Dblp computer vision and pattern recognition

  • At the heart of a pattern recognition system are computer algorithms that are designed to analyze and interpret data.
    The data inputs can be words or texts, images, or audio files.
    Hence, pattern recognition is broader compared to computer vision which focuses on image recognition.
  • Visualization - Find objects that are not visible in the image.
    Recognition - Distinguish or detect objects in the image.
    Sharpening and restoration - Create an enhanced image from the original image.
    Pattern recognition - Measure the various patterns around the objects in the image.
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2020, Seattle, WA, USA, June 14-19, 2020. Computer Vision Foundation 

Can a computer vision task be viewed as a special case of image decomposition?

[Submitted on 2 Dec 2018 ( v1 ), last revised 5 Dec 2018 (this version, v2)] Many seemingly unrelated computer vision tasks can be viewed as a special case of image decomposition into separate layers.

,

Can a dip network capture low-level statistics of a single image?

In this paper we propose a unified framework for unsupervised layer decomposition of a single image, based on coupled "Deep-image-Prior" (DIP) networks.
It was shown [Ulyanov et al] that the structure of a single DIP generator network is sufficient to capture the low-level statistics of a single image.


Categories

Visual computer dblp
Computer vision ebook
Ebay computer vision
Is computer vision dead
Blurred vision due to computer use
Computer vision levels
Is computer vision hard
Computer vision classes
Computer vision ibm
Computer vision companies in singapore
Computer vision lab mit
Computer vision lab syllabus
Computer vision lab stanford
Computer vision lab yifan liu
Computer vision lab gatech
Computer vision lab logo
Computer vision mbzuai
Nba computer vision
Computer vision object detection python
Computer vision object detection algorithms