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RANSAC

Image correspondence pipeline (Panoramas) 1) feature point detection - detect corners using the Harris corner detector. 2) feature point desription - describe features using the Multi-scale oriented patch descriptor. 3) feature matching 4) combine the images together RANSAC; RANdom SAmple Consensus 1) 개요 - Iterative method to fit models to data that can contain outliers. - RANSAC can hopefully p..

AI/Computer Vision 2021. 4. 6. 20:06

Geometric Transformation

2D Transformation 2D linear transformations 1) 개요 - change domain of image 2) Properties of linear transformations - Origin maps to origin - Lines map to lines - Parallel lines remain parallel - Ratios are preserved - Closed under composition 3) parametric transformation $$\begin{bmatrix} x' \\ y' \end{bmatrix} = T \begin{bmatrix} x \\ y \end{bmatrix}$$ - Transformation T is a coordinate-changin..

AI/Computer Vision 2021. 4. 4. 15:50

RNN, LSTM, GRU

RNN 1) sequence data - sequence data is closely connected itself by an order. - MLP or CNN cannot handle sequence data. 2) advantages - Can process any length input - Computation for step t can (in theory) use information from many steps back - Model size doesn't increase for longer input - Same weights applied on every timestep, so there is symmetry in how inputs are processed. 3) disadvantages..

AI/Deep Learning 2021. 4. 2. 15:51

Hough Transform

Hough Transform 1) 개요 - Generic framework for detecting a parametric model. - Edges don't have to be connected. - Lines can be occluded. - Edges vote for the possible models. - Robust to noise. 2) 원리 - Points(x, y) can be explained to parameter space m and b. - Edges can be interpreted as many points in the image. If these points are quantized parameter space, there would be many points of inter..

AI/Computer Vision 2021. 4. 1. 20:06

Image Resampling (Image pyramids)

Image downsampling 1) 개요 - Just deleting even rows and even columns occurs bad condition of images. 2) Aliasing - Occurs when sampling rate is not high enough to capture the amount of detail in the image. - Undersampling can disguise a signal as one of a lower frequency. 3) Anti-aliasing - To avoid aliasing, sampling rate >= 2 * max frequency in the image (Nyquist rate) - Apply a smoothing filte..

AI/Computer Vision 2021. 3. 30. 23:18

Deep residual learning for image recognition; ResNet (2015)

ResNet 1) 개요 - very deep networks using residual connections - 152 layers - ILSVRC 15 winner - Between 20 and 56 layers 'plain' convolutional networks, the deeper network has higher training error and test error.> - Try to resolve degradation problem with deeper network which isn't caused by overfitting. Residual Block - Use network layers to fit a residual mapping instead of directly trying to ..

AI/Paper 2021. 3. 29. 19:23

Going deeper with convolutions; GoogLeNet (2014)

GoogLeNet 1) 개요 - deeper networks, with computational efficiency - 14 ILSVRC winner - 22 layers - Only 5 million parameters (12x fewer parameters than the AlexNet) - Efficient "inception" module - NO FC layers Inception module 1) Naive version - design a local network topology (network within a network) and then stack these modues on top of each other. - apply parallel filter operations on the i..

AI/Paper 2021. 3. 29. 14:57

Very deep convolutional networks for large-scale image recognition; VGGNet (2015)

VGGNet 1) 개요 - Investigate the effect of the convolutional network depth on its accuracy. - 2nd place at ILSVRC (1st : GoogLeNet). But, it is used alot more than GoogLeNet due to simple structure and generalize well to other dataset. - Similar training procedure as AlexNet. 2) Depth (small filters, deeper networks) - The depth of networks were shallow before 2014 ILSVRC. (less than 8 layers) - V..

AI/Paper 2021. 3. 29. 13:23

ImageNet Classification with Deep Convolutional Neural Networks; AlexNet (2012)

AlexNet 1) 개요 - 60 million parameters and 650,000 neurons - Consists of 5 convolutional layers, some max-pooling layers, and 3 fully-connected layers. - Use softmax for the classification. - To make training faster, AlexNet used non-saturating neurons and a GPU. - Use dropout to reduce overfitting in the fully-connected layers. 2) ReLU activation function - LeNet-5 used Tanh function. - AlexNet ..

AI/Paper 2021. 3. 28. 21:53

SIFT (Scale invariant feature transform)

SIFT 1) 개요 - SIFT extracts features that are invariant to scale, rotation and intensity. - SIFT features can be matched reliably across 3D viewpoint and noise. - SIFT includes both an interest point detector and a descriptor. Interest point detector - It is an algorithm that chooses points from an image based on some criterion. - Harris, Min Eigen, and FAST are interest point dector. Descriptor ..

AI/Computer Vision 2021. 3. 26. 15:02