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AI/Computer Vision 검색 결과

해당 글 11건

Camera

Camera 1) Pin-hole camera - Pin-hole camera model is a widely used camera model in computer vision. - It collects light through a small hole to the inside of dark box or room. - Light passes through a single point, the camera center, C, before it is projected onto an image plane. 2) Lens camera - Lenses map bundles of rays from points on the scene to the sensor. 3) Common feature - only use cent..

AI/Computer Vision 2021. 4. 8. 15:43

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

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

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

Harris Corner Detector

Harris corner detector 1) 개요 - Locate interest points where the surrounding neighborhood shows edges in more than one direction. 2) 방법 $$E(u, v) = \sum_{x, y}\: w(x, y)\: [I(x+u, y+v) - I(x,y)]^{2}$$ - shift the window function by (u, v) and compare each pixel before and after by SSD. - w(x, y) : window function -> gives weights to pixels. (e.g. rectangular, gaussian) - I(x+u, y+v) : shifted int..

AI/Computer Vision 2021. 3. 24. 22:16

Morphology

Morphology 1) 개요 - Morphology is a collection of image processing methods. - Usually it is applied to binary images. (each pixel takes only two values; 0 or 1) - Segmentate image and simplify, removal, adjustment, etc. - Useful for cheking object's structure. 2) 방식 - By sliding structuring element (SE) on a image, compute logical operation. -> Doesn't change original image. - The origin of SE's ..

AI/Computer Vision 2021. 3. 24. 14:10

Image filtering (Detecting edges)

Image derivatives 1) 개요 - Intuitively, the edges should have rapid change. By using Gaussian blurring, remove the noises first. The above image is the dreivative without applying Gaussian blurring. - Intensity changes over the image is important information. - x and y derivatives of grayscale image can describe the intensity change. - By gradient, get vector value from the image and can effectiv..

AI/Computer Vision 2021. 3. 24. 00:24

Image filtering (Blurring images)

Blurring image 1) 개요 - Blur the image by using low-pass filter. - Simply this smoothens the image and removes a noises. 2) 방법 - The (grayscale) image I is convolved with various kinds of filters. (2D convolution) - 2D convolution with separable filter is equivalent to two 1D convolutions. 3) Gaussian Filter $$f(i, j) = \frac{1}{2\pi \sigma^{2}}e^{-\frac{i^{2}+j^{2}}{2\sigma^{2}}}$$ - Gaussian bl..

AI/Computer Vision 2021. 3. 23. 18:41