Mohamed Benkedadra- PhD Student at University of Mons
Mohamed Benkedadra
- PhD Student at University of Mons
About
7
Publications
2,943
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27
Citations
Introduction
I write code for fun and for profit.
Current institution
Education
September 2015 - September 2020
Publications
Publications (7)
There are several studies on road lane detection but very few address adverse conditions for acquisition such as sun glare. Loss of details in underexposed images captured facing a low sun leads to misleading road lane detection. High Dynamic Range Imaging methods are used to acquire most details in such scenes. Unfortunately, these techniques are...
Computer vision tasks such as object detection and segmentation rely on the availability of extensive, accurately annotated datasets. In this work, We present CIA, a modular pipeline, for (1) generating synthetic images for dataset augmentation using Stable Diffusion, (2) filtering out low quality samples using defined quality metrics, (3) forcing...
Video surveillance and image acquisition systems represent one of the most active research topics in computer vision and smart city domains. The growing concern for public and workers’ safety has led to a significant increase in the use of surveillance cameras that provide high-definition images and even depth maps when 3D cameras are available. Co...
Questions
Question (1)
Hello,
I have been writing code for a paper and I'm reading an image in RGB space in OpenCV which means that its read as a 3D matrix (HEIGHT x WIDTH x 3 (RGB) ).
I'm flattening the image into a 2D matrix ( NB_PIXELS x 3 (RGB) ) using this code :
image = image.reshape(len(image) * len(image[0]), len(image[0][0]))
This flattening process is an important step in the method that I'm going over in the paper.
Since I can't have the paper depend on a library like NumPy I would like a more elegant way to explain the flattening. Are there any papers that I can reference or any equations, methods ..etc that I can use as a replacement to the code that I shared?
Thank you!
