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*My post explains Keypoint Detection(Landmark Detection), Image Matching, Object Tracking, Stereo Matching, Video Prediction, Optical Flow, Image Captioning.

Computer Vision is the technology which enables a computer to understand and analyze the visual things such as images, videos, etc.

There are many Computer Vision technologies as shown below:

(1) Image Classification(Recognition):

• can classify an entire image into one or more classes(labels) from one or more classes(labels): *Memos:
  • The image can be one frame in a video.
  • There is also Video Classification to classify an entire video into one or more classes(labels) from one or more classes(labels).
• has the method Single-Label Classification which has two methods Binary Classification and Multi-Class Classification.
• has the method Multi-Label Classification.

*Memos:

• Binary Classification can classify an entire image into a single class(label) from two classes(labels).
• Multi-Class Classification can classify an entire image into a single class(label) from more than two classes(labels).
• Multi-Label Classification can classify an entire image into multiple classes(labels) from more than two classes(labels).

(2) Object Localization:

• can localize the objects and interest regions in an image with bounding boxes. *The image can be one frame in a video.

(3) Object Detection:

• can localize and classify the objects and interest regions in an image with classes(labels) and bounding boxes. *The image can be one frame in a video.
• is the combination of Object Localization and Image Classification(Recognition).
• used for Object Tracking.

(4) Image Segmentation:

• can do Object Detection more precisely, differentiating stuff and things with colors: *Memos:
  • Stuff is uncountable things(classes) such as sky, sea, forrest, road, grass, landscape, etc.
  • Things are countable things(classes) such as car, tree, person, animal, flower, etc.
• has the popular methods Semantic Segmentation, Instance Segmentation and Panoptic segmentation: *Memos:
  • Semantic Segmentation is good at differentiating stuff but not good at differentiating things.
  • Instance Segmentation is good at differentiating things but not good at differentiating stuff.
  • Panoptic segmentation:
  • is good at differentiating both stuff and things.
  • is the combination of Semantic Segmentation and Instance Segmentation.
• is used for Medical Imaging(CT scans, MRIs, X-rays, etc), Autonomous Vehicles, Satellite Imagery, Agriculture, Robotics, Surveillance, Industrial Inspection, Face Recognition, etc.