Category Archives: OpenCV

Recurrent Neural Networks II — LSTM

In my previous post, I introduced the basic ideas of Recurrent Neural Networks, as the 2nd post of RNNs, we’ll focus on long short-term memory method. LONG SHORT TERM MEMORY One of the very famous problems of RNNs is the vanishing gradient, the problem is that the influence of a given input on the hidden layer, […]

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Texture Synthesis

What in this post is actually part of my computational photography homework, because I’m recently preparing for interviews, so for reviewing it, I re-implemented this method. WHAT IS IT Texture synthesis is another very interesting application of image processing. What it does is, given a texture sample, generate new texture that is similar with the […]

Also posted in Algorithm, Vision | Tagged , | 2 Responses

Coarse-to-fine Optical Flow (Lucas & Kanade)

OPTICAL FLOW We are always interested in finding the movement of objects from videos, optical flow is one of the most famous methods to do this. Optical flow has lots of uses, such as tracking object, camera correction, mosaics and so on. All optical flow methods are based on the following assumptions: Color constancy (brightness […]

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Denoising Autoencoder

I chose “Dropped out auto-encoder” as my final project topic in the last semester deep learning course, it was simply dropping out units in regular sparse auto-encoder, and furthermore, in stacked sparse auto-encoder, both in visible layer and hidden layer. It does not work well on auto-encoders, except can be used in fine-tune process of stacked sparse […]

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C++ Code For Reading CIFAR-10 Dataset

The CIFAR-10 dataset can be found HERE. It is a very popular multi-channel image dataset for classifier training, as a simplify version of CIFAR-100, it is easier to use for newbies. Here’s a C++ version code for reading this dataset from .bin files into OpenCV matrices.

Also posted in Machine Learning | Tagged , | 2 Responses

Convolutional Neural Networks II

Since the last CNN post, I was working on a new version of CNN, which support multi-layers Conv and Pooling process, I’d like to share some experience here. VECTOR VS HASH TABLE You can see in the last post, I used vector of Mat in convolution steps, it works well when we only have one […]

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Convolutional Neural Networks

WHAT IS CNN A Convolutional Neural Network (CNN) is comprised of one or more convolutional layers, pooling layers and then followed by one or more fully connected layers as in a standard neural network. The architecture of a CNN is designed to take advantage of the 2D structure of an input image (or other 2D input […]

Also posted in Algorithm, Machine Learning | Tagged , , , , | 41 Responses

A Simple Deep Network

During this spring break, I worked on building a simple deep network, which has two parts, sparse autoencoder and softmax regression. The method is exactly the same as the “Building Deep Networks for Classification” part in UFLDL tutorial. For better understanding it, I re-implemented it using C++ and OpenCV.  GENERAL OUTLINE Read dataset (including training data […]

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Poisson Blending II

I re-wrote the Poisson Blending code using C++ and OpenCV. About the Algorithm, see my Previous Poisson Blending post. This time, I just used the most stupid way, just solving the Poisson Equation. You can improve it by using advanced methods. About solving discrete Poisson Equation using Jacobi, SOR, Conjugate Gradients, and FFT, read THIS.   In […]

Also posted in Algorithm, Maths | Tagged , , , , | 16 Responses

Softmax Regression (with OpenCV)

This is the same algorithm with the previous SOFTMAX REGRESSION post. Because I’m going to try to build deeper neural networks for images, so as a review of OpenCV programming, I rewrote the Softmax regression code using OpenCV mat, instead of Armadillo. I used Matlab, Octave, Armadillo a lot these days, it is kind of […]

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