Adaptive Template Matching Method Research Articles

Heart rate detection of ballistocardiogram based on improved DAE and template matching method

Ballistocardiogram (BCG) is a non-invasive physiological signal detection method that can be used for non-contact detection of resting heart rate (RHR) and has been widely used in human health monitoring. However, the BCG signal is vulnerable to noise, making it challenging to accurately measure heart rate. In this paper, we propose a noise reduction model for the BCG signal based on Gramian Angular Field (GAF) and an improved Denoising Autoencoder (DAE), referred to as the GDAE model, to accurately detect heart rate in noise-contaminated signals. First, the Gramian Angular Field transform is used to convert the one-dimensional BCG signal into two-dimensional image information, highlighting the difference between the signal centroid information and noise information; after that, the transformed image is denoised by the Denoising Autoencoder to obtain a denoised BCG signal. After noise reduction, the heart rate of the subject is calculated using the adaptive template matching method. The test proves that under the strong noise interference, the proposed method improves the recall by 6.87% and the accuracy by 6.02% compared with the traditional method, indicating a better detection effect. Furthermore, the comparison test shows that the GDAE model has a significant noise reduction effect on the BCG signal, which improves the practicality of the BCG method for heart rate detection.

Fusion-Based Versatile Video Coding Intra Prediction Algorithm with Template Matching and Linear Prediction.

The new generation video coding standard Versatile Video Coding (VVC) has adopted many novel technologies to improve compression performance, and consequently, remarkable results have been achieved. In practical applications, less data, in terms of bitrate, would reduce the burden of the sensors and improve their performance. Hence, to further enhance the intra compression performance of VVC, we propose a fusion-based intra prediction algorithm in this paper. Specifically, to better predict areas with similar texture information, we propose a fusion-based adaptive template matching method, which directly takes the error between reference and objective templates into account. Furthermore, to better utilize the correlation between reference pixels and the pixels to be predicted, we propose a fusion-based linear prediction method, which can compensate for the deficiency of single linear prediction. We implemented our algorithm on top of the VVC Test Model (VTM) 9.1. When compared with the VVC, our proposed fusion-based algorithm saves a bitrate of 0.89%, 0.84%, and 0.90% on average for the Y, Cb, and Cr components, respectively. In addition, when compared with some other existing works, our algorithm showed superior performance in bitrate savings.

Automated MV markerless tumor tracking for VMAT

Tumor tracking during radiotherapy treatment can improve dose accuracy, conformity and sparing of healthy tissue. Many methods have been introduced to tackle this challenge utilizing multiple imaging modalities, including a template matching based approach using the megavoltage (MV) on-board portal imager demonstrated on 3D conformal treatments. However, the complexity of treatments is evolving with the introduction of VMAT and IMRT, and successful motion management is becoming more important due to a trend towards hypofractionation.We have developed a markerless lung tumor tracking algorithm, utilizing the electronic portal imager (EPID) of the treatment machine. The algorithm has been specifically adapted to track during complex treatment deliveries with gantry and MLC motion. The core of the algorithm is an adaptive template matching method that relies on template stability metrics and local relative orientations to perform multiple feature tracking simultaneously. Only a single image is required to initialize the algorithm and features are automatically added, modified or removed in response to the input images. This algorithm was evaluated against images collected during VMAT arcs of a dynamic thorax phantom. Dynamic phantom images were collected during radiation delivery for multiple lung SBRT breathing traces and an example patient data set. The tracking error was 1.34 mm for the phantom data and 0.68 mm for the patient data.A multi-region, markerless tracking algorithm has been developed, capable of tracking multiple features simultaneously without requiring any other a priori information. This novel approach delivers robust target localization during complex treatment delivery. The reported tracking error is similar to previous reports for 3D conformal treatments.

Repeat pattern segmentation of print fabric based on adaptive template matching

Patterned fabrics are generally constructed from the periodic repetition of a primitive pattern unit. Repeat pattern segmentation of printed fabrics has a very significant impact on the pattern retrieval and pattern defect detection. In this paper, we propose a new approach for repeat pattern segmentation by employing the adaptive template matching method. In contrast to the traditional method for template matching, the proposed algorithm first selects an adaptive size template image in the repeat pattern image based on the size of the original image and its local maximum edge density. Then it uses the sum of absolute differences as the matching features to identify the matched regions in the original image, and the minimum envelope border of the primitive pattern, typically as a parallelogram, can be determined from the results of the four adjacent matched templates. Finally, image traversal base on the obtained parallelogram is implemented over the original image using minimum information loss theory to produce a well-segmented primitive pattern with a complete edge structure. The results from the experiments conducted using an extensive database of real fabric images show that the proposed algorithm has the advantage of rotation invariance and scaling invariance and will not be affected when the background or foreground color is changed.

KERNEL FEATURE CROSS-CORRELATION FOR UNSUPERVISED QUANTIFICATION OF DAMAGE FROM WINDTHROW IN FORESTS

In this study estimation of tree damage from a windthrow event using feature detection on RGB high resolution imagery is assessed. An accurate quantitative assessment of the damage in terms of volume is important and can be done by ground sampling, which is notably expensive and time-consuming, or by manual interpretation and analyses of aerial images. This latter manual method also requires an expert operator investing time to manually detect damaged trees and apply relation functions between measures and volume which are also error-prone. <br><br> In the proposed method RGB images with 0.2 m ground sample distance are analysed using an adaptive template matching method. Ten images corresponding to ten separate study areas are tested. A 13x13 pixels kernel with a simplified linear-feature representation of a cylinder is applied at different rotation angles (from 0° to 170° at 10° steps). The higher values of the normalized cross-correlation (NCC) of all angles are recorded for each pixel for each image. Several features are tested: percentiles (75, 80, 85, 90, 95, 99, max) and sum and number of pixels with NCC above 0.55. Three regression methods are tested, multiple regression (<i>mr</i>), support vector machines (<i>svm</i>) with linear kernel and random forests. The first two methods gave the best results. <br><br> The ground-truth was acquired by ground sampling, and total volumes of damaged trees are estimated for each of the 10 areas. Damaged volumes in the ten areas range from ~1.8 x10<sup>2</sup> m<sup>3</sup> to ~1.2x10<sup>4</sup> m<sup>3</sup>. Regression results show that smv regression method over the sum gives an R-squared of 0.92, a mean of absolute errors (MAE) of 255 m<sup>3</sup> and a relative absolute error (RAE) of 34% using leave-one-out cross validation from the 10 observations. These initial results are encouraging and support further investigations on more finely tuned kernel template metrics to define an unsupervised image analysis process to automatically assess forest damage from windthrow.

KERNEL FEATURE CROSS-CORRELATION FOR UNSUPERVISED QUANTIFICATION OF DAMAGE FROM WINDTHROW IN FORESTS

Abstract. In this study estimation of tree damage from a windthrow event using feature detection on RGB high resolution imagery is assessed. An accurate quantitative assessment of the damage in terms of volume is important and can be done by ground sampling, which is notably expensive and time-consuming, or by manual interpretation and analyses of aerial images. This latter manual method also requires an expert operator investing time to manually detect damaged trees and apply relation functions between measures and volume which are also error-prone. In the proposed method RGB images with 0.2 m ground sample distance are analysed using an adaptive template matching method. Ten images corresponding to ten separate study areas are tested. A 13x13 pixels kernel with a simplified linear-feature representation of a cylinder is applied at different rotation angles (from 0° to 170° at 10° steps). The higher values of the normalized cross-correlation (NCC) of all angles are recorded for each pixel for each image. Several features are tested: percentiles (75, 80, 85, 90, 95, 99, max) and sum and number of pixels with NCC above 0.55. Three regression methods are tested, multiple regression (mr), support vector machines (svm) with linear kernel and random forests. The first two methods gave the best results. The ground-truth was acquired by ground sampling, and total volumes of damaged trees are estimated for each of the 10 areas. Damaged volumes in the ten areas range from ~1.8 x102 m3 to ~1.2x104 m3. Regression results show that smv regression method over the sum gives an R-squared of 0.92, a mean of absolute errors (MAE) of 255 m3 and a relative absolute error (RAE) of 34% using leave-one-out cross validation from the 10 observations. These initial results are encouraging and support further investigations on more finely tuned kernel template metrics to define an unsupervised image analysis process to automatically assess forest damage from windthrow.

ACCURATE SLIDE POSITIONING USING FAST ADAPTIVE-TEMPLATE MATCHING IN AN AUTOMATED MICROSCOPE

In an automated microscope, the stage presents the slide to the optical path in the desired position. This article presents an accurate slide positioning algorithm which finds the relative offset from the zero of off-line calibration when the standard slide with a ring fiduciary mark is loaded on the stage. The relative offset is the difference between the zero by the offset calibration tool and the real-time zero position of the ring mark computed by the scheme. The slide positioning algorithm utilizes the adaptive-template matching method to detect the ring mark precisely, in which a fast sum of absolute differences is employed to reduce the computational complexity of template matching. Computer simulations with real clinical slides containing epithelial debris and nuclei show that the proposed algorithm yields an accurate performance in terms of detecting the ring mark and estimating the relative offset with a great reduction in computational complexity.

A Coarse Elevation Map-based Registration Method for Super-resolution of Three-line Scanner Images

Three-line scanner imagery provides three overlapped images in an along-base direction, and creates a possible avenue to obtain higher quality images through the application of the super-resolution. Accurate co-registration of the three images is a key step for super-resolution. However, discontinuities and occlusions resulting from the 3D-to-2D projection cause mis-registration in traditional 2D-image-level co-registration methods. In this paper, we address this problem by introducing 3D information extracted from image triplets by using GPS/IMU data as an approximation. The core of the proposed method is to use a number of height layers derived from feature points and image partitions, in the form of a coarse elevation map (CEM), as a 3D constraint to restrict registration on the corresponding height. In terms of super-resolution, we also propose a tree-based fast adaptive template matching method for Knife-edge detection to fully automate the SRKE super-resolution algorithm. Experimental results show that the proposed method produces improved registered images and accordingly yields signifi cant resolution enhancement as compared to other methods.

An adaptive template-matching method and its application to the boundary detection of brachial artery ultrasound scans

We developed an adaptive template-matching method for vessel wall boundary detection in brachial artery ultrasound (US) scans. The computer-aided identification of lumen-intima and media-adventitia boundaries of the brachial artery US scan is a critical step for flow-mediated vasodilator response measurement, which has become an increasingly important tool in both clinical and research applications. The adaptive template we propose in this paper is a combination of two parameterized Gaussian envelopes that approximates the US pulse envelope. The algorithm for template matching is cast as a constrained nonlinear least squares problem. A best match between the template and the observed US scan line signal results in a set of optimum parameters of the adaptive template, and the location of the boundaries is directly computed from these parameters. We present the results of applying our method to synthetic and clinical data, showing that the adaptive template-matching method provides superior accuracy in detecting the media-adventitia boundaries of both the near wall and the far wall of the vessel to that of conventional edge-detection methods. (E-mail: psantago@wfubmc.edu)