Detection and Generalization of Spatio-temporal Trajectories for Motion Imagery

In today\u27s world of vast information availability users often confront large unorganized amounts of data with limited tools for managing them. Motion imagery datasets have become increasingly popular means for exposing and disseminating information. Commonly, moving objects are of primary interest in modeling such datasets. Users may require different levels of detail mainly for visualization and further processing purposes according to the application at hand. In this thesis we exploit the geometric attributes of objects for dataset summarization by using a series of image processing and neural network tools. In order to form data summaries we select representative time instances through the segmentation of an object\u27s spatio-temporal trajectory lines. High movement variation instances are selected through a new hybrid self-organizing map (SOM) technique to describe a single spatio-temporal trajectory. Multiple objects move in diverse yet classifiable patterns. In order to group corresponding trajectories we utilize an abstraction mechanism that investigates a vague moving relevance between the data in space and time. Thus, we introduce the spatio-temporal neighborhood unit as a variable generalization surface. By altering the unit\u27s dimensions, scaled generalization is accomplished. Common complications in tracking applications that include occlusion, noise, information gaps and unconnected segments of data sequences are addressed through the hybrid-SOM analysis. Nevertheless, entangled data sequences where no information on which data entry belongs to each corresponding trajectory are frequently evident. A multidimensional classification technique that combines geometric and backpropagation neural network implementation is used to distinguish between trajectory data. Further more, modeling and summarization of two-dimensional phenomena evolving in time brings forward the novel concept of spatio-temporal helixes as compact event representations. The phenomena models are comprised of SOM movement nodes (spines) and cardinality shape-change descriptors (prongs). While we focus on the analysis of MI datasets, the framework can be generalized to function with other types of spatio-temporal datasets. Multiple scale generalization is allowed in a dynamic significance-based scale rather than a constant one. The constructed summaries are not just a visualization product but they support further processing for metadata creation, indexing, and querying. Experimentation, comparisons and error estimations for each technique support the analyses discussed

Hierarchical structure-and-motion recovery from uncalibrated images

This paper addresses the structure-and-motion problem, that requires to find camera motion and 3D struc- ture from point matches. A new pipeline, dubbed Samantha, is presented, that departs from the prevailing sequential paradigm and embraces instead a hierarchical approach. This method has several advantages, like a provably lower computational complexity, which is necessary to achieve true scalability, and better error containment, leading to more stability and less drift. Moreover, a practical autocalibration procedure allows to process images without ancillary information. Experiments with real data assess the accuracy and the computational efficiency of the method.Comment: Accepted for publication in CVI

Challenging Problems in Data Mining and Data Warehousing

Data mining is a process which is used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective marketing strategies as well as increase sales and decrease costs. It depends on constructive data collection and warehousing as well as computer processing. Data mining used to analyze patterns and relationships in data based on what users request. For example, data mining software can be used to create classes of information. When companies centralize their data into one database or program, it is known as data warehousing. Accompanied a data warehouse, an organization may spin off segments of the data for particular users and utilize. While, in other cases, analysts may begin with the type of data they want and create a data warehouse based on those specs. Regardless of how businesses and other entities systemize their data, they use it to support management's decision-making processes

Using Imagery to Identify Abandoned Property in Public Spaces

Generally, the present disclosure is directed to identifying private property that has been illegally dumped or stored for a prolonged period without use. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict whether an item has been abandoned in a public right-of-way based on imagery of the item and record of public right-of-way