Image Template Matching Research Articles

Parallel Vectoring Algorithm for Pattern Matching

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 Steganography Approach for Printed Image Based on Image Complexity and Template Matching

After a digital image is discretized, printed and rescanned, it is usually filtered, rotated, scaled, cropped, con- trast-and-luminance adjusted, as well as distorted by noises. Robustness and invisibility against distortions caused by im- age processing and printing are the essential properties for image watermarking to be applicable in real-world applications. The existing watermark methods used in printed images are less than satisfactory in imperceptibility and robustness. In this paper, the properties of the discretized, printed image and rescanned image in both the spatial and frequency domains are analyzed. Based on these properties, image complexity and template matching theories, this paper proposes a ste- ganography approach for data-hiding in printed image. First, the message to be hidden is coded by using BCH to facilitate the extraction of hidden information. Secondly, based on appropriate image block partition, the local image complexity of the block is calculated to decide whether to embed watermark in this block. Watermark is embedded on the mid-frequency spectrum in DFT domain. Thirdly, two structural template lines are added in the polar coordinate system for detecting any combination of the geometrical distortions. Then, the hidden data can be extracted without using the original image. The experimental results indicate that the proposed method is robust against standard image manipulation operations, basic at- tacks and image publishing system. And the robustness and the invisibility of watermark are also satisfactory in printed images.

Study for Catenary Fault Detection Based on MNP Method

For catenary faults are caused by hot, so infrared thermal imaging technology and image processing are combined in catenary thermal fault detection. In image process, image template matching can be used in target detection of infrared image. As mutual information is similarity measure criterion in image template matching, and weakness of NP window is long computing time, so the paper employ MNP method to instead the original NP window. Experimental results show calculating the joint probability distribution and the matching time of MNP method has advantages. When the template size is 5×5, MNP method has the higher template matching accuracy.

A novel artificial bee colony algorithm based on internal-feedback strategy for image template matching.

Image template matching refers to the technique of locating a given reference image over a source image such that they are the most similar. It is a fundamental mission in the field of visual target recognition. In general, there are two critical aspects of a template matching scheme. One is similarity measurement and the other is best-match location search. In this work, we choose the well-known normalized cross correlation model as a similarity criterion. The searching procedure for the best-match location is carried out through an internal-feedback artificial bee colony (IF-ABC) algorithm. IF-ABC algorithm is highlighted by its effort to fight against premature convergence. This purpose is achieved through discarding the conventional roulette selection procedure in the ABC algorithm so as to provide each employed bee an equal chance to be followed by the onlooker bees in the local search phase. Besides that, we also suggest efficiently utilizing the internal convergence states as feedback guidance for searching intensity in the subsequent cycles of iteration. We have investigated four ideal template matching cases as well as four actual cases using different searching algorithms. Our simulation results show that the IF-ABC algorithm is more effective and robust for this template matching mission than the conventional ABC and two state-of-the-art modified ABC algorithms do.

Template Matching by the Statistical Reach Feature Method

SUMMARYA novel method for image template matching with robustness to various real‐world disturbances is proposed. The method is based on the statistical reach feature method. The method utilizes the set of signs of differences for selected pairs of points from the template image. The selection is performed from the statistical viewpoint. The concrete description of the method is presented plainly. The selection of thresholds in the method is argued. Experiments with various disturbances including illumination change, occlusion, and JPEG noise are described. Analyses based on the noise models are performed to show the theoretical background of the robustness of the method. © 2013 Wiley Periodicals, Inc. Electron Comm Jpn, 96(11): 54–69, 2013; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.11478

A 3D Gluing Defect Inspection System Using Shape-Based Matching Application from Two Cameras

This research is regarding the application of a vision algorithm to identify the operations of a system in order to control the decision making concerning jobs and work pieces recognition that are to be made during system operation in real time. These paper stresses on the vision algorithm used which mainly focus on the shape matching properties to identify defects occur on the product. A new supervised defect detection approach to detect a class of defects in gluing application is proposed. Creating of region of interest in important region of object is discussed. Gaussian smoothing features in determining better image processing and template matching in differentiates between reference and tested image are proposed. This scheme provides high computational savings and results in high defect detection recognition rate. The defects are broadly classified into three classes: 1) gap defect; 2) bumper defect; 3) bubble defect. The defects occur provides with information of height (z-coordinate), length (y-coordinate) and width (x-coordinate). This information gathered from the proposed two camera vision system for conducting 3D transformation. Information gathers used in new correction technique known as Correction of Defect (CoD) where rejected object will be altered to reduce rejected object produced from the system.

Gradient Networks: Explicit Shape Matching Without Extracting Edges

We present a novel framework for shape-based template matching in images. While previous approaches required brittle contour extraction, considered only local information, or used coarse statistics, we propose to match the shape explicitly on low-level gradients by formulating the problem as traversing paths in a gradient network. We evaluate our algorithm on a challenging dataset of objects in cluttered environments and demonstrate significant improvement over state-of-the-art methods for shape matching and object detection.

An Information Hiding Method in Presswork Based on Image Complexity and Template Matching

This paper proposes an information hiding method in presswork based on image and complexity and template matching. First, the message to be hidden is coded by using BCH to facilitate the extraction of hidden information. Secondly, Based on appropriate image block partition, the local image complexity of the block is calculated, which is determined whether to embed watermark in this block. Watermark is embedded on the mid-frequency spectrum in DFT domain. Thirdly, two structural template lines are added in the polar coordinate system for detecting any combination of the geometrical distortions. Then, the hidden data can be extracted without using the original image. The experimental results indicate that the proposed method is robust after high-quality laser printing, offset printing and scanning.

Inspection System for Position of Frame Line in the Mould Based on Machine Vision

In order to meet the online testing requirements in current manufacturing automated production of car foam，the paper proposes a method for detecting the mould that products the foam by using machine vision and introduces the overall solutions of the system. The detection arithmetic mainly includes the image preprocessing、image template matching, interesting region acquisition and the blob detection of image segmentation. The arithmetic is developed in VS2010.Through a series of experiments, the result shows that the arithmetic meets the requirements of inspecting various moulds on the production line.

Development of Contour Size Detection System of Driver Airbag

A new detection system has been designed and developed for detecting the contour size of automobile driver airbag. The detection system is mainly composed of a CCD camera, optical system and a computer which can implement image processing and image recognition. This system uses a CCD sensor as its sensitive element, as well as the basic principle of image processing combined with image segmentation and template matching to determine whether the contour size of airbag is qualified. The experimental results show that the system improves the detecting precision, and speed of assembling automobile airbags. It also solves the problems of a heavy workload by manual operations, inaccurate judgments and low efficiency.

Time–frequency manifold correlation matching for periodic fault identification in rotating machines

For rotating machines, the localized faults of key components generally represent as periodic transient impulses in vibration signals. The existence of background noise will corrupt transient impulses in practice, and will thus increase the difficulty to identify specific faults. This paper combines the concepts of time–frequency manifold (TFM) and image template matching, and proposes a novel TFM correlation matching method to enhance identification of the periodic faults. This method is to conduct correlation matching of a vibration signal in the time–frequency domain by using the TFM with a short duration as a template. By this method, the time–frequency distribution (TFD) of a vibration signal is firstly achieved by the Smoothed Pseudo-Wigner–Ville distribution (SPWVD) method. Then the TFM template is learned to do correlation matching with the TFD of the analyzed signal. Finally, the ridge is extracted from the correlation matching image and the ridge coefficients are analyzed for periodic fault identification. The proposed method takes advantages of the TFM in noise suppression and template matching in object enhancement, and can enhance the fault impulses of interest in a unified scale. The novel method is verified to be superior to traditional enveloping method with providing smoother and clearer fault impulse component via applications to gearbox fault detection and bearing defect identification.

The Application of LabVIEW and IMAQ in Image Acquisition and Processing

This paper which is based on digital image process technology, with LabVIEW and IMAQ Vision,designs a effective method about image acquisition and processing. The system achieve image acquisition,save,playback,gray transformation,denoise,threshold segmentation ,template matching and calculation the center of the target lacation.Virtual Instrument which own flexible hardware and software,has greatly enhanced efficiency and accuracy.

Spatial and spectral morphological template matching

Template matching is a very topical issue in a wide range of imaging applications. Mathematical morphology offers the hit-or-miss transform, an operator which has been successfully applied for template matching in binary images. More recently, it has been extended to grayscale images and even to multivariate images. Nevertheless, these extensions, despite being relevant from a theoretical point-of-view, might lack practical interest due to the inherent difficulty to set up correctly the transform and its parameters (e.g. the structuring functions). In this paper, we propose a new and more intuitive operator which allows for morphological template matching in multivariate images from both a spatial and spectral point of view. We illustrate the potential of this operator in the context of remote sensing.

The Robust Template Matching by Statistical Reach Feature Method

A novel method for image template matching with the robustness for various real-world disturbances is proposed. The method is based on statistical reach feature method. The method utilizes the set of signs of differences for selected pairs of points from the template image. The selection is performed from the statistical view-point. The concrete description of the method is presented plainly. The selection of thresholds in the method is argued. Experiments with various disturbances including illumination change, occlusion, and JPEG noise are shown. The analyses based on the noise models are performed to show theoretical background of the robustness of the method.

Advanced template matching method for estimation of intervertebral kinematics of lumbar spine

Diagnosis of low back pain and other degenerative spinal pathologies can be extremely difficult and, so far, there are not accepted standards. In general, such pathologies are associated with alteration of mechanical properties of spine and, in particular, with the instability of spinal motion. Intervertebral kinematics can be a valuable, objective method to assess the functionality of spinal segments. Fluoroscopic imaging system can provide continuous screening of lumbar tracts during patient's motion, with an acceptable low X-ray dose. Estimation of intervertebral kinematics relies on accurate recognition of vertebrae positions throughout the fluoroscopic sequence: specific vertebrae features are identified and tracked either by manual selection or by automated methods. This study presents a new method of vertebra tracking, based on image template matching of the contour of the vertebral body for an accurate intervertebral kinematics analysis. An image gradient operator was utilized to obtain the vertebral contours; it operates after an edge-preserving smoothing filter designed to reduce low dose X-ray image noise. Once a template is defined for each vertebra, this is used to determine the best vertebral location in each image throughout the fluoroscopic sequence. Accuracy of the proposed method was tested using images of a calibration model. Average error achieved for the intervertebral angle is of the order of 0.4° and approximately 2 mm for the intervertebral centre of rotation. Five fluoroscopic lumbar sequences of healthy volunteers undergoing passive flexion–extension motion were processed. The intervertebral kinematics was compared with other methods (automated and manual) by an estimation of measurement error. Results showed that the current method provides a better representation of the evolution over time of kinematic parameters. In particular, root mean square differences between the current method and a manual selection procedure performed by an experienced and trained clinician resulted 1.3° for the intervertebral angles and 0.9 mm for the intervertebral trajectory. The proposed method provides an effective, automated and objective technique for estimation of intervertebral kinematics of lumbar spine.

Image Template Matching Algorithm for Removing Useless Feature Points

With the aim to remove the unwanted feature points, template matching algorithm which includes pixel-coherence and fixed structure is proposed in this paper. Corner detection algorithm can extract image feature points with the flexibility for illumination variation and affine transformation. This paper gets the special points of plane image by corner detection algorithm and proposes the problem of existing more useless point. Then, template matching algorithm is used to solve the issue and collects the best match points. Finally, the paper compares the template image and the matched image. The experimental results prove that the feature points by template matching algorithm have excellent accurate characteristic for the illumination, transfer and the rotation transform, and they are highly satisfactory.

Bias-error reduction in nanometer resolution microscopic motion tracking vision systems

Microscopic Motion Tracking Systems utilize sub-pixel algorithms to achieve close to nanometer precision motion measurement. This paper characterizes the bias-error of template matching image registration techniques which are commonly used to achieve this level of sub-pixel accuracy. An accurate functional expression for such bias error is derived, which shows how the error depends on image content. Furthermore, this expression is directly utilized in a bias reduction scheme that is shown to significantly reduce the bias error. The newly derived expressions and reduction schemes for bias are applied to a Microscopic Vision System that measures micro-object motion with close to nanometer resolution.

Colour Image Registration and Template Matching using Hypercomplex Phase Correlation

Colour Image Registration and Template Matching using Hypercomplex Phase Correlation

Self‐position estimation of an autonomous mobile robot with variable processing time

AbstractThis paper presents a new self‐position estimation method for an autonomous mobile robot whose processing time can be varied. In this method, the current position of the robot is estimated by image template matching with the normalized correlation coefficient between the input image and stored images. Based on the idea of anytime sensing, the time for self‐position estimation can be varied by changing the image size. In order to realize efficient self‐position estimation, image templates are generated by means of Genetic Algorithm. The usefulness of the proposed method is shown through simulation results using test images and experimental results obtained with a real robot. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(11): 46–58, 2010; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.10223

A Modified PSO Algorithm for Remote Sensing Image Template Matching

Image template matching is essential in image analysis and computer vision tasks. Cross-correlation algorithms are often used in practice, but they are sensitive to nonlinear changes in image intensity and random noise, and are computationally expensive. In this paper, we propose a template-matching algorithm based on a modified particle swarm optimization (PSO) procedure with a mutual information (MI) similarity measure. The influence of MI on the performance of template matching, calculated by different histogram bins, is analyzed first. A modified PSO method (CRI-PSO) is then presented. The proposed algorithm is tested with remote sensing imagery from different sensors and for different seasons. Our experimental results indicate that the proposed approach is robust in practical scenarios and outperforms the standard PSO , multi-start PSO , and cross-correlation algorithms in accuracy and efficiency with our test data. The proposed method can be used for position estimation of aircraft, object recognition, and image retrieval.