Encrypted Image Transmission Over OFDM System

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Encrypted Image Transmission Over OFDM System

The project presents object characteristics analysis using image processing techniques for an automated vision system used at the agricultural field.? In agriculture research of automatic object characteristics detection is essential in monitoring large fields of crops, and thus automatically detects symptoms of object characteristics as soon as they appear on plant leaves. The proposed decision-making system utilizes image content characterization and a supervised classifier type of neural network. Image processing techniques for this kind of decision analysis involve preprocessing, feature extraction and a classification stage. At Processing, an input image will be resized and region of interest selection performed if needed. Here, color and texture features are extracted from the input for network training and classification. Color features like mean, the standard deviation of HSV color space, and texture features like energy, contrast, homogeneity, and correlation. The system will be used to classify the test images automatically to decide object characteristics. For this approach, the automatic classifier NN is used for classification based on learning with some training samples of that same category. This network uses the tangent sigmoid function as the kernel function. Finally, the simulated result shows that the used network classifier provides minimum error during training and better accuracy in classification. Encrypted Image Transmission Over OFDM System.

Encrypted Image Transmission Over OFDM System

Existing method

  1. SVM Classifier
  2. Random tree classifier
  3. K-means clustering


  • Feature Extraction
  • CNN


  • Preprocessing
  • Color Space Conversion
  • Color and Texture Features Extraction
  • NN classifier


  1. Inaccurate results
  2. Need a large number of training datasets
  • Performance is less




Encrypted Image Transmission Over OFDM System


  • [1] M. Bosch, F. Zhu, N. Khanna, C. Boushey, and E. Delp. Combining global and local features for food identification dietary assessment. image processing (ICIP), 201118th IEEE International Conference on, pages 1789? 1792.IEEE, 2011.
  • [2] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines.ACM Transactions on Intelligent Systems and Technology, 2:27:1?27:27, 2011. Software available athttp://www.csie.ntu.edu.tw/ chain/libsvm.
  • [3] M. Chen, K. Dhingra, W. Wu, L. Yang, R. Sukthankar, andJ. Yang. Pfid: Pittsburgh fast-food image dataset. in-age Processing (ICIP), 2009 16th IEEE International Conference on, pages 289?292. IEEE, 2009.
  • [4] G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray.Visual categorization with bags of keypoints. InWorkshopon statistical learning in computer vision, ECCV, volume 1,page 22, 2004.
  • [5] H. Hoashi, T. Joutou, and K. Yanai. Image recognition of 85food categories by feature fusion. multimedia (ISM), 2010IEEE International Symposium on, pages 296? 301. IEEE,2010.

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