Articles: Template Matching In Remote Sensing & Image Processing

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1 Articles: Template Matching In Remote Sensing & Image Processing Ankit kumar 1, Akanksha Agrawal 2 1 Center Head IPCEIT, INICTEL-UNI Lima Peru 2 Assistant Professor Department of Forensic Science Teerthkanker Mahaveer University Moradabad 1 mrankitgoyal@gmail.com 2 akankshaagrawal8914@gmail.com Abstract- Template matching is one of the major problem and has been widely used in tracking, extracting, recognition and many other applications. Recently, Template matching approach has been widely used for much area to find out valuable information. Template matching tries to answer one of the most basic questions about an image? Is there a certain object in that image and where it is. The template is a description of that object hence is an image itself and is used to search the image by computing a difference measure between the template and all possible areas of the image that could match the template. in our paper we give the idea what the template matching and how we implement in remote sensing image. And told the different technique of template matching. Keywords- Template matching, image processing, cc I. INTRODUCTION Digital image processing is the use of computer algorithms to perform image processing on digital images. Image processing is a method to change an image into digital form and perform some operation on it, in order to get an enhanced image or to mine some useful information from it As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. An image may be defined as a twodimensional function, f(x,y) where x and y are spatial (plane)coordinates, and the amplitude of at any pair of coordinates(x,y) is called the intensity or gray level of the image at that point.the field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value.. II. FUNDAMENTAL STEPS IN DIGITAL IMAGE PROCESSING The digital image processing steps can be categorized into two broad areas as the methods whose input and output are images, and methods whose inputs may be images, but whose outputs are attributes extracted from those image Image acquisition is the first process in the digital image processing. In this phase it s provide the image in the digital format. [1].generally, the image acquisition stage involves pre-processing, such as scaling [2]. Image enhancement the idea behind enhancement techniques is to provide the better picture quality for better view. or simply to highlight certain features of interest in an image. [2]. Image restoration is an area that also deals with improving the appearance of an image. Image enhancement is subjective and image restoration [1] is objective [2]. Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Internet. Color image processing involves the study of fundamental concepts in color models and basic color processing in a digital domain. Image color can be used as the basis for extracting features of interest in an image. Wavelets are the foundation for representing images in various degrees of resolution. Wavelet scan be used for image data compression and f or pyramidal representation, in which images a resubdivided successively into smaller regions. 109

2 Compression reducing the storage required to image, or the bandwidth required to transmit it.although storage technology has improved si gnificantlyover the past decade, the same cann ot be said for transmission capacity [1,2]. Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape [2]. Segmentation in this we partition an image into its different parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. [2]. Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, founding either the boundary of a region or all the points in the region itself [2].. Description, also called feature selection, deals with extracting attributes that result in some computable information of interest or are basic for differentiating one class of objects from another. Recognition is the process that assigns a label to an object based on its descriptors. Recognition topic deals with the methods for recognition of individual objects in an image [2]. III. APPLICATION OF IMAGE PROCESSING A. Biomedical Imaging Techniques Various types of imaging devices like X- ray, computer aided tomography image ultrasound, etc. are used extensively for the purpose of medical diagnosis such as CT-scan, X-ray, and MRI are shown in Fig 2 Fig. 2: Examples of (a) CT Scan image of brain, (b) X- ray image of wrist and (c) MRI image of brain, (d) shows the delta of river Ganges,(e) is the glacier flow in Bhutan Himalayas B.. Remotely Sensed Scene Interpretation We show examples of two remotely sensed images in Figure 2 whose color version has been presented in the color figure pages. Figure 2(d) shows the delta of river Ganges in India. The light blue segment represents the sediments in the delta region of the river, the deep blue segment represents the water body, and the deep red regions are mangrove swamps of the adjacent islands. Figure 2 (e) is the glacier flow in Bhutan Himalayas [1,2]. C. Defense surveillance Application of image processing techniques in defense surveillance is an important area of study [1, 2]. The primary task here is to segment different objects in the water body part of the image. After extracting the segments, the parameters like area, location, perimeter, compactness, shape, length, breadth, and aspect ratio are found, to classify each of the segmented objects. D. Content-Based Image Retrieval Retrieval of a query image from a large image archive is an important application in image processing. The advent of large multimedia collection and digital libraries has led to an 110

3 important requirement for development of search tools for indexing and retrieving information from them. [1, 2]. Remote sensing is the gaining the info about an object or phenomenon without making physical contact with the object. In modern usage, the term generally denotes to the use of aerial sensor technologies to detect and classify objects on Earth. There are two main types of remote sensing: passive and active remote sensing. The quality of remote sensing data consists of its spatial, spectral, radiometric and temporal resolutions. Spatial resolution Spectral resolution Radiometric resolution Temporal resolution IV. TEMPLATE MATCHING Template matching is a technique in digital image processing for finding small parts of an image which match a template image [4]. Template matching is a technique used in classifying an object by comparing portions of images with another image. One of the important techniques in Digital image processing is template matching [4]. Template matching is widely used for processing images and pictures. Some of its wide-spread applications object to location, edge detection of images, to plot a route for mobile robot and in image registration techniques. In general, a technique includes its unique algorithm or method, which compares the template image with input image and finds similarity between them. A template is an array of numbers used to detect the presence of a particular configuration of pixels [4]. This is achieved by evaluating the degree of similarity or of dissimilarity (or mismatch) between the template and groups of pixels in every possible position in the image, using a convolution-like approach. Several methods exist for determining the degree of mismatch; in one very simple implementation, the arithmetic difference between the grey levels of the corresponding pixels in the template and the group under test is determined [4,5]. Fig.3: Template Matching Template matching can be subdivided between many approaches: feature-based and templatebased matching. The feature-based approach uses the features of the search and template image. the primary match-measuring metrics to find the best matching location of the template in the source image [5]. The template-based, or global, approach uses the entire template, with generally a sumcomparing metric (using SAD, SSD, crosscorrelation, etc.) that determines the best location by testing all or a sample of the viable test locations within the search image that the template image may match up to. A. Feature-based approach If the template image has strong features, a feature-based approach may be considered; the approach may prove further useful if the match in the search image might be transformed in some fashion [1, 2]. Since this approach does not consider the entirety of the template image, it can be more computationally efficient when working with source images of larger resolution, as the alternative approach, template-based, may require searching potentially large amounts of points in order to determine the best matching location [5]. 111

4 B. Template-based approach For templates without strong features, or for when the bulk of the template image constitutes the matching image, a templatebased approach may be effective. As aforementioned, since template-based template matching may potentially require sampling of a large number of points, it is possible to reduce the number of sampling points [1, 2, 3] by reducing the resolution of the search and template images by the same factor and performing the operation on the resultant downsized images providing a search window of data points within the search image so that the template does not have to search every viable data point, or a combination of both C. Area-based approach Area-based methods are sometimes called as correlation-like methods or template matching: This method is best suited for the templates which have no strong features with image since they operate directly on the bulk of values. Matches are estimated based on the intensity values of both image and template [1, 3]. D. Motion tracking and occlusion handling In instances where the template may not provide a direct match, it may be useful to implement the use of Eigen spaces templates that detail the matching object under a number of different conditions, such as varying perspectives, illuminations, color contrasts, or acceptable matching object poses. For example, if the user was looking for a face, the eigen spaces may consist of images (templates) of faces in different positions to the camera, in different lighting conditions, or with different expressions [5, 6]. It is also possible for the matching image to be obscured, or occluded by an object; in these cases, it is unreasonable to provide a multitude of templates to cover each possible occlusion. For example, the search image may be a playing card, and in some of the search images, the card is obscured by the fingers of someone holding the card, or by another card on top of it, or any object in front of the camera for that matter. In cases where the object is malleable or poseable, motion also becomes a problem, and problems involving both motion and occlusion become ambiguous [6]. In these cases, one possible solution is to divide the template image into multiple subimages and perform matching on each subdivision. E. Template-based matching and convolution This method is normally implemented by first picking out a part of the search image to use as a template: We will call the search image S(x, y), where (x, y) represent the coordinates of each pixel in the search image. We will call the template T(x t, y t), where (xt, yt) represent the coordinates of each pixel in the template. We then simply move the center (or the origin) of the template T(x t, y t) over each (x, y) point in the search image and calculate the sum of products between the coefficients in S(x, y) and T(xt, yt) over the whole area spanned by the template. As all possible positions of the template with respect to the search image are considered, the position with the highest score is the best position [6, 7]. A pixel in the search image with coordinates (xs, ys) has intensity Is (xs, ys) and a pixel in the template with coordinates (xt, yt) has intensity It(xt, yt ). Thus the absolute difference in the pixel intensities is defined as Diff (x s, y s, x t, y t ) = I s (x s, y s ) I t (x t, y t ). The mathematical representation of the idea about looping through the pixels in the search 112

5 SAD ZSAD LSAD (SSD ZSSD The term Pt2(x u, y v) is constant. If the term Pf2(x, y) is approximately constant then the remaining cross correlation term c (u, v) = x,y f(x, y) t(x u, y v) is a measure of the similarity between the image and the feature. Table show the similarity coefficient by the different methods LSSD NCC. image as we translate the origin of the template at every pixel and take the SAD measure is the following [7]. F Template Matching by Cross Correlation Correlation is an important tool in image processing, pattern recognition, and other fields. The correlation between two signals (cross correlation) is a standard approach to feature detection as well as a building block for more sophisticated recognition techniques. Template matching techniques attempt to answer some variation of the following question: The use of cross correlation for template matching is motivated by the distance measure (squared Euclidean distance) [8] d 2 f, t (u, v) = x,y [f(x, y) t(x u, y v)] 2 (The sum is over x, y under the window containing the feature positioned at u, v). In the expansion of d2. d 2 f, t (u, v) = x,y [f 2 (x, y) 2f(x, y)t(x u, y v)+ t 2 (x u, y v)] Fig 2:correlation based similarity measures-summary V. 7. CURRENT RESEARCH IN TEMPLATE MATCHING A wide research is being done in the Image processing technique. 1. Cancer Imaging. 2. Brain Imaging 3. Image processing 4. Imaging Technology 5. Development of automated software- 6. Development of instrumentation VI. CONCLUSION The context of the topic aims at providing the general underlying structure behind the template matching besides these it provide fast computational measures behind the underlying strategies and removes the drawbacks behind the general methodologies applied to a template matching process. 113

6 VII. FUTURE WORK. We all are in midst of revolution ignited by fast development in computer technology and imaging. Against common belief, computers are not able to match humans in calculation related to image processing and analysis. But with increasing sophistication and power of the modern computing, computation will go beyond conventional, Von Neumann sequential architecture and would contemplate the optical execution too. Parallel and distributed computing paradigms are anticipated to improve responses for the image processing results. VIII. REFERENCES [1] R. C. Gonzalez and R. E. Woods, Digital Image Processing,2nd Edition, Prentice Hall, 2002 [2] R. S. Hegadi, Image Processing: Research Opportunities and Challenges. National Seminar on Research in Computers, Coimbatore, India, Dec [3] T. N. Pappas, New Challenges for Image Processing Research. IEEE transactions on image processing, vol. 20, no. 12, December 2011 [4] J. jogleker, Area Based Image Matching Methods A Survey International Journal of Emerging Technology and Advanced Engineering,ISSN , Volume 2, Issue 1, January 2012 [5] T. Mahalakshmi, R. Muthaiah and P. Swaminathan, Review Article: An Overview of Template Matching Technique in Image Processing Research Journal of Applied Sciences, Engineering and Technology, volume 4, year [6] Li, Yuhai, L. Jian, T. Jinwen, X. Honbo. A fast rotated template matching based on point feature. Proceedings of the SPIE 6043, Sep [7] R. Brunelli, Template Matching Techniques in Computer Vision: Theory and Practice, Wiley, ISBN , Hardcover. 348 pages, April [8] J.P. Lewis, Fast Template Matching, Vision Interface 95, Canadian Image Processing and Pattern Recognition Society, Quebec City, Canada, p ,May 15-19, 1995 [9] R.C Gonzalez, Woods, R, Eddins, S "Digital Image Processing using Matlab" Prentice Hall, 2004 [10] J.P. Lewis, Industrial Light & Magic, Fast Normalized Cross-Correlation , Massey University, Auckland, New Zealand, December