DOCSLIB.ORG

QATM: Quality-Aware Template Matching for Deep Learning

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
QATM: Quality-Aware Template Matching for Deep Learning QATM: Quality-Aware Template Matching For Deep Learning Jiaxin Cheng Yue Wu Wael Abd-Almageed Premkumar Natarajan USC Information Sciences Institute, Marina del Rey, CA, USA chengjia@{usc/isi}.edu {yue wu,wamageed,pnataraj}@isi.edu Abstract matches caused by the background pixels. Deformable Di- versity Similarity (DDIS) was introduced in [26], which ex- Finding a template in a search image is one of the core plicitly considers possible template deformation and uses problems many computer vision, such as semantic image se- the diversity of NN feature matches between a template mantic, image-to-GPS verification etc. We propose a novel and a potential matching region in the search image. Co- quality-aware template matching method, QATM, which is occurrence based template matching (CoTM) was intro- not only used as a standalone template matching algorithm, duced in [14] to quantify the dissimilarity between a tem- but also a trainable layer that can be easily embedded into plate and a potential matched region in the search image. any deep neural network. Here, our quality can be inter- These methods indeed improve the performance of template preted as the distinctiveness of matching pairs. Specifically, matching. However, these methods cannot be used in deep we assess the quality of a matching pair using soft-ranking neural networks (DNN) because of two limitations — (1) among all matching pairs, and thus different matching sce- using non-differentiable operations, such as thresholding, narios such as 1-to-1, 1-to-many, and many-to-many will counting, etc. and (2) using operations that are not efficient be all reflected to different values. Our extensive evalu- with DNNs, such as loops and other non-batch operations. ation on classic template matching benchmarks and deep Existing DNN-based methods use simple methods to learning tasks demonstrate the effectiveness of QATM. It not mimic the functionality of template matching [15, 30, 28, only outperforms state-of-the-art template matching meth- 27, 4], such as computing the tensor dot-product [18]1 be- ods when used alone, but also largely improves existing tween two batch tensors of sizes B × H × W × L and deep network solutions. B ×H′ ×W ′ ×L along the feature dimension (i.e., L here), and producing a batch tensor of size B ×H ×W ×H′ ×W ′ containing all pairwise feature dot-product results. Of 1. Introduction and Review course, additional operations like max-pooling may also be applied [30, 31, 18, 7]. Template matching is one of the most frequently used In this paper, we propose the quality-aware template techniques in computer vision applications, such as video matching (QATM) method, which can be used as a stan- tracking[35, 36, 1, 9], image mosaicing [25, 6], object de- dalone template matching algorithm, or in a deep neural tection [12, 10, 34], character recognition [29, 2, 21, 5], and network as a trainable layer with learnable parameters. It 3D reconstruction [22, 23, 16]. Classic template match- takes the uniqueness of pairs into consideration rather than ing methods often use sum-of-squared-differences (SSD) simply evaluating matching score. QATM is composed of or normalized cross correlation (NCC) to calculate a sim- differentiable and batch-friendly operations and, therefore, ilarity score between the template and the underlying im- is efficient during DNN training. More importantly, QATM age. These approaches work well when the transformation is inspired by assessing the matching quality of source and between the template and the target search image is sim- target templates, and thus is able handle different matching ple. However, these methods start to fail when the transfor- scenarios including 1-to-1, 1-to-many, many-to-many and mation is complex or non-rigid, which is common in real- no-matching. Among different matching cases, only the 1- life. In addition, other factors, such as occlusions and color to-1 matching is considered to be high quality due to it’s shifts, make these methods even more fragile. more distinctive than 1-to-many and many-to-many cases. Numerous approaches have been proposed to over- The remainder of paper is organized as follows. Section come these real-life difficulties applying standard tem- 2 discusses motivations and introduces QATM . In Section plate matching. Dekel et al. [11] introduced the Best- 3, the performance of QATM is studied in classic template Buddies-Similarity (BBS) measure, which focuses on the nearest-neighbor (NN) matches to exclude potential and bad 1See numpy.tensordot and tensorflow.tensordot. 11553 matching setting. QATM is evaluated on both semantic im- Matching Cases Not age alignment and image-to-GPS verification problems in 1-to-1 1-to-N M-to-1 M-to-N Matching Section 4. We conclude the paper and discuss future works Quality High Medium Medium Low Very Low in Section 5. QATM (s,t) 1 1/N 1/M 1/MN 1/kT kkSk≈ 0 Table 1: Template matching cases and ideal scores. 2. Quality-Aware Template Matching 2.1. Motivation is therefore not difficult to come up the qualitative assess- In computer vision, regardless of the application, many ment for each case in the Table 1. As a result, the optimal methods implicitly attempt the solve some variant of fol- matched region in S can be found as the place that maxi- lowing problem — given an exemplar image (or image mizes the overall matching quality. We can therefore come patch), find the most similar region(s) of interest in a target up with a quantitative assessment of the matching as shown image. Classic template matching [11, 26, 14], constrained in Eq. (1) template matching [31], image-to-GPS matching [7], and ∗ semantic alignment [18, 19, 8, 13] methods all include some R = arg max{ max Quality(r, t)|t ∈ T } (1) R sort of template matching, despite differences in the details r∈R X of each algorithm. Without loss of generality, we will focus such that the region R in S that maximizes the overall the discussion on the fundamental template matching prob- matching quality will be the optimally matching region. R lem, and illustrate applicability to different problem in later is a fixed size candidate window and we used the size of sections. object as window size in the experiment. One known issue in most of existing template match- ing methods is that typically, all pixels (or features) within 2.2. Methodology the template and a candidate window in the target image are taken into account when measuring their similarity[11]. To make Eq. (1) applicable to template matching, we This is undesirable in many cases, for example when the need to define Quality(s,t), i.e. how to assess the match- background behind the object of interest changes between ing quality between (s,t). In the rest of section, we de- the template and the target image. To overcome this is- rive the quality-aware template matching (QATM) measure, sue, the BBS [11] method relies on nearest neighbor (NN) which is a proxy function of the ideal quality assessment matches between the template and the target, so that it could Quality(s,t). exclude most of background pixels for matching. On top of Let fs and ft be the feature representation of patch s BBS, the DDIS [26] method uses the additional deforma- and t, and ρ(·) is a predefined similarity measure between tion information in NN field, to further improve the match- two patches, e.g. cosine similarity. Given a search patch s, ing performance. we define the likelihood function that a template patch t is Unlike previous efforts, we consider five different tem- matched, as shown in Eq. 2, plate matching scenarios, as shown in Table 1, where t and exp{α · ρ(ft,fs)} s are patches in the template T and search S images, re- L(t|s)= (2) ′ exp{α · ρ(f ′ ,f )} spectively. Specifically, “1-to-1 matching” indicates exact t ∈T t s matching, i.e. two matched objects, “1-to-N” and “M-to-1” where α is a positiveP number and will be discussed later. indicates s or t is a homogeneous or patterned patch caus- This likelihood function can be interpreted as a soft-ranking ing multiple matches, e.g. a sky or a carpet patch, and “M- of the current patch t compared to all other patches in the to-N” indicates many homogeneous/patterned patches both template image in terms of matching quality. It can be alter- S T in and . It is important to note that this formulation natively considered as a heated-up softmax embedding [38], is completely different from the previous NN based formu- which is the softmax activation layer with a learnable tem- lation, because even though t and s are nearest neighbors, perature parameter, i.e. α in our context. their actual relationship still can be any of the five cases con- In this way, we can define the QATM measure as simple sidered. Among four matching cases, only 1-to-1 matching as the product of likelihoods that s is matched in T and t is is considered as high quality. This is due to the fact that in matched in S as shown in Eq. (3). other three matching cases, even though pairs may be highly similar, that matching is less distinctive because of multiple QATM(s,t)= L(t|s) · L(s|t) (3) matched candidates. Which turned out lowering the relia- bility of that pair. Any reasonable similarity measure ρ(·) that gives a high It is clear that the “1-to-1” matching case is the most value when ft and fs are similar, a low value otherwise important, while the “not-matching” is almost useless.
Load more

Recommended publications
  • Template Matching Using Fast Normalized Cross Correlation
    Template Matching using Fast Normalized Cross Correlation Kai Briechle and Uwe D Haneb eck Institute of Automatic Control Engineering Technische Universitat Munc hen Munc hen Germany ABSTRACT In this pap er we present an algorithm for fast calculation of the normalized cross correlation NCC and its applica tion to the problem of template matching Given a template t whose p osition is to b e determined in an image f the basic idea of the algorithm is to represent the template for which the normalized cross correlation is calculated as a sum of rectangular basis functions Then the correlation is calculated for each basis function instead of the whole template Theresultofthe correlation of the template t and the image f is obtained as the weighted sum of the correlation functions of the basis functions Dep ending on the approximation the algorithm can by far outp erform Fouriertransform based implementations of the normalized cross correlation algorithm and it is esp ecially suited to problems where many dierent templates are to b e found in the same image f Keywords Normalized cross correlation image pro cessing template matching basis functions INTRODUCTION A basic problem that often o ccurs image pro cessing is to determine the p osition of a given pattern in an image or part of an image the socalled region of interest This problem is closely related to the determination of a received digital signal in signal pro cessing using eg a matched lter Two basic cases can b e dierentiated The p osition of the pattern is unknown estimate
    [Show full text]
  • Human Face and Facial Parts Detection Using Template Matching Technique
    International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-4, April 2020 Human Face and Facial Parts Detection using Template Matching Technique Payal Bose, Samir Kumar Bandyopadhyay Abstract: Face recognition has become relevant in recent years But still, it is one of the most important tasks. Because face because of its potential applications. The aim of this paper is to detection is very much important for security sections in find out the relevant techniques which give not only better different places, crowd analysis and many more. There are accuracy also the efficient speed. There are several techniques several authors describe several techniques for face available for face detection which give much better accuracy but detection. In 2017, the authors, Shrinath Oza, Abdullah the execution speed is not efficient. In this paper, a normalized Khan, Nilabh Swapnil, Aayush Sharma, Jayashree cross-correlation template matching technique is used to solve [1] this problem. According to the proposed algorithm, first different Chaudhari, implemented an algorithm , the Restricted facial parts are detected likes mouth, eyes, and nose. If any of Boltzmann machine with the rules of deep learning and deep the two facial parts are found successfully then the face can be belief network for face recognition. According to this detected. For matching the templates with the target image, the algorithm, it stores the face images as input in a database template rotates at a certain angle interval. and a neural network used to ensure the efficiency and improvement of the recognition system trained the neural Keywords: Face detection, Face Recognition, Template network greedy layer-wise.
    [Show full text]
  • RESEARCH INVENTY: International Journal of Engineering and Science
    International Refereed Journal of Engineering and Science (IRJES) ISSN (Online) 2319-183X, (Print) 2319-1821 Volume 5, Issue 1 (January 2016), PP.36-40 Introduction of Pattern Recognition with various Approaches 1Tiwary, 2Shiva ramakrishna,3B Sandeep 1,2,3,4( Sreedattha Institute Of Engineering And Science) (Department Of Electronics & Communication Engineering ) ABSTRACT : The objective of this paper is To Familiarize with the fundamental concepts of pattern recognition (PR) & discusses various approaches for same. This paper deals issues: definition of PR (Pattern recognition), PR as System, approaches for PR. Keywords: About five key words in alphabetical order, separated by comma. I. INTRODUCTION In machine learning, PR( pattern recognition) is the assignment of a label to a given input value. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes. Pattern recognition generally aim to provide a reasonable answer for all possible inputs and to perform "most likely" matching of the inputs, taking into account their statistical variation. This is opposed to pattern matching, which look for exact matches in the input with pre-existing patterns. Our goal is to discuss and compare pattern recognition as the best possible Way. II. DEFINITION Before we define PR, we see what is mean by Pattern is a term with pair comprising an observation & a meaning.So PR as per “It is a process of taking raw data & performing action based on the “category” of the pattern.” Or Assignments of inputs to classes. Classification of PR is based on the following two: 1) Supervised classification: Here the input pattern is identified as a member of a pre- defined class.
    [Show full text]
  • OATM: Occlusion Aware Template Matching by Consensus Set Maximization
    OATM: Occlusion Aware Template Matching by Consensus Set Maximization Simon Korman1,3 Mark Milam2 Stefano Soatto1,3 1Weizmann Institute of Science 2Northrop Grumman 3UCLA Vision Lab Abstract We present a novel approach to template matching that is efficient, can handle partial occlusions, and comes with provable performance guarantees. A key component of the method is a reduction that transforms the problem of searching a nearest neighbor among high-dimensional N Figure 1. Instances of the occlusion experiment (Sec. 4.2) A tem- vectors, to searching neighbors among two sets of order plate (overlaid in green) that is 60% occluded by random blocks is √N vectors, which can be found efficiently using range searched for in an image. OATM shows the best results in dealing search techniques. This allows for a quadratic improve- with significant deformation and occlusion (use zoom for detail). ment in search complexity, and makes the method scalable in handling large search spaces. The second contribution increasing the area increases the probability that its corre- is a hashing scheme based on consensus set maximiza- spondent in the target image will be occluded, which causes tion, which allows us to handle occlusions. The resulting the correspondence to fail, unless occlusion phenomena are scheme can be seen as a randomized hypothesize-and-test explicitly taken into account. algorithm, which is equipped with guarantees regarding the In this work we model occlusions explicitly as part of a number of iterations required for obtaining an optimal so- robust template matching process where the co-visible re- lution with high probability. The predicted matching rates gion is assumed to undergo affine deformations of the do- are validated empirically and the algorithm shows a sig- main and range, up to additive noise.
    [Show full text]
  • Matchability Prediction for Full-Search Template Matching Algorithms
    Matchability Prediction for Full-Search Template Matching Algorithms Adrian Penate-Sanchez1, Lorenzo Porzi2,3, and Francesc Moreno-Noguer1 1Institut de Robotica` i Informatica` Industrial, UPC-CSIC, Barcelona, Spain 2Fondazione Bruno Kessler, Trento, Italy 3University of Perugia, Italy Abstract template matching algorithms that perform a full search on all possible transformations of the template guarantee to While recent approaches have shown that it is possible find the global maximum of the distance function, yielding to do template matching by exhaustively scanning the pa- more accurate results at the price of increasing the compu- rameter space, the resulting algorithms are still quite de- tational cost. manding. In this paper we alleviate the computational load Several approaches have focused on improving specific of these algorithms by proposing an efficient approach for characteristics of the template matching framework, such as predicting the matchability of a template, before it is actu- its speed [22, 24], its robustness to partial occlusions [4] or ally performed. This avoids large amounts of unnecessary ambiguities [26] and its ability to select reliable templates computations. We learn the matchability of templates by for rapid visual tracking [2]. Yet, most of these improve- using dense convolutional neural network descriptors that ments have been designed for the algorithms that do not per- do not require ad-hoc criteria to characterize a template. form a full search. In this work we will bring these improve- By using deep learning descriptions of patches we are able ments to algorithms that perform full search, and in particu- to predict matchability over the whole image quite reli- lar we will consider FAst-Match [15], a recent method that ably.
    [Show full text]
  • Arxiv:2007.15817V3 [Cs.CV] 7 May 2021
    Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN Bo Gao1[0000−0003−3930−1815] and Michael W. Spratling1[0000−0001−9531−2813] Department of Informatics, King's College London, London, UK fbo.gao,[email protected] Abstract. Finding a template in a search image is an important task underlying many computer vision applications. Recent approaches perform template matching in a deep feature-space, produced by a convolutional neural network (CNN), which is found to provide more tolerance to changes in appearance. In this article we investigate if enhancing the CNN's encoding of shape information can produce more distinguishable features that improve the performance of template matching. This investigation results in a new template matching method that produces state-of-the-art results on a standard benchmark. To confirm these results we also create a new benchmark and show that the proposed method also outperforms existing techniques on this new dataset. Our code and dataset is available at: https://github.com/iminfine/Deep-DIM. Keywords: Template match · Convolutional neural networks · VGG19. 1 Introduction Template matching is a technique to find a rectangular region of an image that contains a certain object or image feature. It is widely used in many computer vision applications such as object tracking [1, 2], object detection [3, 4] and 3D reconstruction [5, 6]. A similarity map is typically used to quantify how well a template matches each location in an image. Traditional template matching methods calculate the similarity using a range of metrics such as the normalised cross-correlation (NCC), the sum of squared differences (SSD) or the zero-mean normalised cross correlation (ZNCC) applied to pixel intensity or color values.
    [Show full text]
  • Towards Real-Time Object Recognition and Pose Estimation in Point Clouds
    Towards Real-time Object Recognition and Pose Estimation in Point Clouds Marlon Marcon1 a, Olga Regina Pereira Bellon2 b and Luciano Silva2 c 1Dapartment of Software Engineering, Federal University of Technology - Parana,´ Dois Vizinhos, Brazil 2Department of Computer Science, Federal University of Parana,´ Curitiba, Brazil Keywords: Transfer Learning, 3D Computer Vision, Feature-based Registration, ICP Dense Registration, RGB-D Images. Abstract: Object recognition and 6DoF pose estimation are quite challenging tasks in computer vision applications. De- spite efficiency in such tasks, standard methods deliver far from real-time processing rates. This paper presents a novel pipeline to estimate a fine 6DoF pose of objects, applied to realistic scenarios in real-time. We split our proposal into three main parts. Firstly, a Color feature classification leverages the use of pre-trained CNN color features trained on the ImageNet for object detection. A Feature-based registration module conducts a coarse pose estimation, and finally, a Fine-adjustment step performs an ICP-based dense registration. Our proposal achieves, in the best case, an accuracy performance of almost 83% on the RGB-D Scenes dataset. Regarding processing time, the object detection task is done at a frame processing rate up to 90 FPS, and the pose estimation at almost 14 FPS in a full execution strategy. We discuss that due to the proposal’s modularity, we could let the full execution occurs only when necessary and perform a scheduled execution that unlocks real-time processing, even for multitask situations. 1 INTRODUCTION Template matching approaches rely on RANSAC- based feature matching algorithms, following the Object recognition and 6D pose estimation represent pipeline proposed by (Aldoma et al., 2012b).
    [Show full text]
  • Orientation Detection Using a CNN Designed by Transfer Learning of Alexnet
    Proceedings of the 8th IIAE International Conference on Industrial Application Engineering 2020 Orientation Detection Using a CNN Designed by Transfer Learning of AlexNet Fusaomi Nagataa,*, Kohei Mikia, Yudai Imahashia, Kento Nakashimaa Kenta Tokunoa, Akimasa Otsukaa, Keigo Watanabeb and Maki K. Habibc aDepartment of Mechanical Engineering, Faculty of Engineering, Sanyo-Onoda City University Sanyo-Onoda 756-0884, Japan bGraduate School of Natural Science and Technology, Okayama University Okayama 700-8530, Japan cMechanical Engineering Department, School of Sciences and Engineering, The American University in Cairo AUC Avenue, P.O.Box 74, New Cairo 11835, Egypt *Corresponding Author: [email protected] Abstract classification in spite of only two layers. Nagi et al. designed max-pooling convolutional neural networks (MPCNN) for Artificial neural network (ANN) which has four or vision-based hand gesture recognition(1). The MPCNN could more layers structure is called deep NN (DNN) and is classify six kinds of gestures with 96% accuracy and allow recognized as a promising machine learning technique. mobile robots to perform real-time gesture recognition. Convolutional neural network (CNN) has the most used and Weimer et al. also designed a deep CNN architectures for powerful structure for image recognition. It is also known automated feature extraction in industrial inspection that support vector machine (SVM) has a superior ability for process(2). The CNN automatically generates features from binary classification in spite of only two layers. We have massive amount of training image data and demonstrates developed a CNN&SVM design and training tool for defect excellent defect detection results with low false alarm rates.
    [Show full text]
  • Template Matching Approach for Face Recognition System
    International Journal of Signal Processing Systems Vol. 1, No. 2 December 2013 Template Matching Approach for Face Recognition System Sadhna Sharma National Taiwan Ocean University, Taiwan Email: [email protected] Abstract—Object detection or face recognition is one of the Identification (one-to-many matching): Given an most interesting application in the image processing and it is image of an unknown individual, determining that a classical problem in computer vision, having application to person’s identity by comparing (possibly after surveillance, robotics, multimedia processing. Developing a encoding) that image with a database of (possibly generic object detection system is still an open problem, but there have been important successes over the past several encoded) images of known individuals. years from visual pattern. Among the most influential In general, based on the face representation face system is the face recognition system. Face recognition has recognition techniques can be divided into Appearance- become a popular area of research in computer vision and based technique (uses holistic texture features and is one of the most successful applications of image analysis and applied to either whole face or specific regions in a face understanding. Because of the nature of the problem, not image) and Feature-based technique (uses geometric only computer science researchers are interested in it, but facial features mouth, eyes, brows, cheeks etc and neuroscientists and psychologists also. It is the general geometric relationships between them). opinion that advances in computer vision research will provide useful insights to neuroscientists and psychologists A. Appearance Based into how human brain works, and vice versa.
    [Show full text]
  • Object Detection Using Template and HOG Feature Matching
    (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 7, 2020 Object Detection using Template and HOG Feature Matching 1 Marjia Sultana Partha Chakraborty3, Mahmuda Khatun4 Department of Computer Science and Engineering Department of Computer Science & Engineering Begum Rokeya University Comilla University, Cumilla - 3506, Bangladesh Rangpur, Rangpur-5404 Bangladesh Md. Rakib Hasan5 2 Department of Information and Communication Technology Tasniya Ahmed Comilla University, Cumilla - 3506, Bangladesh Institute of Information Technology Noakhali Science and Technology University 6 Noakhali – 3814 Mohammad Shorif Uddin Bangladesh Department of Computer Science and Engineering Jahangirnagar University, Savar, Dhaka-1342, Bangladesh Abstract—In the present era, the applications of computer analysis [2]. For any vision-based image processing vision is increasing day by day. Computer vision is related to the application, object detection is the most integrated part [3]. An automatic recognition, exploration and extraction of the efficient object detection technique enables us to determine necessary information from a particular image or a group of the presence of our desired object from a random scene, image sets. This paper addresses the method to detect the desired regardless of object’s scaling and rotation, changes in camera object from an image. Usually, a template of the desired object is orientation, and changes in the types of illumination. Template used in detection through a matching technique named matching is one of the approaches of great interest in current Template Matching. But it works well when the template image times which has become a revolution in computer vision. is cropped from the original one, which is not always invariant Another widely used approach of object detection is HOG due to various transformations in the test images.
    [Show full text]
  • Deep Learning-Based Template Matching Spike Classification For
    applied sciences Article Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings 1, 1, 1 1 2,3,4,5 In Yong Park y, Junsik Eom y, Hanbyol Jang , Sewon Kim , Sanggeon Park , Yeowool Huh 2,3 and Dosik Hwang 1,* 1 School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; [email protected] (I.Y.P.); [email protected] (J.E.); [email protected] (H.J.); [email protected] (S.K.) 2 Department of Medical Science, College of Medicine, Catholic Kwandong University, Gangneung 25601, Korea; [email protected] (S.P.); [email protected] (Y.H.) 3 Translational Brain Research Center, Catholic Kwandong University, International St. Mary’s Hospital, Incheon 22711, Korea 4 Department of Neuroscience, University of Science & Technology, Daejeon 34113, Korea 5 Center for Neuroscience, Korea Institute of Science and Technology, Seoul 02792, Korea * Correspondence: [email protected]; Tel.: +82-2-2123-5771 Equally contributing authors. y Received: 12 November 2019; Accepted: 30 December 2019; Published: 31 December 2019 Abstract: We propose a deep learning-based spike sorting method for extracellular recordings. For analysis of extracellular single unit activity, the process of detecting and classifying action potentials called “spike sorting” has become essential. This is achieved through distinguishing the morphological differences of the spikes from each neuron, which arises from the differences of the surrounding environment and characteristics of the neurons. However, cases of high structural similarity and noise make the task difficult. And for manual spike sorting, it requires professional knowledge along with extensive time cost and suffers from human bias.
    [Show full text]
  • An Overview of Various Template Matching Methodologies in Image Processing
    International Journal of Computer Applications (0975 – 8887) Volume 153 – No 10, November 2016 An Overview of Various Template Matching Methodologies in Image Processing Paridhi Swaroop Neelam Sharma, PhD M.Tech(Computer Science) Asst. Professor Banasthali Vidhyapeeth Banasthali Vidhyapeeth Rajasthan Rajasthan ABSTRACT MSE(Mean Squared Error) The recognition and classification of objects in images is a ∑∑(Temporary(u, v) − Target(u, v) = emerging trend within the discipline of computer vision Number of pixels community. A general image processing problem is to decide the vicinity of an object by means of a template once the scale wherever, Temporary (u, v) and Target (u, v) are the strength and rotation of the true target are unknown. Template is values of Template image and the input image respectively. primarily a sub-part of an object that‟s to be matched amongst entirely different objects. The techniques of template matching are flexible and generally easy to make use of, that makes it one amongst the most famous strategies of object localization. Template matching is carried out in versatile fields like image processing,signal processing, video compression and pattern recognition.This paper gives brief description of applications and methods where template matching methods were used. General Terms Template matching,computer vision,image processing,object recognition. Fig 1: Example of template matching. An example which describes template matching. This figure Keywords describes template based eye detection.The template is Template Matching, image processing, object recognition. correlated with absolutely distinctive regions of a face image.The regions of a face that offers maximum correlation 1. INTRODUCTION with template refers to the eye region.This approach is Template Matching may be a high-level machine vision effective in both the instances like closed eyes along with method which determines the components of a figure which open eyes and conveys numerous simulation outcome.
    [Show full text]