KEYWORDS: Cameras, Nose, Colorimetry, Information operations, RGB color model, Light sources and illumination, Visual system, Digital cameras, Digital photography, Image processing
Human visual system has the property of perceiving the object color to remain constant regardless of the prevailing
illumination. However, digital cameras usually lack this capability, and the captured images are digitally corrected to
discount the color of the scene light based on the estimated illuminant. Illumination estimation might be erroneous in
some artificial or chromatic lighting conditions. A method was proposed to correct digital photos captured with a
smartphone camera using the smartphone owner's face as the reference. Taking the advantage of the latest smartphones
with two build-in cameras, we could use the front camera to capture the smartphone owner's face and compare with the
saved reference face image in order to estimate the scene illuminant. After that, we could properly adjust the capture
setting for the main camera in order to take a decent target image; or we could automatically correct the target image
based on the estimated illumination by comparing two face images. The method was implemented on the iOS mobile
platform. Experimental result shows that the adjusted images using the proposed method are generally more favorable
than the pictures taken directly by the default camera application.
Automatic license plate recognition (ALPR) is an important capability for traffic surveillance applications, including toll
monitoring and detection of different types of traffic violations. ALPR is a multi-stage process comprising plate
localization, character segmentation, optical character recognition (OCR), and identification of originating jurisdiction
(i.e. state or province). Training of an ALPR system for a new jurisdiction typically involves gathering vast amounts of
license plate images and associated ground truth data, followed by iterative tuning and optimization of the ALPR
algorithms. The substantial time and effort required to train and optimize the ALPR system can result in excessive
operational cost and overhead. In this paper we propose a framework to create an artificial set of license plate images for
accelerated training and optimization of ALPR algorithms. The framework comprises two steps: the synthesis of license
plate images according to the design and layout for a jurisdiction of interest; and the modeling of imaging
transformations and distortions typically encountered in the image capture process. Distortion parameters are estimated
by measurements of real plate images. The simulation methodology is successfully demonstrated for training of OCR.
Natural language color (NLC) was initially developed as a web-based application and then deployed in one
Xerox print driver. NLC changes the image-editing paradigm from the use of curves, sliders, and knobs, to the
use of verbal text-based commands such as "make light green much less yellowish". The technology appeals
to a common user who has no expert knowledge in color science, and this naturally leads one to think about
its use in mobile devices. A prototype GUI design for a language-based color editing on iPhone platform will
be presented that uses several of its haptic interfaces (e.g. "slot-machine", shaking, swiping, etc.). A textual
interface is provided to select a color to be modified within the image and a direction of change for the
modification. A swipe interface is provided to select a magnitude and polarity for the modification. Actions on
the textual and swipe interface are converted to natural language commands that are in turn used to derive a
color transformation that is applied to relevant portions of the image to yield a modified image. The
modifications are displayed in real time to the user.
A method is provided for embedding a UV fluorescent watermark in a color halftone image printed on paper. The
described method implements two different strategies to halftone a watermark region and a background region. One
strategy uses dot-on-dot halftoning to maximize the usage of black ink and minimize ink dispersion, while the other
strategy uses successive-filling halftoning to maximize ink dispersion. An accurate color look-up-table (LUT) is built to
directly transform the colorant values for one halftoning strategy to the colorant values for the other strategy. With the
color transformation applied on one region, the binary outputs in both watermark and background regions halftoned with
different strategies exhibit similar color appearance under normal lighting condition. However, under UV illumination,
due to the fluorescent effect caused by different paper coverages in two regions, the embedded watermark becomes clearly visible.
Compared with colorimetric imaging, multispectral imaging has the advantage of retrieving spectral reflectance factor
of each pixel of a painting. Using this spectral information, pigment mapping is concerned with decomposing the spectrum into its constituent pigments and their relative contributions. The output of pigment mapping is a series of spatial concentration maps of the pigments comprising the painting. This approach was used to study Vincent van Gogh's The Starry Night. The artist's palette was approximated using ten oil pigments, selected from a large database of pigments used in oil paintings and a priori analytical research on one of his self portraits, executed during the same time period. The pigment mapping was based on single-constant Kubelka-Munk theory. It was found that the region of blue sky where the stars were located contained, predominantly, ultramarine blue while the swirling sky and region surrounding the moon contained, predominantly, cobalt blue. Emerald green, used in light bluish-green brushstrokes surrounding the moon, was not used to create the dark green in the cypresses. A measurement of lead white from Georges Seurat's La Grande Jatte was used as the white when mapping The Starry Night. The absorption and scattering properties of this white were replaced with a modern dispersion of lead white in linseed oil and used to simulate the painting's appearance before the natural darkening and yellowing of lead white oil paint. Pigment mapping based on spectral imaging was found to be a viable and practical approach for analyzing pigment composition, providing new insight into an artist's working method, the possibility for aiding in restorative inpainting, and lighting design.
Conference Committee Involvement (1)
Imaging and Printing in a Web 2.0 World IV
4 February 2013 | Burlingame, California, United States
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