EDGE-LABELING USING DICTIONARY-BASED RELAXATION

被引：70

作者：

HANCOCK, ER ^{[1
]}

KITTLER, J ^{[1
]}

机构：

[1] UNIV SURREY,DEPT ELECTR & ELECT ENGN,GUILDFORD GU2 5XH,SURREY,ENGLAND

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1990年 / 12卷 / 02期

关键词：

Contextual classification; edge-labeling; probabilistic relaxation;

D O I：

10.1109/34.44403

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present an improved application of probabilistic relaxation to edge-labeling. The improvement derives from the use of a representation of the edge-process that is internally consistent and which utilizes a more complex description of edge-structure. The particular novelty of the application lies in the use of a dictionary to represent permitted labelings of the entire context-conveying neighborhood of each pixel. This approach is to be contrasted with the use of approximate factorizations which have been employed in previous applications to decompose the neighborhood into object-pairs. We give details of the dictionary approach and the related representation of the edge-process. A comparison with other edge-postprocessing strategies is provided. This leads us to conclude that the dictionary-based approach is a powerful edge-postprocessing tool. It relaxes the demands on the level of filtering that has to be applied to cope with image noise with the benefit of reduced blurring of fine image features. © 1990 IEEE

引用

页码：165 / 181

页数：17

共 50 条

[31] Labeling of MR Brain Image Using Dictionary-basedProbabilistic Relaxation Labeling Technique
Du Huijie
Bian ZhengZhong(Dept. of Biomedical Engineering and Instrument Xi’an Jiaotong University
Chinese Journal of Biomedical Engineering(English Edition), 1997, (03) : 129 - 130
[32] Consistency of dictionary-based Manifold Learning
Koelle, Samson
Zhang, Hanyu
Murad, Octavian-Vlad
Meila, Marina
INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 238, 2024, 238
[33] Dictionary-based compressive Fourier ptychography
Li, Xianye
Li, Li
Liu, Xiaoli
He, Wenqi
Tang, Qijian
Han, Sen
Peng, Xiang
OPTICS LETTERS, 2022, 47 (09) : 2314 - 2317
[34] Accelerated reconstruction of dictionary-based T2 relaxation maps based on dictionary compression and gradient descent search algorithms
Shpringer, Guy
Bendahan, David
Ben-Eliezer, Noam
MAGNETIC RESONANCE IMAGING, 2022, 87 : 56 - 66
[35] Dictionary-Based DGAs Variants Detection
Mahmood, Raja Azlina Raja
Abdullah, Azizol
Hussin, Masnida
Udzir, Nur Izura
ADVANCES ON INTELLIGENT INFORMATICS AND COMPUTING: HEALTH INFORMATICS, INTELLIGENT SYSTEMS, DATA SCIENCE AND SMART COMPUTING, 2022, 127 : 258 - 269
[36] A dictionary-based approach for gene annotation
Pachter, L
Batzoglou, S
Spitkovsky, VI
Banks, E
Lander, ES
Kleitman, DJ
Berger, B
JOURNAL OF COMPUTATIONAL BIOLOGY, 1999, 6 (3-4) : 419 - 430
[37] DICTIONARY-BASED MULTIPLE INSTANCE LEARNING
Shrivastava, Ashish
Pillai, Jaishanker K.
Patel, Vishal M.
Chellappa, Rama
2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2014, : 160 - 164
[38] MAT-free graphic arrangements and a characterization of strongly chordal graphs by edge-labeling
Tran, Tan N.
Tsujie, Shuhei
ALGEBRAIC COMBINATORICS, 2023, 6 (06):
[39] Segmentation of liver computed tomography images using dictionary-based snakes
Shanila N.
Kumar R.S.V.
Ravi R.R.
International Journal of Biomedical Engineering and Technology, 2022, 39 (03): : 283 - 296
[40] Building Thesaurus Lexicon using Dictionary-Based Approach for Sentiment Classification
Park, Seongik
Kim, Yanggon
2016 IEEE/ACIS 14TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING RESEARCH, MANAGEMENT AND APPLICATIONS (SERA), 2016, : 39 - 44

← 1 2 3 4 5 →