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Boundary Detection

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

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 261–270 of 359 papers

Title	Date	Tasks	Status	Hype	Score
Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy	May 12, 2023	Boundary DetectionSegmentation	—Unverified	0	0
Automated image segmentation for detecting cell spreading for metastasizing assessments of cancer development	Jan 1, 2018	Boundary DetectionImage Segmentation	—Unverified	0	0
Automatic Cadastral Boundary Detection of Very High Resolution Images Using Mask R-CNN	Aug 17, 2023	Boundary DetectionInstance Segmentation	—Unverified	0	0
Automatic construction of complex features in Conditional Random Fields for Named Entities Recognition	Sep 1, 2015	Boundary DetectionNamed Entity Recognition (NER)	—Unverified	0	0
Automatic lesion boundary detection in dermoscopy	Nov 23, 2018	Boundary DetectionSegmentation	—Unverified	0	0
Automatic recognition of element classes and boundaries in the birdsong with variable sequences	Jan 23, 2016	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0	0
Automatic Removal of Marginal Annotations in Printed Text Document	Aug 9, 2014	Boundary Detection	—Unverified	0	0
A Word Labeling Approach to Thai Sentence Boundary Detection and POS Tagging	Dec 1, 2016	Boundary DetectionMachine Translation	—Unverified	0	0
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors	Dec 1, 2018	Boundary DetectionChange Point Detection	—Unverified	0	0
B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterization	Feb 14, 2023	Boundary DetectionManagement	—Unverified	0	0

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All datasets RoFT RoFT-chatgpt CoAuthor NYU-Depth V2 TriBERT (in-domain)UruDendro Kinetics 400 PASCAL Context

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	GigaCheck (DN-DAB-DETR)	Accuracy (%)	64.63	—	Unverified
2	RoBERTa + SEP	Accuracy (%)	49.64	—	Unverified
3	PHD + TS ML	Accuracy (%)	23.5	—	Unverified
4	TLE + TS Binary	Accuracy (%)	12.58	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GigaCheck (DN-DAB-DETR)	Accuracy (%)	67.65	—	Unverified
2	RoBERTa + SEP	Accuracy (%)	54.61	—	Unverified
3	TLE + TS Binary	Accuracy (%)	20.02	—	Unverified
4	PHD + TS ML	Accuracy (%)	17.29	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GigaCheck (Mistral-7B-v0.3)	Cohen’s Kappa score	0.42	—	Unverified
2	DeBERTa-v3 (Naive)	Cohen’s Kappa score	0.4	—	Unverified
3	GigaCheck (DN-DAB-DETR)	Cohen’s Kappa score	0.19	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	InvPT	odsF	78.1	—	Unverified
2	PGT (Swin-S)	odsF	78.04	—	Unverified
3	PGT (Swin-T)	odsF	77.05	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GigaCheck (DN-DAB-DETR)	F1@3	0.65	—	Unverified
2	TriBERT (p=2)	F1@3	0.58	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CS-TRD	Average Precision	0.94	—	Unverified
2	INBD	Average Precision	0.75	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CASTANET+ Ensemble	Pairwise F1	0.81	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	InvPT	odsF	73	—	Unverified