In particular, our method provided comparable and promising results with a high prediction accuracy of 89% on the publicly KITTI and Make3D datasets along with a reduction of 40% in the number of trainable parameters compared to the state of the art solutions.
Ranked #1 on Monocular Depth Estimation on Make3D
The inverted pendulum is a non-linear unbalanced system that needs to be controlled using motors to achieve stability and equilibrium.
The objective of this paper is to describe an approach to detect the slip and contact force in real-time feedback.
Determining the distance between the objects in a scene and the camera sensor from 2D images is feasible by estimating depth images using stereo cameras or 3D cameras.
In this paper, we propose an efficient blood vessel segmentation method for the eye fundus images using adversarial learning with multiscale features and kernel factorization.
1 code implementation • 1 Jul 2019 • Md. Mostafa Kamal Sarker, Hatem A. Rashwan, Farhan Akram, Vivek Kumar Singh, Syeda Furruka Banu, Forhad U H Chowdhury, Kabir Ahmed Choudhury, Sylvie Chambon, Petia Radeva, Domenec Puig, Mohamed Abdel-Nasser
Thus, this article aims to achieve precise skin lesion segmentation with minimum resources: a lightweight, efficient generative adversarial network (GAN) model called SLSNet, which combines 1-D kernel factorized networks, position and channel attention, and multiscale aggregation mechanisms with a GAN model.
We propose to add an atrous convolution layer to the conditional generative adversarial network (cGAN) segmentation model to learn tumor features at different resolutions of BUS images.
The proposed classification method can be trained with a small set of images.
1 code implementation • 5 Sep 2018 • Vivek Kumar Singh, Hatem A. Rashwan, Santiago Romani, Farhan Akram, Nidhi Pandey, Md. Mostafa Kamal Sarker, Adel Saleh, Meritexell Arenas, Miguel Arquez, Domenec Puig, Jordina Torrents-Barrena
In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast mass within a region of interest (ROI) in a mammogram.
This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams.
In this paper, an optic disc and cup segmentation method is proposed using U-Net followed by a multi-scale feature matching network.
Diversity of food and its attributes represents the culinary habits of peoples from different countries.
2 code implementations • 25 May 2018 • Vivek Kumar Singh, Santiago Romani, Hatem A. Rashwan, Farhan Akram, Nidhi Pandey, Md. Mostafa Kamal Sarker, Jordina Torrents Barrena, Saddam Abdulwahab, Adel Saleh, Miguel Arquez, Meritxell Arenas, Domenec Puig
This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography.
no code implementations • 25 May 2018 • Md. Mostafa Kamal Sarker, Hatem A. Rashwan, Farhan Akram, Syeda Furruka Banu, Adel Saleh, Vivek Kumar Singh, Forhad U H Chowdhury, Saddam Abdulwahab, Santiago Romani, Petia Radeva, Domenec Puig
The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge.
The results presented highlight the quality of the features detected, in term of repeatability, and also the interest of the approach for registration and pose estimation.