Romain Murenzi

Fast texture database retrieval using extended fractal features

The increase in the number of multimedia databases consisting of images has created a need for a quick method to search these databases for a particular type of image. An image retrieval system will output images from the database similar to the query image in terms of shape, color, and texture. For the scope of our work, we study the performance of multiscale Hurst parameters as texture features for database image retrieval over a database consisting of homogeneous textures. These extended Hurst features represent a generalization of the Hurst parameter for fractional Brownian motion (fBm) where the extended parameters quantize the texture roughness of an image at various scales. We compare the retrieval performance of the extended parameters against traditional Hurst features and features obtained from the Gabor wavelet. Gabor wavelets have previously been suggested for image retrieval applications because they can be tuned to obtain texture information for a number of different scales and orientations. In our experiments, we form a database combining textures from the Bonn, Brodatz, and MIT VisTex databases. Over the hybrid database, the extended fractal features were able to retrieve a higher percentage of similar textures than the Gabor features. Furthermore, the fractal features are faster to compute than the Gabor features.

Two-dimensional directional wavelets in image processing

The two‐dimensional (2‐D) continuous wavelet transform (CWT) is characterized by a rotation parameter, in addition to the usual translations and dilations. This enables is to detect edges and directions in images, provided a directional wavelet is used. First we review the general properties of the 2‐D CWT and describe several useful representations. We describe various classes of wavelets, including the directional ones. Then we turn to the problem of wavelet calibration, in particular, the evaluation of the scale and angle resolving power of a wavelet. Finally we discuss several applications of directional wavelets.

A new motion parameter estimation algorithm based on the continuous wavelet transform

This paper presents a novel motion parameter estimation (ME) algorithm based on the spatio-temporal continuous wavelet transform (CWT). The multidimensional nature of the CWT allows for the definition of a multitude of energy densities by integrating over a subset of the CWT parameter space. Three energy densities are used to estimate motion parameters by sequentially optimizing a state vector composed of velocity, position, and size parameters. This optimization is performed on a frame-by-frame basis allowing the algorithm to track moving objects. The ME algorithm is designed to address real world challenges encountered in the defense industry and traffic monitoring scenarios, such as attaining robust performance in noise and handling obscuration and crossing object trajectories.

Spatio-temporal wavelet transforms for motion tracking

This paper addresses the problem of detecting and tracking moving objects in digital image sequences. The main goal is to detect and select mobile objects in a scene, construct the trajectories, and eventually reconstruct the target objects or their signatures. It is assumed that the image sequences are acquired from imaging sensors. The method is based on spatio-temporal continuous wavelet transforms, discretized for digital signal analysis. It turns out that the wavelet transform can be used efficiently in a Kalman filtering framework to perform detection and tracking. Several families of wavelets are considered for motion analysis according to the specific spatio-temporal transformation. Their construction is based on mechanical parameters describing uniform motion, translation, rotation, acceleration, and deformation. The main idea is that each kind of motion generates a specific signal transformation, which is analyzed by a suitable family of continuous wavelets. The analysis is therefore associated with a set of operators that describe the signal transformations at hand. These operators are then associated with a set of selectivity criteria. This leads to a set of filters that are tuned to the moving objects of interest.

Pose estimation of SAR imagery using the two dimensional continuous wavelet transform

We develop a novel pose estimation technique for synthetic aperture radar image chips based on the 2-D continuous wavelet transform (CWT). The pose estimator exploits a fast approximation of the 2-D CWT so that its computational complexity is comparable to a principle component analysis (PCA) based approach. By incorporating energy from various scales, the CWT approach is more robust than the PCA method. Experiments over the MSTAR database confirm the robustness of the CWT method.

Detection of targets in low-resolution FLIR images using two-dimensional directional wavelets

This paper investigates the use of Continuous Wavelet Transform (CWT) features for detection of targets in low resolution FLIR imagery. We specifically use the CWT features corresponding to the integration of target features at all relevant scales and orientations. These features are combined with non-linear transformations (thresholding, enhancement, morphological operations). We compare our previous results using the Mexican hat wavelet with those obtained using the two types of directional wavelets: the Morlet wavelet and the Cauchy wavelets. The algorithm was tested on the TRIM2 data base.

Effect of signal-to-clutter ratio on template-based ATR

In this work, we evaluate the robustness of template matching schemes for automatic target recognition (ATR) against the effects of clutter layover. The results of our experiments indicate the performance of template matching ATR in various image transform domains against the signal to clutter ratio (SCR). The purpose of these transforms is to enhance the target features in a chip while suppressing features representative of background clutter or simple noise. The ATR experiments were performed for synthetic aperture radar imagery using target chips in the public domain MSTAR database. The transforms include pointwise nonlinearities such as the logarithm and power operations. The templates are generated using the training portion of the MSTAR database at the nominal SCR. Many different ATR parameterizations are considered for each transform domain where templates are built to represent different ranges of aspect angles in uniform angular bins of 5, 10, 15, 30, and 45 degree increments. The different ATRs were evaluated using the testing portion of the database where synthetic clutter was added to lower the SCR.

Image compression quality metrics

Battlefield reconnaissance through tactical surveillance video systems requires transmission of images through a limited bandwidth and capacity to achieve aided target recognition (ATR), of which some lossy compression is indispensable. Based on available resolution, ATR can have three functionality goals: (1) detection of a target, (2) recognition of target classes, and (3) identification of individual target membership. Thus, it is desirable to build an intelligent lookup table which maps a specific ATR goal into an appropriate image compression. Such a table may be built implicitly be employing the exemplar training procedure of artificial neutral networks. In order to illustrate this concept, we will introduce a computational metric called feature persistence measure, useful for x-ray luggage inspections, and further generalized here to capture human performance in a tactical imaging scenario.

Multidimensional wavelets for target detection and recognition

The work described in this paper addresses the use of the four-dimensional continuous wavelet transform (CWT) for automatic target recognition (ATR) and detection. This transform is an overcomplete representation with four coordinates: two spatial, t1 and t2; a rotational coordinate, (theta) ; and a scale coordinate, a. Two central ideas are discussed in connection with the transforms application to target recognition. The first is cross-scale reconstruction, which refers to exploiting the dominate presence of target features across scales. The second is utilizing the non-spatial coordinate space as a working environment for feature extraction and classification. This aspect is unique to the multidimensional wavelet transform, emanating from the inherent redundancy in the transform representation. Some conclusions are drawn in the last section regarding the utility of the CWT for ATR, and the transforms potential as an analysis tool.

Target detection and recognition using two-dimensional isotropic and anisotropic wavelets

Automatic target detection and recognition (ATR) requires the ability to optimally extract the essential features of an object from (usually) cluttered environments. In this regard, efficient data representation domains are required in which the important target features are both compactly and clearly represented, enhancing ATR. Since both detection and identification are important, multidimensional data representations and analysis techniques, such as the continuous wavelet transform (CWT), are highly desirable. First we review some relevant properties of two 2D CWT. Then we propose a two-step algorithm based on the 2D CWT and discuss its adequacy for solving the ATR problem. Finally we apply the algorithm to various images.