A Survey of Feature Extraction Techniques in Content-Based Illicit Image Detection
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Edge Detection of Noisy Images Using 2-D Discrete Wavelet Transform Venkata Ravikiran Chaganti
Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2005 Edge Detection of Noisy Images Using 2-D Discrete Wavelet Transform Venkata Ravikiran Chaganti Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY FAMU-FSU COLLEGE OF ENGINEERING EDGE DETECTION OF NOISY IMAGES USING 2-D DISCRETE WAVELET TRANSFORM BY VENKATA RAVIKIRAN CHAGANTI A thesis submitted to the Department of Electrical Engineering in partial fulfillment of the requirements for the degree of Master of Science Degree Awarded: Spring Semester, 2005 The members of the committee approve the thesis of Venkata R. Chaganti th defended on April 11 , 2005. __________________________________________ Simon Y. Foo Professor Directing Thesis __________________________________________ Anke Meyer-Baese Committee Member __________________________________________ Rodney Roberts Committee Member Approved: ________________________________________________________________________ Leonard J. Tung, Chair, Department of Electrical and Computer Engineering Ching-Jen Chen, Dean, FAMU-FSU College of Engineering The office of Graduate Studies has verified and approved the above named committee members. ii Dedicate to My Father late Dr.Rama Rao, Mother, Brother and Sister-in-law without whom this would never have been possible iii ACKNOWLEDGEMENTS I thank my thesis advisor, Dr.Simon Foo, for his help, advice and guidance during my M.S and my thesis. I also thank Dr.Anke Meyer-Baese and Dr. Rodney Roberts for serving on my thesis committee. I would like to thank my family for their constant support and encouragement during the course of my studies. I would like to acknowledge support from the Department of Electrical Engineering, FAMU-FSU College of Engineering. -
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. -
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. -
Computer Vision: Edge Detection
Edge Detection Edge detection Convert a 2D image into a set of curves • Extracts salient features of the scene • More compact than pixels Origin of Edges surface normal discontinuity depth discontinuity surface color discontinuity illumination discontinuity Edges are caused by a variety of factors Edge detection How can you tell that a pixel is on an edge? Profiles of image intensity edges Edge detection 1. Detection of short linear edge segments (edgels) 2. Aggregation of edgels into extended edges (maybe parametric description) Edgel detection • Difference operators • Parametric-model matchers Edge is Where Change Occurs Change is measured by derivative in 1D Biggest change, derivative has maximum magnitude Or 2nd derivative is zero. Image gradient The gradient of an image: The gradient points in the direction of most rapid change in intensity The gradient direction is given by: • how does this relate to the direction of the edge? The edge strength is given by the gradient magnitude The discrete gradient How can we differentiate a digital image f[x,y]? • Option 1: reconstruct a continuous image, then take gradient • Option 2: take discrete derivative (finite difference) How would you implement this as a cross-correlation? The Sobel operator Better approximations of the derivatives exist • The Sobel operators below are very commonly used -1 0 1 1 2 1 -2 0 2 0 0 0 -1 0 1 -1 -2 -1 • The standard defn. of the Sobel operator omits the 1/8 term – doesn’t make a difference for edge detection – the 1/8 term is needed to get the right gradient -
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. -
Vision Review: Image Processing
Vision Review: Image Processing Course web page: www.cis.udel.edu/~cer/arv September 17, 2002 Announcements • Homework and paper presentation guidelines are up on web page • Readings for next Tuesday: Chapters 6, 11.1, and 18 • For next Thursday: “Stochastic Road Shape Estimation” Computer Vision Review Outline • Image formation • Image processing • Motion & Estimation • Classification Outline •Images • Binary operators •Filtering – Smoothing – Edge, corner detection • Modeling, matching • Scale space Images • An image is a matrix of pixels Note: Matlab uses • Resolution – Digital cameras: 1600 X 1200 at a minimum – Video cameras: ~640 X 480 • Grayscale: generally 8 bits per pixel → Intensities in range [0…255] • RGB color: 3 8-bit color planes Image Conversion •RGB → Grayscale: Mean color value, or weight by perceptual importance (Matlab: rgb2gray) •Grayscale → Binary: Choose threshold based on histogram of image intensities (Matlab: imhist) Color Representation • RGB, HSV (hue, saturation, value), YUV, etc. • Luminance: Perceived intensity • Chrominance: Perceived color – HS(V), (Y)UV, etc. – Normalized RGB removes some illumination dependence: Binary Operations • Dilation, erosion (Matlab: imdilate, imerode) – Dilation: All 0’s next to a 1 → 1 (Enlarge foreground) – Erosion: All 1’s next to a 0 → 0 (Enlarge background) • Connected components – Uniquely label each n-connected region in binary image – 4- and 8-connectedness –Matlab: bwfill, bwselect • Moments: Region statistics – Zeroth-order: Size – First-order: Position (centroid) -
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 -
Computer Vision Based Human Detection Md Ashikur
Computer Vision Based Human Detection Md Ashikur. Rahman To cite this version: Md Ashikur. Rahman. Computer Vision Based Human Detection. International Journal of Engineer- ing and Information Systems (IJEAIS), 2017, 1 (5), pp.62 - 85. hal-01571292 HAL Id: hal-01571292 https://hal.archives-ouvertes.fr/hal-01571292 Submitted on 2 Aug 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. International Journal of Engineering and Information Systems (IJEAIS) ISSN: 2000-000X Vol. 1 Issue 5, July– 2017, Pages: 62-85 Computer Vision Based Human Detection Md. Ashikur Rahman Dept. of Computer Science and Engineering Shaikh Burhanuddin Post Graduate College Under National University, Dhaka, Bangladesh [email protected] Abstract: From still images human detection is challenging and important task for computer vision-based researchers. By detecting Human intelligence vehicles can control itself or can inform the driver using some alarming techniques. Human detection is one of the most important parts in image processing. A computer system is trained by various images and after making comparison with the input image and the database previously stored a machine can identify the human to be tested. This paper describes an approach to detect different shape of human using image processing. -
Edge Detection Low Level
Image Processing Image Processing - Computer Vision Edge Detection Low Level • Edge detection masks • Gradient Detectors Image Processing representation, compression,transmission • Second Derivative - Laplace detectors • Edge Linking image enhancement • Hough Transform edge/feature finding image "understanding" Computer Vision High Level UFO - Unidentified Flying Object Origin of Edges surface normal discontinuity depth discontinuity surface color discontinuity illumination discontinuity Edges are caused by a variety of factors 1 Edge detection Profiles of image intensity edges Step Edge Line Edge How can you tell that a pixel is on an edge? gray value gray gray value gray x x edge edge edge edge Edge Detection by Differentiation Edge detection 1D image f(x) gray value gray 1. Detection of short linear edge segments (edgels) x 2. Aggregation of edgels into extended edges 1st derivative f'(x) threshold |f'(x)| - threshold Pixels that passed the threshold are Edge Pixels 2 Image gradient The discrete gradient The gradient of an image: How can we differentiate a digital image f[x,y]? • Option 1: reconstruct a continuous image, then take gradient • Option 2: take discrete derivative (finite difference) The gradient points in the direction of most rapid change in intensity How would you implement this as a convolution? The gradient direction is given by: The edge strength is given by the gradient magnitude Effects of noise Solution: smooth first Consider a single row or column of the image • Plotting intensity as a function of position gives -
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. -
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. -
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.