We propose a method for automatic target detection and tracking in forward-looking infrared (FLIR) image sequences. We use morphological connected operators to extract and track targets of interest and remove undesirable clutter. The design of these operators is based on general size, connectivity, and motion criteria, using spatial intraframe and temporal interframe information. In a first step, an image sequence is filtered on a frame-by-frame basis to remove background and residual clutter and to enhance the presence of targets. Detections extracted from the first step are passed to a second step for motion-based analysis. This step exploits the spatiotemporal correlation of the data, stated in terms of a connectivity criterion along the time dimension. The proposed method is suitable for piplined implementation or time progressive coding/transmission, since only a few frames are considered at a time. Experimental results, obtained with real FLIR image sequences, illustrating a wide variety of target and clutter variability, demonstrate the effectiveness and robustness of the proposed method.
The automated detection of sea mines remains an increasingly important humanitarian and military task. In recent years, research efforts have been concentrated on developing algorithms that detect mines in complicated littoral environments. Acquired high-resolution side-looking sonar images are often heavily infested with artifacts from natural and man-made clutter. As a consequence, automated detection algorithms, designed for high probability of detection, suffer from a large number of false alarms. To remedy this situation, sophisticated feature extraction and pattern classification techniques are commonly used after detection. In this paper, we propose a nonlinear detection algorithm, based on mathematical morphology, for the robust detection of sea mines. The proposed algorithm is fast and performs well under a variety of sonar modalities and operating conditions. Our approach is based on enhancing potential mine signatures by extracting highlight peaks of appropriate shape and size and by boosting the amplitude of the peaks associated with a potential shadow prior to detection. Signal amplitudes over highlight peaks are extracted using a flat morphological top-hat by reconstruction operator. The contribution of a potential shadow to the detection image is incorporated by increasing the associated highlight amplitude by an amount proportional to the relative contrast between highlight and shadow signatures. The detection image is then thresholded at mid-gray level. The largest p targets from the resulting binary image are then labelled as potential targets. The number of false alarms in the detection image is subsequently reduced to an acceptable level by a feature extraction and classification module. The detection algorithm is tested on two side-scan sonar databases provided by the Coastal Systems Station, Panama City, Florida: SONAR-0 and SONAR-3.
KEYWORDS: Detection and tracking algorithms, Image filtering, Signal to noise ratio, Land mines, Mining, Binary data, Target detection, Multispectral imaging, Linear filtering, Reconstruction algorithms
An unsupervised algorithm is proposed for land mine detection in heavily cluttered multispectral images, based on iterating hybrid multi-spectral morphological filters. The hybrid filter used in each iteration consists of a decorrelating linear transform coupled with a nonlinear morphological detection component. Targets, extracted from the first pass, are used to improve detection results of the subsequent iteration, by helping to update covariance estimates of relevant filter variables. The procedure is stopped after a predetermined number of iterations is reached. Current implementation addresses several weaknesses associated with previous versions of the hybrid morphological approach to land mine detection. Improvement in detection accuracy and speed, robustness with respect to clutter inhomogeneity, and a completely unsupervised operation are the main highlights of the proposed approach. Our experimental investigation reveals substantially superior detection performance and lower false alarm rates over previous schemes. Properties of a graphical user interface (GUI), based on the proposed iterative morphological detection scheme, are also discussed.
KEYWORDS: Detection and tracking algorithms, Target detection, Image filtering, Reconstruction algorithms, Signal to noise ratio, Land mines, Mining, Multispectral imaging, Linear filtering, Signal detection
A new hybrid algorithm, based on combining the decorrelating and packing qualitites of Principal Component (PC) analysis and the shape extracting and filtering properties of Mathematical Morphology, is investigated in the frame-work of land mien detection. The new method is similar in spirit to the MM-MNF algorithm, which is based on a linear pre- filter, followed by a morphological multispectral detection component (MM). The new filter (PC-MM), has a similar concatenated structure, and addresses some of the weaknesses inherent in the linear component of the MM-MNF algorithm; namely, the susceptibility of the MNF transform to clutter inhomogeneity, as well as to variation sin clutter covariance estimation. The PC-MM algorithm addresses the stationarity problem by solely operating on image peaks extracted by a morphological top-hat transform. Therefore, the algorithm is much less susceptible to the present of different textural regions. Subsequently, the peaks in the extracted multispectral top-het image are projected into uncorrelated bands using the principal component (PC) transform. Due to the packing property of the PC transform, the target markers are typically found in the first and second bands in the PC transformed image. The targets are then detected using a variant of the morphological detection scheme. The new method provides a fast and satisfactory first-pass detection result, for images of different clutter homogeneities and target types. The extracted targets, from the first pass, are then issued to improve the detection result in a subsequent iteration, by updating covariance estimates of relevant filter variables.
Automatic mine detection is an area of intense research due to the implications in humanistic and battlefield management related issues. In this paper, we describe a fully automatic and iterative implementation of the nonlinear MM-MNF algorithm and review its performance for detecting landmines in multi-spectral images provided by the Coastal Battlefield Reconnaissance and Analysis program. The MM-MNF algorithm utilizes a powerful linear multi-spectral enhancement tool, called the Maximum Noise Fraction (MNF) transform, in conjunction with a nonlinear detection device based on mathematical morphology. The iterative implementation of this algorithm improves the accuracy of the clutter covariance estimation, which is turn decreases the number of false alarms, as compared to a previously reported implementation. The result are significantly better than the ones obtained from a constant false alarm rate algorithm, known as the RX-algorithm, whose performance was also inferior to the previous implementation of the MM-MNF algorithm.
We study the problem of simulating a class of Gibbs random field models, called morphologically constrained Gibbs random fields, using Markov chain Monte Carlo sampling techniques. Traditional single site updating Markov chain Monte Carlo sampling algorithm, like the Metropolis algorithm, tend to converge extremely slowly when used to simulate these models, particularly at low temperatures and for constraints involving large geometrical shapes. Moreover, the morphologically constrained Gibbs random fields are not, in general, Markov. Hence, a Markov chain Monte Carlo sampling algorithm based on the Gibbs sampler is not possible. We prose a variant of the Metropolis algorithm that, at each iteration, allows multi-site updating and converges substantially faster than the traditional single- site updating algorithm. The set of sites that are updated at a particular iteration is specified in terms of a shape parameter and a size parameter. Computation of the acceptance probability involves a 'test ratio,' which requires computation of the ratio of the probabilities of the current and new realizations. Because of the special structure of our energy function, this computation can be done by means of a simple; local iterative procedure. Therefore lack of Markovianity does not impose any additional computational burden for model simulation. The proposed algorithm has been used to simulate a number of image texture models, both synthetic and natural.
KEYWORDS: Detection and tracking algorithms, Target detection, Land mines, Signal to noise ratio, Mathematical morphology, Reconstruction algorithms, Image filtering, Multispectral imaging, Visualization, Binary data
Automatic mine detection is a critical issue in battle field management. This is expected to lead to better technologies that provide accurate and reliable detection of mines embedded in clutter. In this paper, we review a procedure for automatic mine detection in multispectral data provided by the Coastal Battlefield Reconnaissance and Analysis (COBRA) program. Our procedure is essentially a two-step method that employs the Maximum Noise Fraction (MNF) transform, a powerful enhancement tool for multispectral data, combined with nonlinear morphological operators that do the actual detection. Mathematical morphology is also used to account for the critical step of clutter estimation required by the MNF transform. Results obtained with available, truthed data, show the high success of the proposed method in meeting performance requirements. A low number of midsections is observed, whereas only a small number of false alarms is introduced by the algorithm. The results are better than the ones obtained by means of a constant false alarm rate (CFAR) algorithm provided along with the data.
KEYWORDS: Monte Carlo methods, Binary data, Image filtering, Signal to noise ratio, Image restoration, Image classification, Magnetorheological finishing, Statistical analysis, Image analysis, Digital filtering
Morphological size distributions and densities are frequently used as descriptors of granularity or texture within an image. They have been successfully employed in a number of image processing and analysis tasks, including shape analysis, multiscale shape representation, texture classification, and noise filtering. In most cases however it is not possible to analytically compute these quantities. In this paper, we study the problem of estimating the (discrete) morphological size distribution and density of random images, by means of empirical as well as Monte Carlo estimators. Theoretical and experimental results demonstrate clear superiority of the Monte Carlo estimation approach. Examples illustrate the usefulness of the proposed estimators in traditional image processing and analysis problems.
Automatic mine detection has recently become a subject of great importance to U.S. Navy. A number of approaches to this problem have been suggested so far. Current algorithms however do not provide sufficiently high performance results, especially in cases where mines are embedded in clutter. A thorough and fundamental understanding of target detection and recognition techniques is needed in order to significantly enhance the capabilities of automatic detection systems. We discuss here a possible approach to this problem, based on a theoretical model for image acquisition, that allows mine detection to be formulated as an inverse problem. New and near optimal algorithms may be developed as attempts to solving this problem.
We consider the problem of detecting minelike targets, imaged by means of multispectral sensors, that have been heavily corrupted by clutter. An effective detection approach needs to take into consideration the high correlation that is often present among bands in multispectral images and be robust against clutter. To this end, we here propose a two-step target detection approach. In particular, we first employ the Maximum Noise Fraction transform, in conjunction with vector-morphology, in order to reduce the effect of clutter and enhance the presence of targets. We then discuss a target detection algorithm, based on a morphological image reconstruction/marker fusion approach. We apply this algorithm to the problem of detecting minelike targets present in six-band aerial images, provided to us by the Coastal Systems Station, Naval Surface Warfare Center, Panama City, Florida. The proposed technique is relatively simple and requires only approximate knowledge of target size.
Automatic target detection is the primary goal of many imaging systems both in defense and manufacturing industries. Advances in methods and equipment for image acquisition, processing, and analysis are required to effectively deal with this problem. Towards this goal, we discuss here a target detection algorithm based on mathematical morphology. Mathematical morphology is an image processing tool that is used for designing nonlinear operators for image representation, processing, and analysis. In particular, the proposed approach is based on a morphological reconstruction algorithm for detecting targets of interest appearing on a scene. We apply this algorithm to the problem of detecting minelike targets in multispectral images, provided to us by the Coastal Systems Station, Naval Surface Warfare Center, Panama City, Florida. The proposed technique is relatively simple and only requires approximate knowledge of target size. The algorithm also effectively incorporates fusion of data from different bands. The implementation has been done in the KHOROS signal and image processing environment with encouraging results.
We discuss a number of issues related to the morphological analysis of random shape by means of discrete random set theory. Our purpose here is twofold. First, we would like to demonstrate that, in the discrete case, a number of problems associated with random set theory can be effectively solved. Furthermore, we would like to establish a direct relationship between discrete random sets and binary random fields. To accomplish this, we first introduce the cumulative distribution and capacity functionals of a discrete random set, and review their properties. Under a natural assumption, we show that there exists a one-to-one correspondence between the probability mass function of a discrete binary random field and the cumulative distribution functional of the corresponding discrete random set. The cumulative distribution and capacity functionals are then related to higher-order moments of a discrete binary random field. We show that there exists a direct relationship between the capacity functional of a discrete random set and the capacity functional of the discrete random set obtained by means of dilation, erosion, opening, or closing. These relationships allow us to derive an interesting result, regarding the statistical behavior of elementary morphological filters. Finally, we introduce moments for discrete random sets, and show that the class of opening-based discrete size distributions are higher-order moments of a discrete random set. This last observation allows us to argue that discrete size distributions are good statistical summaries for shape.
In this paper, we review a number of pyramidal image decomposition techniques for image representation and compression. We argue that the design of an efficient pyramidal image decomposition procedure is directly related to the design of an optimal (non-linear in general) image predictor. However, determining such a predictor is not possible in general. To alleviate this problem, we propose four natural constraints, which uniquely identify the `optimal' predictor as being a morphological opening. This choice naturally leads to a morphological pyramidal image decomposition algorithm recently proposed by Heijmans and Toet. Experimental analysis, allows us to study six pyramidal image decomposition techniques, and demonstrate the superiority (in terms of compression performance and computational simplicity) of the Heijmans-Toet algorithm.
We present a unifying approach for the morphological processing of image sequences. The mathematical tool that we choose to work with is lattice theory. Lattice theory allows us to introduce two different, in general, approaches to the problem of morphologically processing image sequences. The first approach formalizes and generalizes the vector approach suggested by Wilson. The second approach is new and extends the vector ideas, proposed by Astola, Haavisto, and Neuvo, regarding median filtering.
The main objective of this paper is to discuss a relatively new approach to the synthesis and analysis of shape. Specifically, we propose to characterize shape by means of random sets. The proposed approach may allow us to develop new shape synthesis and analysis procedures by combining probability theory with mathematical morphology. This combination has the potential of allowing modeling and analysis of shapes of both geometric and random structure.
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