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.
Generally, to acquire an HDR image, many images that cover the entire dynamic range of the scene with different exposure times are required, then these images are fused into one HDR image. This paper proposes an efficient method for the HDR image acquisition with small number of images. First, we estimated scenic dynamic range using two images with different exposure times. These two images contain the upper and lower limit of the scenic dynamic range. Independently of the scene, according to varied exposure times, similar characteristics for both the maximum gray levels in images that include the upper limit and the minimum gray levels in images that include the lower limit are identified. After modeling these characteristics, the scenic dynamic range is estimated using the modeling results. This estimated scenic dynamic range is then used to select the proper exposure times for the acquisition of an HDR image. We selected only three proper exposure times because entire dynamic range of the cameras could be covered by three dynamic range of the cameras with different exposure times. To evaluate the error of the HDR image, experiments using virtual digital camera images were carried out. For several test images, the error of the HDR image using proposed method was comparable to that of the HDR image which utilize more than ten images for the HDR image acquisition.
The current paper proposes an efficient method for edge detection in original and noisy images using Waerden's statistic. Edges represent a significant amount of information on an image. For example, edges reveal the location of objects, their shape and size, and something about their texture. Since edges represent where the intensity of an image moves from a low value to a high value or vice versa, edge detection is often the first step in image segmentation. As a field of image analysis, image segmentation groups pixels into regions to determine the image composition. Therefore, the current paper describes the nonparametric Wilcoxon test and parametric T test based on statistical hypothesis testing for edge detection. Here, the threshold is determined by specifying a significance level, whereas Bovik, Huang, and Munson considered a range of possible test statistic values for the threshold. In the current study, the test statistic is calculated based on pixel gray levels obtained using an edge-height parameter and compared with the threshold determined by a significance level. Experiments were conducted to evaluate the performance of these methods in both original and noisy images. As a result, the Wilcoxon and T test was found to be sensitive to a noisy image, whereas the proposed Waerden test was robust in both noisy and noise-free images under α=0.0005. Furthermore, when compared with Sobel, LoG, and Canny operators, the proposed Waerden test was also more effective in both noisy and noise-free images.