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20172024
most citedColorectal cancer diagnosis from histology images: A comparative study

23 citations · 41 across the 16 of their papers we have counts for

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6 papers · 1 filter

cs.CV2024

Multimodal Crowd Counting with Pix2Pix GANs

Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

Most state-of-the-art crowd counting methods use color (RGB) images to learn the density map of the crowd. However, these methods often struggle to achieve higher accuracy in dense…

cs.CV2024

Curriculum for Crowd Counting -- Is it Worthy?

Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

Recent advances in deep learning techniques have achieved remarkable performance in several computer vision problems. A notably intuitive technique called Curriculum Learning (CL)…

cs.CV2023

Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs

Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

Visual crowd counting estimates the density of the crowd using deep learning models such as convolution neural networks (CNNs). The performance of the model heavily relies on the q…

cs.CV2023

Visual Crowd Analysis: Open Research Problems

Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

Over the last decade, there has been a remarkable surge in interest in automated crowd monitoring within the computer vision community. Modern deep-learning approaches have made it…

cs.CV20225 cited

Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives

Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

Crowd counting is an effective tool for situational awareness in public places. Automated crowd counting using images and videos is an interesting yet challenging problem that has…

cs.CV201923 cited

Colorectal cancer diagnosis from histology images: A comparative study

Junaid Malik, Serkan Kiranyaz, Suchitra Kunhoth +4

Computer-aided diagnosis (CAD) based on histopathological imaging has progressed rapidly in recent years with the rise of machine learning based methodologies. Traditional approach…