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Found 18 papers, 5 papers with code
GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mapping
Specifically, we developed an enhanced GlacierNet2 architecture thatincorporates multiple models, automatic post-processing, and basin-level hydrological flow techniques to improve the mapping of DCGs such that it includes both the ablation and accumulation zones.
R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation
In particular, the proposed model integrates 3D convolution into the Recurrent Residual Neural Network based on U-Net.
Enhanced 3D Human Pose Estimation from Videos by using Attention-Based Neural Network with Dilated Convolutions
In this work, we show a systematic design (from 2D to 3D) for how conventional networks and other forms of constraints can be incorporated into the attention framework for learning long-range dependencies for the task of pose estimation.
Ranked #6 on
3D Human Pose Estimation
on HumanEva-I
Pyramid Point: A Multi-Level Focusing Network for Revisiting Feature Layers
We present a method to learn a diverse group of object categories from an unordered point set.
GanglionNet: Objectively Assess the Density and Distribution of Ganglion Cells With NABLA-N Network
The proposed method shows a robust 97. 49% detection accuracy for ganglion cells, when compared to counts by the expert pathologist, which demonstrates the robustness of GanglionNet.
DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation
We present the Dayton Annotated LiDAR Earth Scan (DALES) data set, a new large-scale aerial LiDAR data set with over a half-billion hand-labeled points spanning 10 square kilometers of area and eight object categories.
COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches
In this paper, we propose a fast and efficient way to identify COVID-19 patients with multi-task deep learning (DL) methods.
TGGLines: A Robust Topological Graph Guided Line Segment Detector for Low Quality Binary Images
Line segment detection is an essential task in computer vision and image analysis, as it is the critical foundation for advanced tasks such as shape modeling and road lane line detection for autonomous driving.
Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks
Several DL architectures have been proposed for classification, segmentation, and detection tasks in medical imaging and computational pathology.
Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis: a comprehensive evaluation with different use cases
Deep Learning (DL) approaches have been providing state-of-the-art performance in different modalities in the field of medical imagining including Digital Pathology Image Analysis (DPIA).
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network
The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches.
Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches
The experimental results show that the proposed DCNN models provide superior performance compared to the existing approaches for nuclei classification, segmentation, and detection tasks.
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches
Furthermore, DL approaches have explored and evaluated in different application domains are also included in this survey.
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
Improved Inception-Residual Convolutional Neural Network for Object Recognition
In this paper, we introduce a new DCNN model called the Inception Recurrent Residual Convolutional Neural Network (IRRCNN), which utilizes the power of the Recurrent Convolutional Neural Network (RCNN), the Inception network, and the Residual network.
Handwritten Bangla Character Recognition Using The State-of-Art Deep Convolutional Neural Networks
In spite of advances in object recognition technology, Handwritten Bangla Character Recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings.
Handwritten Bangla Digit Recognition Using Deep Learning
To improve the performance of Handwritten Bangla Digit Recognition (HBDR), we herein present a new approach based on deep neural networks which have recently shown excellent performance in many pattern recognition and machine learning applications, but has not been throughly attempted for HBDR.
Object Detection by Spatio-Temporal Analysis and Tracking of the Detected Objects in a Video with Variable Background
To detect actual moving object in this work, spatio-temporal blobs have been generated in each frame by spatio-temporal analysis of the image sequence using a three-dimensional Gabor filter.
