The authors propose an asymmetric stereo imaging system that acquires stereoscopic images through the installation of an additional small sub-camera module into a conventional single-lens digital camera. The small sub-camera offers advantages in the proposed system, such as reduced manufacturing cost and a reduction in hardware size. The proposed imaging system provides a practical configuration by considering the variations in focal length, which cause disagreement between the stereoscopic images. In order to resolve the disagreement problem, registration models are established to transform the asymmetric stereo images into symmetric stereo images. Experimental results confirm the accuracy of the registration algorithm and also show subjective evaluation of the images from the proposed system. Some applications of the proposed system, such as automatic horizontal image translation and a depth-based refocusing technique are implemented to show its practicality.
This paper proposes a novel local color correction algorithm that uses three dimensional (3D) point set registration especially for underwater stereo images. Due to the limited visibility in an underwater environment, underwater stereo images are usually captured by a half-mirror-based stereo camera that helps to prevent the unwanted visual fatigues induced by excessive disparities. However, the interaction between the half mirror and the scattered light components in the water may produce strong local color discrepancies. Thus, the proposed algorithm extracts sufficient information on the entire image area between the left and right images using 3D point set registration to counteract these effects. Since the proposed algorithm simultaneously processes all three color channels in the local color space, it is robust to the strong local color discrepancies in underwater stereo images. The proposed algorithm is both subjectively and objectively evaluated by comparing the histograms, the color similarities, and the disparity images before and after color correction. After color correction, the proposed algorithm achieves a color similarity up to 96.67%, while other conventional algorithms show color similarities only up to 90.66%. Although the proposed algorithm is designed for underwater stereo images, it can be used for various stereo images and applications.
In this paper, we proposed a visual fatigue monitoring system based on eye-movement and eye-blink detection. It
analyzes the eye-movement and number of blinks based on the assumption that saccade movement of the eye decreases
and the number of eye blink increases when visual fatigue of viewer is accumulated. The proposed system has an
infrared single camera and an infrared light source. Then, the pupil of the eye can be detected by applying binary
threshold to Purkinje image. The threshold is automatically selected by two constraints which are the eccentricity of
ellipse fitting and the size of the pupil. Finally, total amount of eye movement and the number of eye blinks are
measured by using the positions of the pupil. The results were obtained while watching stereoscopic videos after
personal calibration procedure. The results show that saccade movement of the eye decreases as the visual fatigue of the
viewer is accumulated. However, the number of eye blinks shows large variance along the time axis which implies it is
not proper for visual fatigue monitoring system.
We present a novel method for automatically correcting the radial lens distortion in a zoom lens video camera system. We first define the zoom lens distortion model using an inherent characteristic of the zoom lens. Next, we sample some video frames with different focal lengths and estimate their radial distortion parameters and focal lengths. We then optimize the zoom lens distortion model with preestimated parameter pairs using the least-squares method. For more robust optimization, we divide the sample images into two groups according to distortion types (i.e., barrel and pincushion) and then separately optimize the zoom lens distortion models with respect to divided groups. Our results show that the zoom lens distortion model can accurately represent the radial distortion of a zoom lens.
This paper describes a three-dimensional (3-D) face recognition system based on two different 3-D sensors. These sensors were used to overcome pose variation problems that cannot be effectively solved when working with 2-D images. We acquired input data based on a structured light system and compared them with 3-D faces acquired by a 3-D laser scanner. Due to differing data structures, we generated a selection of probe images and stored images (not only for head pose estimation but also for face recognition). Given an unknown range image, we extracted invariant facial features based on facial geometry and utilized the previously developed error-compensated singular-value decomposition method to estimate a head pose. Distinctive facial shape indices were defined and extracted based on facial curvature characteristics. The extracted indices have a different number and different distribution on each face image. When multiple matching possibilities are involved, dynamic programming (DP) is useful matching algorithm. DP merges data points in order to achieve better point-to-point matching by finding a matching path at minimum cost. Experimental results show that the proposed method obtained a 96.8% face recognition rate when working with 300 individuals under different pose variations.
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