Papers with Code 아카이브
Boundary Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Boundary Detection is a vital part of extracting information encoded in images, allowing for the computation of quantities of interest including density, velocity, pressure, etc.
Source: A Locally Adapting Technique for Boundary Detection using Image Segmentation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/moabitcoin/holy-edge
https://github.com/moothes/salod
Deeply supervised salient object detection with short connections
https://github.com/nicoManthey/Mining-Claims-in-UNHCR-reports-A2
https://github.com/nnizhang/VST
https://github.com/oladeha2/shot_boudary_detector
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
https://github.com/oroszgy/spacy-hungarian-models
Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines
https://github.com/petteriTeikari/vesselNN
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
https://github.com/prijatelj/crowd_noise
https://github.com/rishavroy1264bitmesra/Tensorflow-Boundary-Loss-for-Remote-Sensing-Imagery-Semantic-Segmentation
Boundary Loss for Remote Sensing Imagery Semantic Segmentation
https://github.com/senliuy/Keras_HED_with_model
https://github.com/shallwe999/TransNetV2-SBD-Visualize
TransNet: A deep network for fast detection of common shot transitions
https://github.com/shortpoet/Final-Project
https://github.com/sniklaus/pytorch-hed
https://github.com/soCzech/MasterThesis
TransNet V2: An effective deep network architecture for fast shot transition detection
https://github.com/soccernet/sn-grounding
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
https://github.com/soccernet/sn-spotting
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
https://github.com/sserg-r/srgan
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks
https://github.com/strivebo/image_segmentation_dl
Boundary Loss for Remote Sensing Imagery Semantic Segmentation
https://github.com/tensorpack/tensorpack/tree/master/examples/HED
https://github.com/txyugood/hed
https://github.com/wqliu657/TransNetV2
https://github.com/xavysp/MBIPED
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection
https://github.com/yiskw713/boundary_loss_for_remote_sensing
Boundary Loss for Remote Sensing Imagery Semantic Segmentation
https://github.com/yunfan1202/Delving-into-Crispness
https://github.com/zzmshinnosuke/sketch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.