Data preprocessing and manipulation is the most essential part of the recent deep learning technologies. Data in different areas requires various pre-processing tasks depending on their characteristics or purpose of use. For example, to create a sharp macular image from an ultra-wide-angle fundus image, we rotate, transform, and adjust the original image. We aim to automate the process of fine-tuning the position of the macula, nerve papillae, and blood vessels by matching the size and resolution of the photo with the paired dataset of an existing fundus photo and an ultra-wide fundus photo. This process is largely divided into two steps. First, scaling is a process of adjusting the size of the image by cropping a picture centering on the macula. Second, warping is a process of fine-tuning so that the position of the macula, nerve nipples, and blood vessels of a pair of photos are the same.
The rapid growth of computing power and wireless technology has spurred new technologies such as the massive adoption of mobile devices, the increase in mobile content and services, and the Internet of Things (IoT). As a result, research fields are being actively conducted to extract information by processing data in numerous mobile and network fields. Our researches include modeling user mobility data for GAN-based predictions and constructing discretization and continuity (comprehensive) network dataset for deep learning.
Facial images are particularly challenging subjects in computer vision. A wide spectrum of information including identity, gender, mood, age, and race can be perceived by looking at a single face image. The fact that many contests in the computer vision field are specifically focused on face-related applications (such as face recognition, verification, detection, and so on) is an indication of the distinct difficulties encountered when dealing with facial images. For instance, some topics include transforming face images in terms of attributes, pose, emotion and expressions, predicting facial age progression, and making generated images more realistic.
Lifelogging represents a phenomenon whereby people can digitally record their own daily lives in varying amounts of detail, for a variety of purposes. The record contains a comprehensive dataset of a human’s activities. The data could be used to increase knowledge about how people live their lives. Lifelogging may offer benefits to content-based information retrieval, contextual retrieval, browsing, search, linking, summarization and user interaction. However, there are challenges in managing, analyzing, indexing and providing content-based access to streams of multimodal information derived from lifelog sensors which can be noisy, error-prone and with gaps in continuity due to sensor calibration or failure. We study to select, transform, and archive the valuable and informative records from the huge amount of lifelog data.