Search Results for author:
Found 9 papers, 1 papers with code
Learning Neural Radiance Fields of Forest Structure for Scalable and Fine Monitoring
This work leverages neural radiance fields and remote sensing for forestry applications.
Representation Disentaglement via Regularization by Causal Identification
In this work, we propose the use of a causal collider structured model to describe the underlying data generative process assumptions in disentangled representation learning.
DeepPatent: Large scale patent drawing recognition and retrieval
First, we introduce DeepPatent, a new large-scale dataset for recognition and retrieval of design patent drawings.
Ranked #3 on
Image Retrieval
on DeepPatent
SpectroscopyNet: Learning to pre-process Spectroscopy Signals without clean data
In this work we propose a deep learning approach to clean spectroscopy signals using only uncleaned data.
Deep Spectral CNN for Laser Induced Breakdown Spectroscopy
This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i. e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i. e., calibrate).
Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
We then use our small set of manually labeled patent diagram images via transfer learning to adapt the image search from sketches of natural images to diagrams.
Learning Spatial Relationships between Samples of Patent Image Shapes
Binary image based classification and retrieval of documents of an intellectual nature is a very challenging problem.
Motion Guided LIDAR-camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles
This work proposes a novel motion guided method for target-less self-calibration of a LiDAR and camera and use the re-projection of LiDAR points onto the image reference frame for real-time depth upsampling.
Computational Mapping of the Ground Reflectivity with Laser Scanners
In this investigation we focus on the problem of mapping the ground reflectivity with multiple laser scanners mounted on mobile robots/vehicles.
