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Center for Computer Vision and Visualization, United States

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8 Papers

SNAL: Spatial Network Algebra for Modeling Spatial Networks in Database Systems

Topics: Computational Geometry; Spatial Modeling and Reasoning

Authors: Lin Qi , Huiyuan Zhang and Markus Schneider

Affiliation: University of Florida, United States

Digital Object Identifier: 10.5220/0005880001450152

Proceedings of GISTAM 2016, ISBN: 978-989-758-188-5

Quick Abstract

Spatial networks such as road networks, river networks, telephone networks, and power networks are ubiquitous spatial concepts deployed, for example, in route planning, communication services, high voltage grid topology analysis, and utility management. Current database systems are unable to efficiently handle, represent, store, query, and manipulate large spatial networks. Moreover, data models of spatial networks in a database context are rare due to their inherently complex nature. This paper offers a conceptual foundation called Spatial Network Algebra (SNAL) for designing, characterizing, and representing spatial networks. A general-purpose abstract model is proposed as a specification for a later implementation of spatial networks in different environments such as spatial database systems and GIS.

Related Ontology Subjects/Areas/Topics (3)

Computational Geometry

Computer Vision, Visualization and Computer Graphics

Image Formation and Preprocessing

Trafforithm - A Traffic-aware Shortest Path Algorithm in Real Road Networks with Traffic Influence Factors

Topics: Storage, Indexing and Searching; Geographic Information Retrieval

Authors: Lin Qi and Markus Schneider

Affiliation: University of Florida, United States

Digital Object Identifier: 10.5220/0005350701050112

Proceedings of GISTAM 2015, ISBN: 978-989-758-099-4

Quick Abstract

The shortest path computation between two given locations in a road network is an important problem that finds applications in a wide range of fields. There has been a lot of research efforts targeting at the preciseness and performance of finding shortest paths in road networks. However, rarely of them have really taken into account the influence of traffic factors such as traffic lights, road conditions, traffic jams and turning cost. In other words, existing approaches are rather purely based on the topology of the network, but forgot that there are multiple factors in a real road network that impact the accuracy of the algorithm. The contribution of our paper is twofold. First, we present a generic two-layered framework for moving objects in road networks environment and demonstrate the important role of traffic factors on path finding and route planning. Second, we develop an efficient parallel shortest path algorithm in road networks with the consideration of traffic influence factors. Detailed analysis presented shows that our parallel TRAFFic-aware shortest path algORITHM (Trafforithm), is accurate and practical.

Human Rating of Emotional Expressions - Scales vs. Preferences

Topics: Affective Computing; Observation, Modeling and Prediction of User Behavior; Video and Image Analysis for Physiological Computing; Human Factors in Physiological Computing

Authors: Marco Pasch ¹ , Andrea Kleinsmith ² and Monica Landoni ¹

Affiliations: ¹ University of Lugano, Switzerland , ² University of Florida, United States

Digital Object Identifier: 10.5220/0004727602400245

Proceedings of PhyCS 2014, ISBN: 978-989-758-006-2

Quick Abstract

Human ratings of emotional expressions are the foundation for building and training automatic affect recognition systems. We compare two rating schemes for labeling emotional expressions: likert scales and pair-wise preferences. A statistical analysis shows that while there is a strong correlation between the two schemes, there are also frequent mismatches. Our findings indicate that the schemes perform differently well per affect label. We discuss reasons for this and outline planned future work based on the findings.

Related Ontology Subjects/Areas/Topics (7)

Affective Computing	Methodologies and Methods	Physiological Computing Systems
Applications	Observation, Modeling and Prediction of User Behavior	Video and Image Analysis for Physiological Computing
Human-Computer Interaction

LARGE-SCALE-INVARIANT TEXTURE RECOGNITION

Topics: Recognition and Indexing; Statistical Approach; Pattern Recognition in Image Understanding; Multi-Resolution Vision Techniques

Authors: Muhammad Rushdi and Jeffrey Ho

Affiliation: University of Florida, United States

Digital Object Identifier: 10.5220/0003398904420445

Proceedings of VISAPP 2011/VISIGRAPP, ISBN: 978-989-8425-47-8

Quick Abstract

This paper addresses the problem of texture recognition across large scale variations. Most of the existing methods for texture recognition handle only small-scale variations in test images. We propose using microscopic-scale textures to classify texture images at any coarser scale without prior knowledge of the relative scale. In particular, given a test camera image, we compute the average error of approximating the test texture with patches of the microscopic texture for certain category and scaling factor. Recognition is made by selecting the category with the minimum average error over all categories and scaling factors. Experiments on camera and low-magnification microscopic images show the validity of the proposed method.

Related Ontology Subjects/Areas/Topics (16)

Artificial Intelligence	Human-Computer Interaction	Pattern Recognition
Biomedical Engineering	Image and Video Analysis	Physiological Computing Systems
Biomedical Signal Processing	Methodologies and Methods	Sensor Networks
Computer Vision, Visualization and Computer Graphics	Neurocomputing	Soft Computing
Data Manipulation	Neurotechnology, Electronics and Informatics	Statistical Approach
Health Engineering and Technology Applications

TEXTURE CLASSIFICATION USING SPARSE K-SVD TEXTON DICTIONARIES

Topics: Feature Extraction; Recognition and Indexing; Statistical Approach; Pattern Recognition in Image Understanding; Early Vision and Image Representation

Authors: Muhammad Rushdi and Jeffrey Ho

Affiliation: University of Florida, United States

Digital Object Identifier: 10.5220/0003376101870193

Proceedings of VISAPP 2011/VISIGRAPP, ISBN: 978-989-8425-47-8

Quick Abstract

This paper addresses the problem of texture classification under unknown viewpoint and illumination variations. We propose an approach that combines sparse K-SVD and texton-based representations. Starting from an analytic or data-driven base dictionary, a sparse dictionary is iteratively estimated from the texture data using the doubly-sparse K-SVD algorithm. Then, for each texture image, K-SVD representations of pixel neighbourhoods are computed and used to assign the pixels to textons. Hence, the texture image is represented by the histogram of its texton map. Finally, a test image is classified by finding the closest texton histogram using the chi-squared distance. Initial experiments on the CUReT database show high classification rates that compare well with Varma-Zisserman MRF results.

Related Ontology Subjects/Areas/Topics (21)

Artificial Intelligence	Features Extraction	Neurotechnology, Electronics and Informatics
Biomedical Engineering	Health Engineering and Technology Applications	Pattern Recognition
Biomedical Signal Processing	Human-Computer Interaction	Physiological Computing Systems
Computer Vision, Visualization and Computer Graphics	Image and Video Analysis	Sensor Networks
Data Manipulation	Informatics in Control, Automation and Robotics	Signal Processing, Sensors, Systems Modeling and Control
Early Vision and Image Representation	Methodologies and Methods	Soft Computing
Feature Extraction	Neurocomputing	Statistical Approach

MODELING RADIAL VELOCITY SIGNALS FOR EXOPLANET SEARCH APPLICATIONS

Topic: Time Series and System Modeling

Authors: Prabhu Babu ¹ , Petre Stoica ¹ and Jian Li ²

Affiliations: ¹ Uppsala University, Sweden , ² University of Florida, United States

Digital Object Identifier: 10.5220/0002770601550159

Proceedings of ICINCO 2010, ISBN: 978-989-8425-02-7

Quick Abstract

In this paper, we introduce an estimation technique for analyzing radial velocity data commonly encountered in extrasolar planet detection. We discuss the Keplerian model for radial velocity data measurements and estimate the 3D spectrum (power vs. eccentricity, orbital period and periastron passage time) of the radial velocity data by using a relaxation maximum likelihood algorithm (RELAX). We then establish the significance of the spectral peaks by using a generalized likelihood ratio test (GLRT). Numerical experiments are carried out on a real life data set to evaluate the performance of our method.

Related Ontology Subjects/Areas/Topics (3)

Informatics in Control, Automation and Robotics

Signal Processing, Sensors, Systems Modeling and Control

Time Series and System Modeling

FAST ESTIMATION FOR RANGE IDENTIFICATION IN THE PRESENCE OF UNKNOWN MOTION PARAMETERS

Topic: Adaptive Signal Processing and Control

Authors: Lili Ma ¹ , Chengyu Cao ¹ , Naira Hovakimyan ¹ , Craig Woolsey ¹ and Warren E. Dixon ²

Affiliations: ¹ Virginia Tech, United States , ² University of Florida, United States

Digital Object Identifier: 10.5220/0001642001570164

Proceedings of ICINCO 2007, ISBN: 978-972-8865-84-9

Quick Abstract

A fast adaptive estimator is applied to the problem of range identification in the presence of unknown motion parameters. Assuming a rigid-body motion with unknown constant rotational parameters but known translational parameters, extraction of the unknown rotational parameters is achieved by recursive least square method. Simulations demonstrate the superior performance of fast estimation in comparison to identifier based observers.

Related Ontology Subjects/Areas/Topics (3)

Adaptive Signal Processing and Control

Informatics in Control, Automation and Robotics

Signal Processing, Sensors, Systems Modeling and Control

A CONTENT ORIENTED ARCHITECTURE FOR CONSUMER-TO-BUSINESS E-COMMERCE

Authors: Joshua H. Greenberg ¹ , Douglas Dankel II ¹ and Borko Furht ²

Affiliations: ¹ University of Florida, United States , ² Florida Atlantic University, United States

Digital Object Identifier: 10.5220/0002655200160023

Proceedings of ICEIS 2004, ISBN: 972-8865-00-7

Quick Abstract

Consumer-to-Business (C2B) systems represent the future of eCommerce. Using natural language as a basis, and remaining keenly aware of its potential pitfalls, we describe a software specific communication model based on a new concept called Content Biased Language (CBL). It is shown that the requirements of a C2B system cannot be satisfied with anything less than the stretchability of a CBL. Once this fact has been established, the remainder of this paper discusses a representation for a CBL, as well as an architecture for utilizing that representation. This effort results in the description of a new software quality measure called stretchability, as well as the introduction of perspective domain graphs (PDGs), external open ontological type systems (EOOTS), and global and constituent systems. Finally, the discussion closes with the definition of a new distributed system design called the Content Oriented Architecture (COA).