Human Face Segmentation and Identification

(Also cross-referenced as CAR-TR-695) This thesis considers segmentation and identification of human faces from grey scale images with clutter. The segmentation developed utilizes the elliptical structure of the human head. It uses the information present in the edge map of the image and thr ough some preprocessing separates the head from the background clutter. An ellipse is then fitted to mark the boundary between the head region and the background. The identification procedure finds feature points in the segmented face through a Gabor wave let decomposition and performs graph matching. The segmentation and identification algorithms were tested on a database of 48 images of 16 persons with encouraging results

Detection of Polyps via Shape and Appearance Modeling

Presented at the MICCAI 2008 Workshop on Computational and Visualization Challenges in the New Era of Virtual Colonoscopy, September 6, 2008, New York, USA.This paper describes a CAD system for the detection of colorectal polyps in CT. It is based on stochastic shape and appearance modeling of structures of the colon and rectum, in contrast to the data-driven approaches more commonly found in the literature it derives predictive stochastic models for the features used for classification. The method makes extensive use of medical domain knowledge in the design of the models and in the setting of their parameters. The proposed approach was successfully tested on challenging datasets acquired under a protocol with little colonic preparation; such protocol reduces patient discomfort and potentially improves compliance

Human Face SEGMENTATION AND IDENTIFICATION

This thesis considers segmentation and identification of human faces from grey scale images with clutter. The segmentation developed utilizes the elliptical structure of the human head. It uses the information present in the edge map of the image and through some preprocessing separates the head from the background clutter. An ellipse is then fitted to mark the boundary between the head region and the background. The identification procedure finds feature points in the segmented face through a Gabor wavelet decomposition and performs graph matching. The segmentation and identification algorithms were tested on a database of 48 images of 16 persons with encouraging results. The support of the Advanced Research Projects Agency (ARPA Order No. 8459) and the U.S. Army Topographic Engineering Center under Contract DACA76-92-C-0009 is gratefully acknowledged, as is the help of Sandy German in preparing this paper. Dedication To my grandmother Masooda Begum and my parents Iftikhar A. Sir..

S.A.: A probabilistic model for haustral curvatures with applications to colon cad

Abstract. Among the many features used for classification in computer-aided detection (CAD) systems targeting colonic polyps, those based on differences between the shapes of polyps and folds are most common. We introduce here an explicit parametric model for the haustra or colon wall. The proposed model captures the overall shape of the haustra and we use it to derive the probability distribution of features relevant to polyp detection. The usefulness of the model is demonstrated through its application to a colon CAD algorithm.