All Outputs (12)

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.

Dopamine induces functional extracellular traps in microglia (2021)
Journal Article
Agrawal, I., Sharma, N., Saxena, S., Arvind, S., Chakraborty, D., Chakraborty, D. B., Jha, D., Ghatak, S., Epari, S., Gupta, T., & Jha, S. (2021). Dopamine induces functional extracellular traps in microglia. iScience, 24(1), Article 101968. https://doi.org/10.1016/j.isci.2020.101968

Dopamine (DA) plays many roles in the brain, especially in movement, motivation, and reinforcement of behavior; however, its role in regulating innate immunity is not clear. Here, we show that DA can induce DNA-based extracellular traps in primary, a... Read More about Dopamine induces functional extracellular traps in microglia.

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.

Granulated deep learning and Z-numbers in motion detection and object recognition (2019)
Journal Article
Pal, S. K., Bhoumik, D., & Bhunia Chakraborty, D. (2020). Granulated deep learning and Z-numbers in motion detection and object recognition. Neural Computing and Applications, 32(21), 16533-16548. https://doi.org/10.1007/s00521-019-04200-1

The article deals with the problems of motion detection, object recognition, and scene description using deep learning in the framework of granular computing and Z-numbers. Since deep learning is computationally intensive, whereas granular computing,... Read More about Granulated deep learning and Z-numbers in motion detection and object recognition.

Neighborhood rough filter and intuitionistic entropy in unsupervised tracking (2017)
Journal Article
Chakraborty, D. B., & Pal, S. K. (2018). Neighborhood rough filter and intuitionistic entropy in unsupervised tracking. IEEE Transactions on Fuzzy Systems, 26(4), 2188-2200. https://doi.org/10.1109/TFUZZ.2017.2768322

This paper aims at developing a novel methodology for unsupervised video tracking by exploring the merits of neighborhood rough sets. A neighborhood rough filter is designed in this process for initial labeling of continuous moving object(s) even in... Read More about Neighborhood rough filter and intuitionistic entropy in unsupervised tracking.

Granular Flow Graph, Adaptive Rule Generation and Tracking (2016)
Journal Article
Pal, S. K., & Chakraborty, D. B. (2017). Granular Flow Graph, Adaptive Rule Generation and Tracking. IEEE Transactions on Cybernetics, 47(12), 4096-4107. https://doi.org/10.1109/TCYB.2016.2600271

A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking. In the process, several new concepts and operations are introduced, and methodolog... Read More about Granular Flow Graph, Adaptive Rule Generation and Tracking.

Neighborhood granules and rough rule-base in tracking (2015)
Journal Article
Chakraborty, D. B., & Pal, S. K. (2016). Neighborhood granules and rough rule-base in tracking. Natural Computing, 15(3), 359-370. https://doi.org/10.1007/s11047-015-9493-6

This paper deals with several new methodologies and concepts in the area of rough set theoretic granular computing which are then applied in video tracking. A new concept of neighborhood granule formation over images is introduced here. These granule... Read More about Neighborhood granules and rough rule-base in tracking.

Unsupervised tracking, roughness and quantitative indices (2013)
Journal Article
Pal, S. K., & Chakraborty, D. (2013). Unsupervised tracking, roughness and quantitative indices. Fundamenta Informaticae, 124(1-2), 63-90. https://doi.org/10.3233/FI-2012-825

This paper presents a novel methodology for tracking a single moving object in a video sequence applying the concept of rough set theory. The novelty of this technique is that it does not consider any prior information about the video sequence unlike... Read More about Unsupervised tracking, roughness and quantitative indices.

Granulation, rough entropy and spatiotemporal moving object detection (2012)
Journal Article
Chakraborty, D., Shankar, B. U., & Pal, S. K. (2013). Granulation, rough entropy and spatiotemporal moving object detection. Applied Soft Computing, 13(9), 4001-4009. https://doi.org/10.1016/j.asoc.2012.09.003

A new spatio-temporal segmentation approach for moving object(s) detection and tracking from a video sequence is described. Spatial segmentation is carried out using rough entropy maximization, where we use the quad-tree decomposition, resulting in u... Read More about Granulation, rough entropy and spatiotemporal moving object detection.

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.