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Found 16 papers, 3 papers with code
PAg-NeRF: Towards fast and efficient end-to-end panoptic 3D representations for agricultural robotics
In this work we present PAg-NeRF which is a novel NeRF-based system that enables 3D panoptic scene understanding.
Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots
Monitoring plants and fruits at high resolution play a key role in the future of agriculture.
Panoptic One-Click Segmentation: Applied to Agricultural Data
In weed control, precision agriculture can help to greatly reduce the use of herbicides, resulting in both economical and ecological benefits.
Explicitly incorporating spatial information to recurrent networks for agriculture
In agriculture, the majority of vision systems perform still image classification.
Towards Autonomous Crop-Agnostic Visual Navigation in Arable Fields
Autonomous navigation of a robot in agricultural fields is essential for every task from crop monitoring to weed management and fertilizer application.
Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture
By generating virtual temporal samples, we demonstrate that it is possible to train a lightweight RNN to perform semantic segmentation on two challenging agricultural datasets.
A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management
In this work, we obviate this assumption and introduce a rapidly deployable approach able to operate on any field without any weed species assumptions prior to deployment.
Towards Unsupervised Weed Scouting for Agricultural Robotics
This greatly limits deployability as classification systems must be retrained for any field with a different set of weed species present within them.
Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting - Combined Colour and 3D Information
This paper presents a 3D visual detection method for the challenging task of detecting peduncles of sweet peppers (Capsicum annuum) in the field.
Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification
Fine-grained classification is a relatively new field that has concentrated on using information from a single image, while ignoring the enormous potential of using video data to improve classification.
Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions
We evaluate these action recognition techniques under ideal conditions, as well as their sensitivity in more challenging conditions (variations in scale and translation).
Joint Recognition and Segmentation of Actions via Probabilistic Integration of Spatio-Temporal Fisher Vectors
The final classification decision for each frame is then obtained by integrating the class probabilities at the frame level, which exploits the overlapping of the temporal windows.
Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification
We propose a local modelling approach using deep convolutional neural networks (CNNs) for fine-grained image classification.
Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients
In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification.
Bags of Affine Subspaces for Robust Object Tracking
We propose an adaptive tracking algorithm where the object is modelled as a continuously updated bag of affine subspaces, with each subspace constructed from the object's appearance over several consecutive frames.
Summarisation of Short-Term and Long-Term Videos using Texture and Colour
We present a novel approach to video summarisation that makes use of a Bag-of-visual-Textures (BoT) approach.
