plagatux.es
Review of OpenCV 2 Computer Vision Application Programming Cookbook | La plaga Tux
http://plagatux.es/2011/06/review-of-opencv-2-computer-vision-application-programming-cookbook
Los tuxes con ansias de aprender acechan. Software libre, programación, LaTeX y puede que algo más …. Tutoriales – Plagatux. Review of OpenCV 2 Computer Vision Application Programming Cookbook. Review of OpenCV 2 Computer Vision Application Programming Cookbook. Miércoles, 29 de junio de 2011. Acabo de terminar de leerme el libro “OpenCV 2 Computer Vision Application Programming Cookbook” y quería compartir con todos un análisis personal del mismo. El patrón de diseño “Estrategia” introducido...Entre los...
developerstation.org
DeveloperStation.ORG: March 2013
http://www.developerstation.org/2013_03_01_archive.html
Video Lectures and Talks. Saturday, March 9, 2013. Reading and Writing cv: Mat in OpenCV in C. In C api for OpenCV, you often come across writing and reading matrices as text files (analogous to saving .mat files in MATLAB/Octave). This is a simple way of doing it. You could even save multiple matrices in a single .xml/.yaml. However, I couldn't find ways to read more than 1 matrix which was written to file and being read using Python API. Posted by Rahul Kavi. Matrix types in using imshow and imwrite.
developerstation.org
DeveloperStation.ORG: February 2014
http://www.developerstation.org/2014_02_01_archive.html
Video Lectures and Talks. Wednesday, February 5, 2014. Fun with my Raspberry Pi. I ordered a Raspberry Pi recently. I've heard so much about this device in the recent past, I decide to try it out. I was reminded of few microprocessor(fun class) and electrical engineering(not so fun time) classes that I took in my undergrad. I wrote a simple program that could turn a Stepper Motor using a motor controller. Connected to a Raspberry Pi. HDMI cable to connect to a TV or monitor. For powering the stepper motor.
developerstation.org
DeveloperStation.ORG: simple Convolutional Neural Net based object recognition
http://www.developerstation.org/2016/03/simple-convolutional-neural-net-based.html
Video Lectures and Talks. Wednesday, March 9, 2016. Simple Convolutional Neural Net based object recognition. I made a simple object recognition module over the last weekend. I wrote a Theano. Based convolutional neural network. I wrote a simple OpenCV. Based image segmentation program. The output from the image segmentation program is passed to the Theano based Convolutional Neural Network. I used 10,000 images for training and 16,000 images for testing. Posted by Rahul Kavi. On the way to Virtual World.
developerstation.org
DeveloperStation.ORG: June 2013
http://www.developerstation.org/2013_06_01_archive.html
Video Lectures and Talks. Sunday, June 9, 2013. Logisitic Regression in OpenCV. I finished writing a Logistic Regression classifier in OpenCV. I've seen a lot of posts on the web asking for OpenCV's version of the same but its not available. Its easy to write your own logistic regression classifier. I separated out the cost function and gradient descent algorithm (Batch Gradient Descent). You can replace it with your own optimization algorithm. I posted it on my Github page. You can check it out:.
developerstation.org
DeveloperStation.ORG: August 2015
http://www.developerstation.org/2015_08_01_archive.html
Video Lectures and Talks. Monday, August 10, 2015. Very basic intro to Caffe. This tutorial just gives you a basic idea of what Caffe can do (not for advanced use of Caffe through defining your own layers, using C /Python API) for defining your own network to solve a Computer Vision task. I'm assuming you already have a background in Machine Learning, Statistical Pattern Recognition and have built Neural Networks in the past using some programming language. Create train.txt and test.txt with file...You t...
developerstation.org
DeveloperStation.ORG: March 2016
http://www.developerstation.org/2016_03_01_archive.html
Video Lectures and Talks. Sunday, March 13, 2016. Simple Autoencoder on MNIST dataset. So, I had fun with Theano and trained an Autoencoder on a MNIST dataset. More about Autoencoders is available here. More variants of Autoencoders exist (Sparse, Contractive, etc.) are available with different constraints on the hidden layer representation. I trained an vanilla Autoencoder for 100 epochs with 16 mini batch size and learning rate of 0.01. Posted by Rahul Kavi. Wednesday, March 9, 2016. The output from th...
developerstation.org
DeveloperStation.ORG: About Me
http://www.developerstation.org/p/rahul-kavi-my-reserach-page.html
Video Lectures and Talks. I'm a grad student. I write on DeveloperStation. This blog) to keep a "dairy" of some sort to look-up for myself in the future and to help others in the process. I always like to keep a track of present trends in Technology, Programming. You can follow me on Google Plus. My research publications are listed on my Google Scholar page. Paulo Coelho: I have learned. My Amazon Wish List. Subscribe to: Posts (Atom). Fav Networking, Linux, Computer Vision Blogs.
developerstation.org
DeveloperStation.ORG: Simple Autoencoder on MNIST dataset
http://www.developerstation.org/2016/03/simple-autoencoder-on-mnist-dataset.html
Video Lectures and Talks. Sunday, March 13, 2016. Simple Autoencoder on MNIST dataset. So, I had fun with Theano and trained an Autoencoder on a MNIST dataset. More about Autoencoders is available here. More variants of Autoencoders exist (Sparse, Contractive, etc.) are available with different constraints on the hidden layer representation. I trained an vanilla Autoencoder for 100 epochs with 16 mini batch size and learning rate of 0.01. Posted by Rahul Kavi. Subscribe to: Post Comments (Atom).
developerstation.org
DeveloperStation.ORG: July 2015
http://www.developerstation.org/2015_07_01_archive.html
Video Lectures and Talks. Wednesday, July 8, 2015. Pre-reqs for learning deep learning, unsupervised feature learning, neural networks. So, you are interested in understanding unsupervised feature learning, deep learning, convolutional networks, etc? You have tried various tutorials but you seem to have not understood what the author was talking about. This post lists most of the pre-reqs you will need to understand (not an exhaustive list). Best starting place on the web is Machine Learning on coursera.
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