Proc. SPIE. 6696, Applications of Digital Image Processing XXX
KEYWORDS: Super resolution, Digital image processing, Image processing, Medical diagnostics, Image restoration, Quality measurement, Medical imaging, Image quality, Digital imaging, Human vision and color perception
Digital image rescaling by interpolation has been intensively researched over past decades, and still getting constant
attention from many applications such as medical diagnosis, super-resolution, image blow-up, nano-manufacturing, etc.
However, there are no consented metrics to objectively assess and compare the quality of resized images. Some existing
measures such as peak-signal-to-noise ratio (PSNR) or mean-squared error (MSE), widely used in image restoration
area, do not always coincide with the opinions from viewers. Enlarged digital images generally suffer from two major
artifacts: blurring, zigzagging, and those undesirable effects especially around edges significantly degrade the overall
perceptual image quality. We propose two new image quality metrics to measure the degree of the two major defects,
and compare several existing interpolation methods using the proposed metrics. We also evaluate the validity of image
quality metrics by comparing rank correlations.
Although the resolution of digital imaging device is rapidly increasing these days, digital photography still suffers from resolution limits when we want to enlarge a small picture. It is widely believed that edges play the important role when we evaluate the perceptual quality of resized images, and several edge-directed approaches have been proposed to soothe unwanted edge effects: jagging and blurring Edge-only-directed interpolations generally produce crispier edges than traditional interpolations such as bi-linear or bi-cubic, but they usually take longer time and sometimes create unacceptable defects where their edge estimation is incorrect. In this paper, we will address a new approach to compensate for blurring and aliasing effects using edge and corner cues in a given image.
We developed blob feature analysis-based real-time marker-free motion capture system. Our system can capture actor’s end-effectors and reconstruct 3-dimensional human motions in real-time without any attaching markers or sensors.
To capture robustly various motions of an actor, we proposed blob feature models such as shape model, color model, and spatial model for end-effectors such as a head, hands, and feet. And we introduce weights for each model. According to the clothing conditions of an actor, the proposed method adjusts weights for each model automatically. So, our system can detect and distinguish the actor’s end-effectors although the shapes and the color of end-effectors vary due to various poses and the variation of illumination. And our models are very simple to compute, therefore, the motion capture can be real-time process.
Experiments are conducted on a lot of people wearing various clothes under general fluorescent lights. The proposed system can reconstruct actor’s various motions at 30 frames per second with the 99.95% success rate of the detection of an actor’s end-effectors. So, we confirmed that the proposed motion capture system could stably reconstruct motions of a lot of people wearing various clothes in real-time.
We have provided the Internet data service for more than 30 million users over broadband network in Korea. At emerging time of introducing Internet data service, downstream data rate was a critical issue. But nowadays, the key issue is not provided the best effort service but guaranteed bandwidth service. PON system can guarantee the both points. In this paper we present the requirement of EPON system for Fiber to The Pole (FTTP) and Fiber to the Home (FTTH). And then we will show some examples of EPON deployment at KT.