Image segmentation is a critical problem in computer vision and other image processing applications. Image segmentation has become quite challenging over the years due to its widespread use in a variety of applications. Image thresholding is a popular image segmentation technique. The segmented image quality is determined by the techniques used to determine the threshold value.A locally adaptive thresholding method based on neighborhood processing is presented in this paper. The performance of locally thresholding methods like Niblack and Sauvola was demonstrated using real-world images, printed text, and handwritten text images. Threshold-based segmentation methods were investigated using misclassification error, MSE and PSNR. Experiments have shown that the Sauvola method outperforms real-world images, printed and handwritten text images in terms of misclassification error, PSNR, and MSE.
Coins have very much importance in human's day to day life, which are used in everyone's daily routine like banks, super markets, vending machines etc. So, there is a basic need to automate the counting and sorting of coins. Coin recognition applications play an important role in industry and computer vision. Many approaches developed for the coin detection and calculate its corresponding values. This paper recognizes Indian coins of different denomination The recognition process consists of three steps, 1) we present a simple and fast method for coin segmentation, based on morphological thresholding technique to remove noise and to enhance the quality of coin image, 2) we applied some simple descriptors like mean intensity, area, perimeter to extract the regional features of the coins used for recognition and sorting and 3) we performed edge detection using statistical operators. Only after detecting the edges in the image the number can be recognized. In this paper we describe the pattern recognition method used for identification of coins in the new coin recognition and sorting system.
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