Lecture 1 - Course Introduction

Computer vision overview, Historical context, Course logistics

[slides] [video]

AdaBoost

Feature Based Object Recognition

  • “Sift” & Object Recognition
  • Problem: To map an entire object (stop sign) to another similar object is incredibly hard - due to camera angles, lighting, occlusion, viewpoint, intrinsic variation.
  • However, there tend to remain features that remain “diagnostic and invariant to changes”
  • Identifying critical features on an object, and match features to another object
  • Using the same building block (diagnostic features in images), we have made another step forward as a field and started to recognize holistic scenes.
    • Spatial Pyramid Matching
      • Features in the images that can give us clues as to which type of thing it is
      • Takes features from diff parts of images in diff resolutions and puts them together, then SVM is done on top of that

PASCAL Dataset

  • Image recognition benchmark dataset
  • Performance increases steadily over the years

Overfitting

  • Overfitting happens very fast with not enough training data
  • Solution: ImageNet - largest possible dataset of pictures, the world of objects, and use it for benchmarking and training

Deformable Part Model

results matching ""

    No results matching ""