tailieunhanh - Object recognition: History and overview

To help you specialized culture and art have added references in the process of learning and study. Invite you to consult the lecture content "Object recognition: History and overview". Each of your content and references for additional lectures will serve the needs of learning and research. | Object Recognition: History and Overview Slides adapted from Fei-Fei Li, Rob Fergus, Antonio Torralba, and Jean Ponce How many visual object categories are there? ~10,000 to 30,000 Biederman 1987 1500-3000 basic-level nouns, ~10 types per basic-level category ~10,000 to 30,000 OBJECTS ANIMALS INANIMATE PLANTS MAN-MADE NATURAL VERTEBRATE MAMMALS BIRDS GROUSE BOAR TAPIR CAMERA So what does object recognition involve? Scene categorization outdoor city Image-level annotation: are there people? outdoor city Object detection: where are the people? Image parsing mountain building tree banner vendor people street lamp Variability: Camera position Illumination Shape parameters Within-class variations? Modeling variability Within-class variations Variability: Camera position Illumination q Alignment Roberts (1965); Lowe (1987); Faugeras & Hebert (1986); Grimson & Lozano-Perez (1986); Huttenlocher & Ullman (1987) Shape: assumed known . | Object Recognition: History and Overview Slides adapted from Fei-Fei Li, Rob Fergus, Antonio Torralba, and Jean Ponce How many visual object categories are there? ~10,000 to 30,000 Biederman 1987 1500-3000 basic-level nouns, ~10 types per basic-level category ~10,000 to 30,000 OBJECTS ANIMALS INANIMATE PLANTS MAN-MADE NATURAL VERTEBRATE MAMMALS BIRDS GROUSE BOAR TAPIR CAMERA So what does object recognition involve? Scene categorization outdoor city Image-level annotation: are there people? outdoor city Object detection: where are the people? Image parsing mountain building tree banner vendor people street lamp Variability: Camera position Illumination Shape parameters Within-class variations? Modeling variability Within-class variations Variability: Camera position Illumination q Alignment Roberts (1965); Lowe (1987); Faugeras & Hebert (1986); Grimson & Lozano-Perez (1986); Huttenlocher & Ullman (1987) Shape: assumed known Recall: Alignment Alignment: fitting a model to a transformation between pairs of features (matches) in two images Find transformation T that minimizes T xi xi ' Recall: Origins of computer vision L. G. Roberts, Machine Perception of Three Dimensional Solids, . thesis, MIT Department of Electrical Engineering, 1963. Alignment: Huttenlocher & Ullman (1987) Variability Camera position Illumination Internal parameters Invariance to: Duda & Hart ( 1972); Weiss (1987); Mundy et al. (1992-94); Rothwell et al. (1992); Burns et al. (1993) General 3D objects do not admit monocular viewpoint invariants (Burns et al., 1993) Projective invariants (Rothwell et al., 1992): Recall: invariant to similarity transformations computed from four points A B C D ACRONYM (Brooks and Binford, 1981) Representing and recognizing object categories is harder. Binford (1971), Nevatia & Binford (1972), Marr & Nishihara (1978) Recognition by components Geons (Biederman 1987) ??? .