All Stories

  1. Measuring Changes in Upper Body Movement Due to Fasting Using a Camera
  2. 'Video-less' person body inactivity monitoring system for older adults at home
  3. On The Necessity of Multi-disciplinarity in The Development of At-home Health Monitoring Platforms for Older Adults: Systematic review (Preprint)
  4. On The Necessity of Multi-disciplinarity in The Development of At-home Health Monitoring Platforms for Older Adults: A Review and Evaluation Framework (Preprint)
  5. Using Deep Learning to Detect the Need for Forest Thinning: Application to the Lungau Region, Austria
  6. A multi-modal garden dataset and hybrid 3D dense reconstruction framework based on panoramic stereo images for a trimming robot
  7. A Robust Deformable Linear Object Perception Pipeline in 3D: From Segmentation to Reconstruction
  8. Identifying Student Struggle by Analyzing Facial Movement During Asynchronous Video Lecture Viewing: Towards an Automated Tool to Support Instructors
  9. When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)
  10. RGB-D-Based Framework to Acquire, Visualize and Measure the Human Body for Dietetic Treatments
  11. Automatic Hierarchical Classification of Kelps Using Deep Residual Features
  12. Classification of Ten Skin Lesion Classes: Hierarchical KNN versus Deep Net
  13. Incremental Unsupervised Domain-Adversarial Training of Neural Networks
  14. 3D Technologies to Acquire and Visualize the Human Body for Improving Dietetic Treatment
  15. Semantic Segmentation of SLAR Imagery with Convolutional LSTM Selectional AutoEncoders
  16. SDF-MAN: Semi-Supervised Disparity Fusion with Multi-Scale Adversarial Networks
  17. Color Homography: Theory and Applications
  18. Deep Image Representations for Coral Image Classification
  19. Consistent Semantic Annotation of Outdoor Datasets via 2D/3D Label Transfer
  20. Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders
  21. Understanding how fish speeds vary by temperature when you have very noisy data