Resource Allocation in Computer Vision

We broadly examine resource allocation in several computer vision problems. We consider human resource or computational resource constraints. Human resources, such as human operators monitoring a camera network, provide reliable information, but are typically limited by the huge amount of data to be processed. Computational resources refer to the resources used by machines, such as running time, to execute the programs. It is important to develop algorithms to make effective use of these resources in computer vision applications. We approach human resource constraints with a frame retrieval problem in a camera network. This work addresses the problem of using active inference to direct human attention in searching a camera network for people that match a query image. We find that by representing the camera network using a graphical model, we can more accurately determine which video frames match the query, and improve our ability to direct human attention. We experiment with different methods to determine from which frames to sample expert information from humans, and discover that a method that learns to predict which frame is misclassified gives the best performance. We approach the problem of allocating computational resource in a video processing task. We consider a video processing application in which we combine the outputs from two algorithms so that the budget-limited computationally more expensive algorithm is run in the most useful video frames to maximize processing performance. We model the video frames as a chain graphical model and extend a dynamic programming algorithm to determine on which frames to run the more expensive algorithm. We perform experiments on moving object detection and face detection to demonstrate the effectiveness of our approaches. Finally, we consider an idea for saving computational resources and maintaining program performance. We work on a problem of learning model complexity in latent variable models. Specifically, we learn the latent variable state space complexity in latent support vector machines using group norm regularization. We apply our method to handwritten digit recognition and object detection with deformable part models. Our approach reduces latent variable state size and performs faster inference with similar or better performance

Development of Coordinated Methodologies for Modeling CO2-Containing Systems in Petroleum Industry.

Masters Degree. University of KwaZulu-Natal, Durban.Clathrate hydrates formation in natural gas processing facilities or transportation pipelines may lead to process and/or safety hazards. On the other hand, a number of applications are suggested on the basis of promoting the gas hydrate formation. Some researchers have investigated separation and purification processes through gas hydrate crystallization technology. Some works report that the hydrate formation is applicable to the gas transportation and storage. Gas hydrate concept is also studied as a potential method for CO2 capture and/or sequestration. Water desalination/sweetening, and refrigeration and air conditioning systems are other proposed uses of hydrates phenomenon. In the realm of food processing and engineering, several studies have been done investigating the application of gas hydrate technology as an alternative to the conventional processes. Accurate knowledge of phase equilibria of clathrate hydrates is crucial for preventing or utilizing the hydrates. It is believed that energy production or extraction from different fossil fuels is responsible for considerable emissions of CO2, as an important greenhouse gas, into the atmosphere. Furthermore, CO2 removal from the streams of natural gas is important for enhancing the gaseous streams’ heating value. Employment of solvent-based processes and technologies for removing the CO2 is a widely employed approach in practical applications. Amine-based or pure amine solutions are the most common choice to remove the produced CO2 in numerous carbon capture systems. Further to the above, ionic liquids (ILs) are capable to be utilized to capture CO2 from industrial streams. Other potential solvent are sodium piperazine (PZ) and glycinate (SG) solutions. Equilibrium absorption of carbon dioxide in the aqueous phase is a key parameter in any solvent-based CO2 capture process designing. The captured CO2, then, can be injected into the hydrocarbon reservoirs. In addition to the fact that injection of CO2 into potential sources is one of the most reliable methodologies for enhanced hydrocarbon recovery, utilizing this process in conjunction with the CO2 capture systems mitigates the greenhouse effects of CO2. One of the most significant variables determining the success of CO2 injection is known to be the minimum miscibility pressure (MMP) of CO2-reservoir oil. This research study concerns implementation of computer-based methodologies called artificial neural networks (ANNs), classification and regression trees (CARTs)/AdaBoost-CART, adaptive neuro-fuzzy inference systems (ANFISs) and least squares support vector machines (LSSVMs) for modeling: (a) phase equilibria of clathrate hydrates in: 1- pure water, 2- aqueous solutions of salts and/or alcohols, and 3- ILs, (b) phase equilibria (equilibrium) of hydrates of methane in ILs; (c) equilibrium absorption of CO2 in amine-based solutions, ILs, PZ solutions, and SG solutions; and (d) MMP of CO2-reservoir oil. To this end, related experimental data have been gathered from the literature. Performing error analysis, the performance of the developed models in representing/ estimating the independent parameter has been assessed. For the studied hydrate systems, the developed ANFIS, LSSVM, ANN and AdaBoost-CART models show the average absolute relative deviation percent (AARD%) of 0.04-1.09, 0.09-1.01, 0.05-0.81, and 0.03-0.07, respectively. In the case of hydrate+ILs, error analysis of the ANFIS, ANN, LSSVM, and CART models showed 0.31, 0.15, 0.08, and 0.10 AARD% of the results from the corresponding experimental values. Employing the collected experimental data for carbon dioxide (CO2) absorption in amine-based solutions, the presented models based on ANFIS, ANN, LSSVM, and AdaBoost-CART methods regenerated the targets with AARD%s between 2.06 and 3.69, 3.92 and 8.73, 4.95 and 6.52, and 0.51 and 2.76, respectively. For the investigated CO2+IL systems, the best results were obtained using CART method as the AARD% found to be 0.04. Amongst other developed models, i.e. ANN, ANFIS, and LSSVM, the LSSVM model provided better results (AARD%=17.17). The proposed AdaBoost-CART tool for the CO2+water+PZ system reproduced the targets with an AARD% of 0.93. On the other hand, LSSVM, ANN, and ANFIS models showed AARD% values equal to 16.23, 18.69, and 15.99, respectively. Considering the CO2+water+SG system, the proposed AdaBoost-CART tool correlated the targets with a low AARD% of 0.89. The developed ANN, ANFIS, and LSSVM showed AARD% of more than 13. For CO2-oil MMP, the proposed AdaBoost-CART model (AARD%=0.39) gives better estimations than the developed ANFIS (AARD%=1.63). These findings revealed the reliability and accuracy of the CART/AdaBoost-CART methodology over other intelligent modeling tools including ANN, ANFIS, and LSSVM

Learning based biological image analysis

The fate of contemporary scientific research in biology and medicine is bound to the advancements in computational methods. The unprecedented data explosion in microscopy and the crescent interest of life scientists in studying more complex and more subtle interactions stimulate the research for innovative computational solutions on challenging real world applications. Extensions and novel formulations of generic and flexible methods based on learning/inference are necessary to cope with the large variety of the produced data and to avoid continuous reimplementation and heavy parameter tuning. This thesis exploits cutting edge machine learning methods based on structured probabilistic models and weakly supervised learning to provide four novel solutions in the areas of large-scale microscopic imaging and multiple objects tracking. Chapter 2 introduces a weakly supervised learning framework to tackle the problem of detecting defect images while mining massive microscopic imagery databases. This thesis demonstrates accurate prediction with low user annotation effort. Chapter 3 presents a learning approach for counting overlapping objects in images based on local structured predictors. This problem has numerous applications in high throughput microscopy screening such as cells counting for drug toxicity assays. Chapter 4 develops a deterministic graphical model to impose temporal consistency in objects counts when dealing with a video sequence. This Chapter shows that global (temporal and spatial) structural inference consistently improves over local (only spatial) predictions. The method developed in Chapter 4 is used in a novel downstream tracking algorithm which is introduced in Chapter 5. This Chapter tackles, for the first time, the difficult problem of tracking heavily overlapping, translucent and indistinguishable objects. The mutual occlusion event handling of such objects is formulated as a novel structured inference problem based on the minimization of a convex multi-commodity flow energy. The optimal weights of the energy terms are learned with partial user supervision using structured learning with latent variables.To support behavioral biologists, we apply this method to the problem of tracking a community of interacting Drosophila larvae