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
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
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.