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Found 5 papers, 1 papers with code
Multimodal registration of FISH and nanoSIMS images using convolutional neural network models
In this study, we use convolutional neural networks (CNNs) for multiscale feature extraction, shape context for computation of the minimum transformation cost feature matching and the thin-plate spline (TPS) model for multimodal registration of the FISH and nanoSIMS images.
High-resolution Ecosystem Mapping in Repetitive Environments Using Dual Camera SLAM
Fragmentary maps, initially produced from the documentation camera images via monocular SLAM, are subsequently scaled and aligned with the localization camera trajectory and finally subjected to a global optimization procedure to produce a unified, refined map.
Simultaneous Localization and Mapping
Vocal Bursts Intensity Prediction
Matching Disparate Image Pairs Using Shape-Aware ConvNets
A graph-based representation for the disparate image pair is generated by constructing an affinity matrix that embeds the distances between feature points in two images, thus modeling the correspondence determination problem as one of graph matching.
Deep Spectral Correspondence for Matching Disparate Image Pairs
The proposed scheme reasons about correspondence between disparate images using high-level global shape cues derived from low-level local feature descriptors.
Local Geometry Inclusive Global Shape Representation
Knowledge of shape geometry plays a pivotal role in many shape analysis applications.
