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Video Text Detection 2014 ed. [Hardback]

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  • Formāts: Hardback, 264 pages, height x width: 235x155 mm, weight: 5384 g, 160 Illustrations, black and white; X, 264 p. 160 illus., 1 Hardback
  • Sērija : Advances in Computer Vision and Pattern Recognition
  • Izdošanas datums: 11-Aug-2014
  • Izdevniecība: Springer London Ltd
  • ISBN-10: 1447165144
  • ISBN-13: 9781447165149
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Video Text Detection 2014 ed.Palielināt
  • Formāts: Hardback, 264 pages, height x width: 235x155 mm, weight: 5384 g, 160 Illustrations, black and white; X, 264 p. 160 illus., 1 Hardback
  • Sērija : Advances in Computer Vision and Pattern Recognition
  • Izdošanas datums: 11-Aug-2014
  • Izdevniecība: Springer London Ltd
  • ISBN-10: 1447165144
  • ISBN-13: 9781447165149
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781447165149.html
Keywords:
This book presents a systematic introduction to the latest developments in video text detection. Opening with a discussion of the underlying theory and a brief history of video text detection, the text proceeds to cover pre-processing and post-processing techniques, character segmentation and recognition, identification of non-English scripts, techniques for multi-modal analysis and performance evaluation. The detection of text from both natural video scenes and artificially inserted captions is examined. Various applications of the technology are also reviewed, from license plate recognition and road navigation assistance, to sports analysis and video advertising systems. Features: explains the fundamental theory in a succinct manner, supplemented with references for further reading; highlights practical techniques to help the reader understand and develop their own video text detection systems and applications; serves as an easy-to-navigate reference, presenting the material in self-contained chapters.
1 Introduction to Video Text Detection
1(18)
1.1 Introduction to the Research of Video Text Detection
1(4)
1.2 Characteristics and Difficulties of Video Text Detection
5(2)
1.3 Relationship Between Video Text Detection and Other Fields
7(1)
1.4 A Brief History of Video Text Detection
8(6)
1.5 Potential Applications
14(5)
References
16(3)
2 Video Preprocessing
19(30)
2.1 Preprocessing Operators
20(6)
2.1.1 Image Cropping and Local Operators
21(1)
2.1.2 Neighborhood Operators
22(3)
2.1.3 Morphology Operators
25(1)
2.2 Color-Based Preprocessing
26(3)
2.3 Texture Analysis
29(5)
2.4 Image Segmentation
34(5)
2.5 Motion Analysis
39(5)
2.6 Summary
44(5)
References
46(3)
3 Video Caption Detection
49(32)
3.1 Introduction to Video Caption Detection
49(2)
3.2 Feature-Based Methods
51(21)
3.2.1 Edge-Based Methods
51(5)
3.2.2 Texture-Based Methods
56(7)
3.2.3 Connected Component-Based Methods
63(4)
3.2.4 Frequency Domain Methods
67(5)
3.3 Machine Learning-Based Methods
72(6)
3.3.1 Support Vector Machine-Based Methods
72(1)
3.3.2 Neural Network Model-Based Methods
72(1)
3.3.3 Bayes Classification-Based Methods
73(5)
3.4 Summary
78(3)
References
78(3)
4 Text Detection from Video Scenes
81(46)
4.1 Visual Saliency of Scene Texts
82(8)
4.2 Natural Scene Text Detection Methods
90(23)
4.2.1 Bottom-Up Approach
91(5)
4.2.2 Top-Down Approach
96(4)
4.2.3 Statistical and Machine Learning Approach
100(6)
4.2.4 Temporal Analysis Approach
106(4)
4.2.5 Hybrid Approach
110(3)
4.3 Scene Character/Text Recognition
113(3)
4.4 Scene Text Dataseis
116(6)
4.5 Summary
122(5)
References
124(3)
5 Post-processing of Video Text Detection
127(18)
5.1 Text Line Binarization
127(6)
5.1.1 Wavelet-Gradient-Fusion Method (WGF)
128(1)
5.1.2 Text Candidates
129(2)
5.1.3 Smoothing
131(1)
5.1.4 Foreground and Background Separation
132(1)
5.1.5 Summary
132(1)
5.2 Character Reconstruction
133(9)
5.2.1 Ring Radius Transform
135(1)
5.2.2 Horizontal and Vertical Medial Axes
136(2)
5.2.3 Horizontal and Vertical Gap Filling
138(1)
5.2.4 Large Gap Filling
139(1)
5.2.5 Border Gap Filling
140(1)
5.2.6 Small Gap Filling
141(1)
5.2.7 Summary
142(1)
5.3 Summary
142(3)
References
143(2)
6 Character Segmentation and Recognition
145(24)
6.1 Introduction to OCR and Its Usage in Video Text Recognition
145(2)
6.2 Word and Character Segmentation
147(7)
6.2.1 Fourier Transform-Based Method for Word and Character Segmentation
149(1)
6.2.2 Bresenham's Line Algorithm
149(1)
6.2.3 Fourier-Moments Features
150(2)
6.2.4 Word Extraction
152(1)
6.2.5 Character Extraction
153(1)
6.2.6 Summary
153(1)
6.3 Character Segmentation Without Word Segmentation
154(5)
6.3.1 GVF for Character Segmentation
155(1)
6.3.2 Cut Candidate Identification
155(2)
6.3.3 Minimum-Cost Pathfinding
157(1)
6.3.4 False-Positive Elimination
158(1)
6.3.5 Summary
159(1)
6.4 Video Text Recognition
159(7)
6.4.1 Character Recognition
160(1)
6.4.2 Hierarchical Classification Based on Voting Method
160(4)
6.4.3 Structural Features for Recognition
164(2)
6.4.4 Summary
166(1)
6.5 Summary
166(3)
References
167(2)
7 Video Text Detection Systems
169(26)
7.1 License Plate Recognition Systems
170(11)
7.1.1 Preprocessing of LPR Systems
172(3)
7.1.2 License Plate Detection
175(1)
7.1.3 Skew Correction
176(1)
7.1.4 Character Segmentation
177(1)
7.1.5 Character Recognition
178(3)
7.2 Navigation Assistant Systems
181(2)
7.3 Sport Video Analysis Systems
183(5)
7.4 Video Advertising Systems
188(3)
7.5 Summary
191(4)
References
191(4)
8 Script Identification
195(26)
8.1 Language-Dependent Text Detection
196(4)
8.1.1 Method for Bangla and Devanagari (Indian Scripts) Text Detection
197(1)
8.1.2 Headline-Based Method for Text Detection
197(2)
8.1.3 Sample Experimental Results
199(1)
8.1.4 Summary
199(1)
8.2 Methods for Language-Independent Text Detection
200(7)
8.2.1 Run Lengths for Multi-oriented Text Detection
201(1)
8.2.2 Selecting Potential Run Lengths
201(1)
8.2.3 Boundary Growing Method for Traversing
202(2)
8.2.4 Zero Crossing for Separating Text Lines from Touching
204(1)
8.2.5 Sample Experiments
205(1)
8.2.6 Summary
206(1)
8.3 Script Identification
207(11)
8.3.1 Spatial-Gradient-Features for Video Script Identification
210(1)
8.3.2 Text Components Based on Gradient Histogram Method
210(2)
8.3.3 Candidate Text Components Selection
212(1)
8.3.4 Features Based on Spatial Information
213(1)
8.3.5 Template Formation for Script Identification
214(3)
8.3.6 Summary
217(1)
8.4 Summary
218(3)
References
218(3)
9 Text Detection in Multimodal Video Analysis
221(26)
9.1 Relevance of Video Text and Other Modalities in Video Analysis
223(4)
9.2 Video Text-Related Multimodality Analysis Models
227(2)
9.3 Text Detection for Multimodal Video Content Analysis
229(14)
9.3.1 Text Detection and Multimodal Analysis in Broadcast Videos
229(3)
9.3.2 Lyrics Analysis for Interpreting Karaoke Music Video
232(4)
9.3.3 Multimodal Video Summarization
236(4)
9.3.4 Web Video Category/Search Through Text Modality
240(3)
9.4 Summary
243(4)
References
244(3)
10 Performance Evaluation
247(8)
10.1 Performance Evaluation Protocols
248(1)
10.2 Benchmark Databases for Video Text Detection
248(1)
10.3 Matching Detected Text Boxes with Ground Truths
249(1)
10.4 Performance Metrics for Video Text Detection
250(2)
10.5 Dataset and Evaluation of Video Text Recognition
252(1)
10.6 Summary
253(2)
References
253(2)
Index 255
Dr. Tong Lu is an Associate Professor in the Department of Computer Science and Technology, and a member of the State Key Lab for Novel Software and Technology at Nanjing University, China.

Dr. Shivakumara Palaiahnakote is a Senior Lecturer in the Faculty of Computer Science and Information Technology at the University of Malaya, Kuala Lumpur, Malaysia.

Dr. Chew Lim Tan is a Professor in the School of Computing at the National University of Singapore.

Dr. Wenyin Liu is a Senior Research Fellow and Associate Director of the Multimedia Software Engineering Research Center at City University of Hong Kong.