,

Hadiyanto²,

Widodo³

2022

RIA

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“…3) Binary Image: Binary Image processing is a process for converting a gray image into a black and white image [21]. The pixel value is changed based on the threshold value, that is, by calculating the average value of the degrees of gray.…”

Section: A Image Processingmentioning

confidence: 99%

Implementation of Gabor Wavelet and Support Vector Machine for Braille Recognition

Perwira¹,

Florestiyanto²,

Nurjanah³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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Visually impaired people use braille letters to write and read, which not all people with normal vision can read. It causes parents of children with impaired vision to have difficulties in assisting them in learning at home. At the same time, the involvement of parents in children's learning assistance is needed to monitor their learning progress. In this research, braille letters are identified through images taken using a scanner as a tool to input the images. Then, the Canny edge detection method is used obtain all the edges of each braille dot. Feature extraction is applied to obtain all characteristics of each letter, and the method used is the Gabor Wavelet. The features which are utilized include standard deviation, mean, variance, and median with a theta angle of 0 0 , 30 0 , 45 0 , 60 0 , 120 0 , 135 0 , 180 0 and wavelengths of 3, 6, 13, 28, and 58. These features are combined and used as test data and training data for the Support Vector Machine (SVM) classification stage and produce letters and words in alphabetic letter forms. Braille letters that can be identified in this research are small letters, capital letters, punctuation marks, and numbers. Tests are carried out using a multi-class confusion matrix scenario to determine the level of accuracy, precision, and recall. Based on the results of the tests conducted using 758 braille letters, the accuracy value is 98.15%; the precision value is 97.66%; and the recall value is 98.28%.

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“…We added an image marker layer to the generator network using the image binarization algorithm. For the algorithm's selection of the binarized plant lesion image, we compared Iterative Self-Organizing Data Analysis Technique (ISODATA) [51], the histogram-based threshold algorithm analysis [52], and the image binarization through image filtering and histograms [53], which are found in Table A4 of Appendix A. Because the number of samples was too small to train the segmentation network, the lesion and leaf area pixel value of the plant lesion image had a more obvious bimodal trend.…”

Section: Image Marker Layermentioning

confidence: 99%

Data Enhancement for Plant Disease Classification Using Generated Lesions

et al. 2020

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Deep learning has recently shown promising results in plant lesion recognition. However, a deep learning network requires a large amount of data for training, but because some plant lesion data is difficult to obtain and very similar in structure, we must generate complete plant lesion leaf images to augment the dataset. To solve this problem, this paper proposes a method to generate complete and scarce plant lesion leaf images to improve the recognition accuracy of the classification network. The advantages of our study include: (i) proposing a binary generator network to solve the problem of how a generative adversarial network (GAN) generates a lesion image with a specific shape and (ii) using the edge-smoothing and image pyramid algorithm to solve the problem that occurs when synthesizing a complete lesion leaf image where the synthetic edge pixels are different and the network output size is fixed but the real lesion size is random. Compared with the recognition accuracy of human experts and AlexNet, it was shown that our method can effectively expand the plant lesion dataset and improve the recognition accuracy of a classification network.

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Image Processing Representation Using Binary Image; Grayscale, Color Image, and Histogram

Cited by 14 publications

References 8 publications

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Implementation of Gabor Wavelet and Support Vector Machine for Braille Recognition

Data Enhancement for Plant Disease Classification Using Generated Lesions

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