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(1 - 4 of 4)
- Title
- REAL-TIME FACEDETECTION ANDRECOGNITION SYSTEM INCOMPLEX BACKGROUNDS
- Creator
- Zhang, Xin
- Date
- 2015, 2015-07
- Description
-
This report provides a fast and reliable system for real-time face detection and recognition in complex backgrounds. Most current face...
Show moreThis report provides a fast and reliable system for real-time face detection and recognition in complex backgrounds. Most current face recognition systems identify faces under constrained conditions, such as constant lighting condition, the same background. In the real world, people need to be recognized in complex backgrounds under different conditions, such as tilted head poses, various facial expressions, dark or strong lighting conditions. Meanwhile, because of large amounts of real-time applications for face recognition, such as intelligent robot, unmanned vehicle, security monitor, the fast face recognition rate needs to be satisfied for the real-time requirement. In this project, a fast and reliable system is designed to real-time detect and recognize faces under various conditions. Frames are obtained directly from VGA camera. Image preprocessing and face detection, collection, recognition are sequentially implemented on the frames. Local binary patterns and Haar features are used for face detection and eye detection. Local binary pattern encodes every pixel of the image for texture extraction, which is several times faster than Haar feature detection. Adaptive boosting algorithm is used for selecting the best weak classifiers and cascading method divides the extracted best classifiers into several stages to enhance detection rate. Affine transformation is implemented to unify the size of detected facial images and align two eyes to the desired position for improving recognition accuracy. 33 Gaussian filter is designed to remove noises of the pre-processed facial images. Principal component analysis (PCA) is used for face recognition, which is fast to identify high-dimensional faces with few principal components.
M.S. in Electrical Engineering, July 2015
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- Title
- IMPACT OF DATA SHAPE, FIDELITY, AND INTER-OBSERVER REPRODUCIBILITY ON CARDIAC MAGNETIC RESONANCE IMAGE PIPELINES
- Creator
- Obioma, Blessing Ngozi
- Date
- 2020
- Description
-
Artificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical...
Show moreArtificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical diagnosis, disease prediction, and treatment, with such interests intensifying in the medical image field. AI can automate various cumbersome data processing techniques in medical imaging such as segmentation of left ventricular chambers and image-based classification of diseases. However, full clinical implementation and adaptation of emerging AI-based tools face challenges due to the inherently opaque nature of such AI algorithms based on Deep Neural Networks (DNN), for which computer-trained bias is not only difficult to detect by physician users but is also difficult to safely design in software development. In this work, we examine AI application in Cardiac Magnetic Resonance (CMR) using an automated image classification task, and thereby propose an AI quality control framework design that differentially evaluates the black-box DNN via carefully prepared input data with shape and fidelity variations to probe system responses to these variations. Two variants of the Visual Geometric Graphics with 19 neural layers (VGG19) was used for classification, with a total of 60,000 CMR images. Findings from this work provides insights on the importance of quality training data preparation and demonstrates the importance of data shape variability. It also provides gateway for computation performance optimization in training and validation time.
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- Title
- Towards Assisting Human-Human Conversations
- Creator
- Nanaware, Tejas Suryakant
- Date
- 2021
- Description
-
The idea of the research is to understand the open-topic conversations and ways to provide assistance to humans who face difficulties in...
Show moreThe idea of the research is to understand the open-topic conversations and ways to provide assistance to humans who face difficulties in initiating conversations and overcome social anxiety so as to be able to talk and have successful conversations. By providing humans with assistive conversational support, we can augment the conversation that can be carried out. The AdvisorBot can also help to reduce the time taken to type and convey the message if the AdvisorBot is context aware and capable of providing good responses.There has been a significant research for creating conversational chatbots in open-domain conversations that have claimed to have passed the Turing Test and can converse with humans while not seeming like a bot. However, if these chatbots can converse like humans, can they provide actual assistance in human conversations? This research study observes and improves the advanced open-domain conversational chatbots that are put in practice for providing conversational assistance.While performing this thesis research, the chatbots were deployed to provide conversational assistance and a human study was performed to identify and improve the ways to tackle social anxiety by connecting strangers to perform conversations that would be aided by AdvisorBot. Through the questionnaires that the research subjects filled during their participation, and by performing linguistic analysis, the quality of the AdvisorBot can be improved so that humans can achieve better conversational skills and are able to clearly convey their message while conversing. The results were further enhanced by using transfer learning techniques and quickly improve the quality of the AdvisorBot.
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- Title
- IMPACT OF DATA SHAPE, FIDELITY, AND INTER-OBSERVER REPRODUCIBILITY ON CARDIAC MAGNETIC RESONANCE IMAGE PIPELINES
- Creator
- Obioma, Blessing Ngozi
- Date
- 2020
- Description
-
Artificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical...
Show moreArtificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical diagnosis, disease prediction, and treatment, with such interests intensifying in the medical image field. AI can automate various cumbersome data processing techniques in medical imaging such as segmentation of left ventricular chambers and image-based classification of diseases. However, full clinical implementation and adaptation of emerging AI-based tools face challenges due to the inherently opaque nature of such AI algorithms based on Deep Neural Networks (DNN), for which computer-trained bias is not only difficult to detect by physician users but is also difficult to safely design in software development. In this work, we examine AI application in Cardiac Magnetic Resonance (CMR) using an automated image classification task, and thereby propose an AI quality control framework design that differentially evaluates the black-box DNN via carefully prepared input data with shape and fidelity variations to probe system responses to these variations. Two variants of the Visual Geometric Graphics with 19 neural layers (VGG19) was used for classification, with a total of 60,000 CMR images. Findings from this work provides insights on the importance of quality training data preparation and demonstrates the importance of data shape variability. It also provides gateway for computation performance optimization in training and validation time.
Show less