no code implementations • 14 Jan 2022 • Xiaojia He, Christof Meile, Suchendra M. Bhandarkar
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.
1 code implementation • 10 Jan 2022 • Brian M. Hopkinson, Suchendra M. Bhandarkar
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
no code implementations • 24 Nov 2018 • Shefali Srivastava, Abhimanyu Chopra, Arun CS Kumar, Suchendra M. Bhandarkar, Deepak Sharma
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.
no code implementations • 12 Sep 2018 • Arun CS Kumar, Shefali Srivastava, Anirban Mukhopadhyay, Suchendra M. Bhandarkar
The proposed scheme reasons about correspondence between disparate images using high-level global shape cues derived from low-level local feature descriptors.
no code implementations • 20 Jul 2017 • Somenath Das, Suchendra M. Bhandarkar
Knowledge of shape geometry plays a pivotal role in many shape analysis applications.