MLCV Lab of the Faculty of Computer Science, Universitas Indonesia, focuses on machine learning-related research, especially for computer vision tasks that involve manipulating, analyzing, and interpreting data from images and videos. The methods used are diverse, from processing detailed information stored at the pixel level to geometrically dividing images into several regions to form highly correlated patterns for a specific task.
Vision
MLCV Lab of the Faculty of Computer Science, Universitas Indonesia aims to be the leading research group in fundamental and applied machine learning and computer vision.
Mission
MLCV Lab of the Faculty of Computer Science, Universitas Indonesia, focuses on machine learning-related research, especially for computer vision tasks that involve manipulating, analyzing, and interpreting data from images and videos. The methods used are diverse, from processing detailed information stored at the pixel level to geometrically dividing images into several regions to form highly correlated patterns for a specific task.
This research area focuses on developing and applying machine learning and deep learning methods to solve complex problems across various domains. Research can be directed toward designing learning algorithms, building robust models, and optimizing training pipelines for structured and unstructured data such as images, videos, text, and sensor data.
This research area focuses on processing, analyzing, and enhancing visual data from images and videos to extract meaningful information. Research can be directed toward developing algorithms for image enhancement, feature extraction, segmentation, motion analysis, and video understanding, enabling more accurate and efficient interpretation of visual content.
This research area focuses on analyzing and interpreting medical data to support disease detection, diagnosis, and clinical decision-making. Research can be directed toward developing data-driven methods for processing medical images, signals, and clinical records, as well as building machine learning models that assist healthcare professionals in understanding complex medical information.
This research area focuses on analyzing and interpreting spatial data to understand patterns and changes on the Earth’s surface. Research can be directed toward processing and modeling data from remote sensing platforms such as satellites, aerial imagery, and drones, as well as developing methods for land use analysis, environmental monitoring, and geospatial mapping.
This research area focuses on analyzing and understanding motion and speech data to enable more natural and intelligent interaction between humans and machines. Research can be directed toward modeling human movement, gesture, and activity, as well as processing and interpreting speech signals for recognition, synthesis, and multimodal communication.
This research area focuses on creating and simulating visual environments to model real-world phenomena and support analysis, visualization, and interaction. Research can be directed toward computer graphics techniques, physical and data-driven simulation, and visual rendering methods for realistic, interactive, or analytical applications.
This research area focuses on understanding the geometric structure of scenes from visual data and reconstructing three-dimensional representations from images and videos. Research can be directed toward camera geometry, 3D reconstruction, depth estimation, and scene modeling to enable accurate spatial understanding of real-world environments.
This research area focuses on analyzing social data and signals to understand patterns, behavior, and interactions. Research can be directed toward processing and modeling signals such as audio, text, sensor, and social media data, as well as applying machine learning methods to extract insights from complex social and temporal information.
