Outputs (5)

Event prediction with rough-fuzzy sets (2022)
Journal Article
Chakraborty, D. B., & Yao, J. T. (2023). Event prediction with rough-fuzzy sets. Pattern Analysis and Applications, 26(2), 691-701. https://doi.org/10.1007/s10044-022-01119-7

This article proposes a new methodology of unsupervised event prediction from videos. Detecting events from videos without prior information is a challenging task, as there are no well-accepted definitions about events in a video. It is commonly know... Read More about Event prediction with rough-fuzzy sets.

Controlling by Showing: i-Mimic: A Video-Based Method to Control Robotic Arms (2022)
Journal Article
Chakraborty, D. B., Sharma, M., & Vijay, B. (2022). Controlling by Showing: i-Mimic: A Video-Based Method to Control Robotic Arms. SN Computer Science, 3(2), Article 124. https://doi.org/10.1007/s42979-022-01014-2

A novel concept of vision-based intelligent control of robotic arms is developed here in this work. This work enables the controlling of robotic arm motion only with visual input, that is, controlling by showing the videos of correct movements. This... Read More about Controlling by Showing: i-Mimic: A Video-Based Method to Control Robotic Arms.

Q-rough sets, flicker modeling and unsupervised fire threat quantification from videos (2022)
Journal Article
Chakraborty, D. B., Detani, V., & Parshv Jigneshkumar, S. (2022). Q-rough sets, flicker modeling and unsupervised fire threat quantification from videos. Displays, 72, Article 102140. https://doi.org/10.1016/j.displa.2021.102140

This article defines new methods for unsupervised fire region segmentation and fire threat detection from the RGB video stream. Here, an unsupervised approach has been developed to determine the threat associated with fire. With this method, the thre... Read More about Q-rough sets, flicker modeling and unsupervised fire threat quantification from videos.

Rough video conceptualization for real-time event precognition with motion entropy (2020)
Journal Article
Chakraborty, D. B., & Pal, S. K. (2021). Rough video conceptualization for real-time event precognition with motion entropy. Information Sciences, 543, 488-503. https://doi.org/10.1016/j.ins.2020.09.021

This article defines a new methodology for pre-recognition of events with object motion analysis in a video without any prior knowledge. This unsupervised application is named as ‘conceptualization’. This conceptualization technique is also tested wi... Read More about Rough video conceptualization for real-time event precognition with motion entropy.

Spatiotemporal approach for tracking using rough entropy and frame subtraction (2011)
Presentation / Conference Contribution
Uma Shankar, B., & Chakraborty, D. Spatiotemporal approach for tracking using rough entropy and frame subtraction

We present here an approach for video image segmentation where spatial segmentation is based on rough sets and granular computing and temporal segmentation is done by consecutive frame subtraction. Then the intersection of the temporal segmentation a... Read More about Spatiotemporal approach for tracking using rough entropy and frame subtraction.