Regularization methods are used widely in image selective smoothing and edge preserving restoration of noisy images. Traditional methods utilize image gradients within regularization function for controlling the smoothing and can produce artifacts when noise levels are higher. In this paper, we consider a robust image adaptive exponent driven regularization for filtering noisy images with salient feature preservation. Our spatially adaptive variable exponent function depends on a continuous switch based on the eigenvalues of structure tensor which identifies noisy edges, and corners with higher accuracy. Structure tensor eigenvalues encode various image features and we consider a spatially varying continuous map which provides multiscale edge maps of natural images. By embedding the structure tensor-based exponent in a well-defined regularization model, we obtain denoising filters which are capable of obtaining good feature preserving image restoration. The GPU-based implementation computes the edge map in real time at 45-60 frames/s depending on the GPU card. Multiscale structure tensor-based spatially adaptive variable exponent provides reliable edge maps and compared with standard edge detectors it is robust under various noisy conditions. Moreover, filtering based on the multiscale variable exponent map method outperforms L0 sparse gradient-based image smoothing and related filters.

In this paper, we are proposing a 3D segmentation and interactive visualization workflow. The segmentation implementation uses a globally convex multiphase active contours without edges. This algorithm has been proven to be initialization independent due to their globally convex formulation and better than other approaches due to robustness to image variations and adaptive energy functionals. The workflow includes a flexible 3D visualization application that can handle very large volumes using multi-resolution hierarchical data formats following the segmentation. We also designed a custom fragment shader that is capable of meaningfully fusing the data from three different volumes: a segmented label volume, a mean value per voxel volume and a skull striped volume for effective visualization without modifying the segmented results. Giving researchers the access to a whole end to end pipeline, from 3D segmentation to custom real time interactive 3D visualization is, in our opinion, a powerful tool focused on an analyst/expert centric workflow.

In the event of natural or man-made disasters, providing rapid situational awareness through video/image data collected at salient incident scenes is often critical to the first responders. However, computer vision techniques that can process the media-rich and data-intensive content obtained from civilian smartphones or surveillance cameras require large amounts of computational resources or ancillary data sources that may not be available at the geographical location of the incident. In this paper, we propose an incident-supporting visual cloud computing solution by defining a collection, computation, and consumption (3C) architecture supporting fog computing at the network edge close to the collection/consumption sites, which is coupled with cloud offloading to a core computation, utilizing software-defined networking (SDN). We evaluate our 3C architecture and algorithms using realistic virtual environment test beds. We also describe our insights in preparing the cloud provisioning and thin-client desktop fogs to handle the elasticity and user mobility demands in a theater-scale application. In addition, we demonstrate the use of SDN for on-demand compute offload with congestion-avoiding traffic steering to enhance remote user quality of experience in a regional-scale application. The optimization between fogs computing at the network edge with core cloud computing for managing visual analytics reduces latency, congestion, and increases throughput.

Edge preserving regularization using partial differential equation (PDE)-based methods although extensively studied and widely used for image restoration, still have limitations in adapting to local structures. We propose a spatially adaptive multiscale variable exponent-based anisotropic variational PDE method that overcomes current shortcomings, such as over smoothing and staircasing artifacts, while still retaining and enhancing edge structures across scale. Our innovative model automatically balances between Tikhonov and total variation (TV) regularization effects using scene content information by incorporating a spatially varying edge coherence exponent map constructed using the eigenvalues of the filtered structure tensor. The multiscale exponent model we develop leads to a novel restoration method that preserves edges better and provides selective denoising without generating artifacts for both additive and multiplicative noise models. Mathematical analysis of our proposed method in variable exponent space establishes the existence of a minimizer and its properties. The discretization method we use satisfies the maximum-minimum principle which guarantees that artificial edge regions are not created. Extensive experimental results using synthetic, and natural images indicate that the proposed multiscale Tikhonov-TV (MTTV) and dynamical MTTV methods perform better than many contemporary denoising algorithms in terms of several metrics, including signal-to-noise ratio improvement and structure preservation. Promising extensions to handle multiplicative noise models and multichannel imagery are also discussed.

Visual feature-based tracking systems need to adapt to variations in the appearance of an object and in the scene for robust performance. Though these variations may be small for short time steps, they can accumulate over time and deteriorate the quality of the matching process across longer intervals. Tracking in aerial imagery can be challenging as viewing geometry, calibration inaccuracies, complex ight paths and background changes combined with illumination changes, and occlusions can result in rapid appearance change of objects. Balancing appearance adaptation with stability to avoid tracking non-target objects can lead to longer tracks which is an indicator of tracker robustness. The approach described in this paper can handle affine changes such as rotation by explicit orientation estimation, scale changes by using a multiscale Hessian edge detector and drift correction by using segmentation. We propose an appearance update approach that handles the 'drifting' problem using this adaptive scheme within a tracking environment that is comprised of a rich feature set and a motion model.

The task of automated object tracking and performance assessment in low frame rate, persistent, wide spatial coverage motion imagery is an emerging research domain. The collection of hundreds to tens of thousands of dense trajectories produced by such automatic algorithms along with the subset of manually verified tracks across several coordinate systems require new tools for effective human computer interfaces and exploratory trajectory visualization. We describe an interactive visualization system that supports very large gig pixel per frame video, facilitates rapid, intuitive monitoring and analysis of tracking algorithm execution, provides visual methods for the inter comparison of very long manual tracks with multi segmented automatic tracker outputs, and a flexible KOLAM Tracking Simulator (KOLAM-TS) middleware that generates visualization data by automating the object tracker performance testing and benchmarking process.