Mobile imagers now possess multi-megapixel sensors. Blur caused by camera motion during the exposure is becoming
more pronounced because the exposure time for the smaller pixel sizes has been increased to attain the same photon
statistics.
We present a method of measuring human hand-eye coordination for mobile imagers. When trying to hold a steady
position, the results indicate that there is a distinct linear-walk motion and a distinct random-walk motion while no
panning motion is intended. By using the video capture mode, we find that the frame to frame variation is typically less
than 2.5 pixels (0.149 degrees). An algorithm has been devised which permits the camera to determine in real-time
when is the optimum moment to for the exposure to begin to best minimize motion blur.
We also observed the edge differences in fully populated "direct" image sensors and Bayer pattern sensors. Because
dominant horizontal and vertical linear motions are present, chromatic shifts are observed in the Bayer sensor in the
direction of motion for certain color transitions.
KEYWORDS: Image processing, High dynamic range imaging, Image compression, Digital filtering, Image filtering, Digital imaging, Cameras, Colorimetry, Image enhancement, Digital cameras
KEYWORDS: Independent component analysis, Principal component analysis, Skin, Reflectivity, Cameras, Digital cameras, RGB color model, Image processing, MATLAB, Color imaging
We present a simple method for estimating the scene illuminant for images obtained by a Digital Still Camera (DSC). The proposed method utilizes basis vectors obtained from known memory color reflectance to identify the memory color objects in the image. Once the memory color pixels are identified, we use the ratios of the red/green and blue/green to determine the most likely illuminant in the image. The critical part of the method is to estimate the smallest set of basis vectors that closely represent the memory color reflectances. Basis vectors obtained from both Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used. We will show that only two ICA basis vectors are needed to get an acceptable estimate.
This paper addresses the Frankle-McCann Retinex algorithm by altering its properties to provide a distance-weighting function to every ratio-product path from a source to a destination pixel. The algorithm is further modified to permit the hard RESET function to be replaced by a piece-wise smoother function that allows ratio-product propagation to slightly exceed the maximum brightness in each visual channel. Investigations of how segmentation can aid in reducing the computational complexity and provide a more realistic white balance are presented.
Digital Still Cameras employ automatic white balance techniques to adjust sensor amplifier gains so that white imaged objects appear white. A color cast detection algorithm is presented that uses histogram and segmentation techniques to select near-neutral objects in the image. Once identified and classified, these objects permit determination of the scene illuminant and implicitly the respective amplifier gains. Under certain circumstances, a scene may contain no near-neutral objects. By using the segmentation operations on non-neutral image objects, memory colors, from skin, sky, and foliage objects, may be identified. If identified, these memory colors provide enough chromatic information to predict the scene illuminant. By combining the approaches from near-neutral objects with those of memory color objects, a reasonable automatic white balance over a wide range of scenes is possible.
This paper describes a hybrid technique of coupling a through-the-lens band-pass illumination sensor with a software segmentation and histogram algorithm to estimate the scene illuminant(s) in a digital still camera and color correct the resulting image. A fuzzy logic algorithm combines the result of the source illumination(s) with the chromaticity determination of near-neutral objects derived from the image pixel data to determine the amount chromatic correction required.
Digital Still Camera images often have undesired color casts due to unusual illuminant sources. This paper describes a novel color correction technique that uses adaptive segmentation techniques to identify the presence of such casts, estimate their chromatic strength, and alter the image's near-neutral color regions to compensate for the cast. The segmentation method identifies most major objects in the scene and their average color.
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