Search Results for author:
Found 16 papers, 5 papers with code
Using Curvilinear Features in Focus for Registering a Single Image to a 3D Object
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.
SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks
The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge.
Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification
This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography.
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network
Diversity of food and its attributes represents the culinary habits of peoples from different countries.
Cultural Vocal Bursts Intensity Prediction
General Classification
+1
REFUGE CHALLENGE 2018-Task 2:Deep Optic Disc and Cup Segmentation in Fundus Images Using U-Net and Multi-scale Feature Matching Networks
In this paper, an optic disc and cup segmentation method is proposed using U-Net followed by a multi-scale feature matching network.
MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams
This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams.
Breast Tumor Segmentation and Shape Classification in Mammograms using Generative Adversarial and Convolutional Neural Network
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.
Support Vector Machine (SVM) Recognition Approach adapted to Individual and Touching Moths Counting in Trap Images
The proposed classification method can be trained with a small set of images.
SLSNet: Skin lesion segmentation using a lightweight generative adversarial network
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.
An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning
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.
Adversarial Learning with Multiscale Features and Kernel Factorization for Retinal Blood Vessel Segmentation
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.
Absolute distance prediction based on deep learning object detection and monocular depth estimation models
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.
Designing and Analyzing the PID and Fuzzy Control System for an Inverted Pendulum
The inverted pendulum is a non-linear unbalanced system that needs to be controlled using motors to achieve stability and equilibrium.
Using The Feedback of Dynamic Active-Pixel Vision Sensor (Davis) to Prevent Slip in Real Time
The objective of this paper is to describe an approach to detect the slip and contact force in real-time feedback.
GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network
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
Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings
The joint utilization of diverse data sources for medical imaging segmentation has emerged as a crucial area of research, aiming to address challenges such as data heterogeneity, domain shift, and data quality discrepancies.
