Neural Radiance Fields: Past, Present, and Future

The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation

True Real Time Pose Independent Face Detection Using Color Information and Skin Region Segmentation

The process of detecting a face from a video in real time is essential in applications such as human surveillance, human computer-interaction, and for further face recognition research purposes. In this paper, the face detection algorithm is divided into four stages namely, Video Database Acquisition (VDA), Frame Sequence Extraction (FSE), Skin Region Detection (SRD), and K-Mean Face Segmentation (KFS). Initially, the videos in MPEG format are converted to JPEG images depending on the user specified frame rate (FSE phase). During this conversion, the face detection process comprising of SRD and KFS phases runs on each of the images that are converted. The skin regions are detected in the images, which act as the input for the K-Mean Face Segmentation phase. The skin region clusters thus obtained are classified as face clusters depending on a threshold value. This algorithm was tested on 18 videos, which were acquired by the SONY DCR TRV-80 camera in the VDA phase, regardless of age, gender, size, race, and skin tones. Furthermore, the varying illumination conditions such as bright sunlight, sufficient light, and dim light conditions, and different orientations of the individuals in the videos were gracefully handled by the system. The time taken to detect and store the normalized faces was comparable to the length of the video and in some cases it was even less. Thus, this system works in True Real Time (TRT)

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio