Improved Algorithms for Incremental Self-Calibrated Reconstruction from Video
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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 -
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. -
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. -
1 Introduction
1: Introduction Ahmad Humayun 1 Introduction z A pixel is not a square (or rectangular). It is simply a point sample. There are cases where the contributions to a pixel can be modeled, in a low order way, by a little square, but not ever the pixel itself. The sampling theorem tells us that we can reconstruct a continuous entity from such a discrete entity using an appropriate reconstruction filter - for example a truncated Gaussian. z Cameras approximated by pinhole cameras. When pinhole too big - many directions averaged to blur the image. When pinhole too small - diffraction effects blur the image. z A lens system is there in a camera to correct for the many optical aberrations - they are basically aberrations when light from one point of an object does not converge into a single point after passing through the lens system. Optical aberrations fall into 2 categories: monochromatic (caused by the geometry of the lens and occur both when light is reflected and when it is refracted - like pincushion etc.); and chromatic (Chromatic aberrations are caused by dispersion, the variation of a lens's refractive index with wavelength). Lenses are essentially help remove the shortfalls of a pinhole camera i.e. how to admit more light and increase the resolution of the imaging system at the same time. With a simple lens, much more light can be bought into sharp focus. Different problems with lens systems: 1. Spherical aberration - A perfect lens focuses all incoming rays to a point on the optic axis. A real lens with spherical surfaces suffers from spherical aberration: it focuses rays more tightly if they enter it far from the optic axis than if they enter closer to the axis. -
Study and Comparison of Different Edge Detectors for Image
Global Journal of Computer Science and Technology Graphics & Vision Volume 12 Issue 13 Version 1.0 Year 2012 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172 & Print ISSN: 0975-4350 Study and Comparison of Different Edge Detectors for Image Segmentation By Pinaki Pratim Acharjya, Ritaban Das & Dibyendu Ghoshal Bengal Institute of Technology and Management Santiniketan, West Bengal, India Abstract - Edge detection is very important terminology in image processing and for computer vision. Edge detection is in the forefront of image processing for object detection, so it is crucial to have a good understanding of edge detection operators. In the present study, comparative analyses of different edge detection operators in image processing are presented. It has been observed from the present study that the performance of canny edge detection operator is much better then Sobel, Roberts, Prewitt, Zero crossing and LoG (Laplacian of Gaussian) in respect to the image appearance and object boundary localization. The software tool that has been used is MATLAB. Keywords : Edge Detection, Digital Image Processing, Image segmentation. GJCST-F Classification : I.4.6 Study and Comparison of Different Edge Detectors for Image Segmentation Strictly as per the compliance and regulations of: © 2012. Pinaki Pratim Acharjya, Ritaban Das & Dibyendu Ghoshal. 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. Study and Comparison of Different Edge Detectors for Image Segmentation Pinaki Pratim Acharjya α, Ritaban Das σ & Dibyendu Ghoshal ρ Abstract - Edge detection is very important terminology in noise the Canny edge detection [12-14] operator has image processing and for computer vision. -
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 -
Detection and Evaluation Methods for Local Image and Video Features Julian Stottinger¨
Technical Report CVL-TR-4 Detection and Evaluation Methods for Local Image and Video Features Julian Stottinger¨ Computer Vision Lab Institute of Computer Aided Automation Vienna University of Technology 2. Marz¨ 2011 Abstract In computer vision, local image descriptors computed in areas around salient interest points are the state-of-the-art in visual matching. This technical report aims at finding more stable and more informative interest points in the domain of images and videos. The research interest is the development of relevant evaluation methods for visual mat- ching approaches. The contribution of this work lies on one hand in the introduction of new features to the computer vision community. On the other hand, there is a strong demand for valid evaluation methods and approaches gaining new insights for general recognition tasks. This work presents research in the detection of local features both in the spatial (“2D” or image) domain as well for spatio-temporal (“3D” or video) features. For state-of-the-art classification the extraction of discriminative interest points has an impact on the final classification performance. It is crucial to find which interest points are of use in a specific task. One question is for example whether it is possible to reduce the number of interest points extracted while still obtaining state-of-the-art image retrieval or object recognition results. This would gain a significant reduction in processing time and would possibly allow for new applications e.g. in the domain of mobile computing. Therefore, the work investigates different corner detection approaches and evaluates their repeatability under varying alterations. -
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) -
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. -
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. -
Comparison of Canny and Sobel Edge Detection in MRI Images
Comparison of Canny and Sobel Edge Detection in MRI Images Zolqernine Othman Habibollah Haron Mohammed Rafiq Abdul Kadir Faculty of Computer Science Faculty of Computer Science Biomechanics & Tissue Engineering Group, and Information System, and Information System, Faculty of Biomedical Engineering Universiti Teknologi Malaysia, Universiti Teknologi Malaysia, and Health Science, 81310 UTM Skudai, Malaysia. 81310 UTM Skudai, Malaysia. Universiti Teknologi Malaysia, [email protected] [email protected] 81310 UTM Skudai, Malaysia. [email protected] ABSTRACT Feature extraction approach in medical magnetic resonance detection method of MRI image of knee, the most widely imaging (MRI) is very important in order to perform used edge detection algorithms [5]. The Sobel operator diagnostic image analysis [1]. Edge detection is one of the performs a 2-D spatial gradient measurement on an image way to extract more information from magnetic resonance and so emphasizes regions of high spatial frequency that images. Edge detection reduces the amount of data and correspond to edges. Typically it is used to find the filters out useless information, while protecting the approximate absolute gradient magnitude at each point in an important structural properties in an image [2]. In this paper, input grayscale image. Canny edge detector uses a filter we compare Sobel and Canny edge detection method. In based on the first derivative of a Gaussian, it is susceptible order to compare between them, one slice of MRI image to noise present on raw unprocessed image data, so to begin tested with both method. Both method of the edge detection with, the raw image is convolved with a Gaussian filter.