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CS-F441: Selected Topics from Computer Science (Deep ... › dl › lecture-12-kt-cv.pdf2 Cite 6375...
Transcript of CS-F441: Selected Topics from Computer Science (Deep ... › dl › lecture-12-kt-cv.pdf2 Cite 6375...
CS-F441: SELECTED TOPICS FROM COMPUTER
SCIENCE (DEEP LEARNING FOR NLP & CV)
Lecture-KT-14: Segmentation (U-Net), Object Detection (Yolo)
Dr. Kamlesh Tiwari,Assistant Professor,
Department of Computer Science and Information Systems,BITS Pilani, Rajasthan-333031 INDIA
Nov 20, 2019 (Campus @ BITS-Pilani July-Dec 2019)
Segmentation
ISBI challenge for segmentation of neuronal structures in electronmicroscopic stacksWorks with very few training images (30/application) and touchingboundary. Yield more precise segmentationData augmentation is essential (mainly shift, rotation and elasticdeformation)
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U-Net1
ISBI DIC-HeLa achieved 77.6% iou as compared to 46.0% secondISBI Cell tracking 2015, achieved 92% IoU as compared to 83%second
1Cite 9576 O. Ronneberger and P.Fischer and T. Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation,
Medical Image Computing and Computer-Assisted Intervention (MICCAI), LNCS-9351, pages 234–241, Springer-2015
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Object Detection
What is there andwhere?
Deformative PartModel, andF-RCNN
Apply the model to an image at multiple locations and scales.High scoring regions are considered detections.
Yolo: apply a single neural network to the full image that divides it intoregions and predicts bounding boxes and probabilities for each region
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Object Detection
What is there andwhere?
Deformative PartModel, andF-RCNN
Apply the model to an image at multiple locations and scales.High scoring regions are considered detections.
Yolo: apply a single neural network to the full image that divides it intoregions and predicts bounding boxes and probabilities for each region
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Object Detection
What is there andwhere?
Deformative PartModel, andF-RCNN
Apply the model to an image at multiple locations and scales.High scoring regions are considered detections.
Yolo: apply a single neural network to the full image that divides it intoregions and predicts bounding boxes and probabilities for each region
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Yolo 2
Conditional probability mapSee https://pjreddie.com/darknet/yolo/
2Cite 6375 Redmon, Joseph and Divvala, Santosh and Girshick, Ross and Farhadi, Ali, You only look once: Unified,
real-time object detection, IEEE conference on computer vision and pattern recognition (CVPR), pages 779–788, 2016
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Yolo
S × S segments, gives B bounding boxes with confidence, and Cclass probabilities. So S × S × (B × 5 + C) values. S:7, B:2, C:20
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Yolo
It is fastSpeed comes at the price of accuracy. Improved to 69%Generalizes wellLatest version YOLOv3 2018
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Thank You!
Thank you very much for your attention3 !
Queries ?
3Credit: https://www.youtube.com/watch?v=NM6lrxy0bxs
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