Vision-based toddler tracking at home

This paper presents a vision-based toddler tracking system for detecting risk factors of a toddler's fall within the home environment. The risk factors have environmental and behavioral aspects and the research in this paper focuses on the behavioral aspects. Apart from common image processing tasks such as background subtraction, the vision-based toddler tracking involves human classification, acquisition of motion and position information, and handling of regional merges and splits. The human classification is based on dynamic motion vectors of the human body. The center of mass of each contour is detected and connected with the closest center of mass in the next frame to obtain position, speed, and directional information. This tracking system is further enhanced by dealing with regional merges and splits due to multiple object occlusions. In order to identify the merges and splits, two directional detections of closest region centers are conducted between every two successive frames. Merges and splits of a single object due to errors in the background subtraction are also handled. The tracking algorithms have been developed, implemented and tested

Computer assisted screening of digital mammogram images

The use of computer systems to assist clinicians in digital mammography image screening has advantages over traditional methods. Computer algorithms can enhance the appearance of the images and highlight suspicious areas. Screening provides a more thorough examination of the images. Any computer system that does screening of digital mammograms contains components to address multiple tasks such as: image segmentation, mass lesion detection and classification, and microcalcification detection and classification. This dissertation provides both effective and efficient improvements to existing algorithms, which segment mammogram images and locate mass lesions. In addition, we provide a new algorithm to evaluate and report the results for mass lesion detection. The algorithm presented for mammogram segmentation uses a histogram based operator to define the boundaries between the different components of a mammogram image. It employs a unique clustering algorithm to produce closed, labeled sets of pixels which represent the distinct image components. The mass location algorithm uses a variation of template matching to locate suspicious areas. An evaluation of potential templates and algorithms is included. The method for testing and recording the results of the mass location algorithm groups suspicious pixels into regions and then compares them to the pathology

Rotational Broadening and Doppler Tomography of the Quiescent X-Ray Nova Centaurus X-4

We present high and intermediate resolution spectroscopy of the X-ray nova Centaurus X-4 during its quiescent phase. Our analysis of the absorption features supports a K3-K5V spectral classification for the companion star, which contributes approximately 75 % of the total flux at Halpha. Using the high resolution spectra we have measured the secondary star's rotational broadening to be V_rot*sin(i) = 43 +/- 6 km/s and determined a binary mass ratio of q=0.17 +/- 0.06. Combining our results for K_2 and q with the published limits for the binary inclination, we constrain the mass of the compact object and the secondary star to the ranges 0.49 < M_1 < 2.49 Msun and 0.04 < M_2 < 0.58 Msun. A Doppler image of the Halpha line shows emission coming from the secondary star, but no hotspot is present. We discuss the possible origins of this emission.Comment: 10 pages, 4 figures, accepted by MNRA

Thermography based breast cancer detection using texture features and minimum variance quantization

In this paper, we present a system based on feature extraction techniques and image segmentation techniques for detecting and diagnosing abnormal patterns in breast thermograms. The proposed system consists of three major steps: feature extraction, classification into normal and abnormal pattern and segmentation of abnormal pattern. Computed features based on Gray Level Co-occurrence Matrices (GLCM) are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 GLCM features are extracted from thermograms. The ability of feature set in differentiating abnormal from normal tissue is investigated using a Support Vector Machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier. To evaluate the classification performance, five-fold cross validation method and Receiver operating characteristic analysis was performed. The verification results show that the proposed algorithm gives the best classification results using K-Nearest Neighbor classifier and a accuracy of 92.5 %. Image segmentation techniques can play an important role to segment and extract suspected hot regions of interests in the breast infrared images. Three image segmentation techniques: minimum variance quantization, dilation of image and erosion of image are discussed. The hottest regions of thermal breast images are extracted and compared to the original images. According to the results, the proposed method has potential to extract almost exact shape of tumors