SCHOOL OF ENGINEERING
However camera manufactures have to spend a considerable amount of time and money on tuning parameter subjectively. The major obstacle of a 'self-tuning' camera is the absence of an image quality assessment (IQA) metric without requiring ideal reference image.We propose a new category of IQA, corrupted-reference image quality assessment (CR-QA), which is designed for evaluating image quality without ideal reference. Therefore it can make the automatic tuning process come true, pointing a way towards "self-tuning" camera.
Detection of Whale Blows in Infrared Video
STUDENTS Sai Babu Arigela, Varun Santhaseelan ADVISORS Vijayan K Asari LOCATION, TIME Kennedy Union 207, 2:40 PM-3:00 PM Electrical and Computer Engineering, Oral Presentation - Graduate Research Computer vision technology has become a blessing to other researchers who have to analyze large amounts of video data. In this research, we aim to reduce the workload on whale researchers by automating the monitoring mechanism used to study whale migration. One of the major tasks in studying the migratory behavior of whales is detection and counting the number of whales that pass through a particular region. However, when long range infrared cameras mounted along the shores are used for monitoring, the whales are not detected. Instead, the spouts generated by whales are monitored. Based on the timing of spouts or whale blows, the number of whales passing through the region is estimated. Our research focuses on methods to detect whale blows in the captured video. Some key observations of whale blows in infrared video are, (1) whale blows have higher intensity with respect to the background, (2) characteristic shape for the blow when it reaches full size, (3) minimum distance between two whale blows, and (4) characteristic variation in the shape of the blow over time. The initial step of the algorithm is to threshold the image. Adaptive thresholding is applied according to the characteristics of the local neighborhood. Characterization metrics called cumulative absolute difference and cumulative difference are defined for eliminating false detections. The final step in detection is the use of a neural network classifier to eliminate other false detections.We have also developed a complimentary method where false detections were eliminated based on the variation of local relative variance measure. Local fractal dimension was then used as the elimination criterion for the final detection. We present results based on all the proposed algorithms as well as the primitive version of a tracking methodology developed based on the timing constraints of whale blows.
Surrogate models and their applications in aerospace engineering
STUDENTS Komahan Boopathy ADVISORS Markus P Rumpfkeil LOCATION, TIME Kennedy Union 207, 3:20 PM-3:40 PM Mechanical and Aerospace Engineering, Oral Presentation - Graduate Research Numerical simulations are extensively used in engineering research to solve real world problems whose analytical solutions are unknown. Despite the advancements made in computer hardware and the deployment of High Performance Computing, there exists an acute imbalance between the requirements and availability of computational power, especially when dealing with high-fidelity CFD simulations. For example, a straightforward airfoil shape optimization requires many optimizer iterations and hence the required number of flow-solves can easily surpass several thousands, potentially demanding enormous computational time and storage. With a relatively meager computational power at hand, the research community has to trade-off accuracy for computational time or limit their design spaces, which may lead to inefficient designs. In order to curb the predicaments involved in high-fidelity simulations, the idea of a surrogate model was introduced. A surrogate model replaces expensive function evaluations with an approximate but inexpensive functional representation. A lot of today's research is focused on studying methods to improve the accuracy of models as well as developing versatile and robust surrogates. Recently, we developed a variable fidelity Kriging surrogate model that is enhanced by Multivariate Interpolation and Regression (MIR) and dynamic training point selection, wherein we use MIR as a local surrogate model that guides the construction of the global Kriging surrogate. The adaptive training point strategy that we use adds training points at locations where the difference between local and global surrogate's prediction differ by a given threshold. The exact function evaluations are called only at these locations and the model is iteratively updated until convergence or a maximum number of evaluations has been reached saving a lot of computational time. We demonstrate the efficiency of our enhanced surrogate on multi-dimensional analytic test functions and discuss potential applications such as building aerodynamic databases, uncertainty quantification, and optimization under uncertainty.
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