DOCSLIB.ORG

The Harris Corner Detection Method Based on Three Scale Invariance Spaces

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Harris Corner Detection Method Based on Three Scale Invariance Spaces View metadata,IJCSI International citation and Journal similar of papers Computer at core.ac.uk Science Issues, Vol. 9, Issue 6, No 2, November 2012 brought to you by CORE ISSN (Online): 1694-0814 provided by Directory of Open Access Journals www.IJCSI.org 18 The Harris Corner Detection Method Based on Three Scale Invariance Spaces Yutian Wang, Yiqiang Chen, Jing Li, Biming Li Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei Province, 066004, China given, ignoring the role of the differential scales in the Abstract establishing of the scale space image. In order to solve the problem that the traditional Harris comer operator hasn’t the property of variable scales and is sensitive to Aiming at the problem of the traditional Harris detectors noises, an improved three scale Harris corner detection without the property of variable scales and is sensitive to algorithm was proposed. First, three scale spaces with the noises, this paper proposed an improved three scale Harris characteristic of scale invariance were constructed using discrete corner detection method. Three scale spaces were Gaussian convolution. Then, Harris scale invariant detector was used to extract comers in each scale image. Finally, supportable constructed through selecting reasonably the and unsupportable set of points were classified according to parameters , S and t that influence the performance of whether the corresponding corners in every scale image support the Harris scale invariant detector. Harris comers in each that of the original images. After the operations to those scale images were extracted by the Harris scale invariant unsupportable set of points, the noised corners and most of detector and the supportable and unsupportable set of unstable corners could be got rid of. The corners extracted by points were classified according to whether the the three and the original scale spaces also had scale invariant corresponding corners in every scale image support that of property. The experiments results proved that, compared with the scale space on the whole Gaussian pyramid, the utilization the original images. The method could remove the noised factor of the image was increased, the calculation time is corners and most of unstable corners effectively, and decreased, and the image was high recurrence rate and stability. extracted and increased the corners with the characteristic Keywords: Harris corner detect, three scale spaces, scale of scale invariance. invariant feature, Gaussian convolution, improved algorithm 1. Introduction 2. Harris corner detect lgorithm Corners detection is the key step in the image processing, The equation of Harris corner detect algorithm [7] is: and the Harris comer detector is based on the gray scales 2 gx g x g y of images, much sensitive to the change of the image M G() s g g g 2 scale. Based on the Lindberg [1]-[2] theory of the scale x y y (1) automatic selection, Mikolajczyk etc. [3]-[4] studied the construction of Harris scale invariant detector in the scale Where, g x is the gradient in x direction; g y is the space of images. But the Harris scale invariant detector couldn’t provide stable key points. The reference [5] gradient in y direction; sG )( is the Gaussian template. researched systematically the parameter spaces constituted The corner response function of Harris algorithm is: by the parameters capable of affecting the performance of det 2MktrMR (2) the Harris scale invariant detector, and the experiments result corrected the conclusion that the Harris scale Where, R is the response function of the corner required; invariant detector is unstable. The reference [6] det M is the Matrix determinant; tr M is the Matrix trace; constructed multi-scale spaces using different integral k is the default constant, and is generally 0.04-0.06. scales. Combined with the image blocking method, the In the practice, the center value R of an image is Harris comers were extracted, realizing the accurate calculated and if the value is the maximum in the location in small scale and removing false and remaining neighborhood and larger than a given threshold, the point reality in large scale. But the proportional relations is regarded as a corner point. between the integral and the differential scales were not Copyright (c) 2012 International Journal of Computer Science Issues. All Rights Reserved. IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 6, No 2, November 2012 ISSN (Online): 1694-0814 www.IJCSI.org 19 In order that the second moment Matrix to detect Harris Traditional Method of Lowe [9]-[10] realized Harris scale corners in the scale space was adaptable to the change of invariance detector. In the process of establishing the scales, the secondary moment Matrix after scale Gaussian pyramid image, Harris corners of every points in adjustment was adopted expressed followed: every sub-scale space were calculated according to M x,,, y equations (1) and (2) , and compared them with the Harris ID corners of 26 neighborhood points located at 3 adjacent 2 I x,, y Ix I y x,, y D scales. If the Harris corner get the local maximum value 2 g xD DI 2 and larger than a given threshold, it is extracted as the Ix I y x,, y D IyD x,, y key point. (3) Where, I is the integral scale; D is the differential Experiments show that, establishing whole pyramid will cost much time, and the number of Harris corners scale, and = ; S is the constant; I and I is the x y extracted from every image is close related to the R gradient in the x and y direction. max of the image. Generally, if the value of is small in an image, the When Rmazx max(R ) , RR .0 01 max , and the value of R at every point of the image is also small, maximum value of 3 3neighborhood is got, the current impossible to be larger than all the 26 neighborhood point is a corner. It is the gradient that is calculated by points. So a little of the image in the pyramid is Harris operators, so it is changed only in directions, contributed, and the number of the corners extracted is independent of the image brightness, that is, the small. Solving the problem would cost more time to algorithm has the feature of rotational invariance. establish more layers of pyramid. The value of is determined by and S, and to different images, the different value of and S would also increase the 3. Improved Harris corner detect algorithm calculation complex and the time. with three scale invariance spaces In this paper, three Gaussian kernel functions with In order to solve the defects that the traditional Harris comer detector is sensitive to scale spaces and noises, an different scales G1 (,,) x y 1 , G2 (,,) x y 2 and improved three scale Harris corner detection algorithm G3 (,,) x y 3 were established, and the four images in was proposed. their scale spaces including the original image were constructed. The corners of the four images were The theory of the scale space is first used to simulate the 2 multi-scale features of images in the computer vision field. extracted by Harris, where 2 t 1 , 3 t 1 . The Lindeberg etc. demonstrated that the Gaussian kernel unstable and noise corners without scale invariance were function is the only linear kernel. The Gaussian kernel removed. Then the corners calculation was increased, function with the variable scales is [8]: assuring the numbers of the corners extracted. The steps 2 2 2 were: 1 xy /2 G x,, y 2 e (1) Based on the original image L0, three space images 2 (4) L1, L2, L3 with different scales were established by three An image in a scale could be expressed the convolution of Gaussian kernel functions with different scales the image and the variable Gaussian kernel function, and , , . the expression of the LOG operator is: Experiments showed that the suitable value of is 0.6- L x,,,,*, y G x y I x y 1.2 and the t is 1.5-2.5. (5) (2) The Harris corners of the four images were extracted according to the equations (1) and (2). Experiment Where, (,)x y is the space coordinate and the little showed that the suitable value of S is 0.4-1.0. represents the little smoothing of the image, and the (3)If the corner a in the image L0 could be found in corresponding scale is small. Large scales correspond to 0 the general view of images, and small scales correspond other 5 5 neighborhood of other images with different to the details of images. scales, it would be regarded as being supported by corners in other scale spaces and the corner could be reserved. Copyright (c) 2012 International Journal of Computer Science Issues. All Rights Reserved. IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 6, No 2, November 2012 ISSN (Online): 1694-0814 www.IJCSI.org 20 Otherwise, the corner would be removed. The number corners were extracted by the improved Harris algorithm. of the reserved corners was z . We set =0.8, t= 2 , and S=0.8 in the experiments. 1 (4) Corners in the images L1, L2, L3 were separated into The first image series shown in the Fig.1 and the Fig. 2 the corners supporting L0 and the corners not supporting adopted the classical testing image and the corresponding L0. The corners collections not supporting L0 in the three image with additional noises. images L1, L2, L3 were recorded as , , a1[] n 1 a2[] n 2 a3[] n 3 . (5) For the corner a1 in the , if it could be found that at least one corner was the corner in the and with 5 5 neighborhood of images L2, and L3, the corner of the in the image L1 would be regarded as the corner with scale invariance.
Load more

Recommended publications
  • Context-Aware Features and Robust Image Representations 5 6 ∗ 7 P
    CAKE_article_final.tex Click here to view linked References 1 2 3 4 Context-Aware Features and Robust Image Representations 5 6 ∗ 7 P. Martinsa, , P. Carvalhoa,C.Gattab 8 aCenter for Informatics and Systems, University of Coimbra, Coimbra, Portugal 9 bComputer Vision Center, Autonomous University of Barcelona, Barcelona, Spain 10 11 12 13 14 Abstract 15 16 Local image features are often used to efficiently represent image content. The limited number of types of 17 18 features that a local feature extractor responds to might be insufficient to provide a robust image repre- 19 20 sentation. To overcome this limitation, we propose a context-aware feature extraction formulated under an 21 information theoretic framework. The algorithm does not respond to a specific type of features; the idea is 22 23 to retrieve complementary features which are relevant within the image context. We empirically validate the 24 method by investigating the repeatability, the completeness, and the complementarity of context-aware fea- 25 26 tures on standard benchmarks. In a comparison with strictly local features, we show that our context-aware 27 28 features produce more robust image representations. Furthermore, we study the complementarity between 29 strictly local features and context-aware ones to produce an even more robust representation. 30 31 Keywords: Local features, Keypoint extraction, Image content descriptors, Image representation, Visual 32 saliency, Information theory. 33 34 35 1. Introduction While it is widely accepted that a good local 36 37 feature extractor should retrieve distinctive, accu- 38 Local feature detection (or extraction, if we want 39 rate, and repeatable features against a wide vari- to use a more semantically correct term [1]) is a 40 ety of photometric and geometric transformations, 41 central and extremely active research topic in the 42 it is equally valid to claim that these requirements fields of computer vision and image analysis.
    [Show full text]
  • Robust Corner and Tangent Point Detection for Strokes with Deep
    Robust corner and tangent point detection for strokes with deep learning approach Long Zeng*, Zhi-kai Dong, Yi-fan Xu Graduate school at Shenzhen, Tsinghua University Abstract: A robust corner and tangent point detection (CTPD) tool is critical for sketch-based engineering modeling. This paper proposes a robust CTPD approach for hand-drawn strokes with deep learning approach, denoted as CTPD-DL. Its robustness for users, stroke shapes and biased datasets is improved due to multi-scaled point contexts and a vote scheme. Firstly, all stroke points are classified into segments by two deep learning networks, based on scaled point contexts which mimic human’s perception. Then, a vote scheme is adopted to analyze the merge conditions and operations for adjacent segments. If most points agree with a stroke’s type, this type is accepted. Finally, new corners and tangent points are inserted at transition points. The algorithm’s performance is experimented with 1500 strokes of 20 shapes. Results show that our algorithm can achieve 95.3% for all-or-nothing accuracy and 88.6% accuracy for biased datasets, compared to 84.6% and 71% of the state-of-the-art CTPD technique, which is heuristic and empirical-based. Keywords: corner detection, tangent point detection, stroke segmentation, deep learning, ResNet. 1. Introduction This work originates from our sketch-based engineering modeling (SBEM) system [1], to convert a conceptual sketch into a detailed part directly. A user friendly SBEM should impose as fewer constraints to the sketching process as possible [2]. Such system usually first starts from hand- drawn strokes, which may contain more than one primitives (e.g.
    [Show full text]
  • 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.
    [Show full text]
  • Pavement Crack Detection by Ridge Detection on Fractional Calculus and Dual-Thresholds
    International Journal of Multimedia and Ubiquitous Engineering Vol.10, No.4 (2015), pp.19-30 http://dx.doi.org/10.14257/ijmue.2015.10.4.03 Pavement Crack Detection by Ridge Detection on Fractional Calculus and Dual-thresholds Song Hongxun, Wang Weixing, Wang Fengping, Wu Linchun and Wang Zhiwei Shaanxi Road Traffic Intelligent Detection and Equipment Engineering Technology Research Center, Chang’an University, Xi’an, China School of Information Engineering, Chang’an University, Xi’an, China songhongx @163.com Abstract In this paper, a new road surface crack detection algorithm is proposed; it is based on the ridge edge detection on fractional calculus and the dual-thresholds on a binary image. First, the multi-scale reduction of image data is used to shrink an original image to eliminate noise, which can not only smooth an image but also enhance cracks. Then, the main cracks are extracted by using the ridge edge detection on fractional calculus in a grey scale image. Subsequently, the resulted binary image is further processed by applying both short and long line thresholds to eliminate short curves and noise for getting rough crack segments. Finally the gaps in cracks are connected with a curve connection function which is an artificial intelligence routine. The experiments show that the algorithm for pavement crack images has the good performance of noise immunity, accurate positioning, and high accuracy. It can accurately locate and detect small and thin cracks that are difficult to identify by other traditional algorithms. Keywords: Crack; Multiscale; Image enhancement; Ridge edge; Threshold 1. Introduction In a time period, any road can be damaged gradually due to traffic loads and the other natural/artificial factors, and cracks are the most common road pavement surface defects that need to mend as early as possible.
    [Show full text]
  • Feature Detection Florian Stimberg
    Feature Detection Florian Stimberg Outline ● Introduction ● Types of Image Features ● Edges ● Corners ● Ridges ● Blobs ● SIFT ● Scale-space extrema detection ● keypoint localization ● orientation assignment ● keypoint descriptor ● Resources Introduction ● image features are distinct image parts ● feature detection often is first operation to define image parts to process later ● finding corresponding features in pictures necessary for object recognition ● Multi-Image-Panoramas need to be “stitched” together at according image features ● the description of a feature is as important as the extraction Types of image features Edges ● sharp changes in brightness ● most algorithms use the first derivative of the intensity ● different methods: (one or two thresholds etc.) Types of image features Corners ● point with two dominant and different edge directions in its neighbourhood ● Moravec Detector: similarity between patch around pixel and overlapping patches in the neighbourhood is measured ● low similarity in all directions indicates corner Types of image features Ridges ● curves which points are local maxima or minima in at least one dimension ● quality depends highly on the scale ● used to detect roads in aerial images or veins in 3D magnetic resonance images Types of image features Blobs ● points or regions brighter or darker than the surrounding ● SIFT uses a method for blob detection SIFT ● SIFT = Scale-invariant feature transform ● first published in 1999 by David Lowe ● Method to extract and describe distinctive image features ● feature
    [Show full text]
  • Accurate Corner Detection Methods Using Two Step Approach by Nitin Bhatia , Megha Chhabra Thapar University
    Global Journal of Computer Science & Technology Volume 11 Issue 6 Version 1.0 April 2011 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975 - 4172 & Print ISSN: 0975-4350 Accurate Corner Detection Methods using Two Step Approach By Nitin Bhatia , Megha Chhabra Thapar University Abstract- : Many image features are proved to be good candidates for recognition. Among them are edges, lines, corners, junctions or interest points in general. Importance of corner detection in digital images is increasing with increasing work in computer vision in imagery. One of the most promising techniques is the one based on Harris corner detection method. This work describes different approaches to detect corner in efficient way. Based on the works carried out by Harris method, the authors have worked upon increasing efficiency using edge detection methods on image, along with applying the Harris on this pre-processed image. Most of the time, such a step is performed as one of the first steps upon which more complicated algorithm rely. Hence, good outcome of such an operation influences the whole vision channel. This paper contains a quantitative comparison of three such modified techniques using Sobel–Harris, Canny-Harris and Laplace-Harris with Harris operator on the basis of distances computed by these methods from user detected corners. Keywords: Corner Detection, Harris, Laplace, Canny, Sobel. Classification: GJCST Classification: I.4.6 Accurate Corner Detection Methods using Two Step Approach Strictly as per the compliance and regulations of: © 2011 Nitin Bhatia , Megha Chhabra. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction inany medium, provided the original work is properly cited.
    [Show full text]
  • A Survey of Feature Extraction Techniques in Content-Based Illicit Image Detection
    Journal of Theoretical and Applied Information Technology 10 th May 2016. Vol.87. No.1 © 2005 - 2016 JATIT & LLS. All rights reserved . ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 A SURVEY OF FEATURE EXTRACTION TECHNIQUES IN CONTENT-BASED ILLICIT IMAGE DETECTION 1,2 S.HADI YAGHOUBYAN, 1MOHD AIZAINI MAAROF, 1ANAZIDA ZAINAL, 1MAHDI MAKTABDAR OGHAZ 1Faculty of Computing, Universiti Teknologi Malaysia (UTM), Malaysia 2 Department of Computer Engineering, Islamic Azad University, Yasooj Branch, Yasooj, Iran E-mail: 1,2 [email protected], [email protected], [email protected] ABSTRACT For many of today’s youngsters and children, the Internet, mobile phones and generally digital devices are integral part of their life and they can barely imagine their life without a social networking systems. Despite many advantages of the Internet, it is hard to neglect the Internet side effects in people life. Exposure to illicit images is very common among adolescent and children, with a variety of significant and often upsetting effects on their growth and thoughts. Thus, detecting and filtering illicit images is a hot and fast evolving topic in computer vision. In this research we tried to summarize the existing visual feature extraction techniques used for illicit image detection. Feature extraction can be separate into two sub- techniques feature detection and description. This research presents the-state-of-the-art techniques in each group. The evaluation measurements and metrics used in other researches are summarized at the end of the paper. We hope that this research help the readers to better find the proper feature extraction technique or develop a robust and accurate visual feature extraction technique for illicit image detection and filtering purpose.
    [Show full text]
  • Edge Detection and Corner Detection
    Edge Detection and Corner Detection Introduction to Computer Vision CSE 152 Lecture 7 CSE 152, Spring 2018 Introduction to Computer Vision Announcements • Homework 2 is due Apr 25, 11:59 PM • Reading: – Chapter 5: Local Image Features CSE 152, Spring 2018 Introduction to Computer Vision Edges CSE 152, Spring 2018 Introduction to Computer Vision Corners CSE 152, Spring 2018 Introduction to Computer Vision Edges What is an edge? A discontinuity in image intensity. Physical causes of edges 1. Object boundaries 2. Surface normal discontinuities 3. Reflectance (albedo) discontinuities 4. Lighting discontinuities (shadow boundaries) CSE 152, Spring 2018 Introduction to Computer Vision Object Boundaries CSE 152, Spring 2018 Introduction to Computer Vision Surface normal discontinuities CSE 152, Spring 2018 Introduction to Computer Vision Boundaries of materials properties CSE 152, Spring 2018 Introduction to Computer Vision Boundaries of lighting CSE 152, Spring 2018 Introduction to Computer Vision Profiles of image intensity edges CSE 152, Spring 2018 Introduction to Computer Vision Noisy Step Edge • Derivative is high everywhere. • Must smooth before taking gradient. CSE 152, Spring 2018 Introduction to Computer Vision Edge is Where Change Occurs: 1-D • Change is measured by derivative in 1D Ideal Edge Smoothed Edge First Derivative Second Derivative • Biggest change, derivative has maximum magnitude • Or 2nd derivative is zero. CSE 152, Spring 2018 Introduction to Computer Vision Numerical Derivatives f(x) x X0-h X0 X0+h Take Taylor series
    [Show full text]
  • Coding Images with Local Features
    Int J Comput Vis (2011) 94:154–174 DOI 10.1007/s11263-010-0340-z Coding Images with Local Features Timo Dickscheid · Falko Schindler · Wolfgang Förstner Received: 21 September 2009 / Accepted: 8 April 2010 / Published online: 27 April 2010 © Springer Science+Business Media, LLC 2010 Abstract We develop a qualitative measure for the com- The results of our empirical investigations reflect the theo- pleteness and complementarity of sets of local features in retical concepts of the detectors. terms of covering relevant image information. The idea is to interpret feature detection and description as image coding, Keywords Local features · Complementarity · Information and relate it to classical coding schemes like JPEG. Given theory · Coding · Keypoint detectors · Local entropy an image, we derive a feature density from a set of local fea- tures, and measure its distance to an entropy density com- puted from the power spectrum of local image patches over 1 Introduction scale. Our measure is meant to be complementary to ex- Local image features play a crucial role in many computer isting ones: After task usefulness of a set of detectors has vision applications. The basic idea is to represent the image been determined regarding robustness and sparseness of the content by small, possibly overlapping, independent parts. features, the scheme can be used for comparing their com- By identifying such parts in different images of the same pleteness and assessing effects of combining multiple de- scene or object, reliable statements about image geome- tectors. The approach has several advantages over a simple try and scene content are possible. Local feature extraction comparison of image coverage: It favors response on struc- comprises two steps: (1) Detection of stable local image tured image parts, penalizes features in purely homogeneous patches at salient positions in the image, and (2) description areas, and accounts for features appearing at the same lo- of these patches.
    [Show full text]
  • An Analysis and Implementation of the Harris Corner Detector
    Published in Image Processing On Line on 2018–10–03. Submitted on 2018–06–04, accepted on 2018–09–18. ISSN 2105–1232 c 2018 IPOL & the authors CC–BY–NC–SA This article is available online with supplementary materials, software, datasets and online demo at https://doi.org/10.5201/ipol.2018.229 2015/06/16 v0.5.1 IPOL article class An Analysis and Implementation of the Harris Corner Detector Javier S´anchez1, Nelson Monz´on2, Agust´ın Salgado1 1 CTIM, Department of Computer Science, University of Las Palmas de Gran Canaria, Spain ({jsanchez, agustin.salgado}@ulpgc.es) 2 CMLA, Ecole´ Normale Sup´erieure , Universit´eParis-Saclay, France ([email protected]) Abstract In this work, we present an implementation and thorough study of the Harris corner detector. This feature detector relies on the analysis of the eigenvalues of the autocorrelation matrix. The algorithm comprises seven steps, including several measures for the classification of corners, a generic non-maximum suppression method for selecting interest points, and the possibility to obtain the corners position with subpixel accuracy. We study each step in detail and pro- pose several alternatives for improving the precision and speed. The experiments analyze the repeatability rate of the detector using different types of transformations. Source Code The reviewed source code and documentation for this algorithm are available from the web page of this article1. Compilation and usage instruction are included in the README.txt file of the archive. Keywords: Harris corner; feature detector; interest point; autocorrelation matrix; non-maximum suppression 1 Introduction The Harris corner detector [9] is a standard technique for locating interest points on an image.
    [Show full text]
  • Lecture 10 Detectors and Descriptors
    This lecture is about detectors and descriptors, which are the basic building blocks for many tasks in 3D vision and recognition. We’ll discuss Lecture 10 some of the properties of detectors and descriptors and walk through examples. Detectors and descriptors • Properties of detectors • Edge detectors • Harris • DoG • Properties of descriptors • SIFT • HOG • Shape context Silvio Savarese Lecture 10 - 16-Feb-15 Previous lectures have been dedicated to characterizing the mapping between 2D images and the 3D world. Now, we’re going to put more focus From the 3D to 2D & vice versa on inferring the visual content in images. P = [x,y,z] p = [x,y] 3D world •Let’s now focus on 2D Image The question we will be asking in this lecture is - how do we represent images? There are many basic ways to do this. We can just characterize How to represent images? them as a collection of pixels with their intensity values. Another, more practical, option is to describe them as a collection of components or features which correspond to “interesting” regions in the image such as corners, edges, and blobs. Each of these regions is characterized by a descriptor which captures the local distribution of certain photometric properties such as intensities, gradients, etc. Feature extraction and description is the first step in many recent vision algorithms. They can be considered as building blocks in many scenarios The big picture… where it is critical to: 1) Fit or estimate a model that describes an image or a portion of it 2) Match or index images 3) Detect objects or actions form images Feature e.g.
    [Show full text]
  • The Harris Corner Detection Method Based on Three Scale Invariance Spaces
    IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 6, No 2, November 2012 ISSN (Online): 1694-0814 www.IJCSI.org 18 The Harris Corner Detection Method Based on Three Scale Invariance Spaces Yutian Wang, Yiqiang Chen, Jing Li, Biming Li Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei Province, 066004, China given, ignoring the role of the differential scales in the Abstract establishing of the scale space image. In order to solve the problem that the traditional Harris comer operator hasn’t the property of variable scales and is sensitive to Aiming at the problem of the traditional Harris detectors noises, an improved three scale Harris corner detection without the property of variable scales and is sensitive to algorithm was proposed. First, three scale spaces with the noises, this paper proposed an improved three scale Harris characteristic of scale invariance were constructed using discrete corner detection method. Three scale spaces were Gaussian convolution. Then, Harris scale invariant detector was used to extract comers in each scale image. Finally, supportable constructed through selecting reasonably the and unsupportable set of points were classified according to parameters , S and t that influence the performance of whether the corresponding corners in every scale image support the Harris scale invariant detector. Harris comers in each that of the original images. After the operations to those scale images were extracted by the Harris scale invariant unsupportable set of points, the noised corners and most of detector and the supportable and unsupportable set of unstable corners could be got rid of. The corners extracted by points were classified according to whether the the three and the original scale spaces also had scale invariant corresponding corners in every scale image support that of property.
    [Show full text]