Stephen DelMarco

The design of wavelets for image enhancement and target detection

Detecting dim targets in infrared imagery remains a challenging task. Several techniques exist for detecting bright, high contrast targets such as CFAR detectors, edge detection, and spatial thresholding. However, these approaches often fail for detection of targets with low contrast relative to background clutter. In this paper we exploit the transient capture capability and directional filtering aspect of wavelets to develop a wavelet based image enhancement method. We develop an image representation, using wavelet filtered imagery, which facilitates dim target detection. We further process the wavelet-enhanced imagery using the Michelson visibility operator to perform nonlinear contrast enhancement prior to target detection. We discuss the design of optimal wavelets for use in the image representation. We investigate the effect of wavelet choice on target detection performance, and design wavelets to optimize measures of visual information on the enhanced imagery. We present numerical results demonstrating the effectiveness of the approach for detection of dim targets in real infrared imagery. We compare target detection performance to performance obtained using standard techniques such as edge detection. We also compare performance to target detection performed on imagery enhanced by optimizing visual information measures in the spatial domain. We investigate the stability of the optimal wavelets and detection performance variation, across perspective changes, image frame sample (for frames extracted from infrared video sequences), and image scene content types. We show that the wavelet-based approach can usually detect the targets with fewer false-alarm regions than possible with standard approaches.

General Closed-Form Family of Companders for PAPR Reduction in OFDM Signals Using Amplitude Distribution Modification

In this paper, we characterize a family of closed-form companders for peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM) signals. This new family generalizes the current state-of-the-art companders that are based on modification of the OFDM signal amplitude statistics. We derive the compander and decompander forms for a specific, parameterized member of this family. The larger parameter set characterizing these new companders provides greater design flexibility. The new companders are shown to provide solutions over regions of the operating conditions space where other current companders fail to exist. These new solutions thus widen the application of companders for PAPR reduction, and expand the trade-space over demodulation performance, PAPR reduction, and out-of-band power rejection for compander design. Through numerical result generation and simulation, we generate performance results to show that the new companders offer performance improvements over current companders.

A Theory of Automatic Parameter Selection for Feature Extraction With Application to Feature-Based Multisensor Image Registration

This paper generalizes the previously developed automated edge-detection parameter selection algorithm of Yitzhaky and Peli. We generalize the approach to arbitrary multidimensional, continuous or discrete parameter spaces, and feature spaces. This generalization enables use of the parameter selection approach with more general image features, for use in feature-based multisensor image registration applications. We investigate the problem of selecting a suitable parameter space sampling density in the automated parameter selection algorithm. A real-valued sensitivity measure is developed which characterizes the effect of parameter space sampling on feature set variability. Closed-form solutions of the sensitivity measure for special feature set relationships are derived. We conduct an analysis of the convergence properties of the sensitivity measure as a function of increasing parameter space sampling density. For certain parameter space sampling sequence types, closed-form expressions for the sensitivity measure limit values are presented. We discuss an approach to parameter space sampling density selection which uses the sensitivity measure convergence behavior. We provide numerical results indicating the utility of the sensitivity measure for selecting suitable parameter values.

Application of a dynamic feature selection algorithm to multi-sensor image registration

Image registration is usually a required first processing step for such activities as surveillance, video tracking, change detection, and remote sensing. Often, different sensors are used for the collection of the test and reference imagery. The sensor phenomenology differences can present problems for automatic selection of registration algorithm parameters because of different cross-sensor feature manifestation. In previous work involving edge-based multisensor image registration, we applied a previously-developed automated approach to parameter selection, designed specifically for edge detection. In this work, we adapt and apply a dynamic feature selection algorithm (DFSA) that we recently developed for use in registration algorithm selection for registering images with varying scene content type. We adapt and apply the DFSA to the problem of selecting appropriate registration algorithm parameter values in an edge-based registration algorithm. The approach involves generating test-to-reference feature match scores over a sampling of the transform hypothesis space. The approach is scene-adaptive thereby requiring no a priori information on image scene content. Furthermore, in the DFSA we leverage prior match score calculation generated in a hierarchical grid search to reduce additional computational expense. We give a brief overview of the registration algorithmic framework, and present a description of the dynamic feature selection algorithm. Numerical results are presented for performing test SAR-to-reference EO image registration to show the registration convergence performance improvement resulting from use of the DFSA. Numerical results are generated over images exhibiting different scene content types. We also evaluate the effect of match score normalization on the registration convergence performance improvement.

Multiple template-based image matching using alpha-rooted quaternion phase correlation

In computer vision applications, image matching performed on quality-degraded imagery is difficult due to image content distortion and noise effects. State-of-the art keypoint based matchers, such as SURF and SIFT, work very well on clean imagery. However, performance can degrade significantly in the presence of high noise and clutter levels. Noise and clutter cause the formation of false features which can degrade recognition performance. To address this problem, previously we developed an extension to the classical amplitude and phase correlation forms, which provides improved robustness and tolerance to image geometric misalignments and noise. This extension, called Alpha-Rooted Phase Correlation (ARPC), combines Fourier domain-based alpha-rooting enhancement with classical phase correlation. ARPC provides tunable parameters to control the alpha-rooting enhancement. These parameter values can be optimized to tradeoff between high narrow correlation peaks, and more robust wider, but smaller peaks. Previously, we applied ARPC in the radon transform domain for logo image recognition in the presence of rotational image misalignments. In this paper, we extend ARPC to incorporate quaternion Fourier transforms, thereby creating Alpha-Rooted Quaternion Phase Correlation (ARQPC). We apply ARQPC to the logo image recognition problem. We use ARQPC to perform multiple-reference logo template matching by representing multiple same-class reference templates as quaternion-valued images. We generate recognition performance results on publicly-available logo imagery, and compare recognition results to results generated from standard approaches. We show that small deviations in reference templates of sameclass logos can lead to improved recognition performance using the joint matching inherent in ARQPC.

Video Aided Navigation for Small UAVs

Small Unmanned Aerial Vehicles (UAVs) offer great potential to increase the war fighter’s short term situational awareness. Small UAVs are low cost and light weight, offering soldiers the ability to rapidly deploy a dedicated surveillance asset, thereby immediately increasing their tactical advantage over nearby adversaries. While this short look ahead is invaluable in modern tactical environments, the effectiveness of a small UAV is highly dependent on the operator’s ability to manage the vehicle’s flight path while interpreting the video surveillance data received. In this paper, we will discuss a video tracking approach to reduce operator workload. First, the operator designates a target or location on the ground to track. A video tracker then follows that designation by specifying waypoints and sensor look angles to keep that location in the center of the field of view.

Dynamic Algorithm Selection for Multi-Sensor Image Registration

Modern sensors have a range of modalities including SAR, EO, and IR. Registration of multimodal imagery from such sensors is becoming an increasingly common pre-processing step for various image exploitation activities such as image fusion for ATR. Over the past decades, several approaches to multisensor image registration have been developed. However, performance of these image registration algorithms is highly dependent on scene content and sensor operating conditions, with no single algorithm working well across the entire operating conditions space. To address this problem, in this paper we present an approach for dynamic selection of an appropriate registration algorithm, tuned to the scene content and feature manifestation of the imagery under consideration. We consider feature-based registration using Harris corners, Canny edge detection, and CFAR features, as well as pixel-based registration using cross-correlation and mutual information. We develop an approach for selecting the optimal combination of algorithms to use in the dynamic selection algorithm. We define a performance measure which balances contributions from convergence redundancy and convergence coverage components calculated over sample imagery, and optimize the measure to define an optimal algorithm set. We present numerical results demonstrating the improvement in registration performance through use of the dynamic algorithm selection approach over results generated through use of a fixed registration algorithm approach. The results provide registration convergence probabilities for geo-registering test SAR imagery against associated EO reference imagery. We present convergence results for various match score normalizations used in the dynamic selection algorithm.

Logo recognition using alpha-rooted phase correlation in the radon transform domain

Alpha-rooted phase correlation (ARPC) is a recently-developed variant of classical phase correlation that includes a Fourier domain image enhancement operation. ARPC combines classical phase correlation with alpha-rooting to provide tunable image enhancement. The alpha-rooting parameters may be adjusted to provide a tradeoff between height and width of the ARPC main lobe. A high narrow main lobe peak provides high matching accuracy for aligned images, but reduced matching performance for misaligned logos. A lower, wider peak trades matching accuracy on aligned logos, for improved matching performance on misaligned imagery. Previously, we developed ARPC and used it in the spatial domain for logo recognition as part of an overall automated document analysis problem. However, spatial domain ARPC performance can be sensitive to logo misalignments, including rotational misalignment. In this paper we use ARPC as a match metric in the radon transform domain for logo recognition. In the radon transform domain, rotational misalignments correspond to translations in the radon transform angle parameter. These translations are captured by ARPC, thereby producing rotation-invariant logo matching. In the paper, we first present an overview of ARPC, and then describe the logo matching algorithm. We present numerical performance results demonstrating matching tolerance to rotational misalignments. We demonstrate robustness of the radon transform domain rotation estimation to noise. We present logo verification and recognition performance results using the proposed approach on a public domain logo database. We compare performance results to performance obtained using spatial domain ARPC, and state-of-the-art SURF features, for logos in salt-and-pepper noise.

A Progressive Refinement Approach to Aerial Image Registration Using Local Transform Perturbations

Spatial domain log-polar approaches have demonstrated success for video image registration. However, the log-polar representation is sensitive to origin location. This drawback often necessitates performing a parameter sweep over log-polar origin location, which can be time-consuming. In this paper we present an alternative approach that is appropriate for small to moderate scale, rotational, and skew misalignments but allows large translational offset. We use a form of robust phase correlation to estimate the gross translation, then perform a local search over log-polar origin to fine tune the translation. We sequentially estimate affine transform parameters by maximizing a measure of registration solution verity. We also investigate the effect of scale and rotational initial alignment errors on the robustness of the initial phase correlation to estimate gross translation. We present results using video imagery acquired from a real aerial video surveillance system.

A smart iterative algorithm for multisensor image registration

Realtime multisensor image registration algorithms must be computationally efficient. Often, simplifying assumptions are made to reduce computational time. However, these simplifications usually trade registration convergence performance for reduced runtime. For non-realtime applications where computational resources are not severely limited, this tradeoff may be reversed to improve convergence performance at the expense of increased computational cost. To this end we introduce a smart iterative approach to minimize mis-registrations and thus optimize registration convergence probability. The approach involves performing a registration sweep over a smart sampling of parameters governing feature generation. This approach involves use of two components; a feature sensitivity measure (FSM) and a registration verification metric (VM). The FSM measures the effect of parameter values on feature set variability. This measure enables choice of a suitable parameter sampling density to use for performing iterative registration solution search. The VM provides feedback on the registration solution verity in the absence of ground truth and is used to identify a converged solution. First, we provide an overview of the registration framework used to generate convergence results. Next we introduce the FSM and present mathematical properties. We then describe the VM and present the iterative algorithm. We present numerical results illustrating FSM convergence with increasing parameter sampling density for Canny edge features in SAR imagery. We illustrate use of FSM convergence behavior to select a suitable parameter sampling density for use in the iterative algorithm. Finally, SAR-to-EO registration performance results are presented showing improved convergence probability.