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                                   16.2.5 Flowers, Leaves, and Birds: Some Specialized Problems . . . 511
                               16.3 Image Classification in Practice . . . . . ... .. ... .. .. ... . 512
                                   16.3.1 Codes for Image Features .. .. ... .. ... .. .. ... . 513
                                   16.3.2 Image Classification Datasets . . . . . . . . . . . . . . . . . . 513
                                   16.3.3 Dataset Bias . . . . . .. .. .. ... .. ... .. .. ... . 515
                                   16.3.4 Crowdsourcing Dataset Collection . . . . . .. .. .. ... . 515
                               16.4 Notes . . . . . . .. .. .. ... .. .. ... .. ... .. .. ... . 517

                            17 Detecting Objects in Images                                      519
                               17.1 The Sliding Window Method . . . . . . . . . . . . . . . . . . . . . . 519
                                   17.1.1 Face Detection . .. ... .. .. ... .. ... .. .. ... . 520
                                   17.1.2 Detecting Humans . ... .. .. ... .. ... .. .. ... . 525
                                   17.1.3 Detecting Boundaries . . . . . . . . . . . . . . . . . . . . . . 527
                               17.2 Detecting Deformable Objects . . . . . . . .. .. ... .. .. ... . 530
                               17.3 The State of the Art of Object Detection . . . . ... .. .. ... . 535
                                   17.3.1 Datasets and Resources . . . . . ... .. ... .. .. ... . 538
                               17.4 Notes . . . . . . .. .. .. ... .. .. ... .. ... .. .. ... . 539

                            18 Topics in Object Recognition                                     540
                               18.1 What Should Object Recognition Do? . ... .. ... .. .. ... . 540
                                   18.1.1 What Should an Object Recognition System Do? . . . . . . . 540
                                   18.1.2 Current Strategies for Object Recognition . . . . . . . . . . . 542
                                   18.1.3 What Is Categorization? . . . . . . . . . . . . . . . . . . . . . 542
                                   18.1.4 Selection: What Should Be Described? .. ... .. .. ... . 544
                               18.2 Feature Questions . .. .. ... .. .. ... .. ... .. .. ... . 544
                                   18.2.1 Improving Current Image Features .. .. ... .. .. ... . 544
                                   18.2.2 Other Kinds of Image Feature . . . . . . . . . . . . . . . . . . 546
                               18.3 Geometric Questions . . . . ... .. .. ... .. ... .. .. ... . 547
                               18.4 Semantic Questions . . . . . ... .. .. ... .. ... .. .. ... . 549
                                   18.4.1 Attributes and the Unfamiliar . . . . . . . . . . . . . . . . . . 550
                                   18.4.2 Parts, Poselets and Consistency . . . . . . . . . . . . . . . . . 551
                                   18.4.3 Chunks of Meaning . . . . . . . . . . . . . . . . . . . . . . . . 554


                            VI   APPLICATIONS AND TOPICS                                       557

                            19 Image-Based Modeling and Rendering                               559
                               19.1 Visual Hulls . . ... .. .. ... .. .. ... .. ... .. .. ... . 559
                                   19.1.1 Main Elements of the Visual Hull Model . . . . . . . . . . . . 561
                                   19.1.2 Tracing Intersection Curves . .. ... .. ... .. .. ... . 563
                                   19.1.3 Clipping Intersection Curves . . . . . . . . . . . . . . . . . . 566
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