Search Results for author:
Found 15 papers, 8 papers with code
1D-Convolutional transformer for Parkinson disease diagnosis from gait
Our experimental results show that our approach is effective for detecting the different stages of Parkinson's disease from gait data, with a final accuracy of 88%, outperforming other state-of-the-art AI methods on the Physionet gait dataset.
Automatic counting of planting microsites via local visual detection and global count estimation
One of the main problems when planning planting operations is the difficulty in estimating the number of mounds present on a planting block, as their number may greatly vary depending on site characteristics.
Automating lichen monitoring in ecological studies using instance segmentation of time-lapse images
These cameras are used by ecologists in Newfoundland and Labrador to subsequently analyze and manually segment the images to determine lichen thalli condition and change.
HCT: Hybrid Convnet-Transformer for Parkinson's disease detection and severity prediction from gait
Our hybrid architecture exploits the strengths of both Convolutional Neural Networks (ConvNets) and Transformers to accurately detect PD and determine the severity stage.
VisiTherS: Visible-thermal infrared stereo disparity estimation of human silhouette
To address the aforementioned challenges, this paper proposes a novel approach where a high-resolution convolutional neural network is used to better capture relationships between the two spectra.
Automatic counting of mounds on UAV images: combining instance segmentation and patch-level correction
Counting the number of mounds is generally conducted through manual field surveys by forestry workers, which is costly and prone to errors, especially for large areas.
Transformers for 1D Signals in Parkinson's Disease Detection from Gait
This paper focuses on the detection of Parkinson's disease based on the analysis of a patient's gait.
Multiple Convolutional Features in Siamese Networks for Object Tracking
Siamese trackers demonstrated high performance in object tracking due to their balance between accuracy and speed.
MFST: Multi-Features Siamese Tracker
Based on this advanced feature representation, our algorithm achieves high tracking accuracy, while outperforming several state-of-the-art trackers, including standard Siamese trackers.
Coarse-to-Fine Object Tracking Using Deep Features and Correlation Filters
Motivated by this observation, and by the fact that discriminative correlation filters(DCFs) may provide a complimentary low-level information, we presenta novel tracking algorithm taking advantage of both approaches.
Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait
To our knowledge, this is the state-of-the-start performance in Parkinson's gait recognition.
Posture recognition using an RGB-D camera : exploring 3D body modeling and deep learning approaches
The first method is based on convolutional features extracted from 2D images.
Multi-Branch Siamese Networks with Online Selection for Object Tracking
In this paper, we propose a robust object tracking algorithm based on a branch selection mechanism to choose the most efficient object representations from multi-branch siamese networks.
Reproducible Evaluation of Pan-Tilt-Zoom Tracking
Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in computer vision for many years.
A Bag of Words Approach for Semantic Segmentation of Monitored Scenes
This paper proposes a semantic segmentation method for outdoor scenes captured by a surveillance camera.
