Quantifying uncertainty for coherent structures

Field Alignment is a useful and often necessary preprocessing step in contemporary geophysical state and parameter estimation of coherent structures. In an advance, we introduce a new framework for using Field Alignment to quantify uncertainty from an ensemble of coherent structures. Our method, called Coalescence, discovers the mean field under non-trivial misalignments of fields with complex shapes, which is especially difficult to calculate in the presence of sparse observations. We solve the associated Field Alignment problem using novel constraints derived from turbulent displacement spectra. In conjunction with a continuation method called Scale Cascaded Alignment (SCA), we are able to extract simpler explanations of the error between fields before cascading to more complex deformation solutions. For coherent structures, SCA and Coalescence have the potential to change the way uncertainty is quantified and data is assimilated. We illustrate utility here in a Nowcasting application. Keywords

On computing global similarity in images

The retrieval of images based on their visual similarity to an example image is an important and fascinating area of research. Here, a method to characterize visual appearance for determining global similarity in images is described. Images are filtered with Gaussian derivatives and geometric features are computed from the filtered images. The geometric features used here are curvature and phase. Two images may be said to be similar if they have similar distributions of such features. Global similarity may, therefore, be deduced by comparing histograms of these features. This allows for rapid retrieval and examples from collection of gray-level and trademark images are show

NO REFERENCE IMAGE QUALITY ASSESSMENT

A no-reference image quality assessment (NR-IQA) technique can measure the visual distortion in an image without any reference image data. NR-IQA aims to predict the image quality based on the quality perceived by the Human Visual System (HVS). Image distortions can be caused through the acquisition, compression or transmission of digital images. From the several types of image distortions, JPEG and JPEG2000 compression distortions, addition of white noise, Gaussian blur, and fast fading are the most common. Several approaches were proposed to tackle this problem, some were distortion specific and some were general purpose. Of these, Convolutional Neural Networks (CNN) based approaches have proven to be efficient in predicting quality of the images. Most of these models are trained and tested only for single distortion general purpose images, but in the real world, the images contain more than one distortion type. This Work mainly focusses on using deep convolutional neural networks (DCNNs) for NR-IQA, identifying the different distortion types that are present in the image using distortion type classifiers and also, find the distortion quality of each distortion types using a network of DCNNs. We name this novel approach to be multiple DCNN (MDCNN). We fine tune the networks with different activation functions, optimizers and different tunable parameters in CNNs for the better accuracy. Also, we experiment on different patch sizes that can affect the performance of the system. This proposed model is trained on the LIVE II database and its performance is tested on the CSIQ, and TID 2008 databases which are single distortion. These models achieved high correlation coefficients and accuracy scores on these databases. We further provide the visualization of the inner layers of the DCNN for better understanding of the image quality

Real-Time Data Driven Wildland Fire Modeling

We are developing a wildland fire model based on semi-empirical relations that estimate the rate of spread of a surface fire and post-frontal heat release, coupled with WRF, the Weather Research and Forecasting atmospheric model. A level set method identifies the fire front. Data are assimilated using both amplitude and position corrections using a morphing ensemble Kalman filter. We will use thermal images of a fire for observations that will be compared to synthetic image based on the model state.Comment: 8 pages, 4 figures. ICCS 0

Morphing Ensemble Kalman Filters

A new type of ensemble filter is proposed, which combines an ensemble Kalman filter (EnKF) with the ideas of morphing and registration from image processing. This results in filters suitable for nonlinear problems whose solutions exhibit moving coherent features, such as thin interfaces in wildfire modeling. The ensemble members are represented as the composition of one common state with a spatial transformation, called registration mapping, plus a residual. A fully automatic registration method is used that requires only gridded data, so the features in the model state do not need to be identified by the user. The morphing EnKF operates on a transformed state consisting of the registration mapping and the residual. Essentially, the morphing EnKF uses intermediate states obtained by morphing instead of linear combinations of the states.Comment: 17 pages, 7 figures. Added DDDAS references to the introductio

Tracking Object Motion Across Aspect Changes for Augmented Reality

A model registration system capable of tracking an object through distinct aspects in real-time is presen- ted. The system integrates tracking, pose determ- ination, and aspect graph indexing. The track- ing combines steerable lters with normalized cross- correlation, compensates for rotation in 2D and is adaptive. Robust statistical methods are used in the pose estimation to detect and remove mismatches. The aspect graph is used to determine when features will disappear or become dicult to track and to pre- dict when and where new features will become track- able. The overall system is stable and is amenable to real-time performance

Adaptive Tracking and Model Registration Across Distinct Aspects

A model registration system capable of tracking an object through distinct aspects in real-time is pre- sented. The system integrates tracking, pose deter- mination, and aspect graph indexing. The track- ing combines steerable lters with normalized cross- correlation, compensates for rotation in 2D and is adaptive. Robust statistical methods are used in the pose estimation to detect and remove mismatches. The aspect graph is used to determine when features will disappear or become dicult to track and to predict when and where new features will become trackable. The overall system is stable and is amenable to real- time performance

Image Retrieval Using Scale-Space Matching

The retrieval of images from a large database of images is an important and emerging area of research. Here, a technique to retrieve images based on appearance that works effectively across large changes of scale is proposed. The database is initially filtered with derivatives of a Gaussian at several scales. A user defined template is then created from an image of an object similar to those being sought. The template is also filtered using Gaussian derivatives. The template is then matched with the filter outputs of the database images and the matches ranked according to the match score. Experiments demonstrate the technique on a number of images in a database. No prior segmentation of the images is required and the technique works with viewpoint changes up to 20 degrees and illumination changes. 1 Introduction The advent of multi-media and large image collections in several different domains brings forth a necessity for image retrieval systems. These systems will This work was s..

On multi-scale differential features and their representations for image retrieval and recognition

Visual appearance is described as a cue with which we discriminate images. It has been conjectured that appearance similarity emerges from similarity between features of image surfaces. However, the design of effective appearance features and their efficient representations is an open problem. In this dissertation, appearance features are developed by decomposing image brightness surfaces differentially in space, and in scale. Image representations constructed from multi-scale differential features are compared to determine appearance similarity. The first part of this thesis explores image structure in scale and space. Multi-scale differential features are generated by filtering images with Gaussian derivatives at multiple scales (GMDFs). This provides a robust local characterization of the brightness surface; filtered outputs can be transformed to seek rotation, illumination, view and scale tolerance. Differential features are also shown to be descriptive; both local and global representations of images can be composed from them. The second part of this thesis begins by illustrating local and global representations including feature-templates, -graphs, -ensembles and -distributions. It continues by developing one algorithm, CO-1, in detail. In this algorithm, two robust differential-features, the orientation of the local gradient and the shape-index, are selected for constructing representations. GMDF distributions of the first type are used to represent images and euclidean distance measure is used to determine similarity between representations. The first application of CO-1 is to image retrieval, a task central to developing search and organization tools for digital multimedia collections. CO-1 is applied to example-based browsing of image collections and trademark: retrieval, where appearance similarity can be important for adjudicating relevance. The second application of this work is to image-based and view-based object recognition. Results are demonstrated for face recognition using several standard collections. The central contribution of this work in the words of a reviewer is “… in the simplicity and elegance of the approach of using low-level multi-scale differential image structure.” We posit that this thesis highlights the utility of exploring differential image structure to synthesize features effective in a wide range of appearance-based retrieval and recognition tasks