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.
Conference Committee Involvement (1)
Real-Time Image Processing III
16 January 2006 | San Jose, California, United States