Klasifikasi Beras Menggunakan Metode K-Means Clustering Berbasis Pengolahan Citra Digital

Rice plants (Oryza Sativa L.) are important food crops that have become a staple food for more than half of the world's population. In Indonesia rice is the main commodity in supporting community food. The process of processing rice into rice is done in two ways, namely the processing of pulverized and modern processing using a grinding tool. Rice is an important component in daily food. There are several types on the market, namely: fragrant pandan rice, IR 64, IR 42, C 4, and others. With the variety of forms and types of rice on the market, there are many weaknesses that humans have in perceiving the classification of rice using the senses of vision. Therefore, digital image processing techniques are needed to help analyze the type of rice. This study aims to analyze the type of rice using the K-Means Clustering method based on RGB colors. Before the K-Means calculation, the RGB color feature extraction process must be carried out to get the red value, green value, blue value in each image. The results of this study found that image processing to determine the type of rice using the k-means clustering method can help users to know the type of rice.

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Identification and Classification of Lung Nodules Using Neural Networks

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i6.4799 ◽

2021 ◽

Vol 12 (6) ◽

pp. 1956-1961

Author(s):

Dr. S. Gnanavel Et al.

Keyword(s):

Lung Cancer ◽

Image Processing ◽

Feature Extraction ◽

Digital Image Processing ◽

Digital Image ◽

Extraction Process ◽

Health Concern ◽

Image Processing Techniques ◽

Processing Techniques

Lung cancer is a serious health concern, which is also one of the major types of cancer that has a profound impact on the overall cancer mortality rates. The detection of lung cancer nodules is quite a challenge as the major challenge is the structure of the cancer nodules; here the cells are imbricated with each other. The prediction and classification of lung cancer is done by applying digital image processing techniques to the acquired input images of the nodules. This methodology also aids early detection which in turns reduces the criticality of the condition and provides scope for early intervention and treatment. The prediction methodology involves extracting several features of the lung cancer cell and then applying pattern-based prediction techniques. In recent times, owing to the fact that the time and execution parameters are very important aspects to detect the abnormality of the fast-spreading cancer cells, digital image processing techniques are being widely deployed. The fundamental factors of this research are the quality of image assessment and the precision of feature extraction. Following our proposed methodology, a clear picture of the region of interest is obtained which acts as a basis for the feature extraction process. Here an overall evaluation of the digital image processing techniques used by previous scholars for the finding and classification of lung cancer nodules have also been emphasised.

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Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

Computational Intelligence and Neuroscience ◽

10.1155/2018/1312787 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 5

Author(s):

Nhat-Duc Hoang

Keyword(s):

Image Processing ◽

Computational Intelligence ◽

Asphalt Pavement ◽

Classification Model ◽

Laplacian Pyramid ◽

Pavement Condition ◽

Crack Patterns ◽

Image Processing Techniques ◽

Processing Techniques

To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features. LSSVM is employed for data classification. In addition, the model construction phase of LSSVM requires a proper setting of the regularization and kernel function parameters. This study relies on DFP to fine-tune these two parameters of LSSVM. A dataset consisting of 500 image samples and five class labels of alligator crack, diagonal crack, longitudinal crack, no crack, and transverse crack has been collected to train and verify the established approach. The experimental results show that the Laplacian pyramid is really helpful to enhance the pavement images and reveal the crack patterns. Moreover, the hybridization of LSSVM and DFP, named as DFP-LSSVM, used with the Laplacian pyramid at the level 4 can help us to achieve the highest classification accuracy rate of 93.04%. Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying.

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Classification Of Wheat Grains Using Image Processing Techniques Based Neuro-Fuzzy System Model

Journal of Intelligent Systems with Applications ◽

10.54856/jiswa.201805018 ◽

2018 ◽

pp. 57-61

Author(s):

Ahmet Kayabasi ◽

Kadir Sabanci ◽

Abdurrahim Toktas

Keyword(s):

Image Processing ◽

Fuzzy System ◽

Absolute Error ◽

Data Set ◽

Wheat Grains ◽

Image Processing Techniques ◽

Neuro Fuzzy ◽

Length Width ◽

Processing Techniques

In this study, an image processing techniques (IPTs) and a Sugeno-typed neuro-fuzzy system (NFS) model is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the NFS model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then NFS model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The proposed NFS model numerically calculate the outputs with mean absolute error (MAE) of 0.0312 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based NFS model can be successfully applied to classification of wheat grains.

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