Scale-invariant feature transform - Wikipedia
The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999.[1] Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving. SIFT keypoints of objects are first
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
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
Canny edge detector — skimage 0.26.0rc0.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
Canny edge detection applied to a photograph The purpose of detecting sharp changes in image brightness is to capture important events and changes in properties of the world. It can be shown that under rather general assumptions for an image formation model, discontinuities in image brightness are likely to correspond to:[2][3] discontinuities in depth, discontinuities in surface orientation, chan
)
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
Canny edge detector - Wikipedia
2.3 Gradient magnitude thresholding or lower bound cut-off suppression
エッジ検出 - Wikipedia
エッジ検出（エッジけんしゅつ、英: edge detection）は、画像処理やコンピュータビジョンの用語で、特徴検出 (feature detection) や特徴抽出 (feature extraction) の一種であり、デジタル画像の画像の明るさが鋭敏に、より形式的に言えば不連続に変化している箇所を特定するアルゴリズムを指す。 画像の明るさの鋭敏な変化を検出する目的は、実世界の属性の重要な事象や変化を捉えることである。一般的な画像形成モデルにおいて、画像の明るさの不連続な変化は次のような事象と一致する可能性がある。 深さが不連続である。 面の向きが不連続である。 材質が変化している。 照明が変化している。 理想的には、画像にエッジ検出を施すことで、物体の境界を示す連続する曲線が得られ、面の向きが不連続に変化している部分（要するに角）も曲線として得られる。したがって、エッジ検出を画像
Feature engineering - Wikipedia
This article needs to be updated. Please help update this article to reflect recent events or newly available information. (February 2024) Feature engineering is a preprocessing step in supervised machine learning and statistical modeling[1] which transforms raw data into a more effective set of inputs. Each input comprises several attributes, known as features. By providing models with relevant i
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
sobel — SciPy v1.15.1 Manual
Visual descriptor - Wikipedia
Harris corner detector - Wikipedia
Corner detection — skimage 0.26.0rc0.dev0 documentation
Corner detection - Wikipedia
skimage.feature — skimage 0.26.0rc0.dev0 documentation