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Visar resultat 1 - 5 av 15 avhandlingar som matchar ovanstående sökkriterier.
1. Riemannian Manifold-Based Modeling and Classification Methods for Video Activities with Applications to Assisted Living and Smart Home
Sammanfattning : This thesis mainly focuses on visual-information based daily activity classification, anomaly detection, and video tracking through using visual sensors. The main reasons for adopting visual-information based methods are due to: (i) vision plays a major role in recognition/classification of activities which is a fundamental issue in a human-centric system; (ii) visual sensor-based analysis may possibly offer high performance with minimum disturbance to individuals' daily lives. LÄS MER
2. Stochastic Modeling for Video Object Tracking and Online Learning: manifolds and particle filters
Sammanfattning : Classical visual object tracking techniques provide effective methods when parameters of the underlying process lie in a vector space. However, various parameter spaces commonly occurring in visual tracking violate this assumption. LÄS MER
3. Manifold learning and representations for image analysis and visualization
Sammanfattning : We present a novel method for manifold learning, i.e. identification of the low-dimensional manifold-like structure present in a set of data points in a possibly high-dimensional space. The main idea is derived from the concept of Riemannian normal coordinates. LÄS MER
4. Partial Balayage and Related Concepts in Potential Theory
Sammanfattning : This thesis consists of three papers, all treating various aspects of the operation partial balayage from potential theory.The first paper concerns the equilibrium measure in the setting of two dimensional weighted potential theory, an important measure arising in various mathematical areas, e.g. LÄS MER
5. Visual Object Tracking and Classification Using Multiple Sensor Measurements
Sammanfattning : Multiple sensor measurement has gained in popularity for computer vision tasks such as visual object tracking and visual pattern classification. The main idea is that multiple sensors may provide rich and redundant information, due to wide spatial or frequency coverage of the scene, which is advantageous over single sensor measurement in learning object model/feature and inferring target state/attribute in complex scenarios. LÄS MER