Philippe Burlina

A support vector method for anomaly detection in hyperspectral imagery

This paper presents a method for anomaly detection in hyperspectral images based on the support vector data description (SVDD), a kernel method for modeling the support of a distribution. Conventional anomaly-detection algorithms are based upon the popular Reed-Xiaoli detector. However, these algorithms typically suffer from large numbers of false alarms due to the assumptions that the local background is Gaussian and homogeneous. In practice, these assumptions are often violated, especially when the neighborhood of a pixel contains multiple types of terrain. To remove these assumptions, a novel anomaly detector that incorporates a nonparametric background model based on the SVDD is derived. Expanding on prior SVDD work, a geometric interpretation of the SVDD is used to propose a decision rule that utilizes a new test statistic and shares some of the properties of constant false-alarm rate detectors. Using receiver operating characteristic curves, the authors report results that demonstrate the improved performance and reduction in the false-alarm rate when using the SVDD-based detector on wide-area airborne mine detection (WAAMD) and hyperspectral digital imagery collection experiment (HYDICE) imagery

Kernel fully constrained least squares abundance estimates

A critical step for fitting a linear mixing model to hyperspectral imagery is the estimation of the abundances. The abundances are the percentage of each end member within a given pixel; therefore, they should be non-negative and sum to one. With the advent of kernel based algorithms for hyperspectral imagery, kernel based abundance estimates have become necessary. This paper presents such an algorithm that estimates the abundances in the kernel feature space while maintaining the non-negativity and sum-to-one constraints. The usefulness of the algorithm is shown using the AVIRIS Cuprite, Nevada image.

Adaptive target detection in foliage-penetrating SAR images using alpha-stable models

Detecting targets occluded by foliage in foliage-penetrating (FOPEN) ultra-wideband synthetic aperture radar (UWB SAR) images is an important and challenging problem. Given the different nature of target returns in foliage and nonfoliage regions and very low signal-to-clutter ratio in UWB imagery, conventional detection algorithms fail to yield robust target detection results. A new target detection algorithm is proposed that (1) incorporates symmetric alpha-stable (SalphaS) distributions for accurate clutter modeling, (2) constructs a two-dimensional (2-D) site model for deriving local context, and (3) exploits the site model for region-adaptive target detection. Theoretical and empirical evidence is given to support the use of the SalphaS model for image segmentation and constant false alarm rate (CFAR) detection. Results of our algorithm on real FOPEN images collected by the Army Research Laboratory are provided.

Validating retinal fundus image analysis algorithms: Issues and a proposal

This paper concerns the validation of automatic retinal image analysis (ARIA) algorithms. For reasons of space and consistency, we concentrate on the validation of algorithms processing color fundus camera images, currently the largest section of the ARIA literature. We sketch the context (imaging instruments and target tasks) of ARIA validation, summarizing the main image analysis and validation techniques. We then present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks, and capable of running submitted software automatically on the data stored, with clear and widely agreed-on performance criteria, to provide a fair comparison.

Fast Hyperspectral Anomaly Detection via SVDD

We present a method for fast anomaly detection in hyperspectral imagery (HSI) based on the support vector data description (SVDD) algorithm. The SVDD is a single class, non-parametric approach for modeling the support of a distribution. A global SVDD anomaly detector is developed that utilizes the SVDD to model the distribution of the spectra of pixels randomly selected from the entire image. Experiments on wide area airborne mine detection (WAAMD) hyperspectral data show improved receiver operating characteristic (ROC) detection performance when compared to the local SVDD detector and other standard anomaly detectors (including RX and GMRF). Furthermore, one-second processing time using desktop computers on several 256 times 256 times 145 datacubes is achieved.

An error resilient scheme for image transmission over noisy channels with memory

This correspondence addresses the use of a joint source-channel coding strategy for enhancing the error resilience of images transmitted over a binary channel with additive Markov noise. In this scheme, inherent or residual (after source coding) image redundancy is exploited at the receiver via a maximum a posteriori (MAP) channel detector. This detector, which is optimal in terms of minimizing the probability of error, also exploits the larger capacity of the channel with memory as opposed to the interleaved (memoryless) channel. We first consider MAP channel decoding of uncompressed two-tone and bit-plane encoded grey-level images. Next, we propose a scheme relying on unequal error protection and MAP detection for transmitting grey-level images compressed using the discrete cosine transform (DCT), zonal coding, and quantization. Experimental results demonstrate that for various overall (source and channel) operational rates, significant performance improvements can be achieved over interleaved systems that do not incorporate image redundancy.

Automated detection of drusen in the macula

Age related macular degeneration (AMD) is a condition of the retina that occurs with individuals over 50. AMD is characterized by the formation of drusen in the macula. This condition leads to a deterioration of foveal vision and eventually functional blindness. Automatically screening atrisk individuals may allow the detection of intermediate stage AMD where it is still treatable using anti-VEGH therapy. One of the difficulties in detecting and locating drusen is that their aspect (shape, texture, color, extent) varies significantly, and because of this it is often difficult to build a classifier. To address this difficulty we use a two pronged approach based on (a) multiscale analysis and (b) kernel based anomaly detection. We show experimental results on examples of fundus images taken from healthy and affected patients.

Tracking cell motion using GM-PHD

We present a method for tracking the movement of multiple cells and their lineage. We use the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, a multi-target tracking algorithm, to track the motion of multiple cells over time and to keep track of the lineage of cells as they spawn. We describe a spawning model for the GM-PHD filter as well as modifications to the original GM-PHD algorithm to track lineage. Experimental results are provided illustrating the approach for dense cell colonies.

Electronic image stabilization using multiple visual cues

Image stabilization is a key preprocessing step in dynamic image analysis and deals with the removal of unwanted image motion in a video sequence. This paper presents an integrated algorithm for the problem of image stabilization. The algorithm combines various visual cues such as points and horizon lines, and relies on an extended Kalman filter for the estimation of parameters of interest. We study both calibrated and uncalibrated stabilization cases, and consider the problem of the selection of model dynamics for the estimation of warping parameters. Experimental results from video sequences generated from off-road vehicle platforms show good performance of stabilization algorithm.

Knowledge-Based Control of Vision Systems

We propose a framework for the development of vision systems that incorporate, along with the executable computer algorithms, the problem-solving knowledge required to obtain optimal performance from them. In this approach, the user provides the input data, specifies the vision task to be performed, and then provides feedback in the form of qualitative evaluations of the results obtained. These assessments are interpreted in a knowledge-based framework to automatically select algorithms and set parameters until results of the desired quality are obtained. This approach is illustrated on two real applications, and examples from the knowledge bases developed are discussed in detail.