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PATTERN RECOGNITION IN IMAGE PROCESSING
 
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notes-https://viden.io/knowledge/image-processing-1
Views: 9328 LearnEveryone
OpenCV Shape Recognition - Tutorial 1
 
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Here is my first video of a 3-part tutorial series teaching you how to do basic shape recognition using OpenCV Python. Facebook: https://www.facebook.com/roboticperception/ Email: [email protected]
Views: 30785 Robotic Perception
Image Processing Made Easy - MATLAB Video
 
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Explore the fundamentals of image processing with MATLAB. Download Image Processing Resource Kit: https://goo.gl/jHuo2p Get a Free MATLAB Trial: https://goo.gl/C2Y9A5 Ready to Buy: https://goo.gl/vsIeA5 Cameras are everywhere, even in your phone. You might have a new idea for using your camera in an engineering and scientific application, but have no idea where to start. While image processing can seem like a black art, there are a few key workflows to learn that will get you started. In this webinar we explore the fundamentals of image processing using MATLAB. Through several examples we will review typical workflows for: Image enhancement – removing noise and sharpening an image Image segmentation – isolating objects of interest and gathering statistics Image registration – aligning multiple images from different camera sources Previous knowledge of MATLAB is not required. About the Presenter: Andy The' holds a B.S. in Electrical Engineering from Georgia Institute of Technology and a B.A. in Business from Kennesaw State University. Before joining MathWorks, Andy spent 12 years as a field applications engineer focused on embedded processors at Texas Instruments, and 3 years as a product marketing manager for real-time software at IntervalZero.
Views: 253582 MATLAB
Shape Recognition in MATLAB (Rectangle & Circle)
 
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It is an image processing MATLAB project performed under the course of BUET-EEE212 (Numerical Technique Laboratory Sessional). This code can recognize rectangular and circular shape from a given image.
Views: 8467 Zakir Hasan
Computer Vision with MATLAB for Object Detection and Tracking
 
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Download a trial: https://goo.gl/PSa78r See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Computer vision uses images and video to detect, classify, and track objects or events in order to understand a real-world scene. In this webinar, we dive deeper into the topic of object detection and tracking. Through product demonstrations, you will see how to: Recognize objects using SURF features Detect faces and upright people with algorithms such as Viola-Jones Track single objects with the Kanade-Lucas-Tomasi (KLT) point tracking algorithm Perform Kalman Filtering to predict the location of a moving object Implement a motion-based multiple object tracking system This webinar assumes some experience with MATLAB and Image Processing Toolbox. We will focus on the Computer Vision System Toolbox. About the Presenter: Bruce Tannenbaum works on image processing and computer vision applications in technical marketing at MathWorks. Earlier in his career, he developed computer vision and wavelet-based image compression algorithms at Sarnoff Corporation (SRI). He holds an MSEE degree from University of Michigan and a BSEE degree from Penn State. View example code from this webinar here: http://www.mathworks.com/matlabcentral/fileexchange/40079
Views: 62260 MATLAB
How computers learn to recognize objects instantly | Joseph Redmon
 
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Ten years ago, researchers thought that getting a computer to tell the difference between a cat and a dog would be almost impossible. Today, computer vision systems do it with greater than 99 percent accuracy. How? Joseph Redmon works on the YOLO (You Only Look Once) system, an open-source method of object detection that can identify objects in images and video -- from zebras to stop signs -- with lightning-quick speed. In a remarkable live demo, Redmon shows off this important step forward for applications like self-driving cars, robotics and even cancer detection. Check out more TED talks: http://www.ted.com The TED Talks channel features the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and more. Follow TED on Twitter: http://www.twitter.com/TEDTalks Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: https://www.youtube.com/TED
Views: 402995 TED
Corner Detection - OpenCV with Python for Image and Video Analysis 13
 
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Welcome to a corner detection with OpenCV and Python tutorial. The purpose of detecting corners is to track things like motion, do 3D modeling, and recognize objects, shapes, and characters. sample code and text-based tutorial https://pythonprogramming.net/corner-detection-python-opencv-tutorial/ https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 43380 sentdex
Scikit Learn Machine Learning SVM Tutorial with Python p. 2 -  Example
 
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In this machine learning tutorial, we cover a very basic, yet powerful example of machine learning for image recognition. The point of this video is to get you familiar with machine learning in Python with sklearn, but also to show you that the actual machine learning part is the easy part. Playlist link: https://www.youtube.com/watch?v=URTZ2jKCgBc&list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3&index=2 The real hard part is everything else. Getting data, organizing data, labeling data, scaling data.... and more. sample code: http://pythonprogramming.net http://seaofbtc.com http://sentdex.com http://hkinsley.com https://twitter.com/sentdex Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6
Views: 209043 sentdex
Labeling of objects in an image using segmentation in Matlab
 
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We separate the objects in an image and label them to identify each individually...functions like regionprops() can be used to further extract features from these objects.
Views: 74309 rashi agrawal
Image Recognition and Python Part 1
 
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Sample code for this series: http://pythonprogramming.net/image-recognition-python/ There are many applications for image recognition. One of the largest that people are most familiar with would be facial recognition, which is the art of matching faces in pictures to identities. Image recognition goes much further, however. It can allow computers to translate written text on paper into digital text, it can help the field of machine vision, where robots and other devices can recognize people and objects. Here, our goal is to begin to use machine learning, in the form of pattern recognition, to teach our program what text looks like. In this case, we'll use numbers, but this could translate to all letters of the alphabet, words, faces, really anything at all. The more complex the image, the more complex the code will need to become. When it comes to letters and characters, it is relatively simplistic, however. How is it done? Just like any problem, especially in programming, we need to just break it down into steps, and the problem will become easily solved. Let's break it down! First, we know we want to show the program an image, and have it compare it to patterns that it knows to make an educated guess on what the current image is. This means we're going to need some "memory" of sorts, filled with examples. In the case of this tutorial, we'd like to do image recognition for the numbers zero through nine. So we'd like to be able to show it any random 2, and have it know the image to be a 2 based on the previous examples of 2's that it has seen and memorized. Next, we need to consider how we'll do this. A computer doesn't read text like we read text. We naturally put things together into a pattern, but a machine just reads the data. In the case of a picture, it reads in the image data, and displays, pixel by pixel, what it is told to display. Past that, a machine makes no attempt to decide whether it is showing a couch or a bird. So, our database of what examples are will actually be pixel information. To keep things simple, we should probably "threshold" the images. This means we store everything as black or white. In RGB code, that's a 255, 255, 255, or 0, 0, 0. That is per pixel. Sometimes there is alpha too! What we can then do is take any image, and, if the pixel coloring is say greater than 125, we could say, this is more of a "white" and convert it to 255 (the entire pixel). If it is less than 125 or equal to it, we could say this is more of a "black" and convert it to black. This might be problematic in some circumstances where we have a dark color on a darker color, usually a type of image meant to fool machines. We could have something in place instead to find the "middle" color on average for the current image, and threshold anything lighter to white and anything darker to black. This works very well for two-dimensional images of things like characters, but less well for things with shading that are meant to accompany the image, say of something like a ball. Once we've done this, all we need to do is save the string of pixel definitions for a bunch of "example" texts. We can start with a bunch of fonts, plus some hand drawn examples. There are data dumps of a bunch of examples. This is an example of "training" our data. If we have a decently sized database, then we are ready to try to compare some numbers. A good idea would be to hand-draw an example for your program to compare to. To compare, we'd just simply do the same thing to the question-image. We'd threshold the image into black or white pixels, then we take that pixel list, and compare it to all of our examples. In the end, we will have so many possible "hits." Whichever character has the most "hits" is likely to be the correct one. Done, we've recognized that image. If you think about it, this is actually very similar to how we humans recognize things. Naturally, many children do not immediately distinguish between couches and love seats. What is the difference many of them ask. There is a bit of a grey area between them, and they have many similarities. Generally, a lot of learning comes by example. After seeing hundreds of couches, thousands of chairs, and hundreds of love-seats, a person soon begins to easily distinguish between them, because they have quite a bit of sample data to compare to. This is even how we read text. A number 5 really does mean nothing to a baby. They only begin to learn what a number 5 is as they are shown it over and over, being told it is "5." Eventually, they understand that to be a 5, and they can see 5 in multiple font types and still recognize it to be a 5. Sentdex.com Facebook.com/sentdex Twitter.com/sentdex
Views: 166081 sentdex
Build a TensorFlow Image Classifier in 5 Min
 
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In this episode we're going to train our own image classifier to detect Darth Vader images. The code for this repository is here: https://github.com/llSourcell/tensorflow_image_classifier I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Challenge: The challenge for this episode is to create your own Image Classifier that would be a useful tool for scientists. Just post a clone of this repo that includes your retrained Inception Model (label it output_graph.pb). If it's too big for GitHub, just upload it to DropBox and post the link in your GitHub README. I'm going to judge all of them and the winner gets a shoutout from me in a future video, as well as a signed copy of my book 'Decentralized Applications'. This CodeLab by Google is super useful in learning this stuff: https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/?utm_campaign=chrome_series_machinelearning_063016&utm_source=gdev&utm_medium=yt-desc#0 This Tutorial by Google is also very useful: https://www.tensorflow.org/versions/r0.9/how_tos/image_retraining/index.html This is a good informational video: https://www.youtube.com/watch?v=VpDonQAKtE4 Really deep dive video on CNNs: https://www.youtube.com/watch?v=FmpDIaiMIeA I love you guys! Thanks for watching my videos and if you've found any of them useful I'd love your support on Patreon: https://www.patreon.com/user?u=3191693 Much more to come so please SUBSCRIBE, LIKE, and COMMENT! :) edit: Credit to Clarifai for the first conv net diagram in the video Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 572191 Siraj Raval
Basic shape information for object-based image analysis classification
 
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ใช้โปรมแกรม e-cog ทำ
Views: 264 Art-
Image Processing and Pattern Recognition
 
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In just a few seconds you can find out if you suffer from skin cancer, thanks to a research conducted at CICESE by Dr. Josué Álvarez in the Optics Department. En tan sólo unos segundos puedes saber si padeces cáncer de piel, gracias a una investigación realizada en el CICESE, a cargo del doctor Josué Álvarez, investigador del Departamento de Óptica.
Views: 815 CICESEciencia
Android Studio Tutorial - Analyze Image using Microsoft Cognitive Services
 
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Facebook : http://facebook.com/edmtdev Link source : http://linkshrink.net/7VJ5bg This tutorial use Microsoft Cognitive Services Vision API Analyze an image This feature returns information about visual content found in an image. Use tagging, descriptions and domain-specific models to identify content and label it with confidence. Apply the adult/racy settings to enable automated restriction of adult content. Identify image types and color schemes in pictures. android development tutorial, android programming tutorial, android app development tutorial, android tutorial for beginners , android app tutoria, android studio tutorial, learn android programming , android developer tutorial , android programming, android development, android studio tutorial for beginners, android course, android training, android development course, android app development course
Views: 16959 EDMT Dev
A friendly introduction to Convolutional Neural Networks and Image Recognition
 
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A friendly explanation of how computer recognize images, based on Convolutional Neural Networks. All the math required is knowing how to add and subtract 1's. (Bonus if you know calculus, but not needed.) For a brush up on Neural Networks, check out this video: https://www.youtube.com/watch?v=BR9h47Jtqyw
Views: 213276 Luis Serrano
Signature recognition using image processing
 
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Algorithms applied on signatures for feature extraction using image processing to verify them as authentic.
Views: 10611 Kavita kewl
Mastering Contours Detection : Computer Vision with OpenCV [Udemy]
 
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This video is a part of the Hands on Computer Vision with OpenCV & Python course on udemy.com To watch the full series and have access to the discussion forums, join this course. Use this link to get a massive discount !! https://www.udemy.com/hands-on-computer-vision-with-opencv-python/?couponCode=OPENCV15
Views: 29299 Shrobon Biswas
How to do Object Detection with OpenCV [LIVE]
 
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I'll be using OpenCV + Python to detect strawberries in an image. This will take about 45 minutes and it'll be less than 100 lines of code. Code for this video is here: https://github.com/llSourcell/Object_Detection_demo_LIVE Please subscribe! And like. And comment. That's what keeps me going. More learning resources: http://docs.opencv.org/2.4/doc/tutorials/tutorials.html https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html https://www.youtube.com/watch?v=lJYEup-0gJo https://realpython.com/blog/python/face-recognition-with-python/ https://gravityjack.com/news/opencv-python-3-homebrew/ http://www.simplecv.org/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com And please support me on Patreon!: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 146539 Siraj Raval
11.7: Computer Vision: Blob Detection - Processing Tutorial
 
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In this computer vision tutorial, I build on top of the color tracking example and demonstrate a technique known as "blob detection" to track multiple objects of the same color. Support this channel on Patreon: https://patreon.com/codingtrain Send me your questions and coding challenges! Contact: https://twitter.com/shiffman Links discussed in this video: Computer Vision for Artists and Designers by Golan Levin: http://www.flong.com/texts/essays/essay_cvad/ Image Processing in Computer Vision: http://openframeworks.cc/ofBook/chapters/image_processing_computer_vision.html Source Code for the Video Lessons: https://github.com/CodingTrain/Rainbow-Code p5.js: https://p5js.org/ Processing: https://processing.org For More Computer Vision videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6aG2RJHErXKSWFDXU4qo_ro For More Coding Challenges: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6ZiZxtDDRCi6uhfTH4FilpH Help us caption & translate this video! http://amara.org/v/QbrM/
Views: 59334 The Coding Train
Practical OpenCV 3 Image Processing with Python : Extracting Contours from Images | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2umHwNh]. In this video, we will segment binary images by extracting contours of arbitrary shapes and sizes. • Find and draw contours in a binary Image • Fit polygons to contours to approximate their shape • Use Hu moments to match contours For the latest Application development video tutorials, please visit http://bit.ly/1VACBzh Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 5312 Packt Video
DIP Lecture 25: Active shape models
 
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ECSE-4540 Intro to Digital Image Processing Rich Radke, Rensselaer Polytechnic Institute Lecture 25: Active shape models (5/11/15) 0:00:10 Deformable image registration 0:03:27 Training data: image correspondences 0:07:20 Aligning sets of image correspondences: Procrustes analysis 0:12:38 Principal component analysis (PCA) 0:17:58 PCA algorithm 0:26:46 Fitting shape models 0:31:32 Estimating PCA mode coefficients for new data 0:36:45 What points in the image should we use? 0:39:39 Active shape model algorithm 0:45:44 Example: fitting faces in images and video 0:47:38 Example: fitting organ models in medical images 0:56:22 YEEAAAAH For more info, see: T.F. Cootes, C.J. Taylor, D.H. Cooper, J. Graham Active Shape Models - Their Training and Application http://dx.doi.org/10.1006/cviu.1995.1004
Views: 15509 Rich Radke
Template Matching - OpenCV with Python for Image and Video Analysis 11
 
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Welcome to another OpenCV with Python tutorial, in this tutorial we're going to cover a fairly basic version of object recognition. The idea here is to find identical regions of an image that match a template we provide, giving a certain threshold. For exact object matches, with exact lighting/scale/angle, this can work great. An example where these conditions are usually met is just about any GUI on the computer. The buttons and such are always the same, so you can use template matching. Pair template matching with some mouse controls and you've got yourself a web-based bot! To start, you will need a main image, and a template. You should take your template from the exact "thing" you are looking for in the image. I will provide an image as an example, but feel free to use an image of your favorite website or something like that. Sample code and text-based tutorial: https://pythonprogramming.net/template-matching-python-opencv-tutorial/ https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 86213 sentdex
K-means & Image Segmentation - Computerphile
 
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K-means sorts data based on averages. Dr Mike Pound explains how it works. Fire Pong in Detail: https://youtu.be/ZoZMMg1r_Oc Deep Dream: https://youtu.be/BsSmBPmPeYQ FPS & Digital Video: https://youtu.be/yniSnYtkrwQ Dr. Mike's Code: % This script is the one mentioned during the Computerphile Image % Segmentation video. I chose matlab because it's a popular tool for % quickly prototyping things. Matlab licenses are pricey, if you don't have % one (or, like me, work for an organisation that does) try Octave as a % good free alternative. This code should work in Octave too. % Load in an input image im = imread('C:\Path\Of\Input\Image.jpg'); % In matlab, K-means operates on a 2D array, where each sample is one row, % and the features are the columns. We can use the reshape function to turn % the image into this format, where each pixel is one row, and R,G and B % are the columns. We are turning a W,H,3 image into W*H,3 % We also cast to a double array, because K-means requires it in matlab imflat = double(reshape(im, size(im,1) * size(im,2), 3)); % I specify that initialisation shuold sample points at % random, rather than anything complex like kmeans++ initialisation. % Kmeans++ takes a long time if you are using 256 classes. % Perform k-means. This function returns the class IDs assigned to each % pixel, and in this case we also want the mean values for each class - % what colour is each class. This can take a long time if the value for K % is large, I've used the sampling start strategy to speed things up. % While KMeans is running, it will show you the iteration count, and the % number of pixels that have changed class since last iteration. This % number should get lower and lower, as the means settle on appropriate % values. For large K, it's unlikely that we will ever reach zero movement % (convergence) within 150 iterations. K = 3 [kIDs, kC] = kmeans(imflat, K, 'Display', 'iter', 'MaxIter', 150, 'Start', 'sample'); % Matlab can output paletted images, that is, grayscale images where the % colours are stored in a separate array. This array is kC, and kIDs are % the grayscale indices. colormap = kC / 256; % Scale 0-1, since this is what matlab wants % Reshape kIDs back into the original image shape imout = reshape(uint8(kIDs), size(im,1), size(im,2)); % Save file out, you need to subtract 1 from the image classes, since once % stored in the file the values should go from 0-255, not 1-256 like matlab % would do. imwrite(imout - 1, colormap, 'C:\Path\Of\Output\Image.png'); http://www.facebook.com/computerphile https://twitter.com/computer_phile This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: http://bit.ly/nottscomputer Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com
Views: 160059 Computerphile
Finding Circles in Images Using MATLAB
 
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Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Use a MATLAB app designed to help you easily detect circles in images. For more on Circle Finder, visit: https://www.mathworks.com/matlabcentral/fileexchange/34365-circle-finder Circular Hough transforms detect circles in images. There are several parameters which can be manipulated to enhance the performance of the imfindcircles functionality. This video describes the use of the Circle Finder app, which provides an interactive environment for changing parameters and options, and can be used to immediately visualize the effects of the calculation
Views: 19408 MATLAB
Image Analysis: Evaluating Particle Shape
 
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Dr. Jeff Bodycomb of HORIBA Scientific (www.horiba.com/particle) discusses how image analysis technologies can be used to measure particle shape parameters.
Views: 1293 HORIBA Scientific
Image Analysis - SSY095 - Aircraft recognition using Fourier descriptors
 
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Video used to test the shape recognition algorithm produced in a 30 hours project.
Views: 223 Frejjan
Object Detection and Recogition
 
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http://www.willowgarage.com/blog/2010/09/20/scalable-object-recognition
Views: 214947 WillowGaragevideo
Drawing and Writing on Image - OpenCV with Python for Image and Video Analysis 3
 
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In this OpenCV with Python tutorial, we're going to be covering how to draw various shapes on your images and videos. It's fairly common to want to mark detected objects in some way, so we the humans can easily see if our programs are working as we might hope. Text-based tutorial and sample code: https://pythonprogramming.net/drawing-writing-python-opencv-tutorial/ http://pythonprogramming.net https://twitter.com/sentdex
Views: 99317 sentdex
Image Moments
 
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This video is part of the Udacity course "Introduction to Computer Vision". Watch the full course at https://www.udacity.com/course/ud810
Views: 12308 Udacity
Image Processing in C# Basic Program
 
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Converting an image into gray image in C# language. It is very easy and follow whatever i did in this video. If you need this code then leave your email down below! Please hit like and subscribe me if this video is helpful for you!
Views: 45688 Sangeeta Sunchu
Feature Extraction in 2D color Images (Concept of Search by Image) || Gridowit
 
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Tags for this Video: search by image, content based image search, content based image retrieval, CBIR, Feature extraction of an image, Multimedia Information Retrieval, working of google search by Image, Generic Multimedia Object Indexing Approach, GEMINI approach Multimedia Information Retrieval Information Retrieval and its management how search by image works
Views: 10599 GridoWit
How we teach computers to understand pictures | Fei Fei Li
 
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When a very young child looks at a picture, she can identify simple elements: "cat," "book," "chair." Now, computers are getting smart enough to do that too. What's next? In a thrilling talk, computer vision expert Fei-Fei Li describes the state of the art — including the database of 15 million photos her team built to "teach" a computer to understand pictures — and the key insights yet to come. TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes (or less). Look for talks on Technology, Entertainment and Design -- plus science, business, global issues, the arts and much more. Find closed captions and translated subtitles in many languages at http://www.ted.com/translate Follow TED news on Twitter: http://www.twitter.com/tednews Like TED on Facebook: https://www.facebook.com/TED Subscribe to our channel: http://www.youtube.com/user/TEDtalksDirector
Views: 661668 TED
Image Analysis, Edge Detection (Canny) - OpenCV for Python Tutorial 03.1
 
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Image Analysis, Edge Detection (Canny) - OpenCV for Python Tutorial 03 Source code: http://adf.ly/14455699/pythonedgedetection Document: http://adf.ly/14455699/opencv-edgedetection-canny OpenCV Projects & Tutorials for Python [Tutorial 1]: Install Python OpenCV library for Visual Studio [Tutorial 2]: Create python project in Visual Studio and use OpenCV library [Tutorial 3]: OpenCV Python - Image Analysis, Edge Detection (Sobel, Scharr, Laplacian) [Tutorial 3.1]: OpenCV Python - Image Analysis, Edge Detection (Canny) [Tutorial 4]: OpenCV Python - Multi Objects tracking [Tutorial 5]: OpenCV Python - Motion objects tracking [Tutorial 6]: OpenCV Python - Face recognition [Tutorial 7]: OpenCV Python - Car license recognition [Tutorial 8]: OpenCV Python - Hand gesture [Tutorial 9]: OpenCV Python - Logo recognition ------------------------------------------------------------------------------------------------------ Blog: http://jackyle.com (English) | http://jackyle.xyz (Vietnamese)
Views: 1764 Jacky Le
Object detection tracking and counting using image processing
 
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This video aims to show how moving objects can be detected, tracked and counted using image processing. This video is a real time application where the scene is acquired by a webcam placed above the scene. The video shows also the accuracy of the developed system for counting objects and the speed which is related to this processing time per frame that is around 7.5 - 8.0 ms.
Views: 3825 SniPer
Detecting and Recognizing Text in Natural Images
 
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Text in natural images possesses rich information for image understanding. Detecting and recognizing text facilitates many important applications. From a computer vision perspective, text is a structured object made of characters arranged in a line or curve. The unique characteristics of text makes its detection and recognition problems different than that of general objects. In the first part of this talk, I will introduce our recent work on text detection, where we decompose long text into smaller segments and the links between them. A fully-convolutional neural network model is proposed to detect both segments and links at different scales in a single forward pass. In the second part, I will introduce our work on text recognition, where we tackle the structural recognition problem with an end-to-end neural network that outputs character sequences from image pixels. We further incorporate a learnable spatial transformer into this network, in order to handle text of irregular shape with robustness.  See more at https://www.microsoft.com/en-us/research/video/detecting-and-recognizing-text-in-natural-images/
Views: 6123 Microsoft Research
Image processing    Edge detection
 
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Image processing Edge detection
Views: 117 Seba Ja
3D Shape Analysis for Object Recognition: Visualisations
 
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Music: Rendezvous Park - down - part I http://creativecommons.org/licenses/by-nc-sa/1.0/fi/deed.en Quadros, A., Underwood, J.P. & Douillard, B. 'An occlusion-aware feature for range images' In Proceedings of the 2012 IEEE International Conference on Robotics and Automation, pp. 4428-4435 http://db.acfr.usyd.edu.au/content.php/237.html?publicationid=856
Views: 954 unisydneyacfr
Morphological Transformations - OpenCV with Python for Image and Video Analysis 9
 
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In this OpenCV with Python tutorial, we're going to cover Morphological Transformations. These are some simple operations that we can perform based on the image's shape. These tend to come in pairs. The first pair we're going to talk about is Erosion and Dilation. Erosion is where we will "erode" the edges. The way these work is we work with a slider (kernel). We give the slider a size, let's say 5 x 5 pixels. What happens is we slide this slider around, and if all of the pixels are white, then we get white, otherwise black. This may help eliminate some white noise. The other version of this is Dilation, which basically does the opposite: Slides around, if the entire area isn't black, then it is converted to white. Sample code and text-based tutorial: https://pythonprogramming.net/morphological-transformation-python-opencv-tutorial/ https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 51057 sentdex
ViNotion object detection from moving vehicle (car detection)
 
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Goal: detect objects in video - With specific shape (e.g. cars) - From moving vehicle (moving camera) ViNotion Object Detection - Robust to changes in scene, lighting - Detect object from different viewpoints - Detection in each video image Application - Detect different shapes, e.g. cars, persons, faces, ships, planes - Detection in single picture, or - Detection in motion video Traffic Monitoring - Detect traffic jams, stopping vehicles - Statistics (speed, flow, counter) - Robust tunnel surveillance Mobile Surveillance - Detection from moving vehicle - Mount on car, plane, ship - Police, ambulance, military Automotive - Detect cars, persons around vehicle - Monitor their distance, speed - Safety: automatic braking More information is available from our website: http://www.vinotion.nl
Views: 59167 vinotionNL
shape recognition
 
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Views: 9744 Md.khaled hossain
Lecture 3   Accessing image pixels and planes   OpenCV Python
 
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Accessing the pixels in an image, planes in an image and computing the size and shape of the image.
Views: 569 Notes2Learn
Recognizing shape and dimensions of the test object
 
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1. Initializing position of electrovalves (using stepper motors) 2. Initializing cameras 3. Image analysis 4. Calculations 5. Changing position of electrovalves over tested object 6. Pouring the liquid
Views: 35 Antdesk.com
shape detection
 
02:04
Views: 2572 Beril Sirmacek
ASL2TXT - Sign Language Gesture Recognition - Morphological Processing and Fourier Descriptors
 
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Playing around with image processing techniques to identify sign language gestures. Included: morphological processing and Fourier descriptors. Matlab code available at secs.oakland.edu/~gpcorser
Views: 2860 oaklandcse
Image Segmentation and Shape Analysis for Road-Sign Detection Matlab Project
 
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Project Link : http://kasanpro.com/p/matlab/image-segmentation-shape-analysis-road-sign-detection , Title :Image Segmentation and Shape Analysis for Road-Sign Detection
Views: 1647 kasanpro
OpenCV Face Detection with Raspberry Pi - Robotics with Python p.7
 
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Next, we're going to touch on using OpenCV with the Raspberry Pi's camera, giving our robot the gift of sight. There are many steps involved to this process, so there's a lot that is about to be thrown your way. If at any point you're stuck/lost/whatever, feel free to ask questions on the video and I will try to help where possible. There are a lot of moving parts here. If all else fails, I have hosted my Raspberry Pi image: https://drive.google.com/file/d/0B11p78NlrG-vZzdJLWYxcU5iMXM/view?usp=sharing OpenCV stands for Open Computer Vision, and it is an open source computer vision and machine learning library. To start, you will need to get OpenCV on to your Raspberry Pi. http://mitchtech.net/raspberry-pi-opencv/ Keep in mind, the "make" part of this tutorial will take 9-10 hours on a Raspberry Pi Model B+. The Raspberry Pi 2 will do it in more like 2-4 hours. Either way, it will take a while. I just did it overnight one night. Text-based version and sample code: http://pythonprogramming.net/raspberry-pi-camera-opencv-face-detection-tutorial/ http://pythonprogramming.net https://twitter.com/sentdex
Views: 291859 sentdex
Image Analysis, Color Object Tracking (cvtColor, inRange) - OpenCV for Python Tutorial 03.2
 
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Image Analysis, Color Object Tracking (cvtColor, inRange) - OpenCV for Python Tutorial 03.2 In this tutorial,We will learn how to convert images from one color-space to another, like BGR  Gray, BGR  HSV In addition to that, we will create an application which extracts a colored object in a video Source code: http://adf.ly/14455699/pythorcolorobjecttracking Document: http://adf.ly/14455699/opencv-changingcolorspaces OpenCV Projects & Tutorials for Python [Tutorial 1]: Install Python OpenCV library for Visual Studio [Tutorial 2]: Create python project in Visual Studio and use OpenCV library [Tutorial 3]: OpenCV Python - Image Analysis, Edge Detection (Sobel, Scharr, Laplacian) [Tutorial 3.1]: OpenCV Python - Image Analysis, Edge Detection (Canny) [Tutorial 3.2]: OpenCV Python - Image Analysis, Color Object Tracking [Tutorial 4]: OpenCV Python - Multi Objects tracking [Tutorial 5]: OpenCV Python - Motion objects tracking [Tutorial 6]: OpenCV Python - Face recognition [Tutorial 7]: OpenCV Python - Car license recognition [Tutorial 8]: OpenCV Python - Hand gesture [Tutorial 9]: OpenCV Python - Logo recognition ------------------------------------------------------------------------------------------------------ Blog: http://jackyle.com (English) | http://jackyle.xyz (Vietnamese)
Views: 1296 Jacky Le

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