Automatic target image detection for morphing
In this thesis,we propose a novel approach for automatic target image detection for morphing based on 3D textons and contrast. Given the source image consisting of human frontal face and training images having human and animal faces our algorithm finds the target image automatically from the target database. There are two major advantages of our approach. It solves the problem of manual selection of target image as done by the researchers in morphing community. By detecting it automatically, one may achieve smooth transition from source to destination. Our algorithm aims at finding the best target animal face image considering human face as a source. A histogram model based on 3D textons and contrast is built and chi-square distance is used between the histogram models of source and target images to find the best target. After detecting the target image, the control points for the source and target image are automatically detected using facial geometry, eye map operator and K-means clustering. The superiority of our algorithm over other methods is that it just needs source image and training database and the entire morphing process is done automatically. The experiments were conducted using four class of images that include human, cheetah, lion and monkey respectively in which human class is used as the source. Our target detection results are verified using Structural Similarity Index (SSIM) measure between source and intermediate morphed image. Experiments on a fairly large dataset have been carried out to show the usefulness and capability of our method.
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