Summary of Color Recognition in Challenging Lighting Environments: Cnn Approach, by Nizamuddin Maitlo et al.
Color Recognition in Challenging Lighting Environments: CNN Approach
by Nizamuddin Maitlo, Nooruddin Noonari, Sajid Ahmed Ghanghro, Sathishkumar Duraisamy, Fayaz Ahmed
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel color detection method for computer vision applications using a Convolutional Neural Network (CNN). The method addresses the gap in current techniques by leveraging image segmentation and edge detection to isolate objects in various lighting conditions. By feeding these segmented objects into a CNN trained for color detection, the approach achieves robust results in different lighting scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a computer to recognize colors like humans do. But, just like how our perception of colors changes depending on the lighting, computers have trouble detecting colors too! Researchers are working hard to improve this “color detection” skill for computers. They’ve tried different methods, but there’s still room for improvement. This paper suggests a new way to do it using something called Convolutional Neural Networks (CNNs). It works by first separating objects from the background and then feeding those objects into a special kind of computer program designed specifically to detect colors. The results are really promising! |
Keywords
* Artificial intelligence * Cnn * Image segmentation * Neural network
