Visualization and Intelligent Systems Laboratory
VISLab

 

 

Contact Information

VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

UCR Collaborators:
CSE
ECE
ME
STAT
PSYC
ENTM
BIOL
BPSC
ECON
MATH
BIOENG
MGNT

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Michael Caputo
michael.vislab@gmail.com

Last updated: June 15, 2016

 

 

Context Modeling

Context guided belief propagation for remote sensing image classification

Proposed is a context guided belief propagation (BP) algorithm to perform high spatial resolution multispectral imagery (HSRMI) classification efficiently utilizing superpixel representation. One important characteristic of HSRMI is that different land cover objects possess a similar spectral property that is exploited to speed up the standard BP (SBP) in the classification process. Specifically, we leverage this property of HSRMI as context information to guide messages passing in SBP. Furthermore, the spectral and structural features extracted at the superpixel level are fed into a Markov random field framework to address the challenge of low interclass variation in HSRMI classification by minimizing the discrete energy through context guided BP (CBP).

Visual and Contextual Modeling for the Detection of Repeated m-TBI

Example of fusion between visual model and contextual model on a contralaterally injured rat. Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities. We present an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The visual model utilizes texture features in MRI along with a probabilistic support vector machine. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care.

Dynamic Low-Level Context for the Detection of Mild Traumatic Brain Injury

T2 weighted MR image of half a coronal slice from the rat model dataset. A) Original T2 weighted image. B) Manual detection of the mTBI lesion (highlighted in red). Mild traumatic brain injury (mTBI) appears as low contrast lesions in magnetic resonance (MR) imaging. Standard automated detection approaches cannot detect the subtle changes caused by the lesions. We've proposed and integrated new context features to improve the detection of mTBI lesions. The approach is validated on a temporal mTBI rat model dataset and shown to have improved dice score and convergence compared to other state-of-the-art approaches.

A New Multi-scale Fuzzy Model for Histogram-based Descriptors

We present a general Multi-Scale Fuzzy Model (MSFM) which handles distortions at different scales in Histogram-Based Descriptors(HBDs). This model can be applied both on one-dimensional HBDs and multidimensional HBDs. We then focus on applying MSFM on the widely used Shape Context for a Simplified Multi-scale Fuzzy Shape Context (SMFSC) descriptor. Fuzzy models are barely used in multi-dimensional HBDs due to the significant increase of computational complexity. We show that by introducing an intra-bin point location approximation and an approximate iterative fuzzification approach, the algorithm can be simplified and thus SMFSC hardly increases computational complexity. Experiments on standard shape dataset show that SMFSC improves upon the Inner Distance Shape Context. We also applied SMFSC on Content-Based Product Image Retrieval and the experimental results further validate the effectiveness of our model.

MFSC: A New Shape Descriptor with Robustness to Deformations

We propose a new shape descriptor, Multi-scale Fuzzy Shape Context (MFSC), which is highlighted by its robustness to deformations. A novel multi-scale fuzzy model is presented and applied on the widely used shape descriptor Shape Context to generate MFSC. The multi-scale fuzzy model can handle shape deformations of different scales, which makes MFSC robust to various deformations. Experiments on an articulated shape dataset demonstrate performance improvement gained by MFSC over existing methods. We also applied MFSC on a real-world application, Content-Based Product Image Retrieval, and the experimental results further validate its effectiveness. We make our code and experimental data publicly available for future reference.