Robust shape-based template matching algorithm for target localization under SEM.

Template matching is used for many applications in image processing. Cross Correlation is the basic statistical approach to image registration. It is used for template matching or pattern recognition. Template can be considered a sub-image from the reference image, and the image can be considered as a sensed image. The objective is to establish the correspondence between the reference image and sensed image. It gives the measure of the degree of similarity between an image and template. This paper describes medical image registration by template matching based on Normalized Cross-Correlation (NCC) using Cauchy-Schwartz inequality. The algorithm for template matching using NCC is implemented in MATLAB. The algorithm does the template matching and uses the Cauchy-Schwartz’s inequality to simplify the procedure. The developed algorithm is robust for similarity measure. An experimental result with medical images registration with noise and without noise is shown in the results section.

Template matching of occluded object under low PSNR

Template matching is a fundamental problem in pattern recognition, which has wide applications, especially in industrial inspection. In this paper, we propose a 1-D template matching algorithm which is an alternative for 2-D full search block matching algorithms. Our approach consists of three steps. In the first step the images are converted from 2-D into 1-D by summing up the intensity values of the image in two directions horizontal and vertical. In the second step, the template matching is performed among 1-D vectors using the similarity function sum of square difference. Finally, the decision will be taken based on the value of similarity function. Transformation template image and sub-images in the source image from 2-D grey level information into 1-D information vector reduce the dimensionality of the data and accelerate the computations. Experimental results show that the computational time of the proposed approach is faster and performance is better than three basic template matching methods. Moreover, our approach is robust to detect the target object with changes of illumination in the template also when the Gaussian noise added to the source image.

The main aim of this study is to propose efficient algorithms for image template matching on parallel machines. To our knowledge there is no a template matching algorithm that converts the template image and all candidate blocks in the source image from two-dimensions into one-dimension. This study proposes a Template Matching Vectoring Algorithm (TMVA) that reduces the amount of data to be analyzed by transforming 2-D images into 1-D images. Moreover, to speed up the vectoring pattern matching algorithm this study implements it in parallel. Complexity analysis and experimental results demonstrate that the performance of the proposed algorithm is superior to the other basic template matching algorithms.

A novel method to build a scale‐free network model for template matching is proposed. The scale‐free network model of the template is produced by a kernel histogram. Moreover, those special points that have much larger effects than other points can be selected by a matching algorithm. Based on the scale‐free network model, the matching is implemented by minimising the sum of absolute differences. The proposed algorithm can match the potential object of varying scale in a clutter background. To the best of the authors' knowledge, this is the first template matching algorithm which uses the scale‐free complex network to model template matching.

In order to perform nano-manipulation more accurately, it is necessary to study the relationship between the scanning electron microscope (SEM) image and the actual length. In this paper, based on the secondary electron image, the displacement of 1.81nm in SEM was measured the actual displacement was obtained according to the change of the pixels, so as to realize the nanometer displacement calibration and real-time measurement of the nano robot manipulator in SEM. According to the magnification, resolution and scale bar of SEM image, the mapping relationship between pixels and actual length was established. The obvious feature point and area in the SEM image were selected to be marked. The driving signal was applied to the manipulator of the nano robot, and the template matching algorithm was used to track the feature point and area in real time. The change of the feature point in the SEM image was obtained, and the actual displacement of the manipulator was calculated according to the mapping relationship between the pixel points and the actual displacement. We calibrated the displacement of the nanorobots and measured the actual displacement in real time, which laid a foundation for the precise control of the nanorobots in the future.

We present FPGA logic synthesis algorithms for stochastic fault rate reduction in the presence of both permanent and transient defects. We develop an algorithm for fault tolerant Boolean matching (FTBM), which exploits the flexibility of the LUT configuration to maximize the stochastic yield rate for a logic function. Using FTBM, we propose a robust resynthesis algorithm (ROSE) which maximizes stochastic yield rate for an entire circuit. Finally, we show that existing PLB (programmable logic block) templates for area-aware Boolean matching and logic resynthesis are not effective for fault tolerance, and propose a new robust template with path re-convergence. Compared to the state-of-the-art academic technology mapper Berkeley ABC, ROSE using the proposed robust PLB template reduces the fault rate by 25% with 1% fewer LUTs, and increases MTBF (mean time between failures) by 31%, while preserving the optimal logic depth.

At present, few published template matching algorithms can achieve the performance of commercial software. This paper studies and optimizes the existing geometric feature template matching algorithm, and combines it with the improved Fourier Merlin transform registration algorithm, and finally obtains a fast small target location algorithm. The matching time of the algorithm is 1-4ms, its positioning accuracy is sub-pixel level, the angle accuracy is less than 1 °, and it has good robustness to illumination. The experimental data show that the algorithm meets the requirements of real-time and accurate industrial automation production.

A novel approved algorithm based on template matching is proposed. This algorithm is used to predict the movement of the target and track the target. By utilizing the DESO(Differential Extended State Observer) filter, it is possible to obtain the estimate position of the target in the current image. The template matching algorithm is used to search target around the estimation position. The simulation results of the algorithm are presented. The proposed algorithm demonstrates better performance characteristics than traditional template matching algorithm. Experimental results also demonstrate the effectiveness of this method. Through the simulation experiment, it can be seen that regardless of the goal of uniform motion or motion with variable velocity, DESO motion prediction based on the template matching algorithm can accurately predict the trajectory of the target, and does not depend on the target motion model. The use of DESO is simple, the transition time is short and the tracking performance is satisfying.

As oil palm is cultivated in large-scale plantations, prior and post-planting operations are laborious and time expensive, and also manual implementation of some of these operations often results in incorrect measurements and information. Successful oil palm management in prior and post-planting operations requires effective techniques to collect precise information. Unmanned aerial vehicle (UAV) imagery is a low-cost alternative to field-based assessment but requires the development of methods to easily and accurately extract the required information. The individual oil palm tree detection and height assessment are important and labor-intensive tasks for large-scale plantations where trees taller than 15 m are being replanted due to the high cost of harvesting and low yield. Therefore, in this chapter we demonstrate a general work flow for individual oil palm detection and height assessment using UAV imagery. We explain local maximum (LM) and template matching (TM) techniques as two commonly used approaches in individual tree detection. The accuracy of each method was evaluated on 20 randomly selected plots on UAV image with recall, precision, and F-score method. The F-score for LM method is higher than TM methods which, respectively, are 0.83 and 0.60. From 17,252 oil palm trees in the 20 plots, LM algorithm could detect 1395 (almost 80%) and TM algorithm, 967 (almost 55%). Three hundred fifty-seven and 785 trees have been missed, respectively, in LM and TM approaches. In both cases, background vegetation incorrectly has been labeled as oil palm tree, where 141 and 322 objects have been falsely detected as oil palm tree in LM and TM approaches. LM worked better in almost all of the plots; however the performance decreased in highly dense area. Contradictory to TM approach, in LM approach, shadows did not affect the performance as it was reflected in precision value. In densely cultivated plots due to leaves overlapping of neighboring trees, algorithm failed, and also in sparsely cultivated plots, shadows caused some commission errors. Inherited distortion of UAV image also caused some omission errors in TM approach. Individual detected tree with LM algorithm was used in the next part for oil palm height estimation overlaying with canopy height model.

This letter proposes an algorithm for rotation invariance template matching by combining the circular projection transformation (CPT) and the bounded partial correlation (BPC). CPT is a method that involves rotation invariance for template matching, but calculating the CPT values from the spatial domain is time-consuming. We use the frequency domain approach to reduce the computation time of CPT and the BPC to rapidly skip the positions that dissimilate to a template image. Experiments demonstrated that the proposed algorithm can considerably accelerate the template matching process when scene rotations are present.

Tracking sports motion is a challenging task. This paper presents a comparison of different template matching methods that can be used in such motion tracking applications. Six methods were tested with dynamic and still background conditions. Their performance is analyzed using different video sequences obtained by considering sports such as table tennis, weightlifting, 100m athletics and high jump. Comparison is carried out by means of statistical methods to compare the six different template matching algorithms with the ground truth in terms of accuracy and processing time. The results indicate that, even though there are no statistically significant differences among different template matching algorithms, four out of the six methods have perfect matches with ground truth with respect to the accuracy. Among the six methods considered, the squared difference method is found to be the best in terms of processing time.

The paper presents a new image template matching method for component inspection of electronic assembly systems. To recognize incorrectly assembled components, the input image of the components is matched with standard images, using a template matching algorithm. To produce a rapid inspection system, the processing time of the matching algorithm should be minimized. Since the standard images of all components located in a PCB are stored in the computer, it is desirable to minimize the memory size of the standard image. The discrete wavelet transformation is applied to minimize the image size as well as the processing time. Only 7% memory of the BMP image is used to discriminate between how well, or not, the components have been assembled. Comparative results are presented to verify the usefulness of the new proposed method.

Optimal speed-up algorithms for template matching on SIMD hypercube multiprocessors with restricted local memory

Carbonate sands are composed of relatively few particle types (e.g., Halimeda, coralline algae, corals, mollusks, and foraminifera). The shape of a particular sand grain is highly dependent on the particle type of which it is composed. Previous, studies of modern carbonate environments show that the composition of sand substrates from different subenvironments are dependent on the organisms that inhabit them. These depositional environments can thus be distinguished from each other according to their constituent particle compositions and, therefore, also by analysis of particle shapes. Template (shape) matching can be accomplished only after the digitized shapes have been normalized to a unit-sized circle and registered. Registration involves the simple computation of shape-specific points within, on, or near the 2-dimensional contour of the sand grain. Shapes are subsequently rotated so that all of the shapes are in a similar position relative to their shape-specific points, allowing more meaningful comparisons between particles. After registration, 36 equi-angular radial lengths are calculated for grain from the center of mass to the boundary outline. A template-matching algorithm was devised in order to determine the relative percentages of several reference shape types, representing the constituents contained within 35 samples from 4 carbonate beaches and associ ted subtidal environments from the Florida Keys. Reference shapes may be chosen arbitrarily or obtained by computing average shapes of the various constituents. The precision of the shape classifications may be enhanced by adding supplemental reference shapes to the algorithm. End_of_Article - Last_Page 287------------