We propose an efficient algorithm for colorization of greyscale images. As in prior work, colorization is posed as an
optimization problem: a user specifies the color for a few scribbles drawn on the greyscale image and the color image
is obtained by propagating color information from the scribbles to surrounding regions, while maximizing the local
smoothness of colors. In this formulation, colorization is obtained by solving a large sparse linear system, which normally
requires substantial computation and memory resources. Our algorithm improves the computational performance through
three innovations over prior colorization implementations. First, the linear system is solved iteratively without explicitly
constructing the sparse matrix, which significantly reduces the required memory. Second, we formulate each iteration in
terms of integral images obtained by dynamic programming, reducing repetitive computation. Third, we use a coarseto-
fine framework, where a lower resolution subsampled image is first colorized and this low resolution color image is
upsampled to initialize the colorization process for the fine level. The improvements we develop provide significant speedup
and memory savings compared to the conventional approach of solving the linear system directly using off-the-shelf
sparse solvers, and allow us to colorize images with typical sizes encountered in realistic applications on typical commodity
computing platforms.
We explore sensor scheduling strategies to maximize the operational lifetime of a user-centric image sensor
network. Image sensors are deployed for gathering visual information over a monitored region. Users navigate
within this region by specifying a time-varing desired viewpoint and the network responds with the requested
visual data. By modeling the user's desired viewpoint in a probabilistic framework, we develop a stochastic
formulation of the network lifetime and investigate the camera scheduling strategy that maximizes the expected
value of network lifetime. By suitably abstracting the problem, we present a closed-form solution for the simplistic
case when the monitored region is divided into two parts. Using asymptotic analysis, we then present a simple
camera scheduling strategy for the general case that we conjecture to be optimal. Simulation results demonstrate
a clear advantage of the proposed camera scheduling approach over previously considered alternatives.
KEYWORDS: Calibration, Cameras, Zoom lenses, Distortion, 3D modeling, Digital cameras, Detection and tracking algorithms, 3D image processing, Chaos, Computer engineering
Plane-Based calibration algorithms have been widely adopted for the purpose of camera calibration task. These algorithms have the advantage of robustness compared to self-calibration and flexibility compared to traditional algorithms which require a 3D calibration pattern. While the common assumption is that the intrinsic parameters during the calibration process are fixed, limited consideration has been given to the general case when some intrinsic parameters maybe varying, others maybe fixed or known. We first discuss these general cases for camera calibration. Using a counting argument we enumerate all cases where plane-based calibration may be utilized and list the number of images required for each of these cases. Then we extend the plane-based framework to the problem of cameras with zoom variation, which is the most common case. The approach presented and may be extended to incorporate additional varying parameters described in the general framework. The algorithm is tested using both synthetically generated images and actual images captured using a digital camera. The results indicate that the method performs very well and inherits the advantage of robustness and flexibility from plane-based calibration algorithms.
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