form of Hindi string... ... middle of paper ... ...zontal edges in blurred image. Then one stage is non maximum suppression, it is an edge thinning technique. Then canny operator trace edges through threshold. Differential edge detection can also be used to obtain edges. The result of it is shown in fig2.4. Fig2.4 Binary image as a result of canny edge detector. 2.3.2 Zone based approach: The output of canny edge detection is used to get features. Two [10 10] zones are created
sadness, surprise, normal and disgust emotion. While for fear and happy emotion, we can distinguish then by saying the teeth often appears and there are more canny edge pixels will be detected in the mouth area. Figure 3.4 Process of detecting lips; a) binary output b) Canny edge detection output c) Combination of binary and canny edge Besides mouth, the portion of eyes and eyebrows will be identified based on some requirement such as the eyes must locate at 60% above from bottom border of the
The Canny edge detection algorithm is commonly known as the optimal edge detector. During his research work, Canny's main intentions were to enhance the edge detectors which were already out at that time. Canny was successful in his objective and published a paper entitled "A Computational Approach to Edge Detection" in which he mentions a list of criteria which could improve current methods of edge detection. According to him, low error rate was one of the important criteria. Secondly, the edges
different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Famous techniques of image segmentation which are still being used by the researchers are Edge Detection, Threshold, Histogram, Region based methods, and Watershed Transformation. There are two types of images i.e. gray scale and color images. Image segmentation for color images is totally different from gray scale images. The property of a
CHAPTER 1 INTRODUCTION 1.1 INTRODUCTION: Image segmentation plays a vital role in Image Analysis and computer vision which is considered as the obstruction in the development of image processing technology, Image segmentation has been the subject of intensive research and a wide variety of segmentation techniques has been reported in the last two decades. Image segmentation is a classical and fundamental problem in computer vision. It refers to partitioning an image into several disjoint subsets