Search Results for author:
Found 15 papers, 5 papers with code
Fast and robust curve skeletonization for real-world elongated objects
We consider the problem of extracting curve skeletons of three-dimensional, elongated objects given a noisy surface, which has applications in agricultural contexts such as extracting the branching structure of plants.
Automatic segmentation of trees in dynamic outdoor environments
Segmentation in dynamic outdoor environments can be difficult when the illumination levels and other aspects of the scene cannot be controlled.
A robotic vision system to measure tree traits
The autonomous measurement of tree traits, such as branching structure, branch diameters, branch lengths, and branch angles, is required for tasks such as robotic pruning of trees as well as structural phenotyping.
Robotics
Semantic Segmentation Refinement by Monte Carlo Region Growing of High Confidence Detections
The re-labeling approach employs a region growing mechanism that aggregates these pixels to neighboring areas with high confidence scores and similar appearance.
Apple Flower Detection using Deep Convolutional Networks
To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season.
FreeLabel: A Publicly Available Annotation Tool based on Freehand Traces
Large-scale annotation of image segmentation datasets is often prohibitively expensive, as it usually requires a huge number of worker hours to obtain high-quality results.
Multi-camera calibration with pattern rigs, including for non-overlapping cameras: CALICO
Infrastructure-based approaches are not suitable for stationary camera systems, and pattern-based approaches may constrain camera placement because shared fields of view or extremely large patterns are required.
Gaze Estimation for Assisted Living Environments
Effective assisted living environments must be able to perform inferences on how their occupants interact with one another as well as with surrounding objects.
Probabilistic Semantic Segmentation Refinement by Monte Carlo Region Growing
Experiments using multiple modern semantic segmentation networks and benchmark datasets demonstrate the effectiveness of our approach for the refinement of segmentation predictions at different levels of coarseness, as well as the suitability of the variance estimates obtained in the Monte Carlo iterations as uncertainty measures that are highly correlated with segmentation accuracy.
Deep Convolutional Likelihood Particle Filter for Visual Tracking
We propose a novel particle filter for convolutional-correlation visual trackers.
Unsupervised Spatio-temporal Latent Feature Clustering for Multiple-object Tracking and Segmentation
The feature vectors and the constraints graphs are then provided to the kmeans clustering algorithm to separate the corresponding data points in the latent space.
Tracking Passengers and Baggage Items using Multi-camera Systems at Security Checkpoints
The multiple hypothesis tracking algorithm then keeps track of the temporal identifiers of the targets based on the cluster centroids.
Deep Convolutional Correlation Iterative Particle Filter for Visual Tracking
This work proposes a novel framework for visual tracking based on the integration of an iterative particle filter, a deep convolutional neural network, and a correlation filter.
Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species
We employ a data augmentation and refinement approach to improve the accuracy of the model predictions.
Tracking Passengers and Baggage Items using Multiple Overhead Cameras at Security Checkpoints
Our SSL approach improves object detection by employing a test-time data augmentation and a regression-based, rotation-invariant pseudo-label refinement technique.
