Feature Detectors and Feature Descriptors: Where We Are Now
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Scale Invariant Interest Points with Shearlets
Scale Invariant Interest Points with Shearlets Miguel A. Duval-Poo1, Nicoletta Noceti1, Francesca Odone1, and Ernesto De Vito2 1Dipartimento di Informatica Bioingegneria Robotica e Ingegneria dei Sistemi (DIBRIS), Universit`adegli Studi di Genova, Italy 2Dipartimento di Matematica (DIMA), Universit`adegli Studi di Genova, Italy Abstract Shearlets are a relatively new directional multi-scale framework for signal analysis, which have been shown effective to enhance signal discon- tinuities such as edges and corners at multiple scales. In this work we address the problem of detecting and describing blob-like features in the shearlets framework. We derive a measure which is very effective for blob detection and closely related to the Laplacian of Gaussian. We demon- strate the measure satisfies the perfect scale invariance property in the continuous case. In the discrete setting, we derive algorithms for blob detection and keypoint description. Finally, we provide qualitative justifi- cations of our findings as well as a quantitative evaluation on benchmark data. We also report an experimental evidence that our method is very suitable to deal with compressed and noisy images, thanks to the sparsity property of shearlets. 1 Introduction Feature detection consists in the extraction of perceptually interesting low-level features over an image, in preparation of higher level processing tasks. In the last decade a considerable amount of work has been devoted to the design of effective and efficient local feature detectors able to associate with a given interesting point also scale and orientation information. Scale-space theory has been one of the main sources of inspiration for this line of research, providing an effective framework for detecting features at multiple scales and, to some extent, to devise scale invariant image descriptors. -
Edge Detection of an Image Based on Extended Difference of Gaussian
American Journal of Computer Science and Technology 2019; 2(3): 35-47 http://www.sciencepublishinggroup.com/j/ajcst doi: 10.11648/j.ajcst.20190203.11 ISSN: 2640-0111 (Print); ISSN: 2640-012X (Online) Edge Detection of an Image Based on Extended Difference of Gaussian Hameda Abd El-Fattah El-Sennary1, *, Mohamed Eid Hussien1, Abd El-Mgeid Amin Ali2 1Faculty of Science, Aswan University, Aswan, Egypt 2Faculty of Computers and Information, Minia University, Minia, Egypt Email address: *Corresponding author To cite this article: Hameda Abd El-Fattah El-Sennary, Abd El-Mgeid Amin Ali, Mohamed Eid Hussien. Edge Detection of an Image Based on Extended Difference of Gaussian. American Journal of Computer Science and Technology. Vol. 2, No. 3, 2019, pp. 35-47. doi: 10.11648/j.ajcst.20190203.11 Received: November 24, 2019; Accepted: December 9, 2019; Published: December 20, 2019 Abstract: Edge detection includes a variety of mathematical methods that aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. The same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction. It's also the most important parts of image processing, especially in determining the image quality. -
Hough Transform, Descriptors Tammy Riklin Raviv Electrical and Computer Engineering Ben-Gurion University of the Negev Hough Transform
DIGITAL IMAGE PROCESSING Lecture 7 Hough transform, descriptors Tammy Riklin Raviv Electrical and Computer Engineering Ben-Gurion University of the Negev Hough transform y m x b y m 3 5 3 3 2 2 3 7 11 10 4 3 2 3 1 4 5 2 2 1 0 1 3 3 x b Slide from S. Savarese Hough transform Issues: • Parameter space [m,b] is unbounded. • Vertical lines have infinite gradient. Use a polar representation for the parameter space Hough space r y r q x q x cosq + ysinq = r Slide from S. Savarese Hough Transform Each point votes for a complete family of potential lines: Each pencil of lines sweeps out a sinusoid in Their intersection provides the desired line equation. Hough transform - experiments r q Image features ρ,ϴ model parameter histogram Slide from S. Savarese Hough transform - experiments Noisy data Image features ρ,ϴ model parameter histogram Need to adjust grid size or smooth Slide from S. Savarese Hough transform - experiments Image features ρ,ϴ model parameter histogram Issue: spurious peaks due to uniform noise Slide from S. Savarese Hough Transform Algorithm 1. Image à Canny 2. Canny à Hough votes 3. Hough votes à Edges Find peaks and post-process Hough transform example http://ostatic.com/files/images/ss_hough.jpg Incorporating image gradients • Recall: when we detect an edge point, we also know its gradient direction • But this means that the line is uniquely determined! • Modified Hough transform: for each edge point (x,y) θ = gradient orientation at (x,y) ρ = x cos θ + y sin θ H(θ, ρ) = H(θ, ρ) + 1 end Finding lines using Hough transform -
Fast Image Registration Using Pyramid Edge Images
Fast Image Registration Using Pyramid Edge Images Kee-Baek Kim, Jong-Su Kim, Sangkeun Lee, and Jong-Soo Choi* The Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul, Korea(R.O.K) *Corresponding author’s E-mail address: [email protected] Abstract: Image registration has been widely used in many image processing-related works. However, the exiting researches have some problems: first, it is difficult to find the accurate information such as translation, rotation, and scaling factors between images; and it requires high computational cost. To resolve these problems, this work proposes a Fourier-based image registration using pyramid edge images and a simple line fitting. Main advantages of the proposed approach are that it can compute the accurate information at sub-pixel precision and can be carried out fast for image registration. Experimental results show that the proposed scheme is more efficient than other baseline algorithms particularly for the large images such as aerial images. Therefore, the proposed algorithm can be used as a useful tool for image registration that requires high efficiency in many fields including GIS, MRI, CT, image mosaicing, and weather forecasting. Keywords: Image registration; FFT; Pyramid image; Aerial image; Canny operation; Image mosaicing. registration information as image size increases [3]. 1. Introduction To solve these problems, this work employs a pyramid-based image decomposition scheme. Image registration is a basic task in image Specifically, first, we use the Gaussian filter to processing to combine two or more images that are remove the noise because it causes many undesired partially overlapped. -
Scale Invariant Feature Transform (SIFT) Why Do We Care About Matching Features?
Scale Invariant Feature Transform (SIFT) Why do we care about matching features? • Camera calibration • Stereo • Tracking/SFM • Image moiaicing • Object/activity Recognition • … Objection representation and recognition • Image content is transformed into local feature coordinates that are invariant to translation, rotation, scale, and other imaging parameters • Automatic Mosaicing • http://www.cs.ubc.ca/~mbrown/autostitch/autostitch.html We want invariance!!! • To illumination • To scale • To rotation • To affine • To perspective projection Types of invariance • Illumination Types of invariance • Illumination • Scale Types of invariance • Illumination • Scale • Rotation Types of invariance • Illumination • Scale • Rotation • Affine (view point change) Types of invariance • Illumination • Scale • Rotation • Affine • Full Perspective How to achieve illumination invariance • The easy way (normalized) • Difference based metrics (random tree, Haar, and sift, gradient) How to achieve scale invariance • Pyramids • Scale Space (DOG method) Pyramids – Divide width and height by 2 – Take average of 4 pixels for each pixel (or Gaussian blur with different ) – Repeat until image is tiny – Run filter over each size image and hope its robust How to achieve scale invariance • Scale Space: Difference of Gaussian (DOG) – Take DOG features from differences of these images‐producing the gradient image at different scales. – If the feature is repeatedly present in between Difference of Gaussians, it is Scale Invariant and should be kept. Differences Of Gaussians -
EECS 442 Computer Vision: Homework 2
EECS 442 Computer Vision: Homework 2 Instructions • This homework is due at 11:59:59 p.m. on Friday February 26th, 2021. • The submission includes two parts: 1. To Canvas: submit a zip file of all of your code. We have indicated questions where you have to do something in code in red. Your zip file should contain a single directory which has the same name as your uniqname. If I (David, uniqname fouhey) were submitting my code, the zip file should contain a single folder fouhey/ containing all required files. What should I submit? At the end of the homework, there is a canvas submission checklist provided. We provide a script that validates the submission format here. If we don’t ask you for it, you don’t need to submit it; while you should clean up the directory, don’t panic about having an extra file or two. 2. To Gradescope: submit a pdf file as your write-up, including your answers to all the questions and key choices you made. We have indicated questions where you have to do something in the report in blue. You might like to combine several files to make a submission. Here is an example online link for combining multiple PDF files: https://combinepdf.com/. The write-up must be an electronic version. No handwriting, including plotting questions. LATEX is recommended but not mandatory. Python Environment We are using Python 3.7 for this course. You can find references for the Python standard library here: https://docs.python.org/3.7/library/index.html. -
Exploiting Information Theory for Filtering the Kadir Scale-Saliency Detector
Introduction Method Experiments Conclusions Exploiting Information Theory for Filtering the Kadir Scale-Saliency Detector P. Suau and F. Escolano {pablo,sco}@dccia.ua.es Robot Vision Group University of Alicante, Spain June 7th, 2007 P. Suau and F. Escolano Bayesian filter for the Kadir scale-saliency detector 1 / 21 IBPRIA 2007 Introduction Method Experiments Conclusions Outline 1 Introduction 2 Method Entropy analysis through scale space Bayesian filtering Chernoff Information and threshold estimation Bayesian scale-saliency filtering algorithm Bayesian scale-saliency filtering algorithm 3 Experiments Visual Geometry Group database 4 Conclusions P. Suau and F. Escolano Bayesian filter for the Kadir scale-saliency detector 2 / 21 IBPRIA 2007 Introduction Method Experiments Conclusions Outline 1 Introduction 2 Method Entropy analysis through scale space Bayesian filtering Chernoff Information and threshold estimation Bayesian scale-saliency filtering algorithm Bayesian scale-saliency filtering algorithm 3 Experiments Visual Geometry Group database 4 Conclusions P. Suau and F. Escolano Bayesian filter for the Kadir scale-saliency detector 3 / 21 IBPRIA 2007 Introduction Method Experiments Conclusions Local feature detectors Feature extraction is a basic step in many computer vision tasks Kadir and Brady scale-saliency Salient features over a narrow range of scales Computational bottleneck (all pixels, all scales) Applied to robot global localization → we need real time feature extraction P. Suau and F. Escolano Bayesian filter for the Kadir scale-saliency detector 4 / 21 IBPRIA 2007 Introduction Method Experiments Conclusions Salient features X HD(s, x) = − Pd,s,x log2Pd,s,x d∈D Kadir and Brady algorithm (2001): most salient features between scales smin and smax P. -
Image Segmentation Based on Sobel Edge Detection Yuqin Yao1,A
5th International Conference on Advanced Materials and Computer Science (ICAMCS 2016) Image Segmentation Based on Sobel Edge Detection Yuqin Yao 1,a 1 Chengdu University of Information Technology, Chengdu, 610225, China a email: [email protected] Keywords: MM-sobel, edge detection, mathematical morphology, image segmentation Abstract. This paper aiming at the digital image processing, the system research to add salt and pepper noise, digital morphological preprocessing, image filtering noise reduction based on the MM-sobel edge detection and region growing for edge detection. System in this paper, the related knowledge, and application in various fields and studied and fully unifies in together, the four finished a pair of gray image edge detection is relatively complete algorithm, through the simulation experiment shows that the algorithm for edge detection effect is remarkable, in the case of almost can keep more edge details. Research overview The edge of the image is the most important visual information in an image. Image edge detection is the base of image analysis, image processing, computer vision, pattern recognition and human visual [1]. The ultimate goal is image segmentation; the largest premise is image edge detection. Image edge extraction plays an important role in image processing and machine vision. Proper image detection method is always the research hotspots in digital image processing, there are many methods to achieve edge detection, we expect to find an accurate positioning, strong anti-noise, not false, not missing detection algorithm [2]. The edge is an important feature of an image. Typically, when we take the digital image as input, the image edge is that the gray value of the image is changing radically and discontinuous, in mathematics the point is known as the break point of signal or singular point. -
A PERFORMANCE EVALUATION of LOCAL DESCRIPTORS 1 a Performance Evaluation of Local Descriptors
MIKOLAJCZYK AND SCHMID: A PERFORMANCE EVALUATION OF LOCAL DESCRIPTORS 1 A performance evaluation of local descriptors Krystian Mikolajczyk and Cordelia Schmid Dept. of Engineering Science INRIA Rhone-Alpesˆ University of Oxford 655, av. de l'Europe Oxford, OX1 3PJ 38330 Montbonnot United Kingdom France [email protected] [email protected] Abstract In this paper we compare the performance of descriptors computed for local interest regions, as for example extracted by the Harris-Affine detector [32]. Many different descriptors have been proposed in the literature. However, it is unclear which descriptors are more appropriate and how their performance depends on the interest region detector. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector. Our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations. We compare shape context [3], steerable filters [12], PCA-SIFT [19], differential invariants [20], spin images [21], SIFT [26], complex filters [37], moment invariants [43], and cross-correlation for different types of interest regions. We also propose an extension of the SIFT descriptor, and show that it outperforms the original method. Furthermore, we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT based descriptors perform best. Moments and steerable filters show the best performance among the low dimensional descriptors. Index Terms Local descriptors, interest points, interest regions, invariance, matching, recognition. I. INTRODUCTION Local photometric descriptors computed for interest regions have proved to be very successful in applications such as wide baseline matching [37, 42], object recognition [10, 25], texture Corresponding author is K. -
Scale Invariant Feature Transform - Scholarpedia 2015-04-21 15:04
Scale Invariant Feature Transform - Scholarpedia 2015-04-21 15:04 Scale Invariant Feature Transform +11 Recommend this on Google Tony Lindeberg (2012), Scholarpedia, 7(5):10491. doi:10.4249/scholarpedia.10491 revision #149777 [link to/cite this article] Prof. Tony Lindeberg, KTH Royal Institute of Technology, Stockholm, Sweden Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and recognition developed by David Lowe (1999, 2004). This descriptor as well as related image descriptors are used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for image matching and object recognition under real-world conditions. In its original formulation, the SIFT descriptor comprised a method for detecting interest points from a grey- level image at which statistics of local gradient directions of image intensities were accumulated to give a summarizing description of the local image structures in a local neighbourhood around each interest point, with the intention that this descriptor should be used for matching corresponding interest points between different images. Later, the SIFT descriptor has also been applied at dense grids (dense SIFT) which have been shown to lead to better performance for tasks such as object categorization, texture classification, image alignment and biometrics . The SIFT descriptor has also been extended from grey-level to colour images and from 2-D spatial images to 2+1-D spatio-temporal video. -
Sobel Operator and Canny Edge Detector ECE 480 Fall 2013 Team 4 Daniel Kim
P a g e | 1 Sobel Operator and Canny Edge Detector ECE 480 Fall 2013 Team 4 Daniel Kim Executive Summary In digital image processing (DIP), edge detection is an important subject matter. There are numerous edge detection methods such as Prewitt, Kirsch, and Robert cross. Two different types of edge detectors are explored and analyzed in this paper: Sobel operator and Canny edge detector. The performance of two edge detection methods are then compared with several input images. Keywords : Digital Image Processing, Edge Detection, Sobel Operator, Canny Edge Detector, Computer Vision, OpenCV P a g e | 2 Table of Contents 1| Introduction……………………………………………………………………………. 3 2|Sobel Operator 2.1 Background……………………………………………………………………..3 2.2 Example………………………………………………………………………...4 3|Canny Edge Detector 3.1 Background…………………………………………………………………….5 3.2 Example………………………………………………………………………...5 4|Comparison of Sobel and Canny 4.1 Discussion………………………………………………………………………6 4.2 Example………………………………………………………………………...7 5|Conclusion………………………………………………………………………............7 6|Appendix 6.1 OpenCV Sobel tutorial code…............................................................................8 6.2 OpenCV Canny tutorial code…………..…………………………....................9 7|Reference………………………………………………………………………............10 P a g e | 3 1) Introduction Edge detection is a crucial step in object recognition. It is a process of finding sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene. In short, the goal of edge detection is to produce a line drawing of the input image. The extracted features are then used by computer vision algorithms, e.g. recognition and tracking. A classical method of edge detection involves the use of operators, a two dimensional filter. An edge in an image occurs when the gradient is greatest. -
View Matching with Blob Features
View Matching with Blob Features Per-Erik Forssen´ and Anders Moe Computer Vision Laboratory Department of Electrical Engineering Linkoping¨ University, Sweden Abstract mographic transformation that is not part of the affine trans- formation. This means that our results are relevant for both This paper introduces a new region based feature for ob- wide baseline matching and 3D object recognition. ject recognition and image matching. In contrast to many A somewhat related approach to region based matching other region based features, this one makes use of colour is presented in [1], where tangents to regions are used to de- in the feature extraction stage. We perform experiments on fine linear constraints on the homography transformation, the repeatability rate of the features across scale and incli- which can then be found through linear programming. The nation angle changes, and show that avoiding to merge re- connection is that a line conic describes the set of all tan- gions connected by only a few pixels improves the repeata- gents to a region. By matching conics, we thus implicitly bility. Finally we introduce two voting schemes that allow us match the tangents of the ellipse-approximated regions. to find correspondences automatically, and compare them with respect to the number of valid correspondences they 2. Blob features give, and their inlier ratios. We will make use of blob features extracted using a clus- tering pyramid built using robust estimation in local image regions [2]. Each extracted blob is represented by its aver- 1. Introduction age colour pk,areaak, centroid mk, and inertia matrix Ik. I.e.