Binary Template Matching Research Articles

Visual Positioning Algorithm Based on Micro Assembly Line

To achieve mass production of microelectronic devices, this paper designs a microelectronic device detection scheme based on monocular vision, on the assembly robot platform of micro-electronic devices. By analyzing the principle of camera calibration and distortion and performing internal parameter calibration of the camera with Zhang Zhengyou calibration method, this paper establishes a hand-eye calibration model of the relationship between the robot’s motion and the relative position of the camera, so as to calculate the transformation matrix from the camera model to the gripper. In the end, the paper proposes an accurate positioning method which combined boundary tracking algorithm based on binary image and template matching algorithm, identifying the type of workpieces through barcode.

Image stitching and partitioning algorithms for infrared thermal human-body images

The thermal infrared image of the human body directly reflects the temperature distribution of the human body surface. Based on in-depth analysis, the infrared image can provide intelligent diagnosis assistance for human diseases. This paper proposed two preprocessing algorithms, i.e., upper-lower body image-stitching and body image partitioning, for medical infrared image analysis. In the image stitching stage, the human body is first extracted from the background by local thresholding based on the characteristics of the actual imaging environment. Then the upper and lower body images are aligned and fused using binary and grayscale template matching. In the image partitioning stage, the key points of the part area are determined by the extremum-point analysis of the human contour. The human body is then partitioned into regions including head, trunk, limbs, etc. Experiments show that the proposed preprocessing algorithms produce satisfactory results in image-stitching and portioning, and can effectively support the quantitative and qualitative analysis of human body temperature distribution.

Binary Template Matching for Morphological Dilation Enhancement in Navigation Radar Imaging

Radar imaging system is strongly influenced by other supporting systems. There is a motor system that provides angular information to the display system, and Digital Signal Processing (DSP) system that provides the main information for radar imaging. The new approach, we substitute a DC servo motor with a DC stepper motor as radar antenna rotator in our navigation radar system development. Different from the use of servo motors that can provide angle movements smoothly, the new motor system provides angle information of 0.56 degrees in every step, results in empty pixel gaps in every 0.56 degrees in radar Plan Position Indicator (PPI) image. The width of the empty pixel gaps becomes wider when the cell array of raw data is increasing regarding the image plotting process. In this paper, we proposed a new morphological dilation method to the radar raw data based on binary template matching to accommodate the various width of empty pixel gaps before the radar raw data are plotted into the radar PPI image. By this method, the morphological dilation will only be applied to the raw data that meet the same criteria as the binary template. Otherwise, the raw data will be left as they are. The result shows that there is 150.52% pixel data addition in the empty pixel gaps from the original image, and 48.44% increase of the morphological dilation without binary template matching method.

Perspectives of human verification via binary QRS template matching of single-lead and 12-lead electrocardiogram.

ObjectiveThis study aims to validate the 12-lead electrocardiogram (ECG) as a biometric modality based on two straightforward binary QRS template matching characteristics. Different perspectives of the human verification problem are considered, regarding the optimal lead selection and stability over sample size, gender, age, heart rate (HR).MethodsA clinical 12-lead resting ECG database, including a population of 460 subjects with two-session recordings (>1 year apart) is used. Cost-effective strategies for extraction of personalized QRS patterns (100ms) and binary template matching estimate similarity in the time scale (matching time) and dissimilarity in the amplitude scale (mismatch area). The two-class person verification task, taking the decision to validate or to reject the subject identity is managed by linear discriminant analysis (LDA). Non-redundant LDA models for different lead configurations (I,II,III,aVF,aVL,aVF,V1-V6) are trained on the first half of 230 subjects by stepwise feature selection until maximization of the area under the receiver operating characteristic curve (ROC AUC). The operating point on the training ROC at equal error rate (EER) is tested on the independent dataset (second half of 230 subjects) to report unbiased validation of test-ROC AUC and true verification rate (TVR = 100-EER). The test results are further evaluated in groups by sample size, gender, age, HR.Results and discussionThe optimal QRS pattern projection for single-lead ECG biometric modality is found in the frontal plane sector (60°-0°) with best (Test-AUC/TVR) for lead II (0.941/86.8%) and slight accuracy drop for -aVR (-0.017/-1.4%), I (-0.01/-1.5%). Chest ECG leads have degrading accuracy from V1 (0.885/80.6%) to V6 (0.799/71.8%). The multi-lead ECG improves verification: 6-chest (0.97/90.9%), 6-limb (0.986/94.3%), 12-leads (0.995/97.5%). The QRS pattern matching model shows stable performance for verification of 10 to 230 individuals; insignificant degradation of TVR in women by (1.2–3.6%), adults ≥70 years (3.7%), younger <40 years (1.9%), HR<60bpm (1.2%), HR>90bpm (3.9%), no degradation for HR change (0 to >20bpm).

A New Ear Recognition Method Based on Differential Geometry

The human ear is unique and stability and it has broad application prospects in the field of pattern recognition .In this paper, a novel ear recognition method based on differential geometry is proposed. Firstly, we get the normalization ear image through the image preprocessing. Secondly, according to the character of ear recognition, a method based on differential geometry was used to extract ear anatomy feature points. Finally, the best match is found from a database by using binary template matching. The proposed method can be performed automatically for subject recognition by using 2D ear intensity image. And it can efficiently overcome the influence of illumination and rotation change for ear recognition. Experimental results show that proposed method is simple, robust and better in accuracy and efficiency than previous methods.

Region based matching for print process identification

For quality inspection of security printing systems it is necessary to measure the displacement between printing processes. We present a new approach for region based matching of color images. Maximally stable extremal regions are extracted from image color channels and are the basis for matching. Binary template matching is performed between pairs of regions taken from the corresponding color channels of different images and a displacement vector is derived for each matching pair of regions. Clustering of measured displacements taken from sequences of sample images allows the estimation of the accuracy of printing processes and the alignment of printing processes. Results of an experimental application to banknote printing process inspection are given.

Overexpression of folate binding protein and mesothelin are associated with uterine serous carcinoma

Purpose Folate receptor alpha (FOLR1) is a membrane bound receptor involved in the transport of folate as well as other regulatory cellular processes. The purpose of this study was to examine the expression of FOLR1 in uterine cancers and to identify changes in gene expression that are associated with overexpression of FOLR1. Experimental design Fifty-eight frozen uterine cancer specimens were stained for FOLR1 using immunohistochemistry and results were correlated with transcript expression noted on quantitative PCR. Total RNA from 16 cases of uterine serous carcinoma (USC) was analyzed for gene expression using the Affymetrix HG-U133A and HG-U133B GeneChip set. USCs overexpressing FOLR1 were compared to cancers with an absence of FOLR1 using binary comparison and template matching of data was used to identify genes that correlate with FOLR1 expression. Selected targets from this analysis were evaluated by quantitative PCR as well as in an independent set of USC represented in quadruplicate on a tissue microarray (TMA). Results Overexpression of FOLR1 was observed in 11/16 (69%) of USC and 0/10 normal endometrium cases using frozen tissue specimens. Binary comparison between FOLR1 positive and negative cases identified 121 genes altered by 2-fold at p < 0.01 of which 45 are well correlated with FOLR1 expression pattern. Using quantitative PCR, both mesothelin (MSLN) and PTGS1 (COX1) were significantly increased in FOLR1 overexpressing tumors ( p = 0.014 and p = 0.006 respectively). TMA confirmed that overexpression of FOLR1 and MSLN respectively occurred in 23/48 (48%) and 17/54 (32%) of pure USC. Conclusion Both FOLR1 and MSLN are cell surface targets that are co-expressed at high levels in USC and are appealing targets for biologic therapy.

Target detection using local fuzzy thresholding and binary template matching in forward-looking infrared images

A new small-target detection method in forward-looking infrared images (FLIR) is proposed. The goal is to identify target locations, with low false alarms, in thermal infrared images of a natural battlefield. Unlike previous approaches, the proposed method deals with small-target detection in low-contrast images, and it is able to determine centers of targets accurately. The method comprises three distinct stages. The first stage is called center-surround difference, whose function is to find salient regions in an input image. In the second stage, local fuzzy thresholding is applied to the region of interest that is chosen from the result of the first step. The extracted binary objects are potential targets that will be classified as valid targets or clutters. Finally, using size and affinity measurements, the potential targets are compared with target templates to discard clutters in the third stage. In the experiments, many natural infrared images are used to prove the effectiveness of the proposed method. The proposed method is compared to previously reported approaches to verify its efficiency.

Efficient Template Matching Algorithm for Binary Image based on Scanning-type Upper Limit Estimation

An efficient algorithm for binary template matching is proposed. For efficient search, object images to be checked is divided into two regions: R-region and P-region. In R-region, an upper limit of correlation coefficients can be derived as an interval estimation based on mathematical analysis of correlations of the object image and a pivot image. In P-region, another interval estimation is formalized based on the number of white and black pixels in a template and the object image. By use of these interval estimations, a scanning-type upper limit estimation of correlation coefficients can be formalized for the efficient search algorithm. Experiments show the effectiveness and efficiency of the proposed search algorithm.

Automatic target detection using binary template matching

This paper presents a new automatic target detection (ATD) algorithm to detect targets such as battle tanks and armored personal carriers in ground-to-ground scenarios. Whereas most ATD algorithms were developed for forward-looking infrared (FLIR) images, we have developed an ATD algorithm for charge-coupled device (CCD) images, which have superior quality to FLIR images in daylight. The proposed algorithm uses fast binary template matching with an adaptive binarization, which is robust to various light conditions in CCD images and saves computation time. Experimental results show that the proposed method has good detection performance.

Duplicate document detection by template matching

We discuss some operational issues pertaining to the detection of duplicates in the databases of bitmapped binary document images, and reason that efficient and effective duplicate document detection probably needs a combination of an efficient primary detector and an effective subordinate detector to be achieved. An algorithm that executes binary pattern template matching by cross-correlation is proposed as a duplicate document detection methodology. The template matching operation is amenable to pixel-parallel computation on serial architecture computers by bitwise integer operations. A description of the algorithm is accompanied by a discussion of issues that arise in its practical implementation. Duplicate detection by template matching is especially well suited to facsimile (i.e. fax) databases, in particular for detecting the single feed-multiple transmissions that often dominate the occurrence of duplicates in fax databases. Detailed experimental results presented for fax documents demonstrate that template matching is suitable as both a primary detector when conducted with small template and search area sizes, and a subordinate detector when conducted with moderate template and search area sizes.

The First Stage in Two-Stage Template Matching

This paper formulates the problem encountered in the first stage of two-stage, binary template matching as a set of hypotheses to be tested, including a hypothesis of ``no object.'' Two new statistics R and G are proposed, based on a likelihood ratio, and are compared to the sum of absolute differences and a correlation measure by analytical approximations and Monte Carlo experiments. Statistical power and a measure of sensitivity to the true location of the object are the criteria. Parameters are the numbers of 1's in object and image, subtemplate size, and parameters reflecting intensity distortion between template and object. One of the proposed statistics R is much more computationally intensive than the other G. Although R is more powerful than G and the other statistics, G is generally more sensitive to the true object location. Statistic G is also more powerful than the sum of absolute differences and correlation. All statistics are robust to incomplete knowledge of distortion parameters. Experiments on Landsat images confirm the sensitivity of G and recommend it for application in the first stage.