References - University of Tasmania · 2014-11-18 · REFERENCES 123 Popovici, V., & Thiran, J.P....
Transcript of References - University of Tasmania · 2014-11-18 · REFERENCES 123 Popovici, V., & Thiran, J.P....
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A Face detection tables
This appendix contains the face detection true positive and false positive counts made
by the three main methods compared in chapter 6. These counts are for detections on
the MIT/CMU frontal face testing sets A, B and C, as described in section 4.1. They
may be comparable with other detection experiments on these images, although this
thesis used its own annotations which means such comparisons won’t be exact.
Fig. 6.11 contains a graph of these counts.
126
Appendix A Face detection tables 127
Table A.1: Face detection true and false positive counts for binary detection, binary
detection followed by hill-climbing and confidence mapping
B Confidence-based ROC curves
This appendix contains the individual ROC curves plotted during rotated object de-
tection using the confidence-based methods described in chapter 6. As in section 5.4,
graphs are shown in pairs, with the first showing the angle range increasing from 0◦ to
the approximately best range, and the second showing the angle range increasing from
there to 90◦.
The curves compared in section 6.4 follow the best points from each pair of these
graphs, using the method described in section 2.7.3.1.
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Appendix B Confidence-based ROC curves 129
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Figure B.2: ROC curves for fish detection by rotated cascades using binary detection
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Appendix B Confidence-based ROC curves 130
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varying the cascade random angle range
Appendix B Confidence-based ROC curves 131
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Figure B.4: ROC curves for fish detection by rotated cascades using confidence map-
ping, varying the cascade random angle range
Appendix B Confidence-based ROC curves 132
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Figure B.5: ROC curves for seahorse segment detection on rotated images using binary
detection followed by hill-climbing, varying the cascade random angle range
Appendix B Confidence-based ROC curves 133
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Figure B.6: ROC curves for seahorse segment detection on rotated images using con-
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Appendix B Confidence-based ROC curves 134
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Figure B.7: ROC curves for seahorse segment detection by rotated cascades using
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Appendix B Confidence-based ROC curves 135
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(d) Seahorse bodies, 10◦..90◦ range
Figure B.8: ROC curves for seahorse segment detection by rotated cascades using
confidence mapping, varying the cascade random angle range
C Example images with detections
This appendix shows example face, fish and seahorse images with the detections made
by the methods compared in chapter 6. The fish and seahorse segment ‘binary cascade
detections’ are also the detections made in chapter 5 when the angle step is 15◦.
136
Appendix C Example images with detections 137
(a) Original image
(b) Binary cascade detections (c) Hill-climbed detections
(d) Binary cascade detections after merging
(numbers show neighbours)
(e) Hill-climbed detections after merging
(numbers show neighbours)
Figure C.1: Example face image with binary detections and hill-climbed detections
Appendix C Example images with detections 138
(a) No failure tolerance confidence map
(line lengths show ln(confidence))
(b) Failure tolerance=1 confidence map
(line lengths show ln(confidence))
(c) No failure tolerance confidence sum (d) Failure tolerance=1 confidence sum
(e) No failure tolerance detections
(numbers show confidence)
(f) Failure tolerance=1 detections
(numbers show confidence)
Figure C.2: Example face image with detections made by confidence mapping
Appendix C Example images with detections 139
(a) Attribute proportion=0.8 confidence map
(line lengths show ln(confidence))
(b) Attribute proportion=0.9 confidence map
(line lengths show ln(confidence))
(c) Attribute proportion=0.8 confidence sum (d) Attribute proportion=0.9 confidence sum
(e) Attribute proportion=0.8 detections
(numbers show confidence)
(f) Attribute proportion=0.9 detections
(numbers show confidence)
Figure C.3: Example face image with detections made by confidence mapping with
virtual attribute subsetting
Appendix C Example images with detections 140
(a) Original image
(b) Binary cascade detections (c) Hill-climbed detections
(d) Binary cascade detections after merging
(numbers show neighbours)
(e) Hill-climbed detections after merging
(numbers show neighbours)
Figure C.4: Example fish image with binary detections and hill-climbed detections
made on rotated images, angle step=15◦, angle range=30◦
Appendix C Example images with detections 141
(a) No failure tolerance confidence map
(line lengths show ln(confidence))
(b) Failure tolerance=1 confidence map
(line lengths show ln(confidence))
(c) No failure tolerance confidence sum (d) Failure tolerance=1 confidence sum
(e) No failure tolerance detections
(numbers show confidence)
(f) Failure tolerance=1 detections
(numbers show confidence)
Figure C.5: Example fish image with detections made by confidence mapping on ro-
tated images, angle step=15◦, angle range=30◦
Appendix C Example images with detections 142
(a) Attribute proportion=0.8 confidence map
(line lengths show ln(confidence))
(b) Attribute proportion=0.9 confidence map
(line lengths show ln(confidence))
(c) Attribute proportion=0.8 confidence sum (d) Attribute proportion=0.9 confidence sum
(e) Attribute proportion=0.8 detections
(numbers show confidence)
(f) Attribute proportion=0.9 detections
(numbers show confidence)
Figure C.6: Example fish image with detections made by confidence mapping with
virtual attribute subsetting on rotated images, angle step=15◦, angle range=30◦
Appendix C Example images with detections 143
(a) Original image
(b) Binary cascade detections (c) Hill-climbed detections
(d) Binary cascade detections after merging
(numbers show neighbours)
(e) Hill-climbed detections after merging
(numbers show neighbours)
Figure C.7: Example fish image with binary detections and hill-climbed detections
made by rotated cascades, angle step=15◦, angle range=30◦
Appendix C Example images with detections 144
(a) No failure tolerance confidence map
(line lengths show ln(confidence))
(b) Failure tolerance=1 confidence map
(line lengths show ln(confidence))
(c) No failure tolerance confidence sum (d) Failure tolerance=1 confidence sum
(e) No failure tolerance detections
(numbers show confidence)
(f) Failure tolerance=1 detections
(numbers show confidence)
Figure C.8: Example fish image with detections made by confidence mapping with
rotated cascades, angle step=15◦, angle range=30◦
Appendix C Example images with detections 145
(a) Attribute proportion=0.8 confidence map
(line lengths show ln(confidence))
(b) Attribute proportion=0.9 confidence map
(line lengths show ln(confidence))
(c) Attribute proportion=0.8 confidence sum (d) Attribute proportion=0.9 confidence sum
(e) Attribute proportion=0.8 detections
(numbers show confidence)
(f) Attribute proportion=0.9 detections
(numbers show confidence)
Figure C.9: Example fish image with detections made by confidence mapping with
virtual attribute subsetting and rotated cascades, angle step=15◦, angle range=30◦
Appendix C Example images with detections 146
Figure C.10: Original seahorse images
Appendix C Example images with detections 147
(a) Binary cascade detections
(b) Binary cascade detections after merging (numbers show neighbours)
Figure C.11: Example seahorse images with body (red) and head (green) detections
made on rotated images by binary cascades, angle step=15◦, head angle range=5◦,
body angle range=10◦
Appendix C Example images with detections 148
(a) Hill-climbed detections
(b) Hill-climbed detections after merging (numbers show neighbours)
Figure C.12: Example seahorse images with body (red) and head (green) detections
made on rotated images by binary cascades followed by hill-climbing, angle step=15◦,
head angle range=5◦, body angle range=10◦
Appendix C Example images with detections 149
(a) Confidence map (line lengths show
ln(confidence)
(b) Confidence sum
(c) Detections (numbers show confidence)
Figure C.13: Example seahorse images with body (red) and head (green) detections
made on rotated images by confidence mapping, angle step=15◦, head angle range=20◦,
body angle range=15◦
Appendix C Example images with detections 150
(a) Matched from binary cascade detections (fig. C.11)
(b) Matched from confidence mapped segment detections (fig. C.13)
Figure C.14: Whole seahorse detections matched from segment detections (numbers
show match cost)