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).

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.

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.

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.

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.

This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care.

Human gait properties can be affected by various environmental contexts such as walking surface and carrying objects. In this paper, we propose a novel approach for individual recognition by combining different gait classifiers with the knowledge of environmental contexts to improve the recognition performance. Different classifiers are designed to handle different environmental contexts, and context specific features are explored for context characterization. In the recognition procedure, we can determine the probability of environmental contexts in any probe sequence according to its context features, and apply the probabilistic classifier combination strategies for the recognition. Experimental results demonstrate the effectiveness of the proposed approach.