...Learning OpenCV Gary Bradski and Adrian Kaehler Beijing · Cambridge · Farnham · Köln · Sebastopol · Taipei · Tokyo Learning OpenCV by Gary Bradski and Adrian Kaehler Copyright © 2008 Gary Bradski and Adrian Kaehler. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (safari.oreilly.com). For more information, contact our corporate/institutional sales department: (800) 998-9938 or corporate@oreilly.com. Editor: Mike Loukides Production Editor: Rachel Monaghan Production Services: Newgen Publishing and Data Services Cover Designer: Karen Montgomery Interior Designer: David Futato Illustrator: Robert Romano Printing History: September 2008: First Edition. Nutshell Handbook, the Nutshell Handbook logo, and the O’Reilly logo are registered trademarks of O’Reilly Media, Inc. Learning OpenCV, the image of a giant peacock moth, and related trade dress are trademarks of O’Reilly Media, Inc. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and O’Reilly Media, Inc. was aware of a trademark claim, the designations have been printed in caps or initial caps. While every precaution has been taken in the preparation of this...
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...Neural Networks for Matching in Computer Vision Giansalvo Cirrincione1 and Maurizio Cirrincione2 Department of Electrical Engineering, Lab. CREA University of Picardie-Jules Verne 33, rue Saint Leu, 80039 Amiens - France exin@u-picardie.fr Universite de Technologie de Belfort-Montbeliard (UTBM) Rue Thierry MIEG, Belfort Cedex 90010, France maurizio.cirricione@utbm.fr 1 2 Abstract. A very important problem in computer vision is the matching of features extracted from pairs of images. At this proposal, a new neural network, the Double Asynchronous Competitor (DAC) is presented. It exploits the self-organization for solving the matching as a pattern recognition problem. As a consequence, a set of attributes is required for each image feature. The network is able to find the variety of the input space. DAC exploits two intercoupled neural networks and outputs the matches together with the occlusion maps of the pair of frames taken in consideration. DAC can also solve other matching problems. 1 Introduction In computer vision, structure from motion (SFM) algorithms recover the motion and scene parameters by using a sequence of images (very often only a pair of images is needed). Several SFM techniques require the extraction of features (corners, lines and so on) from each frame. Then, it is necessary to find certain types of correspondences between images, i.e. to identify the image elements in different frames that correspond to the same element in the scene. This paper...
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...T.C. BAHÇEŞEHİR UNIVERSITY VISION BASED TARGET TRACKING CONTROLLED SENTRY Capstone Project Fikret Taygun Duvan İSTANBUL, 2011 T.C. BAHÇEŞEHİR UNIVERSITY FACULTY OF ENGINEERING DEPARTMENT OF MECATRONICS ENGINEERING VISION BASED TARGET TRACKING CONTROLLED SENTRY Capstone Project Fikret Taygun Duvan Advisor: Dr. Khalid Abidi İSTANBUL, 2010 T.C. BAHÇEŞEHİR UNIVERSITY FACULTY OF ENGINEERING DEPARTMENT OF MECATRONICS ENGINEERING Name of the project: Vision Based Target Tracking Controlled Sentry Name/Last Name of the Student: Fikret Taygun Duvan Date of Thesis Defense: 23/01/2011 I hereby state that the graduation project prepared by Your Name (Title Format) has been completed under my supervision. I accept this work as a “Graduation Project”. Dr. Khalid ABIDI I hereby state that I have examined this graduation project by Your Name (Title Format) which is accepted by his supervisor. This work is acceptable as a graduation project and the student is eligible to take the graduation project examination. Asst. Prof. Yalçın Çekiç Head of the Department of Mechatronics Engineering We hereby state that we have held the graduation examination of Your Name and agree that the student has satisfied all requirements. THE EXAMINATION COMMITTEE Committee Member 1. Khalid ABIDI 2. ………………………….. 3. ………………………….. Signature ……………………….. ……………………….. ……………………….. ACADEMIC HONESTY PLEDGE In keeping with Bahçeşehir University Student Code of...
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...Driverless cars – is it safe to take the human out of the loop? From the beginning of the world humans always wanted to make their life easier. They produced more and more complicated tools for hunting, then for harvesting and other branches of human activity – these tools allowed them to make their job without using their bare hands. Time was passing, humanity was evolving newer technologies, people knew more and more about the world and laws that rule it. But one thing that every human-being wanted the most was faster way of travelling. The first step was inviting wheel. Later humans built carts, coaches and many other vehicles propelled with animal’s or person’s strength. But still it was too slow and too less automatic for people. The change came in 1769, when Nicolas-Joseph Cugnot presented his steam vehicle. Then everything started to evolve faster – and we step to nowadays, where we have many different types of cars, even the ones fueled by water or electricity. In our century, where artificial intelligence isn’t a Sci-Fi subject only but also reality, people started to think about using it in car’s safety and driving system’s development. One of the results is Google’s self-driving car, which is travelling in states of Nevada, Florida and California, where autonomous vehicles are already legalized. This technology is evolving all the time and maybe there will come the day where whole world will be able to use such kind of cars. But there comes the question – is it safe...
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...Edge detection using Fuzzy Logic and Automata Theory Title Page By Takkar Mohit Supervisor A Thesis Submitted to In Partial Fulfillment of the Requirements for the Degree of Master of Engineering in Electronics & Communication December 2014 . Table of Contents Title Page i CERTIFICATE ii COMPLIANCE CERTIFICATE iii THESIS APPROVAL CERTIFICATE iv DECLARATION OF ORIGINALITY v Acknowledgment vi Table of Contents vii List of Figures x Abstract xiii Chapter 1 Introduction 1 1.1 Edge Detection: Analysis 3 1.1.1 Fuzzy Logic in Image Processing 4 1.1.2 Fuzzy Logic for Edge Detection 5 1.1.3 Cellular Learning Automata 6 Chapter 2 Literature Review 7 2.1 Edge Detection: Methodology 7 2.1.1 First Order Derivative Edge Detection 7 2.1.1.1 Prewitts Operator 7 2.1.1.2 [pic] Sobel Operator 8 2.1.1.3 Roberts Cross Operator 11 2.1.1.4 Threshold Selection 11 2.1.2 Second Order Derivative Edge Detection 11 2.1.2.1 Marr-Hildreth Edge Detector 11 2.1.2.2 Canny Edge Detector 12 2.1.3 Soft Computing Approaches to Edge Detection 13 2.1.3.1 Fuzzy Based Approach 14 2.1.3.2 Genetic Algorithm Approach 14 2.1.4 Cellular Learning Automata 15 Chapter 3 Fuzzy Image Processing 18 3.1 Need for Fuzzy Image Processing 19 3.2 Introduction to Fuzzy sets and Crisp sets 20 3.2.1 Classical sets (Crisp sets) 20 3.2.2 Fuzzy sets 21 3.3 Fuzzification 22 3.4 Membership Value Assignment 22 3.5...
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...Lockett, Roblee Page 1 of 48 6/3/2003 GENETIC ALGORITHM BASED DESIGN AND IMPLEMENTATION OF MULTIPLIERLESS TWODIMENSIONAL IMAGE FILTERS by Douglas J. Lockett and Christopher D. Roblee ********* Senior Capstone Design Project Submitted in partial fulfillment of the requirements for the degree of Bachelor of Science Department of Electrical and Computer Engineering Union College Steinmetz Hall Schenectady, New York 12308 U.S.A. Submitted May 30, 2003 Final Project Report Senior Capstone Design Project, Department of Electrical and Computer Engineering Union College, 2003. © 2003 Douglas Lockett, Christopher Roblee Lockett, Roblee Page 2 of 48 6/3/2003 Table of Contents: Abstract……………………………………………………………………………….3 1. Introduction…………………………………………………………………………..4 2. Theory of Multiplierless Arithmetic………………………………………………...5 3. Image Filters 3.1. Motivations for IIR vs. FIR……………………………………………………....7 3.2. Edge Detection …………………………………………………………………..8 3.3. Canny Edge Detection……………………………………………………………9 4. Genetic Algorithms 4.1. Motivations……………………………………………………………………...10 4.2. Basic Theory…………………………………………………………………….10 4.3. Description of the Designed Genetic Algorithm………………………………..13 4.3.1. Fitness Function Definition and Crossover Selection…………………...17 4.3.2. Magnitude Response and Relative Error………………………………...19 4.3.3. GA Parameters…………………………………………………………...19 5. Results 5.1. Magnitude Frequency Analysis ……………………………………………...…21 5.2. Spatial Analysis…………………………………………………………………24...
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...Robotics 104-1 Final project: A self-driving mobile platform Lou, Seddik, Benjamin Derville and Chun-Kan Gary Chow Abstract— In this paper, we present our work on building an autonomous car. We first describe our choosen approach and the reason behind this choice, we then describe what we manage to implement and then we finish by reviewing our work done. I. INTRODUCTION Autonomous driving is maybe the most popular current project in machine learning, and especially deep learning. Billions of dollars are assigned to create self-driving car. Google lead the movement with their Google Car created from their lab Google X, followed by Tesla who manage to quickly catch up with their big fleet of cars driving on the roads at each instant. Uber just created a research center for this work, GM partner with Lyft, George Hotz, an americain car made his own home-made simple self-driving car and even Nvidea created a demo. In this sense, this work is not really original or innovative. We however thought this work was not yet popularized to students projects. This works is also interesting because it’s not ’achieved’ even by the current state-of-the-art, there are still lots of works to be done. II. T HEORY will be very hard to distinguish between them and not useful for a long-term solution. Additionally we would encounter a major problem when defining the color and shape of a car. So instead we design an machine-learning algorithm which uses a similar approach...
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...Essay A driverless car, also known as a self-driving car is an autonomous car which can perform the actions of the human being, as if a man were driving a traditional car. We can say that the car is independent of the human as the car only needs to be programmed with the destination. The mechanical part of the vehicle is held by the car its own. Moreover, to function, the car has some specific technology, for example laser, radar, GPS and computer vision. An example of an approved case of self-driving cars can be Google’s. In 2011 the state of Nevada was the first jurisdiction in the United States to pass a law concerning the operation of driverless cars. This law was turned into effect by March 2012 and the Nevada Department of Motor Vehicles gave the first license for a self-driven car in May 2012. This license was given to Google’s car which was in this case a Toyota Prius. Google got involved with this issue as it is trying to develop technology for driverless vehicles. In addition, the project is currently being led by Google engineer Sebastian Thrun, director of the Stanford Artificial Intelligence Laboratory and the co-inventor of Google Street View. To develop this system, Google also had to hire 15 engineers. This topic about self-driving cars involves also other issues such as hardware and software, social and ethical issues and the social impact. As regards hardware and software involved, it integrates Google Maps with various hardware sensors and artificial...
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...EEN 538: DIGITAL IMAGE PROCESSING Optical Character Recognition (OCR) using binary image processing with MATLAB Abstract- Nowadays, Optical Recognition is becoming a very important tool in several fields: medicine, physics, cosmology, traffic (plate numbers), etc. We can also use this to recognize character for example to digitalize a book. We will talk about this last topic in this report: Optical Character Recognition (OCR). I. INTRODUCTION Once we have the b&w image we can start the segmentation process. To do that we can use the function “bwconncomp”. This function returns us a struct from where we can obtain the characters because it gives us all the connected components. Thus, we can use it to get all the character even if they have 2 or 3 objects. This function returns us the pixels of the connected components (characters) but we have to figure out from those, the coordinates of the character in the original matrix (row and columns). To do this, we will obtain the centroid of every connected component and from it and using the first and last pixel detected of the connect component, we can figure out the exact coordinates of the image. The idea is as follows: Firstly, we can to convert the number that the function returns us to a column and a row. We can do this using the total rows of the original image. Once we have the first and last pixel detected of the connect component in (row, column) we can figure out directly the x-coordinates of the character in the image...
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...Different Change Detection Techniques Table of Contents Introduction...................................................3 Digital Change Detection Process...............................4 Description of the most commonly used change detection methods.5 I. Post-Classification Comparison..........................5 II. Direct Classification...................................6 III. Principal Component Analysis (PCA)......................6 IV. Image Differencing......................................8 V. Change Vector Analysis (CVA)............................9 Relative accuracy of the most commonly used change detection methods........................................................9 I. Post-Classification Comparison.........................10 II. Direct Classification..................................11 III. Principal Component Analysis (PCA).....................11 IV. Image Differencing.....................................12 V. Change Vector Analysis (CVA) Conclusion....................................................14 References....................................................15 Introduction Remote sensing change detection has been defined as the process of identifying change in the state of an object or phenomena through the detection of differences between two or more sets of images taken of the same area on different dates (Wang, 1993). The underlying assumption is that changes on the ground cause significant changes...
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...As the number of people using computers increases, so is the increase in number of people, who suffer from symptoms, which are commonly known as Computer Vision Syndrome (CVS). This condition results from focusing eyes on a computer or other display device for long and uninterrupted period. The symptoms include headaches, blurred vision, fatigue, eyestrain, dry eyes, irritated eyes, double vision, dizziness, and difficulty in refocusing the eyes. These improper lighting conditions (i.e. glare or bright overhead lighting) or air moving past the eyes (e.g. overhead vents, direct air from a fan, etc.) [1] further aggravate these symptoms. Blinking helps to spread the tear film and moisten the surface of the eye, otherwise reduced rate of blinking leads to Dry Eye. Typical complaints experienced by intensive computing work (more than 3 hours per day) include dryness, redness, burning, sandy-gritty eye irritation or sensitivity to light and eye fatigue [2]. According to the National Institute for Occupational Safety and Health, computer vision syndrome affects about 90% of the people who spend three hours or more a day at a...
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...ISSN: 2277-3754 ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 7, January 2013 Recognition for Handwritten English Letters: A Review Nisha Sharma, Tushar Patnaik, Bhupendra Kumar Abstract -- Character recognition is one of the most interesting and challenging research areas in the field of Image processing. English character recognition has been extensively studied in the last half century. Nowadays different methodologies are in widespread use for character recognition. Document verification, digital library, reading bank deposit slips, reading postal addresses, extracting information from cheques, data entry, applications for credit cards, health insurance, loans, tax forms etc. are application areas of digital document processing. This paper gives an overview of research work carried out for recognition of hand written English letters. In Hand written text there is no constraint on the writing style. Hand written letters are difficult to recognize due to diverse human handwriting style, variation in angle, size and shape of letters. Various approaches of hand written character recognition are discussed here along with their performance. Fig 1.Major Steps of an OCR System Index Terms— Offline Hand written Character Recognition, Pre-Processing, Feature Extraction, Classification, Post Processing. I. INTRODUCTION Optical Character Recognition (OCR) is one of the most fascinating and challenging areas of pattern...
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...CHANGE MANAGEMENT MENTOR GRAPHICS THE ROLE OF VISION ORGB 2605 Group 5 Taylor Nicholson - A00839192 Courtenay Anderson - A00665468 Wendy McLeod - A00545182 Ana Cielo Tanjuaquio - A00748789 When Gerald Langelar first imagined his “Vision” for Mentor Graphics he failed to remember that, “It is imperative that change is aligned with a clear vision and business strategy and that subsequent activities and interventions are coordinated and consistent.” (Victor and Franckeiss pg. 249) 1. Mentor Graphics used vision several different ways over the years. Their initial vision of “Build something people will buy” was, as stated in the text, an unarticulated vision. The vision of the company is meant to be inspiring, motivating, and resonate emotion. Mentor Graphics was thought to have missed the mark with this by creating a far too simple vision. The initial intentions were good but the process in the creation of the subsequent visions, which were primarily good, however, as time passed, became vague, confusing, and ultimately the vision returned to the original vision of getting back to the core principles of the organization. 2. The multiple visions both strengthened and weakened the company during the process. The visions strengthened the company by being able to connect the vision to the organization's inner voice. This was true of the original vision, however the two of visions; the “10X Imperative” and “Changing the Way the World Designs Together” were...
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...1 The CASE Journal Volume 4, Issue 2 (Spring 2008) Table of Contents Click on the article or case title to go to that page Editorial Policy Letter from the Editor Case Abstracts Cases “Sally’s Dilemma: Making Tough Choices in Collaborative Visioning” Karl A. Hickerson, David J. O’Connell & Arun K. Pillutla, St. Ambrose University “The Death of a Salesman Revisited: Part A” Herbert Sherman, Long Island University & Daniel James Rowley, University of Northern Colorado “The Death of a Salesman Revisited: Part B” Herbert Sherman, Long Island University & Daniel James Rowley, University of Northern Colorado “Customer Service at the Jewish Community Center” Edward Demarais, Salem State College, Sandra Sheckman, & Gina Vega, Salem State “The Frozen Production Line” Anton Massman, U.S. Air Force, Elaine Davis & Janell M. Kurtz, St. Cloud State University “Dow Chemical and Agent Orange in Vietnam” Cedric Dawkins, California State Polytechnic University – Pomona Membership Form Page 2 The CASE Journal Volume 4, Issue 2 (Spring 2008) EDITORIAL POLICY The audience for this journal includes both practitioners and academics and thus encourages submissions from a broad range of individuals. The CASE Journal invites submissions of cases designed for classroom use. Cases from all business disciplines will be considered. Cases must be factual, and releases must be available where necessary. All cases must be accompanied by an instructor’s manual ...
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...como podemos darnos cuenta en que áreas podemos crecer o mejorar para hacer más fuertes nuestras fortalezas y defendernos de las debilidades. Lo que mas me gusto de la conferencia fue que compartió experiencias reales, y anécdotas que le han pasado durante su estancia en ese trabajo, ya que realmente podemos saber a que nos enfrentaremos, en este caso, si llegamos a trabajar en una consultoría. También me gusto que fuera directamente al grano, y explico de manera breve pero muy clara todo lo que se requería, además de que respondió varias dudas de varios alumnos. Como ya mencioné, me parece muy interesante ver como un Ingeniero Industrial puede dedicarse a varias cosas, y algunas muy distintas entre ellas, por lo cual creo nos da una visión hacía el futuro muy amplia para ver hacia donde queremos dirigirnos. Otra cosa que me gusto fueron los diferentes tips para las entrevistas que dio, ya que creo es algo que todos podremos utilizar, cuando sea necesario. Realmente yo no le cambiaría nada, ya que realmente cumplió con explicarnos todo lo que el realiza, así como la empresa donde trabaja y sobre los distintos proyectos en los que ha sido...
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