In video surveillance schemes, the motion object detection plays a significant role. To subtract the object background, a segmentation technique based on feature extraction is utilized in which the change in the training rate makes an alteration in the background. Thereafter, the extracted features are trained by using the self‐organizing map (SOM) network in which the weight parameters in the network is optimized with the help of artificial bee colony (ABC) optimization algorithm, so, the proposed methodology is named as HSOM‐ABC technique. This methodology is carried out to perform the classification process in this research. Initially, the whole dataset is preprocessed with the help of grayscale conversion method which converts the original image into grayscale color. After this, fuzzy c‐means clustering is applied to perform the segmentation process and this method divides the foreground and background parts efficiently. Then, feature extraction is done with the help of local binary pattern method which extract the relevant features from the segmented image. Finally, HSOM‐ABC method is proposed to accurate classification process. Hence, the moving objects are identified by categorizing the background and foreground images. MatLab platform is chosen for the proposed work simulation and the performance is evaluated by means of different parameters and it is compared with new existing approaches. Experimental outcomes show that the proposed strategy achieves higher precision value than any other existing methods.
Objective: Diabetic retinopathy is a critical pathological disease condition which affects the lives of millions of people everyday. Exudates found in the eye are one of the important signs of Diabetic retinopathy. This work aims to segment exudates for faster detection and treatment of Diabetic retinopathy.Methods: This paper proposes a robust and efficient method to segment exu-dates. Initial pre-processing work applies adaptive unsharp masking which sharps the areas based on the level of smoothness in the image preventing accentuation of noise. Optic disc is removed by active contour model. The exudates are then segmented by Renyi’s Entropy based thresholding which choses the optimal threshold for segmentation, exploiting Renyi’s entropy da-ta.Results: The performance of the proposed system was evaluated and found better than state of art results giving accuracy, sensitivity and specificity 94.5%, 95.1% and 96.2% respectively.Conclusion: Effective computer aided system is essential for accurate exudates detection. The proposed algorithm utilises the advantages of adaptive unsharp masking in medical image pro-cessing along with Renyi’s entropy based thresholding to detect Exudates, which performs better than traditional thresholding techniques.
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