FCN (Fully Convolutional Network)：ディープラーニングによるSemantic Segmentation手法
画像の領域を分割するタスクをSegmentation(領域分割)と呼び、Semantic Segmentationは「何が写っているか」で画像領域を分割するタスクのことを指す。 画像を物体領域単位で分類する物体認識や物体検出に対して、Semantic Segmentationは画像をpixel単位でどのクラスに属するか分類する。そのためPixel-labelingとも呼ばれる。 もちろんディープラーニング以前から様々な手法が提案されている。 https://news.mynavi.jp/article/cv_future-35/ https://news.mynavi.jp/article/cv_future-36/
Feature Visualization
If we want to understand individual features, we can search for examples where they have high values — either for a neuron at an individual position, or for an entire channel. We used the channel objective to create most of the images in this article. If we want to understand a layer as a whole, we can use the DeepDream objective , searching for images the layer finds “interesting.” And if we want
Scale-invariant feature transform - Wikipedia
The detection and description of local image features can help in object recognition. The SIFT features are local and based on the appearance of the object at particular interest points, and are invariant to image scale and rotation. They are also robust to changes in illumination, noise, and minor changes in viewpoint. In addition to these properties, they are highly distinctive, relatively easy
Minimum bounding box - Wikipedia
A sphere enclosed by its axis-aligned minimum bounding box (in 3 dimensions) In geometry, the minimum bounding box or smallest bounding box (also known as the minimum enclosing box or smallest enclosing box) for a point set S in N dimensions is the box with the smallest measure (area, volume, or hypervolume in higher dimensions) within which all the points lie. When other kinds of measure are used
Sobel operator - Wikipedia
A color picture of an engine The Sobel operator applied to that image The Sobel operator, sometimes called the Sobel–Feldman operator or Sobel filter, is used in image processing and computer vision, particularly within edge detection algorithms where it creates an image emphasising edges. It is named after Irwin Sobel and Gary M. Feldman, colleagues at the Stanford Artificial Intelligence Laborat
医用画像 - Wikipedia
医用画像（いようがぞう、英: Medical imaging）は、主に病気の診断および治療のために、人体内部の構造や機能を画像として可視化する検査技術である。 医用画像には、単純X線撮影、コンピュータ断層撮影（CT）、核磁気共鳴画像法（MRI）、超音波断層画像（US）、核医学検査、血管造影などがある。単純X線撮影やCTでは、組織によりX線の吸収率が異なることを利用して画像を得る。また超音波検査の場合、超音波により組織内のエコーから内部構造を知ることができる。 本来、画像を生成するよう設計されていなかった測定手法や記録手法（脳波や脳磁図）も一種の地図のように表せるデータを生成することから、医用画像の一種とみなすこともできるが、一般にはこれらは医用画像には含まれない。 医療施設では、主に診療放射線技師が医用画像の撮影を行う。また、撮影された医用画像を医学的に解釈する医師を放射線診断医あるいは画
Local binary patterns - Wikipedia
Local binary patterns (LBP) is a type of visual descriptor used for classification in computer vision. LBP is the particular case of the Texture Spectrum model proposed in 1990.[1][2] LBP was first described in 1994.[3][4] It has since been found to be a powerful feature for texture classification; it has further been determined that when LBP is combined with the Histogram of oriented gradients (H
Corner detection - Wikipedia
Harris and Stephens[5] improved upon Moravec's corner detector by considering the differential of the corner score with respect to direction directly, instead of using shifted patches. (This corner score is often referred to as autocorrelation, since the term is used in the paper in which this detector is described. However, the mathematics in the paper clearly indicate that the sum of squared dif
Canny edge detector — skimage 0.24.0rc2.dev0 documentation
Note Go to the end to download the full example code. or to run this example in your browser via Binder Canny edge detector# The Canny filter is a multi-stage edge detector. It uses a filter based on the derivative of a Gaussian in order to compute the intensity of the gradients.The Gaussian reduces the effect of noise present in the image. Then, potential edges are thinned down to 1-pixel curves
Edge detection - Wikipedia
Approaches[edit] There are many methods for edge detection, but most of them can be grouped into two categories, search-based and zero-crossing based. The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a comput
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Feature (computer vision) - Wikipedia
In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of fe
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scipy.ndimage.find_objects — SciPy v1.13.0 Manual
scipy.ndimage.sobel — SciPy v1.13.0 Manual
scipy.ndimage.histogram — SciPy v1.13.0 Manual
Medical imaging - Wikipedia
Visual descriptor - Wikipedia
GitHub - opencv/opencv: Open Source Computer Vision Library
Harris corner detector - Wikipedia
Corner detection — skimage 0.23.3rc0.dev0 documentation
https://scikit-image.org/docs/dev/api/skimage.feature.html