Matching Items (2)
Filtering by
- All Subjects: engineering
- All Subjects: Multimedia systems
- Resource Type: Text
Description
Bridging semantic gap is one of the fundamental problems in multimedia computing and pattern recognition. The challenge of associating low-level signal with their high-level semantic interpretation is mainly due to the fact that semantics are often conveyed implicitly in a context, relying on interactions among multiple levels of concepts or low-level data entities. Also, additional domain knowledge may often be indispensable for uncovering the underlying semantics, but in most cases such domain knowledge is not readily available from the acquired media streams. Thus, making use of various types of contextual information and leveraging corresponding domain knowledge are vital for effectively associating high-level semantics with low-level signals with higher accuracies in multimedia computing problems. In this work, novel computational methods are explored and developed for incorporating contextual information/domain knowledge in different forms for multimedia computing and pattern recognition problems. Specifically, a novel Bayesian approach with statistical-sampling-based inference is proposed for incorporating a special type of domain knowledge, spatial prior for the underlying shapes; cross-modality correlations via Kernel Canonical Correlation Analysis is explored and the learnt space is then used for associating multimedia contents in different forms; model contextual information as a graph is leveraged for regulating interactions among high-level semantic concepts (e.g., category labels), low-level input signal (e.g., spatial/temporal structure). Four real-world applications, including visual-to-tactile face conversion, photo tag recommendation, wild web video classification and unconstrained consumer video summarization, are selected to demonstrate the effectiveness of the approaches. These applications range from classic research challenges to emerging tasks in multimedia computing. Results from experiments on large-scale real-world data with comparisons to other state-of-the-art methods and subjective evaluations with end users confirmed that the developed approaches exhibit salient advantages, suggesting that they are promising for leveraging contextual information/domain knowledge for a wide range of multimedia computing and pattern recognition problems.
ContributorsWang, Zhesheng (Author) / Li, Baoxin (Thesis advisor) / Sundaram, Hari (Committee member) / Qian, Gang (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis presents a multi-modal motion tracking system for stroke patient rehabilitation. This system deploys two sensor modules: marker-based motion capture system and inertial measurement unit (IMU). The integrated system provides real-time measurement of the right arm and trunk movement, even in the presence of marker occlusion. The information from the two sensors is fused through quaternion-based recursive filters to promise robust detection of torso compensation (undesired body motion). Since this algorithm allows flexible sensor configurations, it presents a framework for fusing the IMU data and vision data that can adapt to various sensor selection scenarios. The proposed system consequently has the potential to improve both the robustness and flexibility of the sensing process. Through comparison between the complementary filter, the extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the particle filter (PF), the experimental part evaluated the performance of the quaternion-based complementary filter for 10 sensor combination scenarios. Experimental results demonstrate the favorable performance of the proposed system in case of occlusion. Such investigation also provides valuable information for filtering algorithm and strategy selection in specific sensor applications.
ContributorsLiu, Yangzi (Author) / Qian, Gang (Thesis advisor) / Olson, Loren (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2010