Document Type: Original Article


Department of Electrical Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran.


The main purpose of this paper was to introduce an efficient algorithm for fault identification in fruits images. First, input image was de-noised using the combination of Block Matching and 3D filtering (BM3D) and Principle Component Analysis (PCA) model. Afterward, in order to reduce the size of images and increase the execution speed, refined Discrete Cosine Transform (DCT) algorithm was utilized. Finally, for segmentation, fuzzy clustering algorithm with spatial information was applied on the compressed image. Implementation results in MATLAB environment and based on the gathered data by the author showed that the proposed algorithm contains a good capability in de-noising. Also, in the proposed method, identification accuracy of faulty regions in fruit was higher than other methods. The major advantage of the proposed method was its high speed which makes it appropriate for real time applications. 

Graphical Abstract


  • Using the combination of BM3D and PCA model lead to reduce the noise
  • The modified DCT algorithm leads to compress the image
  • Applying the Spatially Fuzzy-c-means algorithm on compressed image leads to cluster the image into two or three classes
  • The main advantage of the proposed algorithm is its high speed, as we can utilize this algorithm in on-line applications


Main Subjects

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