Computer Vision Research Discipline

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A Computer Vision Research Discipline is a computing research discipline that studies automated vision and automated vision algorithms (to develop automated vision systems).



References

2020

  • (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Computer_vision Retrieved:2020-8-13.
    • Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.

      Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.

      The scientific discipline of computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner or medical scanning device. The technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems.

      Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, visual servoing, 3D scene modeling, and image restoration.


2020

   3D computer vision 
   Action and behavior recognition 
   Adversarial learning, adversarial attack and defense methods
   Biometrics, face, gesture, body pose
   Computational photography, image and video synthesis 
   Datasets and evaluation 
   Efficient training and inference methods for networks 
   Explainable AI, fairness, accountability, privacy, transparency and ethics in vision 
   Image retrieval 
   Low-level and physics-based vision 
   Machine learning architectures and formulations 
   Medical, biological and cell microscopy 
   Motion and tracking 
   Neural generative models, auto encoders, GANs
   Optimization and learning methods 
   Recognition (object detection, categorization) 
   Representation learning, deep learning 
   Scene analysis and understanding 
   Segmentation, grouping and shape 
   Transfer, low-shot, semi- and un- supervised learning 
   Video analysis and understanding 
   Vision + language, vision + other modalities 
   Vision applications and systems, vision for robotics and autonomous vehicles
   Visual reasoning and logical representation

2009

  • Master's Degree in Statistics at the University of Chicago. http://www.stat.uchicago.edu/admissions/ms-degree.html
    • Computer Vision: Object recognition and detection, in medical imaging, regular photos, digitized documents and a variety of other sources - is a recurrent and critical issue in science, industry and modern communications. The faculty includes specialists in the analysis of visual signals.