Minimum Average Correlation Energy Filter Research Articles

Genre Classification of Traditional Malay Music Using Spectrogram Correlation

A method to classify the genre of traditional Malay music using spectrogram correlation is described. The method can be divided into three distinct parts consisting of spectrogram construction that retains the most salient feature of the music, template construction that takes into account the variations in music within a genre as well as the music progresses, and template matching based on spectrogram image cross-correlation with unconstrained minimum average correlation energy filters. Experiments conducted with seven genres of traditional Malay music show that the recognition accuracy is dependent on the number of segments used to construct the filter templates, which in turn is related to the length of music segment used. Despite using a small dataset, an average recognition rate of 61.8 percent was obtained for music segments lasting 180 seconds using six relatively short excerpts.

Sub‐population prediction using enhanced correlation filters

Minimum average correlation energy (MACE) filters are initially developed and widely used for image pattern recognition tasks. A novel method leveraging the enhanced MACE filters is proposed to tackle classification problems from a new perspective. By employing 1D Fourier transform, establishing new identification metric, and improving numerical stability, the proposed method constructs an enhanced correlation filter to select a sub-population of the un-labelled data, and subsequently outputs labels. Experiments show our method achieves 100% precision on multiple datasets considered, including two public benchmarks and one obtained from semiconductor industry addressing an emerging task.

Fourier Locally Linear Soft Constrained MACE for facial landmark localization

This paper proposes a novel nonlinear correlation filter for facial landmark localization. Firstly, we prove that SVM as a classifier can also be used for localization. Then, soft constrained Minimum Average Correlation Energy filter (soft constrained MACE) is proposed, which is more resistent to overfittings to training set than other variants of correlation filter. In order to improve the performance for the multi-mode of the targets, locally linear framework is introduced to our model, which results in Fourier Locally Linear Soft Constraint MACE (FL2 SC-MACE). Furthermore, we formulate the fast implementation and show that the time consumption in test process is independent of the number of training samples. The merits of our method include accurate localization performance, desiring generalization capability to the variance of objects, fast testing speed and insensitivity to parameter settings. We conduct the cross-set eye localization experiments on challenging FRGC, FERET and BioID datasets. Our method surpasses the state-of-arts especially in pixelwise accuracy.

A new approach for image stitching technique using Dynamic Time Warping (DTW) algorithm towards scoliosis X-ray diagnosis

Consider a set of images of a single object, or scenery, taken from different viewpoints and time. Panorama image creation is the process of stitching such images into a single coordinate system to generate a wider viewing panoramic image. Image stitching consists of two processes which are image registration and image blending. In image registration, parts of two overlapping or consecutive images are considered to find an appropriate merging position and transformation to combine the images. In image blending, the intensities of pixels along the stitching line are modified so that they flow naturally without any noticeable break. In this paper, we propose a novel method that utilizes the Dynamic Time Warping (DTW) algorithm to match pairs of images for image stitching. We also perform a dimension reduction scheme that significantly reduces the computational complexity of the standard DTW without affecting its performance. The effectiveness of the proposed method is demonstrated in stitching 50 pairs of medical X-ray images and its performance is compared to those of normalized cross correlation (NCC), Minimum Average Correlation Energy (MACE) filters, sum-of-square-differences (SSD) and sum-of-absolute-differences (SAD). For the database used, the dimensionally reduced DTW outperforms the NCC, MACE, SSD and SAD methods in accuracy and average execution time. The method also outperforms two widely used stitching programs available on the internet called Hugin and Autostitch.

Cancelable face verification using optical encryption and authentication.

In a cancelable biometric system, each instance of enrollment is distorted by a transform function, and the output should not be retransformed to the original data. This paper presents a new cancelable face verification system in the encrypted domain. Encrypted facial images are generated by a double random phase encoding (DRPE) algorithm using two keys (RPM1 and RPM2). To make the system noninvertible, a photon counting (PC) method is utilized, which requires a photon distribution mask for information reduction. Verification of sparse images that are not recognizable by direct visual inspection is performed by unconstrained minimum average correlation energy filter. In the proposed method, encryption keys (RPM1, RPM2, and PDM) are used in the sender side, and the receiver needs only encrypted images and correlation filters. In this manner, the system preserves privacy if correlation filters are obtained by an adversary. Performance of PC-DRPE verification system is evaluated under illumination variation, pose changes, and facial expression. Experimental results show that utilizing encrypted images not only increases security concerns but also enhances verification performance. This improvement can be attributed to the fact that, in the proposed system, the face verification problem is converted to key verification tasks.

A new approach to medical image stitching using minimum average correlation energy filter and peak to side-lobe ratio

Standard X-ray images using conventional screen-film technique have a limited field of view and failed to visualize the entire long bone on a single image. To produce images with whole body parts, digitized images from the films that contain portions of the body parts are assembled using image stitching. This article presents a new medical image stitching method that uses minimum average correlation energy filters to identify and merge pairs of X-ray medical images. The effectiveness of the proposed method is demonstrated in the experiments involving two databases that contain a total of 40 pairs of overlapping and nonoverlapping images. Then the experimental results are compared to those of the normalized cross correlation (NCC) method. It is found that the proposed method outperforms the NCC method in identifying both the overlapping and nonoverlapping medical images. The efficacy of the proposed method is further vindicated by its average execution time which is about five times shorter than that of the NCC method. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 166–171, 2012

Face verification using local binary pattern–unconstrained minimum average correlation energy correlation filters

In this paper, we present a novel approach for face verification using local binary pattern (LBP) operators and optical correlation filters. Due to selected filter type and LBP method, different performances would have been expected. We let LBP operate on training images to form local binary pattern-unconstrained minimum average correlation energy (LBP-UMACE) filters as an optical correlation filter to enhance recognition rates and reduce error rates simultaneously. As a result, we demonstrate that the designed filters have better performance compared with UMACE filters. Moreover, the proposed filters are easy to implement and can be used in fast and reliable face verification systems.

Development of Automated Image Stitching System for Radiographic Images

Standard X-ray images using conventional screen-film technique have a limited field of view that is insufficient to show the full bone structure of large hands on a single frame. To produce images containing the whole hand structure, digitized images from the X-ray films can be assembled using image stitching. This paper presents a new medical image stitching method that utilizes minimum average correlation energy filters to identify and merge pairs of hand X-ray medical images. The effectiveness of the proposed method is demonstrated in the experiments involving two databases which contain a total of 40 pairs of overlapping and non-overlapping hand images. The experimental results are compared with that of the normalized cross-correlation (NCC) method. It is found that the proposed method outperforms the NCC method in classifying and merging the overlapping and non-overlapping medical images. The efficacy of the proposed method is further indicated by its average execution time, which is about five times shorter than that of the other method.

Perturbation-enhanced feature correlation filter for robust iris recognition

Visual pattern of human iris provides rich texture information for personal identification. However, it is challenging to match intra-class iris images with large variations in applications. This study proposes a perturbation-enhanced feature correlation filter (PFCF) for robust iris matching. PFCF is developed based on quad-phase minimum average correlation energy filter, but it has two significant improvements. First, PFCF is performed on Gabor filtered iris images to encode both local and global features. On the one hand, Gabor images can enhance the local details of iris texture. On the other hand, correlation filters describe the regional appearance information and measure the global similarity between iris images efficiently. Secondly, artificially perturbed iris images are generated to model intra-class variations. Also, a set of additional correlation filters are developed accordingly as the gallery templates. The decision is determined by the fusion result of multiple correlation filters. Therefore PFCF not only takes the advantages of Gabor images and correlation filters but also enlarges the amount of enrolled templates for robust iris matching. Extensive experiments on three challenging iris image databases demonstrate that the proposed method outperforms the state-of-the-art methods according to its robustness against deformation, rotation, occlusion, blurring and illumination changes in iris images.

Lower‐quarter‐based face verification using correlation filter

Correlation filters have been applied successfully in pattern matching needed in face verification. In this paper, we propose a face verification technique based on half the lower face using the minimum average correlation energy (MACE) filter which has the ability to satisfy the correlation peak at the origin, and minimise the average correlation energy. Our experiments show that the application of the MACE filter for face verification using the lower‐left or ‐right parts of the face generates authentication with at least 80% accuracy. Authentication accuracy using the MACE filter based on the whole face increases to at least 88%, but requires approximately twice as much response time.

基于最小平均相关能量滤波器的目标识别技术

基于最小平均相关能量滤波器的目标识别技术

Anomaly Detection in Electroencephalogram Signals Using Unconstrained Minimum Average Correlation Energy Filter

Problem statement: Electroencepharogram (EEG) is an extremely complex signal with very low signal to noise ratio and these attributed to difficulty in analyzing the signal. Hence for detecting abnormal segment, a distinctive method is required to train the technologist to distinguish the anomalous in EEG data. The objective of this study was to create a framework to analyze EEG signals recorded from epileptic patients by evaluating the potential of UMACE filter to detect changes in single-channel EEG data during routine epilepsy monitoring. Approach: Normally, the peak to side lobe ratio (PSR) of a UMACE filter was employed as an indicator if a test data is similar to an authentic class or vice versa, however in this study, the consistent changes of the correlation output known as Region Of Interest (ROI) was plotted and monitored. Based on this approach, a novel method to analyze and distinguish variances in scalp EEG as well as comparing both normal and abnormal regions of the patient’s EEG was assessed. The performance of the novelty detection was examined based on the onset and end time of each seizure in the ROI plot. Results: Results showed that using ROI plot of variances one can distinguish irregularities in the EEG data. The advantage of the proposed technique was that it did not require large amount of data for training. Conclusion: As such, it was feasible to perform seizure analysis as well as localizing seizure onsets. In short, the technique can be used as a guideline for faster diagnosis in a lengthy EEG recording.

The correntropy MACE filter

The minimum average correlation energy (MACE) filter is well known for object recognition. This paper proposes a nonlinear extension to the MACE filter using the recently introduced correntropy function. Correntropy is a positive definite function that generalizes the concept of correlation by utilizing second and higher order moments of the signal statistics. Because of its positive definite nature, correntropy induces a new reproducing kernel Hilbert space (RKHS). Taking advantage of the linear structure of the RKHS it is possible to formulate the MACE filter equations in the RKHS induced by correntropy and obtained an approximate solution. Due to the nonlinear relation between the feature space and the input space, the correntropy MACE (CMACE) can potentially improve upon the MACE performance while preserving the shift-invariant property (additional computation for all shifts will be required in the CMACE). To alleviate the computation complexity of the solution, this paper also presents the fast CMACE using the fast Gauss transform (FGT). We apply the CMACE filter to the MSTAR public release synthetic aperture radar (SAR) data set as well as PIE database of human faces and show that the proposed method exhibits better distortion tolerance and outperforms the linear MACE in both generalization and rejection abilities.

Enhancing the correntropy MACE filter with random projections

The minimum average correlation energy (MACE) filter is a well known correlation filter for object recognition. Recently, a nonlinear extension to the MACE filter using the correntropy function in feature space has been introduced. Correntropy is a positive definite function that generalizes the concept of correlation by utilizing higher order moment information of signal structure. Since the MACE is a spatial matched filter for an image class, the correntropy MACE (CMACE) can potentially improve its performance. Both the MACE and CMACE are basically memory-based algorithms and due to the high dimensionality of the image data, the computational cost of the CMACE filter is one of the critical issues in practical applications. We propose to use a dimensionality reduction method based on random projections (RP), which has emerged as a powerful method for dimensionality reduction in machine learning. We apply the CMACE filter with random projection (CMACE-RP) to face recognition and show that it indeed outperforms the traditional linear MACE in both generalization and rejection abilities with small degradation in performance, but great savings in storage and computational complexity.

Significance of image representation for face verification

In this paper we discuss the significance of representation of images for face verification. We consider three different representations, namely, edge gradient, edge orientation and potential field derived from the edge gradient. These representations are examined in the context of face verification using a specific type of correlation filter, called the minimum average correlation energy (MACE) filter. The different representations are derived using one-dimensional (1-D) processing of image. The 1-D processing provides multiple partial evidences for a given face image, one evidence for each direction of the 1-D processing. Separate MACE filters are used for deriving each partial evidence. We propose a method to combine the partial evidences obtained for each representation using an auto-associative neural network (AANN) model, to arrive at a decision for face verification. Results show that the performance of the system using potential field representation is better than that using the edge gradient representation or the edge orientation representation. Also, the potential field representation derived from the edge gradient is observed to be less sensitive to variation in illumination compared to the gray level representation of images.

An associative memory readout for ESNs with applications to dynamical pattern recognition

The use of echo state networks (ESN) to find patterns in time (dynamical pattern recognition) has been limited. This paper argues that ESNs are particularly well suited for dynamical pattern recognition and proposes a linear associative memory (LAM) as a novel readout for ESNs. From the class of LAMs, the minimum average correlation energy (MACE) filter is adopted because of its high rejection characteristics that allow its use as a detector in the automatic pattern recognition literature. In the ESN application, the MACE interprets the states of the ESN as a two-dimensional "image", one dimension being time and the other the processing element index (space). An optimal template image for each class, which associates ESN states with the class label, can be analytically computed using training data. During testing, ESN states are correlated with each template image and the class label of the template with the highest correlation is assigned to the input pattern. The ESN-MACE combination leads to a nonlinear template matcher with robust noise performance as needed in non-Gaussian, nonlinear digital communication channels. A real-world data experiment for chemical sensing with an electronic nose is included to demonstrate the validity of this approach. Moreover, the proposed readout can also be used with liquid state machines eliminating the need to convert spike trains into continuous signals by binning or low-pass filtering.

Rotationally invariant pattern recognition by use of linear and nonlinear cascaded filters

We discuss the merits of using single-layer (linear and nonlinear) and multiple-layer (nonlinear) filters for rotationally invariant and noise-tolerant pattern recognition. The capability of each approach is considered with reference to a two-class, rotation-invariant, character recognition problem. The minimum average correlation energy (MACE) filter is a linear filter that is generally accepted to be optimal for detecting signals that are free from noise. Here it is found that an optimized MACE filter cannot differentiate between the characters E and F in a rotation-invariant manner. We have found, however, that this task is possible when a single optimized linear filter is used to achieve the required response when a nonlinear threshold function is included after the filter. We show that this structure can be cascaded to form a multiple-layer, cascaded filter and that the capability of such a system is enhanced by its increased noise tolerance in the character recognition problem. Finally, we show the capability of a two-layer cascade as a means to detect different species of bacteria in images obtained from a phase-contrast microscope.

Illumination-tolerant face verification of low-bit-rate JPEG2000 wavelet images with advanced correlation filters for handheld devices

Face recognition on mobile devices, such as personal digital assistants and cell phones, is a big challenge owing to the limited computational resources available to run verifications on the devices themselves. One approach is to transmit the captured face images by use of the cell-phone connection and to run the verification on a remote station. However, owing to limitations in communication bandwidth, it may be necessary to transmit a compressed version of the image. We propose using the image compression standard JPEG2000, which is a wavelet-based compression engine used to compress the face images to low bit rates suitable for transmission over low-bandwidth communication channels. At the receiver end, the face images are reconstructed with a JPEG2000 decoder and are fed into the verification engine. We explore how advanced correlation filters, such as the minimum average correlation energy filter [Appl. Opt. 26, 3633 (1987)] and its variants, perform by using face images captured under different illumination conditions and encoded with different bit rates under the JPEG2000 wavelet-encoding standard. We evaluate the performance of these filters by using illumination variations from the Carnegie Mellon University's Pose, Illumination, and Expression (PIE) face database. We also demonstrate the tolerance of these filters to noisy versions of images with illumination variations.

Cascaded linear shift-invariant processors in optical pattern recognition

We study a cascade of linear shift-invariant processing modules (correlators), each augmented with a nonlinear threshold as a means to increase the performance of high-speed optical pattern recognition. This configuration is a special class of multilayer, feed-forward neural networks and has been proposed in the literature as a relatively fast best-guess classifier. However, it seems that, although cascaded correlation has been proposed in a number of specific pattern recognition problems, the importance of the configuration has been largely overlooked. We prove that the cascaded architecture is the exact structure that must be adopted if a multilayer feed-forward neural network is trained to produce a shift-invariant output. In contrast with more generalized multilayer networks, the approach is easily implemented in practice with optical techniques and is therefore ideally suited to the high-speed analysis of large images. We have trained a digital model of the system using a modified backpropagation algorithm with optimization using simulated annealing techniques. The resulting cascade has been applied to a defect recognition problem in the canning industry as a benchmark for comparison against a standard linear correlation filter, the minimum average correlation energy (MACE) filter. We show that the nonlinear performance of the cascade is a significant improvement over that of the linear MACE filter in this case.

Recent advances to nonlinear minimum average correlation energy filters

We present recent advances in the development of nonlinear extensions to the minimum average correlation energy (MACE) filter. The MACE filter and its variations have been applied to the area of automatic target detection and recognition (ATD/R). Nonlinear extensions (Fisher and Principe, 1994) have been presented based on a statistical formulation of the optimization criterion, of which the linear MACE filter is a special case. The method by which nonlinear topologies can be incorporated into the filter design is reviewed. We present recent advances to this nonlinear method as well as new experimental results applying the technique to inverse synthetic aperture radar (ISAR) data. The methods described result in faster convergence times and significantly better classification performance.