Pattern rocognition and image analysis 1st Edition by Helder Araujo, Ana Maria Mendonca, Armando Pinho, Maria Ines Torres ISBN 9783642021725 3642021727

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ISBN 10: 3642021727 
ISBN 13: 9783642021725
Author: Helder Araujo, Ana Maria Mendonca, Armando Pinho, Maria Ines Torres

Pattern rocognition and image analysis 1st Edition Table of contents:

1. Invited Talks
2. Inference and Learning for Active Sensing, Experimental Design and Control
3. The Principle of Maximum Expected Utility
4. Myopic Decision Making
5. Sequential Decision Making
6. A Common Solution Framework
7. Demonstrations
8. Active Sensing Example
9. Experimental Design to Choose among Scientific Models
10. Control Example: Particles with Force-Fields
11. References
12. Large Scale Online Learning of Image Similarity through Ranking
13. The Similarity Learning Model and Algorithm
14. Experiments
15. References
16. Inpainting Ideas for Image Compression
17. Computer Vision
18. Improving Scene Recognition through Visual Attention
19. Introduction
20. Visual Attention
21. Scene Recognition
22. Experiments and Results
23. Conclusions
24. References
25. Smoothed Disparity Maps for Continuous American Sign Language Recognition
26. Introduction
27. System Overview
28. Vision-Based Features
29. Stereo Vision-Based Features
30. Extending Hand Tracking with Stereo Features
31. Experimental Results
32. Summary
33. References
34. Human Action Recognition Using Optical Flow Accumulated Local Histograms
35. Introduction
36. Features and Action Descriptor
37. Oriented Histograms Computation
38. Experiments
39. Methodology
40. Classification Results
41. The Shortest Sequence to Recognize an Action
42. Recognizing Activity Changes
43. Discussion and Conclusions
44. References
45. Trajectory Modeling Using Mixtures of Vector Fields
46. Introduction
47. Overall Idea
48. Generative Motion Model
49. Learning the Fields
50. Estimation Criterion: Marginal MAP
51. The E-step
52. The M-step
53. Experimental Results
54. Conclusions
55. References
56. High-Speed Human Detection Using a Multiresolution Cascade of Histograms of Oriented Gradients
57. Introduction
58. Description of the Method
59. Multiresolution Cascade
60. HOG Features Pyramid
61. Training and Detection
62. Experimental Results
63. Conclusions
64. References
65. Face-to-Face Social Activity Detection Using Data Collected with a Wearable Device
66. Introduction
67. TheBadge
68. Features Extraction
69. The Classification System
70. Experiment and Results
71. Conclusions and Future Works
72. References
73. Estimating Vehicle Velocity Using Image Profiles on Rectified Images
74. Introduction
75. Image Rectification
76. Intensity Profile
77. Velocity Estimation
78. Results
79. Conclusions
80. References
81. Kernel Based Multi-object Tracking Using Gabor Functions Embedded in a Region Covariance Matrix
82. Introduction
83. Region Covariance Descriptor
84. Descriptors Dissimilarity and Update
85. Improvement to Occlusion
86. Kernel Particle Filter Tracking
87. Results
88. Conclusions
89. References
90. Autonomous Configuration of Parameters in Robotic Digital Cameras
91. Introduction
92. Configuration of the Camera Parameters
93. Experimental Results
94. Conclusions
95. References
96. Facial Reconstruction and Alignment Using Photometric Stereo and Surface Fitting
97. Introduction
98. Image Acquisition
99. Photometric Stereo
100. Image Alignment
101. Results
102. Conclusion
103. References
104. Facial Feature Extraction and Change Analysis Using Photometric Stereo
105. Introduction
106. Nose and Nasion Detection
107. Eye Detection
108. Results
109. Conclusion
110. References
111. Match Selection in Batch Mosaicing Using Mutual Information
112. Introduction
113. Topology Estimation Using Extended Kalman Filter
114. Definitions
115. Implementation
116. Experimental Results
117. Discussion
118. Conclusions
119. References
120. Space Carving Acceleration Using Uncertainty Measurements
121. Introduction
122. Some Space Carving Algorithm Problems
123. Sources of Uncertainty
124. Proposed Algorithm
125. Membership Functions
126. Aggregation Function
127. Decision Rule
128. Results
129. Conclusions and Future Work
130. References
131. Mean Shift Parallel Tracking on GPU
132. Introduction
133. Serial Mean Shift Tracking Algorithm
134. Target Model Representation
135. Candidate Model Representation
136. Similarity Function (SF)
137. Mean Shift Vector
138. Parallel MS Tracking Algorithm on GPU
139. Color Space Partition
140. Parallel Tracking Algorithm with CUDA
141. Experiments
142. Data Set and Performance Comparision
143. Effect of Bandwidth between CPU and GPU on the Parallel Algorithm
144. Conclusions
145. References
146. Local Boosted Features for Pedestrian Detection
147. Introduction
148. Approach
149. Boosted Features
150. Boosted Feature Selection
151. Implementation Details
152. Histogram of Oriented Gradients
153. Boosted Features
154. Detection Images
155. Experiments
156. Conclusions
157. References
158. Plane Filtering for the Registration of Urban Range Laser Imagery
159. Introduction
160. State of the Art
161. Introduction to the Problem
162. Proposed Method
163. Experimental Results
164. Conclusions and Future Work
165. References
166. Real-Time Motion Detection for a Mobile Observer Using Multiple Kernel Tracking and Belief Propagati
167. Introduction
168. Global Motion Estimation
169. Kernel Tracking with SSD
170. Global Tracking Model and Maintenance
171. Local Motion Detection Using Belief Propagation
172. Experimental Results
173. Conclusions Future Work
174. References
175. Robust and Efficient Multipose Face Detection Using Skin Color Segmentation
176. Introduction
177. Skin Color Model
178. Invariant Features
179. Experimental Results
180. Results on CVL Database
181. Classification on a Video Sequence
182. Conclusions
183. References
184. Image Analysis and Processing
185. Contour Regularity Extraction Based on String Edit Distance
186. Motivation
187. Background and Notation
188. Extended Freeman Chain Codes
189. Edit Distance
190. Problem Definition and Solution Outline
191. String Fusion Operation
192. Finding Similar Regions
193. Prototype Construction
194. Building Prototypes from a Contour Set
195. Experimental Results
196. Conclusions and Future Work
197. References
198. Cell Division Detection on the Arabidopsis Thaliana Root
199. Introduction
200. Methodology
201. Nuclei Detection
202. Nuclei Division Detection
203. Temporal Filtering of False Division Detections
204. Results
205. Conclusion
206. References
207. HOG-Based Decision Tree for Facial Expression Classification
208. Introduction
209. Selection of Discriminative Features
210. Bottom-Up Hierarchical Tree Learning
211. Results
212. Conclusions
213. References
214. Class Representative Visual Words for Category-Level Object Recognition
215. Introduction
216. Class Representative Visual Words
217. Cluster Precision Maximization
218. Experimental Results
219. Conclusion
220. References
221. Multi-Scale Image Analysis Applied to Radioastronomical Interferometric Data
222. Background
223. Wavelet Representation Using the “`a Trous” Algorithm
224. A Catalogued Radio Image and Its Wavelet Decomposition
225. Our Proposal
226. Results and Discussion
227. References
228. Robust Statistical Estimation Applied to Automatic Lip Segmentation
229. Introduction
230. Mouth-Region Detection
231. Robust Statistical Estimation
232. Cluster Identification and Results
233. Cluster Grouping and Results Using Probabilistic and Spatial Cluster Analysis
234. Evaluation Framework
235. Conclusions and Future Work
236. References
237. Textural Features for Hyperspectral Pixel Classification
238. Introduction
239. Textural Features
240. Hyperspectral Database
241. Experimental Results
242. Results Using the Best Band for Grey Level Classification
243. Results Using Individual Bands
244. Results for Clusters of Bands
245. Conclusions
246. References
247. A New Clustering Algorithm for Color Image Segmentation
248. Introduction
249. Self – organising Feature Map and Learning Process
250. Proposed Clustering Algorithm
251. Principle of the Algorithm
252. Clustering Algorithm
253. Application to Color Image Classification
254. Conclusion
255. References
256. A Top-Down Approach for Automatic Dropper Extraction in Catenary Scenes
257. Introduction
258. Overview of the Proposed Approach
259. Automatic Dropper Detection
260. Dropper Rough Localization
261. Dropper Anchor Point Detection
262. Dropper Classification
263. Alignment on Catenary Stave Models
264. Experiments and Results
265. Conclusion and Future Works
266. References
267. A Scale Invariant Interest Point Detector for Discriminative Blob Detection
268. Introduction
269. Curvature Analysis on Images
270. Discriminant Blob Detection Using Trajectories
271. Experimental Results
272. Conclusions
273. References
274. Enhancing In-Vitro IVUS Data for Tissue Characterization
275. Introduction
276. Plaque Characterization in Hybrid Data
277. Data Processing
278. Features Extraction
279. Enhancing In-Vitro Data for Tissue Characterization
280. Experimental Results
281. Conclusions
282. References
283. Toward Robust Myocardial Blush Grade Estimation in Contrast Angiography
284. Introduction
285. Previous Work
286. ProposedMethod
287. Detection of the Blush Area
288. Tracking by Optical Flow
289. Vessel Detection
290. Measuring Opacity in the Blush Area
291. Extraction of Blush Parameters
292. Preliminary Results
293. Conclusion and Future Works
294. References
295. Multi-spectral Texture Characterisation for Remote Sensing Image Segmentation
296. Introduction
297. The Texem Model
298. Segmentation with Inter-scale Post-fusion
299. Multi-spectral Local Difference Texems – MLDT
300. Experimental Data
301. Results
302. Conclusions
303. References
304. Texture Measuring by Means of Perceptually-Based Fineness Functions
305. Introduction
306. Perceptually-Based Fineness Functions
307. Assessment Collection
308. Fitting the Function
309. Results
310. Conclusions
311. References
312. High Performance Circle Detection through a GPU Rasterization Approach
313. Introduction
314. Improving the Hough Transform on the GPU
315. Error Compensation
316. Empirical Results
317. Qualitative Analysis
318. Quantitative Analysis
319. Computational Savings
320. Summary and Conclusions
321. References
322. Appearance and Shape Prior Alignments in Level Set Segmentation
323. Introduction
324. Level Set for Segmentation Task
325. Appearance Model
326. Shape Model
327. Numerical Algorithms
328. Experiments and Applications
329. Conclusions
330. References
331. The Diagonal Split: A Pre-segmentation Step for Page Layout Analysis and Classification
332. Introduction
333. The Diagonal Split
334. Formulation
335. Experiments and Results
336. Layout Generation
337. Layout Distance Definition
338. Classification and Retrieval
339. Results
340. Conclusions
341. References
342. Robust Super-Resolution Using a Median Filter for Irregular Samples
343. Introduction
344. Median Filter for Irregular Samples
345. Performance Analysis of Robust Noise Removal Methods
346. Conclusions
347. References
348. Left Ventricle Segmentation from Heart MDCT
349. Introduction
350. Left Ventricle Segmentation
351. Principal Axis Estimation
352. Reference Phase Segmentation
353. Full Exam Segmentation
354. Evaluation
355. Methods
356. Results
357. Conclusions and Future Work
358. References
359. A Comparison of Wavelet Based Spatio-temporal Decomposition Methods for Dynamic Texture Recognition
360. Introduction
361. Discrete Wavelet Transform and Filter Banks
362. Video Analysis Using Wavelet Decomposition
363. Spatial Wavelet Decomposition
364. Temporal Wavelet Decomposition
365. 2D+T Wavelet Decomposition Using Filter Product
366. J. R. Smith {it et al}. Wavelet Decomposition
367. Computing Dynamic Texture Descriptors
368. Results
369. Conclusion
370. References
371. A Fast Anisotropic Mumford-Shah Functional Based Segmentation
372. Introduction
373. Material and Methods
374. The ROF Denoising Model
375. The Chan-Vese Segmentation Model
376. The Link between Chan-Vese and ROF Model
377. The Model Problem
378. Anisotropic Mumford-Shah Model
379. The Link between the Anisotropic Mumford-Shah Model and ROF Model
380. Numerical Results
381. Discussion and Further Work
382. References
383. Labeling Still Image Databases Using Graphical Models
384. Introduction
385. Related Work
386. The Probability Model
387. Reviewing the FEP Algorithm
388. Experiments
389. Labeling on Still Image Databases
390. Evaluating Computational Complexity
391. Conclusions
392. References
393. Pattern Recognition
394. AdaBoost Multiple Feature Selection and Combination for Face Recognition
395. Introduction
396. Proposal Overview
397. Kernel Filters
398. Gabor Features for Face Recognition
399. Differential Features
400. Harris Corner Detector
401. Feature Selection and Combination by AdaBoost
402. Fusion Architectures
403. AdaBoost Algorithm
404. Experiments and Discussion
405. Conclusions
406. References
407. Median Graph Computation by Means of a Genetic Approach Based on Minimum Common Supergraph and Maxim
408. Introduction
409. Definitions and Notation
410. Generalized Median Graph
411. Theoretical Properties of the Median Graph
412. A New Genetic Algorithm
413. Experiments
414. Conclusions and Future Work
415. References
416. Fatty Liver Characterization and Classification by Ultrasound
417. Introduction
418. Problem Formulation
419. Classifier
420. Experimental Results
421. Conclusions
422. References
423. Sleep/Wakefulness State from Actigraphy
424. Introduction
425. Problem Formulation
426. Sleep/Wakefulness State Estimation
427. Experimental Results
428. Conclusions
429. References
430. A Random Extension for Discriminative Dimensionality Reduction and Metric Learning
431. Introduction
432. Linear Dimensionality Reduction and Metric Learning
433. MCML with Randomized Landmark Points
434. Experimental Results
435. Concluding Remarks
436. References
437. Minimum Error-Rate Training in Statistical Machine Translation Using Structural SVMs
438. Introduction
439. Minimum Error-Rate Training
440. Structural Support Vector Machines
441. Minimum Error-Rate Training Using SSVMs
442. Experimental Setup
443. Baseline System
444. Results
445. Previous Work and Discussion
446. References
447. Morpheme-Based Automatic Speech Recognition of Basque
448. Introduction
449. Morphological Units
450. Getting the Morphological Units
451. Using the Morphemes
452. TaskandCorpora
453. Experimental Evaluation
454. Conclusions
455. References
456. On a Kernel Regression Approach to Machine Translation
457. Introduction
458. The Training Process
459. The Linear Regression
460. The Decoding Problem
461. The Aliasing Problem
462. The Unknown n-Grams Problem
463. Adding Scores to the Search
464. Experimental Results
465. Conclusions and Future Work
466. References
467. Experimental Analysis of Insertion Costs in a Na”{ı}ve Dynamic MDF-Tree
468. Introduction
469. New Incremental Indexing Approximation
470. Rebuilding the Tree
471. Experiments
472. Conclusions
473. References
474. An Approach to Estimate Perplexity Values for Language Models Based on Phrase Classes
475. Introduction
476. Description of Language Models
477. Baseline Language Models
478. LMs Based on Phrase Classes
479. Estimating PP Value
480. Approach1
481. Approach2
482. Experimental Results
483. Conclusions
484. References
485. An Adaptive Approach for Affine-Invariant 2D Shape Description
486. Introduction
487. Proposed Method
488. Adaptive Estimation of the Region-of-Support
489. Affine-Invariant Shape Descriptor
490. Our Shape Descriptor under General Affine Transformations
491. Experimental Results
492. Conclusions
493. References
494. Towards Real-Time Human Action Recognition
495. Introduction
496. Related Work
497. Human Detection
498. Feature Extraction
499. Correlation-Based Human Detection
500. Action Recognition
501. Computation of Component Angles
502. Experimental Results
503. Conclusions
504. References
505. Using One-Class Classification Techniques in the Anti-phoneme Problem
506. Introduction
507. Segment-Based Speech Recognition
508. The Anti-phoneme Problem
509. Modelling All Phonemes with Gaussians
510. The Incorrect Segment Sampling Algorithm
511. Using General Counter-Example Generation
512. Experiments
513. The Speech Recognition Environment
514. Testing
515. Results
516. Conclusions and Future Work
517. References
518. Index of Balanced Accuracy: A Performance Measure for Skewed Class Distributions
519. Introduction
520. Evaluation of Classifier Performance in Imbalanced Domains
521. The New Performance Evaluation Method
522. The Dominance Index
523. The Balanced Accuracy Graph
524. Index of Balanced Accuracy
525. A Theoretical Example
526. Experiments
527. The Results
528. Conclusions and Further Extensions
529. References
530. Chromatographic Pattern Recognition Using Optimized One-Class Classifiers
531. Introduction
532. One-Class Classifier Optimization
533. Classifier Performance Assessment Using ROC Analysis
534. Automatic Estimation of the Rejection Parameter
535. Recognition of Chromatographic Profiles Using One-Class Classifiers
536. Experimental Results
537. Conclusions
538. References
539. Variable Order Finite-Context Models in DNA Sequence Coding
540. Introduction
541. Low Order Finite-Context Models
542. Variable Order Finite-Context Models
543. Tree-Structured Representation of VOFCM
544. Experimental Results
545. Conclusions
546. References
547. Characterizing Graphs Using Spherical Triangles
548. Introduction
549. Graphs Embedded into Manifolds
550. Geometric Preliminaries
551. Hausdorff Distance
552. Multidimensional Scaling
553. Experiments
554. Conclusion and Future Work
555. References
556. Score Fusion by Maximizing the Area under the ROC Curve
557. Introduction
558. Score Fusion by Maximizing the AUC
559. Score Normalization
560. Score Fusion Model
561. AUC Maximization
562. Notes on the Implementation of the Algorithm
563. Extensions of the Algorithm
564. Experiments
565. Conclusions
566. References
567. Gender Recognition from a Partial View of the Face Using Local Feature Vectors
568. Introduction
569. Methodology
570. Feature Extraction
571. Classification
572. Experiments
573. Description of the Database
574. Description of Experiments
575. Discussion of Results
576. Conclusions
577. References
578. Melodic Track Identification in MIDI Files Considering the Imbalanced Context
579. Introduction
580. Methodology
581. Track Feature Extraction
582. Resampling
583. Classifiers
584. Performance Measures in Class Imbalance Problems
585. Experimental Results
586. Datasets
587. Experimental Design
588. Experiment I
589. Experiment II
590. Conclusions and Future Work
591. References
592. Bernoulli HMMs at Subword Level for Handwritten Word Recognition
593. Introduction
594. Hidden Markov Models
595. Subunit Models Based on HMMs
596. Bernoulli HMM
597. Experiments
598. Concluding Remarks and Future Work
599. References
600. Simultaneous Identity and Expression Recognition Using Face Geometry on Low Dimensional Manifolds
601. Introduction
602. Active Appearance Models
603. Shape and Texture Models
604. Model Fitting
605. Laplacian EigenMaps
606. Simultaneous Identity and Facial Expression Recognition
607. Experimental Results

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