Abstract:
Image processing is a form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or, a set of characteristics or parameters related to the image. Image segmentation is the process by which• individual image pixels are grouped into partitions, according to some intrinsic properties of image i.e. gray levels, contrast, color, texture, shape etc.
This report is a study and implementation of Image segmentation technique to microscopic image and a fingerprint recognition system based on Minutiae based matching quite frequently used in various fingerprint algorithms and techniques. Image Segmentation is one of the most important concerns in digital image Processing. It's a long standing problem in computer vision. Often it is viewed as an ill-defined problem in comparison to other vision tasks which have apparently well defined objectives, such as detection, recognition, and tracking. It is fair to say that computer vision or image understanding is all about parsing images. Different fundamental algorithms of image segmentation are implemented like Edge Linking & Boundary Detection, Region Growing (Similarity Based Segmentation), Iterative Thresholding Method. A new approach of Image Segmentation based on Marker-controlled watershed segmentation. A segmentation based problem to count number of white and black nano particles approximately in a microscopic image of a ceramic material is successfully solved.
The approach mainly involves extraction of minutiae points from the sample fingerprint images and then performing fingerprint matching based on the number of minutiae pairings among two fingerprints in question. Our implementation mainly incorporates image enhancement, image segmentation, feature (minutiae) extraction and minutiae matching. It finally generates a percent score which tells whether two fingerprints match or not. The project is coded in MATLAB.
