Face Detection from Images Using Support Vector Machine

Detection of patterns in images using classifiers is one of the most promising topics of research in the field of computer vision. A large number of practical applications for face detection exist and contemporary work even suggests that any specialized detectors can be approximated by using fast detection classifiers. In this project, I have developed an algorithm which will detect face from the input image with less false detection rate using combined effects of computer vision concepts. This algorithm utilizes the concept of recognizing skin color, detecting edges and extracting different features from face. The result is supported by the statistics obtained from calculating the parameters defining the parts of the face. The project also implements the highly powerful concept of Support Vector Machine that is used for the classification of images into face and non-face class. This classification is based on the training data set and indicators of luminance value, chrominance value, saturation value, elliptical value and nose, eye & mouth map values

IOBSERVER: species recognition via computer vision

This paper is about the design of an automated computer vision system that is able to recognize the species of fish individuals that are classified into a fishing vessel and produces a report file with that information. This system is called iObserver and it is a part of project Life-iSEAS (Life program).A very first version of the system has been tested at the oceanographic vessel “Miguel Oliver”. At the time of writing a more advanced prototype is being tested onboard other oceanographic vessel: “Vizconde de Eza”. We will describe the hardware design and the algorithms used by the computer vision software.Peer Reviewe

Content based image retrieval for bio-medical images

Content Based Image Retrieval System (CBIR) is used to retrieve images similar to the query image. These systems have a wide range of applications in various fields. Medical subject headings, key words, and bibliographic references can be augmented with the images present within the articles to help clinicians to potentially improve the relevance of articles found in the querying process. In this research, image feature analysis and classification techniques are explored to differentiate images found in biomedical articles which have been categorized based on modality and utility. Examples of features examined in this research include: features based on different histograms of the image, texture features, fractal dimensions etc. Classification algorithms used for categorization were 1) Mean shift clustering 2) Radial basis clustering. Different combinations of features were selected for classification purposes and it was observed that features incorporating soft decision based HSV histogram features give the best results. A library of features was then developed which can be used in RapidMiner. Experimental results for various combinations of features have also been included --Abstract, page iii

Feature-based tracking of multiple people for intelligent video surveillance.

Intelligent video surveillance is the process of performing surveillance task automatically by a computer vision system. It involves detecting and tracking people in the video sequence and understanding their behavior. This thesis addresses the problem of detecting and tracking multiple moving people with unknown background. We have proposed a feature-based framework for tracking, which requires feature extraction and feature matching. We have considered color, size, blob bounding box and motion information as features of people. In our feature-based tracking system, we have proposed to use Pearson correlation coefficient for matching feature-vector with temporal templates. The occlusion problem has been solved by histogram backprojection. Our tracking system is fast and free from assumptions about human structure. We have implemented our tracking system using Visual C++ and OpenCV and tested on real-world images and videos. Experimental results suggest that our tracking system achieved good accuracy and can process videos in 10-15 fps.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .A42. Source: Masters Abstracts International, Volume: 45-01, page: 0347. Thesis (M.Sc.)--University of Windsor (Canada), 2006

Occlusion handling in multiple people tracking

Object tracking with occlusion handling is a challenging problem in automated video surveillance. Occlusion handling and tracking have always been considered as separate modules. We have proposed an automated video surveillance system, which automatically detects occlusions and perform occlusion handling, while the tracker continues to track resulting separated objects. A new approach based on sub-blobbing is presented for tracking objects accurately and steadily, when the target encounters occlusion in video sequences. We have used a feature-based framework for tracking, which involves feature extraction and feature matching

A Second Order TV-type Approach for Inpainting and Denoising Higher Dimensional Combined Cyclic and Vector Space Data

In this paper we consider denoising and inpainting problems for higher dimensional combined cyclic and linear space valued data. These kind of data appear when dealing with nonlinear color spaces such as HSV, and they can be obtained by changing the space domain of, e.g., an optical flow field to polar coordinates. For such nonlinear data spaces, we develop algorithms for the solution of the corresponding second order total variation (TV) type problems for denoising, inpainting as well as the combination of both. We provide a convergence analysis and we apply the algorithms to concrete problems.Comment: revised submitted versio

AN AUTOMATED DENTAL CARIES DETECTION AND SCORING SYSTEM FOR OPTIC IMAGES OF TOOTH OCCLUSAL SURFACE

Dental caries are one of the most prevalent chronic diseases. Worldwide 60 to 90 percent of school children and nearly 100 percent of adults experienced dental caries. The management of dental caries demands detection of carious lesions at early stages. The research of designing diagnostic tools in caries has been at peak for the last decade. This research aims to design an automated system to detect and score dental caries according to the International Caries Detection and Assessment System (ICDAS) guidelines using the optical images of the occlusal tooth surface. There have been numerous works that address the problem of caries detection by using new imaging technologies or advanced measurements. However, no such study has been done to detect and score caries with the use of optical images of the tooth surface. The aim of this dissertation is to develop image processing and machine learning algorithms to address the problem of detection and scoring the caries by the use of optical image of the tooth surface