Fueled by the development of advanced driver assistance system (ADAS), autonomous vehicles, and the proliferation of cameras and sensors, automotive is becoming a rich new domain for innovations in imaging technology. This paper presents an overview of ADAS, the important imaging and computer vision problems to solve for automotive, and examples of how some of these problems are solved, through which we highlight the challenges and opportunities in the automotive imaging space.
Camera calibration is an important problem for stereo 3-D cameras since the misalignment between the two
views can lead to vertical disparities that significantly degrade 3-D viewing quality. Offline calibration during
manufacturing is not always an option especially for mass produced cameras due to cost. In addition, even if
one-time calibration is performed during manufacturing, its accuracy cannot be maintained indefinitely because
environmental factors can lead to changes in camera hardware. In this paper, we propose a real-time stereo
calibration solution that runs inside a consumer camera and continuously estimates and corrects for the misalignment
between the stereo cameras. Our algorithm works by processing images of natural scenes and does not
require the use of special calibration charts. The algorithm first estimates the disparity in horizontal and vertical
directions between the corresponding blocks from stereo images. Then, this initial estimate is refined with two
dimensional search using smaller sub-blocks. The displacement data and block coordinates are fed to a modified
affine transformation model and outliers are discarded to keep the modeling error low. Finally, the estimated
affine parameters are split by half and misalignment correction is applied to each view accordingly. The proposed
algorithm significantly reduces the misalignment between stereo frames and enables a more comfortable
3-D viewing experience.
Viewing comfort is an important concern for 3-D capable consumer electronics such as 3-D cameras and TVs.
Consumer generated content is typically viewed at a close distance which makes the vergence-accommodation
conflict particularly pronounced, causing discomfort and eye fatigue. In this paper, we present a Stereo Auto
Convergence (SAC) algorithm for consumer 3-D cameras that reduces the vergence-accommodation conflict on
the 3-D display by adjusting the depth of the scene automatically. Our algorithm processes stereo video in realtime
and shifts each stereo frame horizontally by an appropriate amount to converge on the chosen object in that
frame. The algorithm starts by estimating disparities between the left and right image pairs using correlations of
the vertical projections of the image data. The estimated disparities are then analyzed by the algorithm to select
a point of convergence. The current and target disparities of the chosen convergence point determines how much
horizontal shift is needed. A disparity safety check is then performed to determine whether or not the maximum
and minimum disparity limits would be exceeded after auto convergence. If the limits would be exceeded, further
adjustments are made to satisfy the safety limits. Finally, desired convergence is achieved by shifting the left
and the right frames accordingly. Our algorithm runs real-time at 30 fps on a TI OMAP4 processor. It is tested
using an OMAP4 embedded prototype stereo 3-D camera. It significantly improves 3-D viewing comfort.
Sharpness is an important attribute that contributes to the
overall impression of image quality. As digital photography
becomes more and more popular, digital photo enhancement has been
a topic of great interest. In this paper, we investigate two
issues related to digital photo sharpness. 1) How do we
quantitatively measure the sharpness of a digital image? 2) What is
the preferred sharpness of a digital image, and what is the relation
between preferred sharpness and sharpness detection threshold? Both
issues are of practical use to the digital photography market.
First, we present the design and properties of three sharpness metrics
to answer the first question. Next, we describe psychophysical experiments to investigate the second question. It is found that 1) the sharpness metric Digital Sharpness Scale (DSS) and Average Edge Transition Slope (AETS) are highly correlated to the perceived sharpness; 2) Both DSS and AETS predict sharpness equality with acceptable error; 3) the sharpness detection threshold is relatively consistent across subjects and across image contents, compared with the sharpness preference; 4) the average level of preferred sharpness is consistently higher than the detection threshold across image contents and across subjects, which implies that observers in general prefer a sharpened image to the original image; and 5) the preferred level of sharpness has a strong dependency on image content.
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