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A Combined Approach of Harris-SIFT Feature Detection for Image Mosaicing

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A Combined Approach of Harris-SIFT Feature Detection for Image Mosaicing International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 3 Issue 5, May - 2014 A Combined Approach of Harris-SIFT Feature Detection for Image Mosaicing Monika B. Varma, Prof. Kinjal Mistree C.E Department, Chotubhai Gopalbhai Patel Institute of Technology Uka Tarsadia University Gujarat, India. Abstract—This Image Mosaicing is a process of assembling appropriate transformations via a warping operation and multiple overlapping images of the same scene into a large image. merging the overlapping regions of warped Images, it is The output of the image Mosaicing operation is the union of the possible to construct a single image indistinguishable from a two or more overlapping input images. This technology is widely single large image of the same object, covering the entire used in photography, digital video, motion analysis, medical visible area of the scene. The basis of the Image Mosaicing image processing, remote sensing image processing, document technique is to find the common part in the overlap input image processing and other fields. images and then smoothly blend them to produce final Panorama. This paper describes a combined approach of Harris-SIFT feature detection for Image Mosaicing. Firstly, feature points are The procedure of the Image Mosaicing Consists of two basic detected by using Harris corner detector, then after SIFT Concept: 1) Image Registration and 2) Image Blending and descriptor is computed to store feature vector for each detected Warping[6], as shown in the Fig 1. keypoints and then feature matching is applied. The RANSAC Homography algorithm is used to detect wrong matches for improving the stability of the algorithm and for estimating the transformation model. Finally Image Blending procedure is used for smooth and seamless panorama. IJERT Combined approach of Harris corner detector and stableIJERT invariant SIFT descriptor is reliable in extracting the translation, scale and rotation invariant features, less affected by illumination issues and increases the speed of the feature detection process which also affects the matching accuracy. Keywords— Feature Detector, SIFT descriptor, RANSAC I. INTRODUCTION Image mosaic technology is one of the gradual developing Fig.1 Basic Block Diagram of Image Mosaicing digital image processing technologies applied to various fields. The field of view of human eye is restricted to 200x135º, in A. Image Acquisition order to increase the field of view 360x180º panorama is used. First, In this stage overlapping image of the same scene is Image Mosaicing or Image Stitching is a technique to create captured from any mobile, digital cameras to create a full panorama, where images are assembled with some common mosaiced image. The input images can be of any type ex: field of view. In many fields there is always a need of full view image outdoor scene images, aerial images, satellite images, images, the most traditional application is the construction of medical images, document images etc. large aerial and satellite photographs from collections of images, document image analysis, medical imaging[5], field of B. Image Registration photography, 3D rebuilding of objects etc. The core of a Mosaicing procedure is the Image An Image Mosaicing is a synthetic composition generated Registration of the overlapping parts of the images we want to from a sequence of images and it can be obtained by merge together. Image Registration refers to the geometric understanding the geometric relation between the images. The alignment of a set of images. The set may consist of two or geometric relations are the coordinate transformations that more digital images taken of a single scene at different times, relate the different image coordinate systems. By applying the from different sensors, or from different viewpoints. This IJERTV3IS052229 www.ijert.org 2373 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 3 Issue 5, May - 2014 means, the best transformation is computed between the input The mapping functions constructed during the previous step two images and then make the geometric alignment of those are used to transform the sensed image and thus to register the images. One of the images is referred to as the references or images. source and the second image is referred to as the target or sensed. C. Image Blending The majority of the registration methods consist of the following four steps: Feature Detection, Feature Matching, The next step, following registration, is image integration. Transform model estimation, Image Resampling and It is a process of overlaying images together on a bigger transformation[1]. canvas. The images are placed appropriately on the bigger The feature detection is the first step, if the feature are canvas using registration transformation to get final mosaic. detected accurately and speedy then further process accurate Images aligned , even after undergoing geometric corrections, in accurate way. The Feature can be salient and distinctive require further processing to eliminate distortions and objects (closed-boundary regions, edges, contours, line discontinuities. Since the parts are recorded under different intersections, corners, etc.) are manually or, preferably, conditions, including weather, lighting and noise they may automatically detected. There are the two main approaches for have different gray level characteristics. This may cause seams Feature detection: Area based methods and Feature based to be apparent between two different parts. The seams can be methods. Second approach of feature detection is based on the very noticeable, and they often interfere with the perception of extraction of the salient structures features like edges, corners, the details of the picture. Blending is applied to make seamless region etc. Corners are important local features in the images. stitching. Two popular methods of blending the images are, They are the points having high curvature and lie in the One is alpha blending or feathering also called linear blending junction of the different brightness region of the images[8]. and another is Gaussian pyramid. They are the distinct and have a higher chance of being detected in different views of the same scene. II. METHODOLOGY The corner based feature detector are Harris, SUSAN etc. In the process of Image Mosaicing, a variety of different which are descriptor less. Harris corner detector[3] finds the situations may be encountered, such as the translation of the feature points at fixed scale[8]. This detectors takes the less image, image rotation, and different scales image mosaic. time. Here, feature detectors are used to find areas of interest Therefore, the requirement to the applicability of algorithm is in the images. A feature descriptor is used to build a also increasing. description of a local feature. Disadvantage of Harris corner detector is that it is not scale invariant and though it is The most two popular algorithms used for the feature rotational invariant it is not that much reliable[9]. There are detection are Harris corer detector and SIFT, both are having some scale and rotation invariant feature detector or advantages and disadvantages. descriptors for ex: SIFT, SURF, BRIEF etc. whose descriptorIJERTIJERT can be used for further feature matching. Harris corner detector is not Scale invariant and though it is rotation invariant it is not that much reliable. SIFT which is After feature has been detected from the both reference and a stable method to detect feature points but the computation in sensed images, they can be matched by means of the image the procedure of image pyramid construction, key point intensity values in their close neighborhoods, feature spatial location determination by extreme value detection and the distribution and other symbolic feature description. Again computation cost is too much time consuming. Therefore feature matching is divided into further two categories: 1) instead of applying them individually, it is profitable to use Area-based and 2) feature based[1]. them together to take the advantage of both and to overcome the disadvantage of each other. hence computing time can be After the feature correspondence has been established the shorter. mapping function is constructed. It should transform the sensed image to overlay it over the reference image. SIFT descriptor operation is performed only to those Homography is the mapping between two spaces which often features which are extracted by Harris algorithm; hence the used to represent the correspondence between two images of computing cost will be reduced. Thus here by applying this the same scene . It’s widely useful for images where multiple approach, i.e. by using Harris corner detector and SIFT images are taken from a rotating camera having a fixed descriptor in combined manner we get the scale and rotation camera center ultimately warped together to produce a invariant features, less affected by illumination issues. The panoramic view. In homography undesired corners which do idea of applying Harris algorithm before SIFT descriptor not belong to the overlapping are removed[12]. Homography reduces the computational time of SIFT feature detector calculation can be performed by Least Mean Square algorithm for detecting the features in the image and increases algorithm, RANSAC etc. RANSA (Random Sample the feature matching capability of features detected by Harris Consensus)[7] is a common and robust accepted way to refine detector and the image
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