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Audio-Based Productivity Forecasting Of Construction Cyclic Activities, Chris A. Sabillon
Audio-Based Productivity Forecasting Of Construction Cyclic Activities, Chris A. Sabillon
Electronic Theses and Dissertations
Due to its high cost, project managers must be able to monitor the performance of construction heavy equipment promptly. This cannot be achieved through traditional management techniques, which are based on direct observation or on estimations from historical data. Some manufacturers have started to integrate their proprietary technologies, but construction contractors are unlikely to have a fleet of entirely new and single manufacturer equipment for this to represent a solution. Third party automated approaches include the use of active sensors such as accelerometers and gyroscopes, passive technologies such as computer vision and image processing, and audio signal processing. Hitherto, most …
Facial Expression Analysis Via Transfer Learning, Xiao Zhang
Facial Expression Analysis Via Transfer Learning, Xiao Zhang
Electronic Theses and Dissertations
Automated analysis of facial expressions has remained an interesting and challenging research topic in the field of computer vision and pattern recognition due to vast applications such as human-machine interface design, social robotics, and developmental psychology. This dissertation focuses on developing and applying transfer learning algorithms - multiple kernel learning (MKL) and multi-task learning (MTL) - to resolve the problems of facial feature fusion and the exploitation of multiple facial action units (AUs) relations in designing robust facial expression recognition systems. MKL algorithms are employed to fuse multiple facial features with different kernel functions and tackle the domain adaption problem …
Feature Pruning For Action Recognition In Complex Environment, Adarsh Nagaraja
Feature Pruning For Action Recognition In Complex Environment, Adarsh Nagaraja
Electronic Theses and Dissertations
A significant number of action recognition research efforts use spatio-temporal interest point detectors for feature extraction. Although the extracted features provide useful information for recognizing actions, a significant number of them contain irrelevant motion and background clutter. In many cases, the extracted features are included as is in the classification pipeline, and sophisticated noise removal techniques are subsequently used to alleviate their effect on classification. We introduce a new action database, created from the Weizmann database, that reveals a significant weakness in systems based on popular cuboid descriptors. Experiments show that introducing complex backgrounds, stationary or dynamic, into the video …
Efficient Techniques For Relevance Feedback Processing In Content-Based Image Retrieval, Danzhou Liu
Efficient Techniques For Relevance Feedback Processing In Content-Based Image Retrieval, Danzhou Liu
Electronic Theses and Dissertations
In content-based image retrieval (CBIR) systems, there are two general types of search: target search and category search. Unlike queries in traditional database systems, users in most cases cannot specify an ideal query to retrieve the desired results for either target search or category search in multimedia database systems, and have to rely on iterative feedback to refine their query. Efficient evaluation of such iterative queries can be a challenge, especially when the multimedia database contains a large number of entries, and the search needs many iterations, and when the underlying distance measure is computationally expensive. The overall processing costs, …
Evolutionary Optimization Of Support Vector Machines, Fred Gruber
Evolutionary Optimization Of Support Vector Machines, Fred Gruber
Electronic Theses and Dissertations
Support vector machines are a relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This thesis deals with the problem of model selection with support vector machines, that is, the problem of finding the optimal parameters that will improve the performance of the algorithm. It is shown that genetic algorithms provide an effective way to find the optimal …