English title

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Rice classification and detection of its quality as a main field in the modern agriculture is attracted many researchers in recent years. This problem is a major issue in the scientific and commercial fields associated with modern agriculture. Different processing techniques in recent years are applied to recognize various types of agricultural products. There are also several color-based and texture-based features to achieve the desired results in this classification procedure. In this paper, the problem of rice categorization and quality detection is considered using sparse non-negative matrix factorization algorithm. This technique includes non-negative matrix factorization method with sparsity constraint to achieve dictionaries that represent the structural content of rice variety. Also, these dictionaries are corrected in such a way to yield the dictionaries with least coherence values to each other. The results of the proposed classifier based on the learned models are compared with the results obtained from the neural network and support vector machine classifiers. Simulation results show that the proposed method based on the combinational features is able to identify the type of rice grain and determine its quality with high accuracy rate.
 

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Classification of brain tumors using MRI images along with medical knowledge can lead to proper decision-making on the patient's condition. Also, classification of benign or malignant tumors is one of the challenging issues due to the need for detailed analysis of tumor tissue. Therefore, addressing this field using image processing techniques can be very important. In this paper, various types of texture-based and statistical-based features are used to determine the type of brain tumor and different types of features are applied in this classification procedure. Sparse coding and dictionary learning techniques are used to learn the over-complete models based on the characteristics of each data category. The classification process is carried out based on the calculated energy of the sparse coefficients. Also, the results of this categorization are compared with the results of the classification based on the neural network and support vector machine. The simulation results show that the proposed method based on the selected combinational features and learning the over-complete dictionaries can be able to classify the types of brain tumors precisely.