Undergraduate Project Update Presentation Day
CCDS Undergrad Project Update Presentation Day, held on February 8, 2024. Eight groups, under the supervision of CCDS mentors, showcased their progress and findings. The presentations encompassed a diverse range of topics and research endeavors. It was a culmination of dedicated efforts and collaborative work within the CCDS community. The event provided a platform for students to share their achievements and insights with peers and faculty members alike.
Boundary-Enhanced Attention for Satellite Imagery dataScience
Satellite image classification presents unique challenges distinct from traditional urban scene datasets, including significant class imbalance and the scarcity of comprehensive examples within single frames. While recent advancements in semantic segmentation and metric learning have shown promise in urban scene datasets, their direct applicability to satellite image classification remains uncertain. This paper introduces a novel approach, the Boundary Attention (BA) Loss , specifically designed to address these challenges in satellite imagery. BA emphasizes the significance of boundary regions within satellite imagery, aiming to mitigate information relation complexity by directing enhanced attention to minority classes and improving attention mechanisms along class boundaries. Through comprehensive experimental evaluation and comparison with existing methods, this paper demonstrates the effectiveness and adaptability of the BA method, paving the way for more accurate and robust satellite image classification systems. The proposed BA method offers a tailored solution that stands to significantly improve classification accuracy in the context of satellite image analysis.
Non-Rigid Distortion Removal via Coordinate Based Image Representation
maging through turbulent refractive medium (e.g., hot air, in-homogeneous gas, fluid flow) is challenging, since the non-linear light transport through the medium (e.g. refraction and scattering) causes non-rigid distortions in perceived images. However, most computer vision algorithms rely on sharp and distortion-free images to achieve the expected performance. Removal of these non-rigid image distortions is therefore critical and beneficial for many vision applications, from segmentation to recognition. To resolve the distortion and blur introduced by air turbulence, conventional turbulence restoration methods leverage optical flow, regions fusion and blind deconvolution to recover images. One avenue that is underexplored for this problem is the use of coordinate based image representations. These methods represent images as the parameters of a neural network ,and they can be used to deform the image grid itself to account for turbulence. In this research, we aim to extend this idea to unseen images with meta learning that can remove both air and water distortions without much customization.
Related publications:
- Unsupervised Non-Rigid Image Distortion Removal via Grid Deformation, ICCV 2021
Adaptive LLM-based Tutor for Personalized Python Learning
Because of their varied backgrounds and skill levels, students in the field of programming education frequently confront a variety of difficulties. Personalized learning is typically not supported by traditional learning platforms, which reduces their efficacy. Our goal is to construct an intelligent tutor system based on LLMs that can solve problems and reason in order to provide students with tutor-like guidance. Additionally, we want to establish engaging interactions between students and tutors and during these exchanges, we would like to learn as much as possible about the tutors’ internal decision-making process. Furthermore, in order to deliver a more approachable and natural experience that is in line with the learner’s needs and the curriculum objectives, the system will need to recognize and monitor, as much as possible, the individual preferences and mental state of the learners.
Real-Time Adaptive model for Satellite Image Classification in Dynamic Disaster Environments
During catastrophic events like floods or wildfires, satellite imagery is essential for comprehending the conditions on the ground. Unfortunately, conventional computer vision models find it difficult to adjust to the quickly changing environment that occurs during catastrophes. As a result, the precision with which impacted regions are classified is reduced, making it more difficult to identify flooded areas or destroyed buildings. This research’s main goal is to overcome these obstacles by creating an adaptive computer vision model that is specially made for classifying satellite images. This model will have the capability to generalize across a variety of disaster scenarios and will dynamically adapt to changing conditions by including real-time adaptation mechanisms. As a result, during catastrophes, we will be able to use satellite imagery to gather more precise and fast information, which will increase human safety and disaster response. Our earlier research on the categorization of urban buildup, and land usage and land cover will be expanded upon in this study.
Related publications
- Automatic Detection of Natural Disaster Effect on Paddy Field from Satellite Images using Deep Learning Techniques, 8th International Conference on Control and Robotics Engineering (ICCRE), Niigata, Japan, April 21-23, 2023.
- Attention Toward Neighbors: A Context-Aware Framework of High-Resolution Image Segmentation, 28th IEEE International Conference on Image Processing (ICIP), Anchorage, Alaska, USA, 2021
- Understanding the Urban Environment from Satellite Images with New Classification Method—Focusing on Formality and Informality, Sustainability, vol 14, issue 7, MDPI, 2022
- LULC Segmentation of RGB Satellite Image using FCN-8″, in the proceedings of 3rd SLAAI International Conference on Artificial Intelligence, 2019, Sri Lanka and in Communications in Computer and Information Science, Book Chapter, Springer, 2019.
