Maximum Average Correlation Height Filter 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.

Tracking of a Fixed-Shape Moving Object Based on the Gradient Descent Method.

Tracking moving objects is one of the most promising yet the most challenging research areas pertaining to computer vision, pattern recognition and image processing. The challenges associated with object tracking range from problems pertaining to camera axis orientations to object occlusion. In addition, variations in remote scene environments add to the difficulties related to object tracking. All the mentioned challenges and problems pertaining to object tracking make the procedure computationally complex and time-consuming. In this paper, a stochastic gradient-based optimization technique has been used in conjunction with particle filters for object tracking. First, the object that needs to be tracked is detected using the Maximum Average Correlation Height (MACH) filter. The object of interest is detected based on the presence of a correlation peak and average similarity measure. The results of object detection are fed to the tracking routine. The gradient descent technique is employed for object tracking and is used to optimize the particle filters. The gradient descent technique allows particles to converge quickly, allowing less time for the object to be tracked. The results of the proposed algorithm are compared with similar state-of-the-art tracking algorithms on five datasets that include both artificial moving objects and humans to show that the gradient-based tracking algorithm provides better results, both in terms of accuracy and speed.

Recognition and Tracking of Objects in a Clustered Remote Scene Environment

Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer Vision. Computer vision is one of the most active research fields that lies at the intersection of deep learning and machine vision. This paper presents an efficient ensemble algorithm for the recognition and tracking of fixed shape moving objects while accommodating the shift and scale invariances that the object may encounter. The first part uses the Maximum Average Correlation Height (MACH) filter for object recognition and determines the bounding box coordinates. In case the correlation based MACH filter fails, the algorithms switches to a much reliable but computationally complex feature based object recognition technique i.e., affine scale invariant feature transform (ASIFT). ASIFT is used to accommodate object shift and scale object variations. ASIFT extracts certain features from the object of interest, providing invariance in up to six affine parameters, namely translation (two parameters), zoom, rotation and two camera axis orientations. However, in this paper, only the shift and scale invariances are used. The second part of the algorithm demonstrates the use of particle filters based Approximate Proximal Gradient (APG) technique to periodically update the coordinates of the object encapsulated in the bounding box. At the end, a comparison of the proposed algorithm with other state-of-the-art tracking algorithms has been presented, which demonstrates the effectiveness of the proposed algorithm with respect to the minimization of tracking errors.

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.

Active contour‐based clutter defiance scheme for correlation filters

The negative impact of clutters on correlation output still remains the problem not handled in correlation pattern recognition (CPR) paradigm. Some of the known impacts caused by the presence of clutters include pronounced side-lobe generation and reduction in height of correlation peak. The occurrence of the side-lobes results in the distortion of actual correlation output while making the class decision a difficult task. Whereas, reduction in correlation height may cause class decisions to go wrong. The authors propose a novel clutter defiance strategy based on object segmentation through active contours approach thus evading a negative effect on the correlation output. Instead of allowing clutters to participate in CPR-based classification decision they recommend blocking irrelevant details to avoid distortion in the correlation process. An appropriate tweaking in the optimal logarithmic maximum average correlation height filter design yields a performance gain up to 80% in the correlation peak to side-lobe ratio, thus making it well pronounced to classify the true class objects correctly. The improvement remains consistent throughout the range of in-plane angular distortions against synthetically cluttered challenging images.

Approximate Proximal Gradient-Based Correlation Filter for Target Tracking in Videos: A Unified Approach

Video cameras are among the most commonly used devices throughout the world which results in imaging technology being one of the most important areas for research and development. Imaging technology requires constant research as it is used in crucial applications such as video conferencing and surveillance. In the field of image processing, motion detection and estimation are fundamental steps in extracting information on objects segmented from their backgrounds. In this paper, a cohesive approach is presented that uses two algorithms for motion estimation and detection. The proposed method is able to detect moving objects using maximum average correlation height (MACH) filter. Upon obtaining the accurate coordinates of an object of interest from the MACH filter, the next part of the algorithm starts tracking the object. For tracking, a particle filter is used to estimate the motion of the object using a Markov chain. To enhance the accuracy of particle filter, an approximate proximal gradient algorithm is employed for unconstrained minimization of the particles which restricts the tracking process to target templates (most essential information) only. Finally, a comparison between the proposed algorithm and recent similar algorithms is made that demonstrates the minimization of tracking errors using the proposed technique.

The influence of additional phase modulation of an amplitude liquid crystal STLM on the image recognition characteristics in the invariant optical digital correlator

We present the results of measurements of additional phase modulation characteristics of a serial amplitude liquid crystal spatial light modulator HoloEye LC 2002. It is found in which way the phase shift of the liquid crystal spatial light modulator depends on the applied signal. The mathematical simulation of the performance of an invariant diffractive optical-digital correlator based on a liquid crystal spatial light modulator with the amplitude-dependent phase shift is carried out using previously measured data. The correlation filters used in the work are an optimal tradeoff maximum average correlation height filter and a minimum noise and correlation energy optical correlation filter. A method for correlation filters optimization was proposed to compensate for the recognition error caused by the presence of the additional phase modulation. In some cases, the optimization allows one not only to compensate for the recognition error, but also to reduce it.

Composite filtering strategy for improving distortion invariance in object recognition

Correlation-based pattern recognition filtering methods such as the eigenextended maximum average correlation height (EEMACH) filter is considered an effective tool in object recognition applications. However, these approaches require exclusive training for all possible distortions including in-plane as well as out-of-plane rotation, scale and illumination variations. The overall training process is exhaustive and requires training of filter banks to handle specific types of distortion separately. To overcome the aforementioned limitations, the authors propose a new difference of Gaussian (DoG)-based logarithmically preprocessed EEMACH filter which can manage multiple distortions in a single training instance while ensuring inherent control over illumination variations. The DoG-based logarithmic treatment exploits inherent capabilities of logarithmic preprocessing to manage scale and in-plane rotations. By reducing the number of classifier instances to one third, it not only reduces the computation complexity of the process to 33%, but also enhances the object recognition performance. The cumulative improvement is up to 2.73% in case of rotations and 10.8% in case of scaling by incorporating reinforced edges due to DoG operation. The resultant filter displays significantly enhanced recognition performance leading to a higher percentage of correct machine decisions, especially when an input scene contains multiple distortions.

Information Authentication of Three-Dimensional Photon Counting Double Random Phase Encryption Using Nonlinear Maximum Average Correlation Height Filter

In this paper, we propose a nonlinear maximum average correlation height (MACH) filter for information authentication of photon counting double random phase encryption (DRPE). To enhance the security of DRPE, photon counting imaging can be applied because of its sparseness. However, under severely photon-starved conditions, information authentication of DRPE may not be implemented successfully. To visualize the photon counting DRPE, a three-dimensional imaging technique such as integral imaging can be used. In addition, a nonlinear MACH filter can be utilized for helping the information authentication. Therefore, in this paper, we use integral imaging and nonlinear MACH filter to implement the information authentication of photon counting DRPE. To verify our method, we implement optical experiments and computer simulation.

Optimization-based methodology for training set selection to synthesize composite correlation filters for face recognition

Face recognition has been addressed with pattern recognition techniques such as composite correlation filters. These filters are synthesized from training sets which are representative of facial classes. For this reason, the filter performance depends greatly on the appropriate selection of the training set. This set can be selected either by a filter designer or by a conventional method. This paper presents an optimization-based methodology for the automatic selection of the training set. Given an optimization algorithm, the proposed methodology uses its main mechanics to iteratively examine a given set of available images in order to find the best subset for the training set. To this end, three objective functions are proposed as optimization criteria for training set selection. The proposed methodology was evaluated by undertaking face recognition under variable illumination and facial expressions. Four optimization algorithms and three composite correlation filters were used to test the proposed methodology. The Maximum Average Correlation Height filter designed by Grey Wolf Optimizer obtained the best performance under homogeneous illumination and facial expressions, while the Unconstrained Nonlinear Composite Filter designed by either Grey Wolf Optimizer or (1+1)-Evolution Strategy obtained the best performance under variable illumination. The proposed methodology selects training sets for the synthesis of composite filters with competitive results comparable to the results reported in the face recognition literature.

Spatial Distorted Target Recognition Based on Improved Maximum Average Correlation Height Algorithm

Spatial distorted target is very hard to be recognized for complexity and variety of targets, which has restricted the development of pattern recognition technology to a great extent. Joint transform correlator is one of the key equipments to detect and recognize distorted targets. The appearance and development of maximum average correlation height (MACH) algorithm is introduced in this paper. Based on the principle the algorithm and experimental analysis, an improved maximum average correlation height algorithm fit for joint transform correlator is proposed, which has powerful capability of suppressing background noise and widening distortion tolerance. Target images with different shapes including scale or angular distortion constitute MACH filter in frequency domain, which is projected to space domain as reference template including varieties of attitude. To show the feasibility of the algorithm, an airplane with angular distortion in sky is processed by MACH filter as an example. Simulation and optical experimental results are given in this paper. The experiments show the angular distortion tolerance can reach up to 15 degrees. The actual effect of the improved MACH filter algorithm is confirmed very well.

Scale Distorted Target Recognition Based on Machine Vision

<div> <p class="9"><span>Machine vision means to carry out measurement or judgment with machine instead of human eyes. In the field of target recognition, optical correlation technology is a main way to realize machine vision. Targets can be recognized and located with high precision</span><span> </span><span>taking advantage of optoelectronic hybrid joint </span><span>transform</span><span> correlator (OHJTC). However, when scale or angular distortion of the detected target exists relative to the reference template, the intensity of correlation peaks will decrease to a great extent, which restricted the recognition results greatly. In this paper, the development and principle of maximum average correlation height (MACH)</span><span> </span><span>algorithm is introduced. Through amounts of experiments, t</span><span>he control</span><span>ling</span><span> parameters</span><span> </span><span>of the synthesized filter</span><span> are optimized</span><span>, which makes MACH filter suppress background noise and widen recognition range of targets</span><span>. To show the feasibility of this algorithm, simulative and optical experiments of the improved MACH filter are carried out. As an example, the recognition results of a fighter target in sky</span><span> </span><span>are given, which shows </span><span>the </span><span>scale</span><span> </span><span>distortion tolerance can reach up </span><span>to </span><span>±</span><span>23%</span><span>. The actual effect of the improved MACH filter algorithm has been confirmed very well</span><span>.</span><span> <o:p></o:p></span></p> </div>

Trends in correlation-based pattern recognition and tracking in forward-looking infrared imagery.

In this paper, we review the recent trends and advancements on correlation-based pattern recognition and tracking in forward-looking infrared (FLIR) imagery. In particular, we discuss matched filter-based correlation techniques for target detection and tracking which are widely used for various real time applications. We analyze and present test results involving recently reported matched filters such as the maximum average correlation height (MACH) filter and its variants, and distance classifier correlation filter (DCCF) and its variants. Test results are presented for both single/multiple target detection and tracking using various real-life FLIR image sequences.

Maximum Average Correlation Height Filter Based on Pulsed Neural Network for Distorted Target Recognition

A novel method for image recognition using Pulsed Coupled Neural Network (PCNN) based on Maximum Average Correlation Height (MACH) filtering is proposed. Firstly based on the property of synchronous pulses oscillation for the similar group of neurons in PCNN, segment image to extract edge information, and this effectively suppresses noise. Subsequently the synthesized MACH is adopted to excellently realize the distorted target recognition using appropriate filter parameters depending on different targets. The simulations results show that the output correlation peak is obvious, and validate the effectiveness and accuracy of the method for distortion target recognition.

Distorted Target Recognition Based on Prewitt Operator Combined with MACH Filter

The distorted (scale and rotation etc.) target images and the training images are edge extracted by Prewitt operator. The Maximum Average Correlation Height filter is synthesized with the training images. The low frequency information of the distorted target images and the filter is enhanced. Then the edge extracted distorted target image is filtered by the Maximum Average correlation Height filter. Thereby, the distortion tolerance of the Maximum Average Correlation Height filter is expanded. By this method, when the space edge extraction combines with frequency domain filtering, the scale distortion tolerance is 0.76~1.56 times; the rotation distortion tolerance can reach up to 80 degrees. For the targets exceeding the distortion tolerance range, the intensity of correlation peak becomes weaker and weaker, but some of the targets can still be identified.

Fast spatiotemporal MACH filter for action recognition

Human action recognition has been an active field of research in computer vision community for the last decade. The spatiotemporal MACH (maximum average correlation height) filter approach has proved to be a very efficient method to solve the problem. It captures the intra-class variability and produces a very high response at the spatiotemporal location $$(x,y,t)$$ where the action is present in a video. Its computation cost is significantly lower than any other action recognition approach. However, faster algorithm is always needed to perform a computer vision task in real-time. Therefore, we propose a very efficient algorithm for normalized spatiotemporal MACH filtering for action recognition. It is based on the computations performed both in the frequency domain as well as the spatiotemporal domain exploiting integral video. We compare its speed with that of the relevant traditional algorithms and show that our approach drastically outperforms all of them.

Optical correlator based target detection, recognition, classification, and tracking

A dedicated automatic target recognition and tracking optical correlator (OC) system using advanced processing technology has been developed. Rapidly cycling data-cubes with size, shape, and orientation are employed with software algorithms to isolate correlation peaks and enable tracking of targets in maritime environments with future track prediction. The method has been found superior to employing maximum average correlation height filters for which the correlation peak intensity drops off in proportion to the number of training images. The physical dimensions of the OC system may be reduced to as small as 2 in. × 2 in. × 3 in. (51 mm × 51 mm × 76 mm) by modifying and minimizing the OC components.

3D passive photon counting automatic target recognition using advanced correlation filters

In this Letter, we present results for detecting and recognizing 3D objects in photon counting images using integral imaging with maximum average correlation height filters. We show that even under photon starved conditions objects may be automatically recognized in passively sensed 3D images using advanced correlation filters. We show that the proposed filter synthesized with ideal training images can detect and recognize a 3D object in photon counting images, even in the presence of occlusions and obscuration.

最大平均相关高度滤波算法在畸变目标识别中的应用

Maximum Average Correlation Height(MACH) algorithm was applied on JTC to achieve distortion target recognition to solve the problem of inaccurate recognition for distorted target(the targets rotated in plane or scale changed) in cluttered background with Joint Transform Correlator(JTC).First of all,three performance control parameters of MACH filter were optimized in frequency domain,and then the optimized MACH frequency domain filter was projected to object space;the MACH reference template could be obtained.As practical examples,the optical experiments on warship and car in clutter background were presented.The results show that when the control parameters of the filter are γ=1,α=0 and β=0.1,the obtained MACH reference template is clearest,the correlation peak of the target is sharpest and brightest.In this way,the obtained scale distortion tolerance on the JTC is 28%,and rotation distortion tolerance is 20°.For the target which exceeding the distortion tolerance range,the correlation peak is weaker and weaker,but some can still be recognized.In the field of distorted target detection,the improved MACH filter has a powerful advantage and a good application prospect.

云层背景目标识别的匹配滤波器设计

An improved matched filter design method based on the maximum average correlation height(MACH) principle is proposed to recognize the targets in cloud backgrounds. In the process of MACH filter design, a theoretical model of power spectrum density(PSD) is established by curve fitting after statistically analyzing the real cloud backgrounds’ PSD distribution to replace the traditional white noise model. Various features of flight targets are extracted and processed by threshold to get the training set images. Correlation results of targets in various cloud backgrounds show that the average peak to correlation energy ratio is 0.71%, and the peak to clutter ratio is 0.92 of improved matched filter, the cloud backgrounds are well restrained, and the filter is also distortion-invariant to feat