Object Recognition 2

Computer Vision

Alex Net

3

“ImageNet Classification with Deep Convolutional Neural Networks”, 2012

- ImageNet - ReLU, Dropout, Data augmentation

VGG Net

4

VGG “Very Deep Convolutional Networks for Large-Scale Image Recognition“. 2015

- ILSVRC 2014 Winner (localization and classification) - 3x3 conv, 2x2 pooling and deeper layers

GoogLeNet

5

GoogleNet “Going deeper with convolutions”, 2014

- ILSVRC 2014 - Inception modules, more than 100 layers

ResNet

6

MS ResNet “Deep Residual Learning for Image Recognition”, 2015

- ILSVRC 2015 Winner - Residual Block, 152 layers

R-CNN

7

R-CNN “Rich feature hierarchies for accurate object detection and semantic segmentation”

- Object detection including classification

Object Recognition in 3D

Computer Vision

https://www.youtube.com/watch?v=-l4ih6bpNTQ

https://www.youtube.com/watch?v=C4DO1nPJo1U

https://www.youtube.com/watch?v=lPQZIrdIo0g

https://www.youtube.com/watch?v=BjL-DW4jVEM

Introduction to the Literature

23

Top Conference in Computer Vision

1. CVPR

2. ICCV

3. ECCV

Top Journal in Computer Vision

1. IEEE TPAMI (IF: 9.455, JCR Ranking 2nd ~0.8%)

2. Springer IJCV (IF: 11.541, JCR Ranking 1st ~1.5%)

h-index: a scholar with an index of h has published h papers

each of which has been cited in other papers at least h times

https://scholar.google.com/citations?view_op=top_venues&hl=en&vq=eng_computervisionpatternrecognition

https://www.thecvf.com/ http://ieeexplore.ieee.org/

Oral presentation

Spotlight presentation

Poster presentation

CVPR 2018 conference

2 column < 9 pages

Peer Review by

AC & 3 Reviewers

~ 3300 submissions ~ 970 accepted papers

Final Project

Computer Vision

Topics 1. Stereo Matching Improvement

- 수업시간에 배운 방식으로 최적의 성능 내기 + Dynamic Programming 추가

- 기타 새로운 문제 해결 방식 추가: Occlusion, Hole filling, Boundary noise, thin object, non-textured region

Dataset: http://vision.middlebury.edu/stereo/data/scenes2014/

2. Panorama using Homography

- 신뢰도 높고 더 많은 Matching point pair 찾기: ex) SIFT, etc

- Homography 계산 정확도 향상, tone mapping

Dataset: use your own pictures (more than 10 images)

3. Change Detection

- 개선된 Detection Result

- 노이즈 제거, Clutter 제거, Morphological post procession

Dataset: http://jacarini.dinf.usherbrooke.ca/static/dataset/badWeather/skating.zip

4. CNN based Classification using Tensorflow and MNIST

- 개선된 Classification Result on distorted (affine, occlusion, noisy) test images

제출 내용: Commented Source Code, Result Images, Report Document

Pick one!!!!