Extended Maximum Average Correlation Height Research Articles

Improving the False Alarm Capability of the Extended Maximum Average Correlation Height Filter

The extended maximum average correlation height (EMACH) filter is a potent pattern-detection tool used in image processing and computer vision applications. This filter enhances the effectiveness of the maximum average correlation height (MACH) filter by adding more features and flexibility. Incorporating the benefits of wavelet decomposition, we updated the EMACH filter to enhance its performance. The updated filter offered improved accuracy, robustness, and flexibility in recognizing complex patterns and objects in images with varying lighting conditions, noise levels, and occlusions. To verify the results’ consistency and compare their performance with that of the MACH filter and EMACH filter, performance metrics like peak-to-correlation energy, peak-to-sidelobe ratio, signal-to-noise ratio, and discrimination ratio were computed. Through numerical and experimental studies, we found that the proposed filter enhances the identification rate and decreases the number of false alarms.

SAR Image Target Recognition Based on Monogenic Signal and Sparse Representation

It is necessary to recognize the target in the situation of military battlefield monitoring and civilian real‐time monitoring. Sparse representation‐based SAR image target recognition method uses training samples or feature information to construct an overcomplete dictionary, which will inevitably affect the recognition speed. In this paper, a method based on monogenic signal and sparse representation is presented for SAR image target recognition. In this method, the extended maximum average correlation height filter is used to train the samples and generate the templates. The monogenic features of the templates are extracted to construct subdictionaries, and the subdictionaries are combined to construct a cascade dictionary. Sparse representation coefficients of the testing samples over the cascade dictionary are calculated by the orthogonal matching tracking algorithm, and recognition is realized according to the energy of the sparse coefficients and voting recognition. The experimental results suggest that the new approach has good results in terms of recognition accuracy and recognition time.

Logarithmically Pre-processed EMACH Filter for Enhanced Performance in Target Recognition

The image transformation with respect to mammalian retina-based sensor geometry enables us to resolve some major difficulties in object recognition. Logarithmic mapping is a mathematical transform that converts scale and angular variations to image shifts which helps in classifying target objects correctly. The primary issues dealt with in the design of correlation filters include the ability to suppress clutter and noise, easy detection of the correlation peaks and distortion tolerance. The correlation pattern recognition concept is applied for developing filters that provide invariance to distortions by adopting a suitable training mechanism. But, a separate training requirement for each distortion type is a drawback in correlation filters. By joining a recent variant of correlation filters, i.e. the Extended Maximum Average Correlation Height Filter with the strengths of logarithmic translation, we get a supplementary advantage which is useful in most of the recognition applications. The proposed combination yields a design which can manage multiple distortions in a single training instance with pronounced detection results against complex input scenes.

New two-stage correlation-based approach for target detection and tracking in forward-looking infrared imagery using filters based on extended maximum average correlation height and polynomial distance classifier correlation

A novel approach is proposed to recognize and track multiple identical and/or dissimilar targets in forward-looking infrared (FLIR) image sequences using a combination of an extended maximum average correlation height (EMACH) filter and polynomial distance classifier correlation filter (PDCCF). The EMACH filter and PDCCF are trained a priori using representative training images of targets with expected size and orientation variations. In the first step, the input scene is correlated with all EMACH filters (one for each desired or expected target class). Based on the regions with higher correlation peak values in the combined correlation output, a sufficient number of regions of interest (ROIs) are selected from the input scene. In the second step, a PDCCF is applied to these ROIs to identify target types and reject clutter and background. Moving-target detection and tracking is accomplished by applying this technique independently to all incoming image frames. Independent tracking of target(s) from one frame to the other allows the system to handle complicated situations such as a target disappearing in a few frames and then reappearing in later frames. This method yields robust performance for challenging FLIR imagery in terms of accurate detection and classification as well as tracking of the targets.

Correlation filter: an accurate approach to detect and locate low contrast character strings in complex table environment

Correlation has been used extensively in object detection field. In this paper, two kinds of correlation filters, Minimum Average Correlation Energy (MACE) and Extended Maximum Average Correlation Height (EMACH), are applied as adaptive shift locators to detect and locate smudgy character strings in complex tabular color flight coupon images. These strings in irregular tabular coupon are computer-printed characters but of low contrast and could be shifted out of the table so that we cannot detect and locate them using traditional algorithms. In our experiment, strings are extracted in the preprocessing phase by removing background and then based on geometric information, two correlation filters are applied to locate expected fields. We compare results from two correlation filters and demonstrate that this algorithm is a high accurate approach.

Comparative study of maximum average correlation height filter variants using ladar imagery

We present a study of a family of maximum average correlation height (MACH) filters. MACH filters were introduced by Mahalanobis et al., and several modifications such as the extended MACH (EMACH) and generalized MACH (GMACH) have been introduced to enhance the utility of the MACH filter approach. A comparison between the different filtering approaches and processing techniques is presented for the specific case of laser radar (ladar) imagery. The comparison utilizes both synthetic data for training and testing in one case, and synthetic training data and collected ladar imagery for testing in the second case. The results indicate that the GMACH variant of MACH filters is superior to both MACH and EMACH filters for the case of laser radar.