Temporal Profile Based Small Moving Target Detection Algorithm in Infrared Image Sequences
ABSTRACT A new algorithm is presented which deals with the problem of detecting small moving targets in infrared image sequences that
also contain drifting and evolving clutter. Through development of models of the temporal behavior of the static background,
target and cloud edge on a single pixel basis, the new algorithm employing the connecting line of the stagnation points (CLSP)
of the temporal profile as the baseline is created and tested. The deviation of the temporal profile and its CLSP is analyzed
and it is determined that the distribution of the residual temporal profile obtained by subtracting the baseline from the
temporal profile can be modeled by a Gaussian distribution. The occurrences of the targets have intensity values significantly
different to the distribution of the residual temporal profile. Unlike the conventional 3-D method, this new algorithm operates
on the temporal profile in 1-D space, not in 3-D space, thus having a higher computational efficiency. Experiments with real
IR image sequences have proved the validity of the new approach.
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ABSTRACT: We describe the development and the implementation of a new nonlinear image processing methodology to detect dim targets in infrared images. The methodology is based on mathematical morphology. In a first phase, the image is filtered in order to enhance positively contrasted, isotropic objects with a specified size. Then, a simple motion analysis is carried out, and finally, detection is performed by thresholding. A complete study of the algorithm performance on NATO test sequences is presented. A comparison is made with the Holmes filter and the scale-subtraction filter, a wavelet-based method.Optical Engineering - OPT ENG. 01/1996; 35(7):1886-1893.
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ABSTRACT: This work presents a method for clutter rejection and dim target track detection from infrared (IR) satellite data using neural networks. A high-order correlation method which recursively computes the spatio-temporal cross-correlations between data of several consecutive scans is developed. The implementation of this scheme using a connectionist network is presented. Several important properties of the high-order correlation method which indicate that the resultant filtered images capture all the target information are established. The simulation results obtained with this approach show at least 93% clutter rejection. Further improvement in the clutter rejection rate is achieved by modifying the high-order correlation method to incorporate the target motion dynamics. The implementation of this modified high-order correlation using a high-order neural network architecture is demonstrated. The simulation results indicate at least 97% clutter rejection rate for this method. A comparison is also made between the methods developed here and the conventional frequency domain three-dimensional (3-D) filtering scheme, and the simulation results are providedIEEE Transactions on Aerospace and Electronic Systems 08/1993; · 1.30 Impact Factor
Conference Proceeding: Small target detection using wavelets[show abstract] [hide abstract]
ABSTRACT: Presents a method for the detection of small objects embedded in a noisy background. The detection is performed on the wavelet transformed image. After a preliminary de-noising pass, the objects are separated from background by exploiting the evaluation of Renyi's information at the different decomposition levels of the wavelet transform. We apply the proposed technique to detect microcalcifications in digital mammographic imagesPattern Recognition, 1998. Proceedings. Fourteenth International Conference on; 09/1998