Abdullah Bal

University of South Alabama, Mobile, Alabama, United States

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Publications (26)22.88 Total impact

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    ABSTRACT: A novel target detection technique is developed in this paper for hyperspectral imagery. The proposed technique involves a Gaussian filter to process the reference target signature as well as the unknown pixel signature from the given input scene. After performing correlation operation between the signatures, a post-processing scheme is employed to decide whether the specific pixel contains the target material. Theoretical analyses and computer simulation results presented in this paper show that the technique can successfully overcome the problem of variations in the target signature due to changes in the environment and addition of noise to the input scene. Investigation on real-life hyperspectral imagery proves that the proposed technique can successfully detect the potential targets present in the input scene without generating any significant amount of false alarm.
    Optics and Lasers in Engineering 01/2008; · 1.92 Impact Factor
  • Mohammad S. Alam, Abdullah Bal
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    ABSTRACT: Camera motion estimation in image sequences generally focuses on the recovery of the frames when the camera is mounted on a moving platform. Global motion in video sequences is more complex and involves camera operation, camera motion, and other nontarget motions. Global motion compensation is usually handled by compensating the dominant motion using estimation and segmentation techniques. To enhance tracker performance and accuracy, frame recovery operation plays a crucial role by estimating undesired motion. In this paper, a normalized correlation-based regional template-matching (TM) algorithm has been developed to accurately recover frames before the application of the tracking algorithm. Then, a robust multiple-target-tracking system has been developed using a combination of fringe-adjusted joint transform correlator and TM techniques. Joint transform correlation detects a target optoelectronically, while TM technique is performed digitally. To increase the tracking system speed and decrease the effects of clutter, a subframe segmentation and local deviation-based image-preprocessing algorithm has been proposed. The improved performance of multiple-target-tracking system is tested using real-life forward-looking infrared (IR) imagery video sequences obtained from IR sensors mounted on an airborne platform
    IEEE Transactions on Industrial Electronics 03/2007; · 6.50 Impact Factor
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    ABSTRACT: Pattern recognition in hyperspectral imagery often suffers from a number of limitations, which includes computation complexity, false alarms and missing targets. The major reason behind these problems is that the spectra obtained by hyperspectral sensors do not produce a deterministic signature, because the spectra observed from samples of the same material may vary due to variations in the material surface, atmospheric conditions and other related reasons. In addition, the presence of noise in the input scene may complicate the situation further. Therefore, the main objective of pattern recognition in hyperspectral imagery is to maximize the probability of detection and at the same time minimize the probability of generating false alarms. Though several detection algorithms have been proposed in the literature, but most of them are observed to be inefficient in meeting the objective requirement mentioned above. This paper presents a novel detection algorithm which is fast and simple in architecture. The algorithm involves a Gaussian filter to process the target signature as well as the unknown signature from the input scene. A post-processing step is also included after performing correlation to detect the target pixels. Computer simulation results show that the algorithm can successfully detect all the targets present in the input scene without any significant false alarm.
    01/2007;
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    ABSTRACT: Anomaly analysis is used for various geophysics applications such as determination of geophysical structure's location and border detections. Besides the classical geophysical techniques, artificial intelligence based image processing algorithms have been found attractive for geophysical anomaly analysis. Recently, cellular neural networks (CNN) have been applied to geophysical data and satisfactory results are reported. CNN provides fast and parallel computational capability for geophysical image processing applications due to its filtering structure. The behavior of CNN is defined by two template matrices that are adjusted by a properly supervised learning algorithm. After training stage for geophysical data, Bouguer anomaly maps can be processed and analyzed sequentially. In this paper, CNN learning and processing capability have been improved, combining Wavelet functions and backpropagation learning algorithms. The new architecture is denoted as Wavelet-Cellular Neural networks (Wave-CNN) and it is employed to analyze Bouguer anomaly maps which are important to extract useful information in geophysics. At first, Wave-CNN performance is tested on synthetic geophysical data, which are created by a computer environment. Then, Bouguer anomaly maps of the Dumluca iron ore field have been analyzed and results are reported in comparison to real drilling results.
    Pure and Applied Geophysics 12/2006; 164(1):199-215. · 1.62 Impact Factor
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    ABSTRACT: Often sensor ego-motion or fast target movement causes the target to temporarily go out of the field-of-view leading to reappearing target detection problem in target tracking applications. Since the target goes out of the current frame and reenters at a later frame, the reentering location and variations in rotation, scale, and other 3D orientations of the target are not known thus complicating the detection algorithm has been developed using Fukunaga-Koontz Transform (FKT) and distance classifier correlation filter (DCCF). The detection algorithm uses target and background information, extracted from training samples, to detect possible candidate target images. The detected candidate target images are then introduced into the second algorithm, DCCF, called clutter rejection module, to determine the target coordinates are detected and tracking algorithm is initiated. The performance of the proposed FKT-DCCF based target detection algorithm has been tested using real-world forward looking infrared (FLIR) video sequences.
    Proc SPIE 06/2006;
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    ABSTRACT: In this paper, we propose a novel decision fusion algorithm for target tracking in forward-looking infrared image sequences recorded from an airborne platform. An important part of this study is identifying the failure modes in this type of imagery. Our strategy is to prevent these failure modes from developing into tracking failures. The results furnished by competing ego-motion compensation and tracking algorithms are evaluated based on their similarity to a target model constructed using the weighted composite reference function.
    IEEE Transactions on Image Processing 03/2006; · 3.20 Impact Factor
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    ABSTRACT: Fukunaga-Koontz Transform based technique offers some attractive properties for desired class oriented dimensionality reduction in hyperspectral imagery. In FKT, feature selection is performed by transforming into a new space where feature classes have complimentary eigenvectors. Dimensionality reduction technique based on these complimentary eigenvector analysis can be described under two classes, desired class and background clutter, such that each basis function best represent one class while carrying the least amount of information from the second class. By selecting a few eigenvectors which are most relevant to desired class, one can reduce the dimension of hyperspectral cube. Since the FKT based technique reduces data size, it provides significant advantages for near real time detection applications in hyperspectral imagery. Furthermore, the eigenvector selection approach significantly reduces computation burden via the dimensionality reduction processes. The performance of the proposed dimensionality reduction algorithm has been tested using real-world hyperspectral dataset.
    Proc SPIE 01/2006;
  • A. Bal, M. S. Alam
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    ABSTRACT: Target detection and tracking algorithms deal with the recognition of a variety of target images obtained from a multitude of sensor types, such as forward-looking infrared (FLIR), synthetic aperture radar and laser radar.1,2 Temporary disappearance and then reappearance of the target(s) in the field-of-view may be encountered during the tracking processes. To accommodate this problem, training based techniques have been developed using combination of two techniques; tuned basis functions (TBF) and correlation based template matching (TM) techniques. The TBFs are used to detect possible tentative target images. The detected candidate target images are then introduced into the second algorithm, called clutter rejection module, to determine the target reentering frame and location of the target. The performance of the proposed TBF-TM based reappeared target detection and tracking algorithm has been tested using real-world forward looking infrared video sequences.
    01/2006;
  • A. Bal, M.S. Alam
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    ABSTRACT: A novel automatic target tracking (ATT) algorithm for tracking targets in forward-looking infrared (FLIR) image sequences is proposed in this paper. The proposed algorithm efficiently utilizes the target intensity feature, surrounding background, and shape information for tracking purposes. This algorithm involves the selection of a suitable subframe and a target window based on the intensity and shape of the known reference target. The subframe size is determined from the region of interest and is constrained by target size, target motion, and camera movement. Then, an intensity variation function (IVF) is developed to model the target intensity profile. The IVF model generates the maximum peak value where the reference target intensity variation is similar to the candidate target intensity variation. In the proposed algorithm, a control module has been incorporated to evaluate IVF results and to detect a false alarm (missed target). Upon detecting a false alarm, the controller triggers another algorithm, called template model (TM), which is based on the shape knowledge of the reference target. By evaluating the outputs from the IVF and TM techniques, the tracker determines the real coordinates of one or more targets. The proposed technique also alleviates the detrimental effects of camera motion, by appropriately adjusting the subframe size. Experimental results using real-life long-wave and medium-wave infrared image sequences are shown to validate the robustness of the proposed technique.
    IEEE Transactions on Instrumentation and Measurement 11/2005; · 1.71 Impact Factor
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    ABSTRACT: We have previously shown that polarization enhancement of fingerprint images during the enrolment process improves the performance of the verification and identification processes. In this paper, we present a design and analysis of a new synthetic discriminant function (SDF) for rotation/scale invariant polarization-enhanced fingerprint system. Performance comparison between the proposed SDF and an SDF obtained for traditional fingerprint systems is included.
    Proc SPIE 08/2005;
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    ABSTRACT: An important step in the fingerprint identification system is the reliable extraction of distinct features from fingerprint images. Identification performance is directly related to the enhancement of fingerprint images during or after the enrollment phase. Among the various enhancement algorithms, artificial-intelligence-based feature-extraction techniques are attractive owing to their adaptive learning properties. We present a new supervised filtering technique that is based on a dynamic neural-network approach to develop a robust fingerprint enhancement algorithm. For pattern matching, a joint transform correlation (JTC) algorithm has been incorporated that offers high processing speed for real-time applications. Because the fringe-adjusted JTC algorithm has been found to yield a significantly better correlation output compared with alternate JTCs, we used this algorithm for the identification process. Test results are presented to verify the effectiveness of the proposed algorithm.
    Applied Optics 03/2005; 44(5):647-54. · 1.69 Impact Factor
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    ABSTRACT: The parallel processing capability and adaptive filtering features of dynamic neural networks offer highly efficient feature extraction and enhancement capability for fingerprint images. The most important aspect of the fingerprint enhancement is the extraction of relevant details with respect to distributed complex features. For this purpose, an efficient dynamic neural filtering technique has been proposed in this paper. After the enhancement process, fingerprint identification is/has been achieved using joint transform correlation (JTC) algorithm. Since the fringe-adjusted JTC algorithm has been found to yield significantly better correlation output compared to alternate JTCs, we used it in this study. The identification test results are presented to verify the effectiveness of the proposed enhancement and identification algorithms.
    Proc SPIE 03/2005;
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    ABSTRACT: The performance of target detection and tracking algorithms generally depends on the signature, clutter, and noise that are usually present in the input scene. To evaluate the effectiveness of a given algorithm, it is necessary to develop performance metrics based on the input plane as well as output plane information. We develop two performance metrics for assessing the effects of input plane data on the performance of detection and tracking algorithms by identifying three regions of operation-excellent, average, and risky intervals. To evaluate the performance of a given algorithm based on the output plane information, we utilize several metrics that use primarily correlation peak intensity and clutter information. Since the fringe-adjusted joint transform correlation (JTC) was found to yield better correlation output compared to alternate JTC algorithms, we investigate the performance of two fringe-adjusted JTC (FJTC)-based detection and tracking algorithms using several metrics involving the correlation peak sharpness, signal-to-noise ratio, and distortion invariance. The aforementioned input and output plane metrics are used to evaluate the results for both single/multiple target detection and tracking algorithms using real life forward-looking infrared (FLIR) video sequences.
    Optical Engineering 01/2005; 44(6). · 0.96 Impact Factor
  • A. Bal, M. S. Alam
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    ABSTRACT: Target tracking in forward looking infrared (FLIR) video sequences is challenging problem due to various limitations such as low signal-to-noise ratio, image blurring, partial occlusion, and low texture information, which often leads to missing targets or tracking non-target objects. To alleviate these problems, we propose the application of quadratic correlation filters using subframe approach in FLIR. The proposed filtering technique avoids the disadvantages of pixel-based image preprocessing techniques. The filter coefficients are obtained for desired target class from the training images. For real time applications, the input scene is first segmented to the subframes according to target location information from the previous frame. The subframe of interest is then correlated with correlation filters associated with target class. The obtained correlation output contains higher value that indicates the target location in the region of interest. The simulation results for target tracking in real life FLIR imagery have been reported to verify the effectiveness of the proposed technique.
    01/2005;
  • Abdullah Bal, Mohammad S. Alam
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    ABSTRACT: Moving target tracking is a challenging task and is increasingly becoming important for various applications. In this paper, we have presented target detection and tracking algorithm based on target intensity feature relative to surrounding background, and shape information of target. Proposed automatic target tracking algorithm includes two techniques: intensity variation function (IVF) and template modeling (TM). The intensity variation function is formulated by using target intensity feature while template modeling is based on target shape information. The IVF technique produces the maximum peak value whereas the reference target intensity variation is similar to the candidate target intensity variation. When IVF technique fails, due to background clutter, non-target object or other artifacts, the second technique, template modeling, is triggered by control module. By evaluating the outputs from the IVF and TM techniques, the tracker determines the real coordinates of the target. Performance of the proposed ATT is tested using real life forward-looking infrared (FLIR) image sequences taken from an airborne, moving platform.
    Proc SPIE 01/2005; 54:1846-1852.
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    ABSTRACT: We propose a novel decision fusion algorithm for target tracking in forward-looking infrared (FLIR) image sequences recorded from an airborne platform. The algorithm allows the fusion of complementary ego-motion compensation and tracking algorithms to estimate the position of the target in the current frame among a sequence of frames of FLIR imagery. We identified three modes that contribute to the failure of the tracking system: (1) the sensor ego-motion failure mode, which causes the movement of the target beyond the operational limits of the tracking stage; (2) the tracking failure mode, which occurs when the tracking algorithm fails to determine the correct location of the target in the new frame; (3) the reference-image distortion failure mode, which happens when the reference image accumulates walkoff error, especially when the target is changing in size, shape, or orientation from frame to frame. The strategy in our design is to prevent these failure modes from producing tracking failures. The overall performance of the algorithm is guaranteed to be much better than any individual tracking algorithm used in the fusion. One important aspect of the proposed algorithm is its recoverability: the ability to recover following a failure at a certain frame. The experiments performed on Army Missile Command AMCOM FLIR data set verify the robustness of the algorithm.
    Optical Engineering 01/2005; 44(2). · 0.96 Impact Factor
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    ABSTRACT: One of the most important challenges of fingerprint identification is the extraction of relevant details against distributed complex features. The parallel processing capability and learnable filtering features of cellular neural networks offer highly efficient feature extraction and enhancement capability for fingerprint images. In this paper, we propose to utilize the Widrow learning algorithm with a cellular neural network to efficiently enhance fingerprint details during the enrollment part. To evaluate the performance of the verification-identification part, enhanced fingerprint images are introduced into the fringe-adjusted joint transform correlator architecture for verification of an unknown fingerprint from a database. Comparison between the original and enhanced fingerprint identification and verification results is provided through computer simulation.
    Optical Engineering 01/2005; 44. · 0.96 Impact Factor
  • Abdullah Bal, Mohammad S Alam
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    ABSTRACT: Target tracking in forward-looking infrared (FLIR) video sequences is a challenging problem because of various limitations such as low signal-to-noise ratio (SNR), image blurring, partial occlusion, and low texture information, which often leads to missing targets or tracking nontarget objects. To alleviate these problems, we developed a novel algorithm that involves local-deviation-based image preprocessing as well as fringe-adjusted joint-transform-correlation--(FJTC) and template-matching--(TM) based target detection and tracking. The local-deviation-based preprocessing technique is used to suppress smooth texture such as background and to enhance target edge information. However, for complex situations such as the target blending with background, partial occlusion of the target, or proximity of the target to other similar nontarget objects, FJTC may produce a false alarm. For such cases, the TM-based detection technique is used to compensate FJTC breaking points by use of cross-correlation coefficients. Finally, a robust tracking algorithm is developed by use of both FJTC and TM techniques, which is called FJTC-TM technique. The performance of the proposed FJTC-TM algorithm is tested with real-life FLIR image sequences.
    Applied Optics 10/2004; 43(25):4874-81. · 1.69 Impact Factor
  • Mohammad S. Alam, Abdullah Bal
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    ABSTRACT: The performance of a target tracking algorithm is directly related to global motion compensation performance, if the imaging sensor systems are not stable. Especially, forward looking infra-red (FLIR) video sequences are detrimentally affected by camera motion since the infrared camera mounted on an airborne platform suffer from abrupt discontinuities in motion. Since this global motion could cause the movement of the target outside the operational limits of the tracking algorithm, each frame in FLIR sequences has to be recovered by motion estimation technique. In this paper, a normalized cross correlation based template matching algorithm has been developed to accurately estimate and compensate the global motion before the application of the tracking algorithm. Then, the automatic target tracking algorithm has been applied using fringe-adjusted joint transform correlator (FJTC) based target detection and tracking technique.
    Proc SPIE 10/2004;
  • Abdullah Bal, Mohammad S. Alam, Aed El-Saba
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    ABSTRACT: An important step in the fingerprint identification system is the extraction of relevant details against distributed complex features. Identification performance is directly related to the enhancement of fingerprint images during or after the enrollment phase. Among the various enhancement algorithms, artificial intelligence based feature extraction techniques are attractive due to their adaptive learning properties. In this paper, we propose a cellular neural network (CNN) based filtering technique due to its ability of parallel processing and generating learnable filtering features. CNN offers high efficient feature extraction and enhancement possibility for fingerprint images. The enhanced fingerprint images are then introduced to joint transform correlator (JTC) architecture to identify unknown fingerprint from the database. Since the fringe-adjusted JTC algorithm has been found to yield significantly better correlation output compared to alternate JTCs, we used it for the identification process. Test results are presented to verify the effectiveness of the proposed algorithm.
    Proc SPIE 10/2004;