ScholarGate
アシスタント

手法を比較

選択した手法を並べて確認できます。異なる行はハイライト表示されます。

分水嶺セグメンテーション×ブロブ検出×
分野コンピュータビジョンコンピュータビジョン
系統Machine learningMachine learning
提唱年19791998
提唱者Serge Beucher and Christian LantuéjoulTony Lindeberg
種類Morphological image segmentationMulti-scale feature detection
原典Meyer, F. (1994). Topographic distance and watershed lines. Signal Processing, 38(1), 113–125. DOI ↗Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2), 79–116. DOI ↗
別名Watershed transform, Water shedding segmentationConnected component analysis, Region-based detection
関連55
概要Watershed segmentation is a morphological image processing technique that automatically segments an image into distinct regions by treating image intensity as a topographic landscape where each object corresponds to a valley. Introduced by Beucher and Lantuéjoul in 1979 and refined by Meyer, the watershed algorithm is particularly effective for separating touching or overlapping objects.Blob detection is a technique for identifying regions of interest (blobs)—connected, homogeneous areas that differ from their surroundings—at multiple scales. Introduced by Lindeberg in the context of scale-space theory, blob detection automatically finds and characterizes circular or elliptical objects without requiring a priori knowledge of their size.
ScholarGateデータセット
  1. v1
  2. 2 出典
  3. PUBLISHED
  1. v1
  2. 2 出典
  3. PUBLISHED

検索へ スライドをダウンロード

ScholarGate手法を比較: Watershed Segmentation · Blob Detection. 2026-06-17に以下より取得 https://scholargate.app/ja/compare