
Fahim Faisal Niloy
- Research Assistant, CCDS
PhD Student
University of California Riverside
Research Interests
Machine Learning
Fahim Faisal Niloy, M Ashraful Amin, Amin Ahsan Ali, AKM Mahbubur Rahman
2021 IEEE International Conference on Image Processing (ICIP)
IEEE, pp. 2279–2283
High-resolution image segmentation remains challenging and error-prone due to the enormous size of intermediate feature maps. Conventional methods avoid this problem by using patch based approaches where each patch is segmented independently. However, independent patch segmentation induces errors, particularly at the patch boundary due to the lack of contextual information in very high-resolution images where the patch size is much smaller compared to the full image. To overcome these limitations, in this paper, we propose a novel framework to segment a particular patch by incorporating contextual information from its neighboring patches. This allows the segmentation network to see the target patch with a wider field of view without the need of larger feature maps. Comparative analysis from a number of experiments shows that our proposed framework is able to segment high resolution images with significantly improved mean Intersection over Union and overall accuracy.

PhD Student
University of California Riverside
Machine Learning

Professor
+1 more affiliationDepartment of Information Sciences and Technology
George Mason University, USA
Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

Professor
Department of Computer Science and Engineering
Independent University, Bangladesh
Artificial Intelligence, Machine Learning

Associate Professor
Department of Computer Science and Engineering
Independent University, Bangladesh