Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur's, Otsu and Tsallis functions

By Ashish Kumar Bhandari, Anil Kumar, Girish Kumar Singh. Expert systems with applications, 2015

343 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/6a844e1df7329b761f9b30a707a51a8000c09010

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Adnan Qayyum
2. Syed Muhammad Anwar
3. Muhammad Majid
4. Muhammad Awais
5. J. Anitha
6. Anju Asokan
7. Shiv Ram Dubey

In Computer Vision and Pattern Recognition

1. Adnan Qayyum
2. Syed Muhammad Anwar
3. Muhammad Majid
4. Muhammad Awais
5. J. Anitha
6. Majdi R. Alnowami
7. Anju Asokan
8. Shiv Ram Dubey

In Media Technology

1. Syed Muhammad Anwar
2. Muhammad Majid
3. Muhammad Awais
4. J. Anitha
5. Anju Asokan
6. Adnan Qayyum

Citations in the corpus

• Change detection techniques for remote sensing applications: a survey (2019, methodology): “(Bhandari et al. 2015b) presents a modified artificial bee colony based multilevel thresholding for segmentation of satellite image.”
• Medical Image Analysis using Convolutional Neural Networks: A Review (2017, background): “In recent studies, segmentation has been applied on different types of images [27-29] for applications such as satellite images [30, 31], tomographic maps [32] and medical images [33, 34].”