Threshold selection using the within-class variance in Otsu’s method is generally moderate, yet inappropriate for expressing class statistical distributions. Otsu uses a variance to represent the dispersion of each class based on the distance square from the mean to any data. However, since the optimal threshold is biased toward the larger variance among two class variances, variances cannot be used to denote the real class statistical distributions. Therefore, to express more accurate class statistical distributions, this paper proposes the within-class standard deviation as a criterion for threshold selection, and the optimal threshold is then determined by minimizing the within-class standard deviation. Experimental results confirm that the proposed method produced a better performance than existing algorithms.
Digital still cameras generally use an optical low-pass filter(OLPF) to enhance the image quality by removing high spatial frequencies causing aliasing. While eliminating the OLPF can save manufacturing costs, images captured without using an OLPF include moiré in the high spatial frequency region of the image. Therefore, to reduce the presence of moiré in a captured image, this paper presents a moiré reduction method without the use of an OLPF. First, the spatial frequency response(SFR) of the camera is analyzed and moiré regions detected using patterns related to the SFR of the camera. Using these detected regions, the moiré components represented by the inflection point between the high frequency and DC components in the frequency domain are selected and then removed. Experimental results confirm that the proposed method can achieve moiré reduction while preserving detail information.
To acquire images in low-light environments, it is usually necessary to adopt long exposure times or to resort to
flashes. Flashes, however, often induce color distortion, cause the red-eye effect and can be disturbing to the subjects. On
the other hand, long-exposure shots are susceptible to subject-motion, as well as motion-blur due to camera shake when
performed with a hand-held camera. A recently introduced technique to overcome the limitations of the traditional lowlight
photography is the use of the multi-spectral flash. Multi-spectral flash images are a combination of UV/IR and
visible spectrum information. The general idea is to retrieve the details from the UV/IR spectrum and the color from the
visible spectrum. Multi-spectral flash images, however, are themselves subject to color distortion and noise. In this work,
a method of computing multi-spectral flash images so as to reduce the noise and to improve the color accuracy is
presented. The proposed method is a previously seen optimization method, improved by introducing a weight map used
to discriminate the uniform regions from the detail regions. The optimization target function takes into account the
output likelihood with respect to the ambient light image, the sparsity of image gradients, and the spectral constraints for
the IR-red and UV-blue channels. The performance of the proposed method was objectively evaluated using longexposure
shots as references.
Projectors have become common display devices, not only for office and school presentations, but also for home theater
entertainment. Although a completely dark room is the ideal venue for watching a projected image, in most situations
(including classrooms and conference rooms) the viewing conditions are not completely dark, and ambient light falling
on the screen produces a background light level with the image projected on top. As the background light increases, it
becomes more difficult to see the projected image, which becomes dull and may appear washed out. What is really
happening is that the ambient light reduces the contrast of the image. While the amount of light contributing to the image
remains the same, more light has been projected onto the screen by other light sources. This effect can be reduced by
employing the white-peaking function of a digital light-processing (DLP) projector, which adjusts the white segment of
the color wheel, resulting in more natural and vivid images. Although the chromaticity coordinates for an image
projected with and without white peaking are the same, when white is added to the projected image, the perceived hue
changes. This phenomenon is known as the Abney effect. This paper presents a model of this hue-shift phenomenon and
proposes a hue-correction method. For evaluation purposes, an observer-preference test is conducted on several test
images with and without hue shifts, and z-scores are utilized to compare the results.
Recently, projector is one of the most common display devices not only for presentation at offices and classes, but for
entertainment at home and theater. The use of mobile projector expands applications to meeting at fields and presentation
on any spots. Accordingly, the projection is not always guaranteed on white screen, causing some color distortion.
Several algorithms have been suggested to correct the projected color on the light colored screen. These have limitation
on the use of measurement equipment which can't bring always, also lack of accuracy due to transform matrix obtained
by using small number of patches. In this paper, color correction method using general still camera as convenient
measurement equipment is proposed to match the colors between on white and colored screens. A patch containing 9
ramps of each channel are firstly projected on white and light colored screens, then captured by the camera, respectively,
Next, digital values are obtained by the captured image for each ramp patch on both screens, resulting in different values
to the same patch. After that, we check which ramp patch on colored screen has the same digital value on white screen,
repeating this procedure for all ramp patches. The difference between corresponding ramp patches reveals the quantity of
color shift. Then, color correction matrix is obtained by regression method using matched values. Differently from
previous methods, the use of general still camera allows to measure regardless of places. In addition, two captured
images on white and colored screen with ramp patches inform the color shift for 9 steps of each channel, enabling
accurate construction of transform matrix. Nonlinearity of camera characteristics is also considered by using regression
method to construct transform matrix. In the experimental results, the proposed method gives better color correction on
the objective and subjective evaluation than the previous methods.
In current printing technique, the Color Management System uses the ICC profiles of monitor and printer to perform
color matching. Unfortunately the ICC profile cannot capture all of the monitor color reproduction characteristics,
because such features change when the user acts on the color temperature, brightness and contrast controls, and they also
depend on the kind of backlighting and lifetime of LCD monitor. As a result there is usually an unwanted color
difference between an image displayed on the user monitor and its printed version. Yet, once we are able to produce an
ICC profile that matches the user's monitor characteristics by measuring, then the CMS becomes able to correctly
perform color matching. However, this method is of difficult application, because in general the measuring equipment is
not available and, even then, it takes a long time and new measurements according to monitor color temperature,
brightness and contrast. In this paper we propose a color matching technique based on estimate of the user's environment
through the simple visual test with an output image on monitor and its printed image. The estimated characteristic of
monitor is stored in new ICC profile and applied to color conversion process. Consequently the proposed method
reduced the color difference between image displayed on user monitor and its printed image.
In this paper, to avoid reaching a local minimum and correspond to a variety of real world video sequences, we propose an optimal fast search algorithm. With this objective, according to image property of each block, search strategy is varied adaptively by size of motion in each block. Each block from frames is classified into stationary, small motion and large motion block. We also suggest that the motion vector, which has stationary block such as background or still image, is set by zero, and thus these blocks do not perform search. For the others blocks, by using advantages of conventional search algorithm adaptively, we apply NTSS algorithm for small motion block and DS algorithm for large motion block. The proposed algorithm gives us faster search result and a significant improvement in terms of performance for motion compensated frames and computational complexity.