Evaluation of Conventional Network and Neural Network Techniques For Image Enhancement Parameters
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Abstract
The primary purpose of this study is to evaluate the conventional network and the Neural Network. The evaluation criterion is
based upon edge detection, face detection. Image enhancement improves the quality (clarity) of images for human viewing. Neural
Networks have been developed rapidly during the recent few years and it is extensively applied for the enhancement of the digital image.
For this proposed comparison, the evaluation criteria parameters are implements and analyzes for both neural and conventional network.
One of the purpose of the study was to identify the major factor affecting the image and result are obtained were validated with existing
techniques. This paper focuses on two popular features of image enhancement that are face detection and edge detection. Experiments on
images are implemented to confirm the validity of the proposed analysis. The number of experiments is done on the number of images
which shows that in the edge detection of image the counting of the no. of white pixels are more than the no. of white pixels in
conventional networks. The face detection method counts the single face and multiple faces in the image, which is given for the evaluation.
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Keywords: Conventional networks, edge Detection, Face detection, Neural Networks.
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