Tumor tracking is very important to deal with a cancer in a moving organ in clinical applications such as radiotherapy, HIFU etc. Respiratory monitoring systems are widely used to find location of the cancers in the organs because respiratory signal is highly correlated with the movement of organs such as the lungs and liver. However the
conventional respiratory system doesn’t have enough accuracy to track the location of a tumor as well as they need additional effort or devices to use. In this paper, we propose a novel method to track a liver tumor in real time by extracting respiratory signals directly from B-mode images and using a deformed liver model generated from CT images of the patient. Our method has several advantages. 1) There is no additional radiation dose and is cost effective due to use of an ultrasound device. 2) A high quality respiratory signal can be directly extracted from 2D images of the diaphragm. 3) Using a deformed liver model to track a tumor’s 3D position, our method has an accuracy of 3.79mm in tracking error.
We present a new method for patient-specific liver deformation modeling for tumor tracking. Our method focuses on deforming two main blood vessels of the liver – hepatic and portal vein – to utilize them as features. A novel centerline editing algorithm based on ellipse fitting is introduced for vessel deformation. Centerline-based blood vessel model and various interpolation methods are often used for generating a deformed model at the specific time t. However, it may introduce artifacts when models used in interpolation are not consistent. One of main reason of this inconsistency is the location of bifurcation points differs from each image. To solve this problem, our method generates a base model from one of patient’s CT images. Next, we apply a rigid iterative closest point (ICP) method to the base model with centerlines of other images. Because the transformation is rigid, the length of each vessel’s centerline is preserved while some part of the centerline is slightly deviated from centerlines
of other images. We resolve this mismatch using our centerline editing algorithm. Finally, we interpolate three deformed models of liver, blood vessels, tumor using quadratic B´ezier curves. We demonstrate the effectiveness of the proposed approach with the real patient data.
This paper presents a novel method of using 2D ultrasound (US) cine images during image-guided therapy to accurately track the 3D position of a tumor even when the organ of interest is in motion due to patient respiration. Tracking is possible thanks to a 3D deformable organ model we have developed. The method consists of three processes in succession. The first process is organ modeling where we generate a personalized 3D organ model from high quality 3D CT or MR data sets captured during three different respiratory phases. The model includes the organ surface, vessel and tumor, which can all deform and move in accord with patient respiration. The second process is registration of the organ model to 3D US images. From 133 respiratory phase candidates generated from the deformable organ model, we resolve the candidate that best matches the 3D US images according to vessel centerline and surface. As a result, we can determine the position of the US probe. The final process is real-time tracking using 2D US cine images captured by the US probe. We determine the respiratory phase by tracking the diaphragm on the image. The 3D model is then deformed according to respiration phase and is fitted to the image by considering the positions of the vessels. The tumor’s 3D positions are then inferred based on respiration phase. Testing our method on real patient data, we have found the accuracy of 3D position is within 3.79mm and processing time is 5.4ms during tracking.
A depth captured by a Time-of-Flight (ToF) camera is sometimes distorted when object is close to camera. This problem causes low quality in several applications. In this paper, we take saturation of pixel into account and propose how to correct depth regardless of color or distance of objects using multi-integration time and phase reconstruction methods. The ToF camera captures depth of objects by using the ratio of electron charges which is not saturated for each integration time and by using phase summation equality. For verifying our approach, we used our prototype ToF camera with 480x270 pixel resolution, 16MHz modulated frequency, f/1.6 lens and 40msec integration time. Integration times are set by 16msec and 24msec, respectively. And target object used in test is color chart consisting of 24 standard colors and is located at intervals of 25cm (0.75m to 2.00m). We verified depth of each patch in color chart has identical value.
Time-of-Flight (ToF) cameras are used for a variety of applications because it delivers depth information at a high frame rate. These cameras, however, suffer from challenging problems such as noise and motion artifacts. To increase signal-to-noise ratio (SNR), the camera should calculate a distance based on a large amount of infra-red light, which needs to be integrated over a long time. On the other hand, the integration time should be short enough to suppress motion artifacts. We propose a ToF depth imaging method to combine advantages of short and long integration times exploiting an imaging fusion scheme proposed for color imaging. To calibrate depth differences due to the change of integration times, a depth transfer function is estimated by analyzing the joint histogram of depths in the two images of different integration times. The depth images are then transformed into wavelet domains and fused into a depth image with suppressed noise and low motion artifacts. To evaluate the proposed method, we captured a moving bar of a metronome with different integration times. The experiment shows the proposed method could effectively remove the motion artifacts while preserving high SNR comparable to the depth images acquired during long integration time.
In this paper, we propose an efficient synthetic refocusing method from multiple coded aperture images for 3D user
interaction. The proposed method is applied to a flat panel display with a sensor panel which forms lens-less multi-view cameras. To capture the scene in front of the display, the modified uniformly redundant arrays (MURA) patterns are displayed on the LCD screen without the backlight. Through the imaging patterns on the LCD screen, MURA coded
images are captured in the sensor panel. Instead of decoding all coded images to synthetically generate a refocused
image, the proposed method only decodes one coded image corresponding to the refocusing image at a certain distance after circularly shifting and averaging all coded images. Further, based on the proposed refocusing method, the depth of an object in front of the display is estimated by finding the most focused image for each pixel through a stack of the refocused images at different depth levels. Experimental results show that the proposed method captures an object in front of the display, generates refocused images at different depth levels, and accurately determines the depth of an object including real human hands near the display
In this paper, we present a fast hologram pattern generation method by radial symmetric interpolation. In spatial domain,
concentric redundancy of each point hologram is removed by substituting the calculation of wave propagation with
interpolation and duplication. Also the background mask which represents stationary point in temporal domain is used to
remove temporal redundancy in hologram video. Frames are grouped in predefined time interval and each group shares
the background information, and hologram pattern of each time is updated only for the foreground part. The
effectiveness of proposed algorithm is proved by simulation and experiment.
Optical zoom lenses mounted on a stereo color camera magnify each left and right two-dimensional (2-D) image increasing focal length. However, without adjusting the baseline distance, the optical zoom distorts three-dimensional (3-D) perception because the optical zoom magnifies projected 2-D images not an original 3-D object. We propose a computational approach to stereoscopic zoom that magnifies stereo images without 3-D distortion. We computationally manipulate the baseline distance and convergence angle between left and right images by synthesizing novel view stereo images based on the depth information. We suggest a volume-predicted bidirectional occlusion inpainting method for novel view synthesis. Original color image is warped to the novel view determined by the adjusted baseline and convergence angle. Rear volume of each foreground object is predicted, and the foreground portion of each occlusion region is identified. Then we apply our inpainting method to fill in the foreground and background respectively. Experimental results show that the proposed inpainting method removes the cardboard effect that significantly decreases the perceptual quality of synthesized novel view image but has never been addressed in the literature. Finally, 3-D object presented by stereo images is magnified by the proposed stereoscopic zoom method without 3-D distortion.
In this paper, we propose a novel multi-view generation framework that considers the spatiotemporal consistency of each
synthesized multi-view. Rather than independently filling in the holes of individual generated images, the proposed
framework gathers hole information from each synthesized multi-view image to a reference viewpoint. The method then
constructs a hole map and a SVRL (single view reference layer) at the reference viewpoint before restoring the holes in
the SVRL, thereby generating a spatiotemporally consistent view. A hole map is constructed using depth information of
the reference viewpoint and the input/output baseline length ratio. Thus, the holes in the SVRL can also represent holes
in other multi-view images. To achieve temporally consistent hole filling in the SVRL, the restoration of holes in the
current SVRL is performed by propagating the pixel value of the previous SVRL. Further hole filling is performed using
a depth- and exemplar-based inpainting method. The experimental results showed that the proposed method generates
high-quality spatiotemporally consistent multi-view images in various input/output environments. In addition, the
proposed framework decreases the complexity of the hole-filling process by reducing repeated hole filling.
Respiratory motion tracking has been issues for MR/CT imaging and noninvasive surgery such as HIFU and
radiotherapy treatment when we apply these imaging or therapy technologies to moving organs such as liver, kidney or
pancreas. Currently, some bulky and burdensome devices are placed externally on skin to estimate respiratory motion of
an organ. It estimates organ motion indirectly using skin motion, not directly using organ itself. In this paper, we propose
a system that measures directly the motion of organ itself only using ultrasound image. Our system has automatically
selected a window in image sequences, called feature window, which is able to measure respiratory motion robustly even
to noisy ultrasound images. The organ's displacement on each ultrasound image has been directly calculated through the
feature window. It is very convenient to use since it exploits a conventional ultrasound probe. In this paper, we show that
our proposed method can robustly extract respiratory motion signal with regardless of reference frame. It is superior to
other image based method such as Mutual Information (MI) or Correlation Coefficient (CC). They are sensitive to what
the reference frame is selected. Furthermore, our proposed method gives us clear information of the phase of respiratory
cycle such as during inspiration or expiration and so on since it calculate not similarity measurement like MI or CC but
actual organ's displacement.
Time-of-flight depth sensor provides faster and easier way to 3D scene capturing and reconstruction. The depth
sensor, however, suffers from motion blur caused by any movement of camera or objects. In this manuscript,
we propose a novel depth motion blur pixel detection and elimination method that can be implemented on any
ToF depth sensor with light memory and computation resources. We propose a blur detection method using the
relations of electric charge amount. It detects blur pixel at each depth value calculation step only by checking
the four electric charge values by four internal control signals. Once we detect blur pixels, their depth values are
replaced by any closest normal pixel values. With this method, we eliminate motion blur before we build the
depth image with only few more calculations and memory addition.
This paper describes the method related to correcting color distortion in color imaging. Acquiring color images from
CMOS or CCD digital sensors can suffer from color distortion, which means that the image from sensors is different
from the original image in the color space. The main reasons are the cross-talks between adjacent pixels, the color
pigment characteristic's mismatch with human perception and infra-red (IR) influx to visible channel or red, green, blue
(RGB) due to IR cutoff filter imperfection. To correct this distortion, existing methods use multiplying gain coefficients
in each color channel and this multiplication can cause noise boost and loss of detail information. This paper proposes
the novel method which can not only preserve color distortion correction ability, but also suppress noise boost and loss
of detail information in the color correction process of IR corrupted pixels. In the case of non-IR corruption pixels, the
use of image before color correction instead of IR image makes this kind of method available. Specifically the color and
low frequency information in luminance channel is extracted from the color corrected image. And high frequency
information is from the IR image or the image before color correction. The method extracting the low and high
frequency information use multi-layer decomposition skill with edge preserving filters.
Time-of-flight cameras produce 3D geometry enabling faster and easier 3D scene capturing. The depth camera,
however, suffers from motion blurs when the movement from either camera or scene appears. Unlike other
noises, depth motion blur is hard to eliminate by any general filtering methods and yields the serious distortion
in 3D reconstruction, typically causing uneven object boundaries and blurs. In this paper, we provide a through
analysis on the ToF depth motion blur and a modeling method which is used to detect a motion blur region from
a depth image. We show that the proposed method correctly detects blur regions using the set of all possible
motion artifact models.
We propose a novel markerless 3D facial motion capture system using only one common camera. This system is simple
and easy to transfer facial expressions of a user's into virtual world. It has robustly tracking facial feature points
associated with head movements. In addition, it estimates high accurate 3D points' locations. We designed novel
approaches to the followings; Firstly, for precisely 3D head motion tracking, we applied 3D constraints using a 3D face
model on conventional 2D feature points tracking approach, called Active Appearance Model (AAM). Secondly, for
dealing with various expressions of a user's, we designed 2D face generic models from around 5000 images data and 3D
shape data including symmetric and asymmetric facial expressions. Lastly, for accurately facial expression cloning, we
invented a manifold space to successfully transfer 2D low dimensional feature points to 3D high dimensional points. The
manifold space is defined by eleven facial expression bases.
Recently a Time-of-Flight 2D/3D image sensor has been developed, which is able to capture a perfectly aligned
pair of a color and a depth image. To increase the sensitivity to infrared light, the sensor electrically combines
multiple adjacent pixels into a depth pixel at the expense of depth image resolution. To restore the resolution
we propose a depth image super-resolution method that uses a high-resolution color image aligned with an input
depth image. In the first part of our method, the input depth image is interpolated into the scale of the color
image, and our discrete optimization converts the interpolated depth image into a high-resolution disparity image,
whose discontinuities precisely coincide with object boundaries. Subsequently, a discontinuity-preserving filter is
applied to the interpolated depth image, where the discontinuities are cloned from the high-resolution disparity
image. Meanwhile, our unique way of enforcing the depth reconstruction constraint gives a high-resolution depth
image that is perfectly consistent with its original input depth image. We show the effectiveness of the proposed
method both quantitatively and qualitatively, comparing the proposed method with two existing methods. The
experimental results demonstrate that the proposed method gives sharp high-resolution depth images with less
error than the two methods for scale factors of 2, 4, and 8.
This paper presents a novel Time-of-Flight (ToF) depth denoising algorithm based on parametric noise modeling.
ToF depth image includes space varying noise which is related to IR intensity value at each pixel. By assuming
ToF depth noise as additive white Gaussian noise, ToF depth noise can be modeled by using a power function
of IR intensity. Meanwhile, nonlocal means filter is popularly used as an edge-preserving denoising method
for removing additive Gaussian noise. To remove space varying depth noise, we propose an adaptive nonlocal
means filtering. According to the estimated noise, the search window and weighting coefficient are adaptively
determined at each pixel so that pixels with large noise variance are strongly filtered and pixels with small
noise variance are weakly filtered. Experimental results demonstrate that the proposed algorithm provides good
denoising performance while preserving details or edges compared to the typical nonlocal means filtering.
Breast soft tissues have similar x-ray attenuations to mass tissue. Overlapping breast tissue structure often obscures mass
and microcalcification, essential to the early detection of breast cancer. In this paper, we propose new method to generate
the high contrast mammogram with distinctive features of a breast cancer by using multiple images with different x-ray
energy spectra. On the experiments with mammography simulation and real breast tissues, the proposed method has
provided noticeable images with obvious mass structure and microcalifications.
new 3D video format which consists of one full resolution mono video and half resolution left/right videos is proposed.
The proposed 3D video format can generate high quality virtual views from small amount of input data while preserving
the compatibility for legacy mono and frame compatible stereo video systems. The center view video is the same with
normal mono video data, but left/right views are frame compatible stereo video data. This format was tested in terms of
compression efficiency, rendering capability, and backward compatibility. Especially we compared view synthesis
quality when virtual views are made from full resolution two views or one original view and the other half resolution
view. For frame compatible stereo format, experiments were performed on interlaced method. The proposed format gives
BD bit-rate gains of 15%.
This study aims to promote the cubic effect by reproducing images with depth perception using chromostereopsis in
human visual perception. From psychophysical experiments based on the theory that the cubic effect depends on the
lightness of the background in the chromostereoptic effect and the chromostereoptic reversal effect, it was found that the
luminous cubic effect differs depending on the lightness of the background and the hue combination of the neighboring
Also, the layer of the algorithm-enhancing cubic effect that was drawn from the result of the experiment was classified
into the foreground, middle, and background layers according to the depth of the input image. For the respective
classified layer, the color factors that were detected through the psychophysical experiments were adaptively controlled
to produce an enhanced cubic effect that is appropriate for the properties of human visual perception and the
characteristics of the input image.
A Time-of-Flight (ToF) camera uses a near infrared (NIR) to obtain the distance from the camera to an object. The
distance is calculated from the amount of time shift between the emitted and reflected NIR. ToF cameras generally
modulate NIR with a square wave rather than a sinusoidal wave due to its difficulty in hardware implementation. The
previous method using simple trigonometric function estimates the time shift with the difference of electrons generated
by the reflected square wave. Thus the estimated time shift includes a harmonic distortion caused by the nonlinearity of
trigonometric function. In this paper, we propose a new linear estimation method to reduce the harmonic distortion. For
quantitative evaluation, the proposed method is compared to the previous method using our prototype ToF depth camera.
Experimental results show that the distance obtained by the proposed method is more accurate than that by the previous
Market's demands of digital cameras for higher sensitivity capability under low-light conditions are remarkably
increasing nowadays. The digital camera market is now a tough race for providing higher ISO capability. In this paper,
we explore an approach for increasing maximum ISO capability of digital cameras without changing any structure of an
image sensor or CFA. Our method is directly applied to the raw Bayer pattern CFA image to avoid non-linearity
characteristics and noise amplification which are usually deteriorated after ISP (Image Signal Processor) of digital
cameras. The proposed method fuses multiple short exposed images which are noisy, but less blurred. Our approach is
designed to avoid the ghost artifact caused by hand-shaking and object motion. In order to achieve a desired ISO image
quality, both low frequency chromatic noise and fine-grain noise that usually appear in high ISO images are removed
and then we modify the different layers which are created by a two-scale non-linear decomposition of an image. Once
our approach is performed on an input Bayer pattern CFA image, the resultant Bayer image is further processed by ISP
to obtain a fully processed RGB image. The performance of our proposed approach is evaluated by comparing SNR
(Signal to Noise Ratio), MTF50 (Modulation Transfer Function), color error ∝E*ab and visual quality with reference
images whose exposure times are properly extended into a variety of target sensitivity.
We present a collection of principles to compare two sets
of color primaries for wide gamut displays. A new, algorithmic threedimensional
method to find optimal color primaries both for threeprimary
and multiprimary displays is described. The method was
implemented in a computer program. The resulting optimal color
primary sets are discussed. We show that two-dimensional methods
to find optimal color primaries by using a chromaticity diagram are
inferior to three-dimensional optimization techniques that include luminance
This paper describes the new method for fast auto focusing in image capturing devices. This is achieved by using two defocused images. At two prefixed lens positions, two defocused images are taken and defocused blur levels in each image are estimated using Discrete Cosine Transform (DCT). These DCT values can be classified into distance from the image capturing device to main object, so we can make distance vs. defocused blur level classifier. With this classifier, relation between two defocused blur levels can give the device the best focused lens step. In the case of ordinary auto focusing like Depth from Focus (DFF), it needs several defocused images and compares high frequency components in each image. Also known as hill-climbing method, the process requires about half number of images in all focus lens steps for focusing in general. Since this new method requires only two defocused images, it can save lots of time for focusing or reduce shutter lag time. Compared to existing Depth from Defocus (DFD) which uses two defocused images, this new algorithm is simple and accurate as DFF method. Because of this simplicity and accuracy, this method can also be applied to fast 3D depth map construction.
In this paper, we present a new noise estimation and reduction scheme to restore images degraded by image sensor noise. Since the characteristic of noise deviates according to camera response function (CRF) and the sensitivity of image sensors, we build a noise profile by using test charts for accurate noise estimation. By using the noise profile, we develop simple and fast noise estimation scheme which can be appropriately used for digital cameras. Our noise removal method utilizes the result of the noise estimation and applies several adaptive nonlinear filters to give the best image quality against high ISO noise. Experimental results show that the proposed method yields significantly good performance for images corrupted by both synthetic sensor noise and real sensor noise.
This paper proposes a framework of colour preference control to satisfy the consumer's colour related emotion. A colour
harmony algorithm based on two-colour combinations is developed for displaying the images with several complementary colour pairs as the relationship of two-colour combination. The colours of pixels belonging to complementary colour areas in HSV colour space are shifted toward the target hue colours and there is no colour change for the other pixels. According to the developed technique, dynamic emotions by the proposed hue conversion can be improved and the controlled output image shows improved colour emotions in the preference of the human viewer. The psychophysical experiments are conducted to investigate the optimal model parameters to produce the most pleasant image to the users in the respect of colour emotions.
Digital images captured from CMOS image sensors suffer Gaussian noise and impulsive noise. To efficiently reduce the
noise in Image Signal Processor (ISP), we analyze noise feature for imaging pipeline of ISP where noise reduction
algorithm is performed. The Gaussian noise reduction and impulsive noise reduction method are proposed for proper
ISP implementation in Bayer domain. The proposed method takes advantage of the analyzed noise feature to calculate
noise reduction filter coefficients. Thus, noise is adaptively reduced according to the scene environment. Since noise is
amplified and characteristic of noise varies while the image sensor signal undergoes several image processing steps, it is
better to remove noise in earlier stage on imaging pipeline of ISP. Thus, noise reduction is carried out in Bayer domain
on imaging pipeline of ISP. The method is tested on imaging pipeline of ISP and images captured from Samsung 2M
CMOS image sensor test module. The experimental results show that the proposed method removes noise while
effectively preserves edges.
Image acquisition devices inherently do not have color constancy mechanism like human visual system. Machine color constancy problem can be circumvented using a white balancing technique based upon accurate illumination estimation. Unfortunately, previous study can give satisfactory results for both accuracy and stability under various conditions. To overcome these problems, we suggest a new method: spatial and temporal illumination estimation. This method, an evolution of the Retinex and Color by Correlation method, predicts on initial illuminant point, and estimates scene-illumination between the point and sub-gamuts derived by from luminance levels. The method proposed can raise estimation probability by not only detecting motion of scene reflectance but also by finding valid scenes using different information from sequential scenes. This proposed method outperforms recently developed algorithms.
A series of psychophysical experiments using paired comparison method was performed to investigate various visual
attribute affecting image quality of a mobile display. An image quality difference model was developed to show high
correlation with visual results. The result showed that Naturalness and Clearness are the most significant attributes
among the perceptions. A colour quality difference model based on image statistics was also constructed and it was
found colour difference and colour naturalness are important attributes for predicting image colour quality difference.
We develop a methodology to find the optimal memory color (colors of familiar objects) boundary in YCbCr color space and a local image adjustment technique called preferred color reproduction (PCR) to improve image quality. The optimal memory color boundary covers most familiar object colors taken under various viewing conditions. The PCR algorithm is developed based on the idea that colors of familiar objects (memory colors) are key factors in judging the naturalness of an image. The PCR algorithm is applied to pixels detected as having a memory color. Memory color detection is conducted using color information by checking if an input color is inside the predetermined memory color boundary. The PCR algorithm transforms colors inside the memory color boundary to be shifted toward the boundary of constant interval in the center. The PCR algorithm is applied to skin colors, and psychophysical experiments using real images were conducted to determine the best parameters for the algorithm resulting in the most preferred image.
This paper proposes a method of filtering a digital sensor image to efficiently reduce noise and to improve the sharpness of an image. To reduce the noise in an image captured by conventional image sensor, the proposed noise reduction filter selectively outputs one of results obtained by recursive temporal and spatial noise filtering values. By proposed noise filtering method, image detail can be well preserved and noise filtering artifacts which can be generated along the moving object boundary in image sequences by applying temporal noise filtering are prevented. Since the sharpness of noise filtered image can be inevitably deteriorated by noise filtering, the adaptive noise suppressed sharpening filter is also proposed. The proposed sharpening filter generates filter mask adaptively according to the pixel similarity information within filter mask and can obtain the continuous image quality by the easy-controllable gain control algorithm without noise boost-up in the smooth region.
This study investigates the color error problems posed by large flat panel displays and proposes a subpixel-rendering algorithm to mitigate the problem. The color error problems are caused by Mach band effect and the convergence error of a pixel on large subpixel structured displays and named a color band error. The proposed method includes three processes; a finding process of areas or pixels generating the error, an estimating process of the error, and a correction process of the error. To correct the color band error, we take an error erosion approach, an error concealment approach, and a hybrid approach of the error erosion and the error concealment. In this paper, we experimented to know the threshold where human vision can detect by a psychophysical method. In addition, we applied our proposed method to a commercial 42" plasma display to confirm the effect. The results show that all observers see the color band error at a sharp edge having above 64-gray difference and the converted test images by our algorithm are preferred to the original test images. Finally, this paper reports that the Mach band effect and the convergence error on large subpixel structured display produce color band errors on images having sharp edge and the proposed method effectively corrects the color band errors.
This study has three primary aims circumventing current limitations of color reproduction technologies: firstly, to derive base-line image quality factors from both color printer experts and academic research works. Individual factors were verified by systematic experiments, secondly, to develop a perceptual gamut mapping algorithm covering the image quality and preference factors derived, thirdly, to apply the algorithm to printer driver as acting for a vendor specific perceptual intent. Algorithm of this study tried to optimization between control parameters of gamut mapping and color
shifting factors of preference, e.g. skin, sky and green grass. Profile builder using this algorithm outperforms, in industrial and academic aspects, existing commercial tool and CIE recommended algorithms.
The image processor in digital TV has started to play an important role due to the customers' growing desire for higher quality image. The customers want more vivid and natural images without any visual artifact. Image processing techniques are to meet customers' needs in spite of the physical limitation of the panel. In this paper, developments in image processing techniques for DTV in conjunction with developments in display technologies at Samsung R and D are reviewed. The introduced algorithms cover techniques required to solve the problems caused by the characteristics of the panel itself and techniques for enhancing the image quality of input signals optimized for the panel and human visual characteristics.
On a plasma display panel (PDP), luminous elements of red, green, and blue have different time responses. Therefore, a colored trails and edges appear behind and in front of moving objects. In order to reduce the color artifacts, this paper proposes a motion-based discoloring method. Discoloring values are modeled as linear functions of a motion vector to reduce hardware complexity. Experimental results show that the proposed method has effectively removed the colored trails and edges of moving objects. Moreover, the clear image sequences have been observed compared to the conventional ones.
The preferred skin color reproduction algorithm is developed for the mobile display especially for a portrait image with one person as a main object occupying most of the screen. According to the developed technique, the skin area in an image is detected using color value of each pixel in YCbCr color space. The skin color boundary is defined as a quadrangle in Cb-Cr plane. The colors of pixels belonging to skin area are shifted toward the preferred colors while there is no color change for the other pixels. The psychophysical experiments are conducted to investigate the optimal model parameters providing the most pleasant image to the users. Then, the performance of developed algorithm is tested using the optimal parameters. The result shows that for more than 95% cases, the observers prefer the images treated with the developed algorithm compared to the original image. It is believed that the developed algorithm can be applied to the mobile application to improve the image quality regardless the input sources.
The recent color display market is being focused on displays with larger panel size and larger color gamut. More specifically, for widening the color gamut, multi-primary display (MPD), which is a display having more than the conventional three-channels, has been an important issue. However, developing MPD faces many difficulties such as sustaining display luminance against 3-color displays, embodying a simple H/W structure, and assigning new color signals based on input RGB signals.
The purpose of this study is to propose a method to display color on a new pixel structure for flat-panel displays with six primary colors intended to expand the color gamut, and to discuss a color decomposition algorithm for this new structure to assign signals to 6-channel from an input RGB signal. Special interests of this study are made on minimizing the deterioration of color image quality compared with RGB-based display and maximizing the usage of the widened color gamut. The result of this study was implemented on a prototype 6-color LCD and verified generation of the color signals for 6-color without luminance degradation. The device is capable of reproducing colors like emerald cyan, which cannot be displayed on a conventional RGB display. In addition, hardware implementation on FPGA verified commercial viability of the algorithm.
The theoretical approach is introduced to design the optimal chromaticities for primaries of a display with a given size of triangular color gamut in xy-plane. Optimal primaries are defined as a set of chromaticities of red, green and blue primaries with fixed white point that most optimally satisfying four criteria, i.e. gamut size, gamut shape, coverage of object colors and hue of the primaries, in the visually uniform color space, CIECAM02. It is assumed that the optimal gamut should cover that of sRGB and have similar maximum chroma for each hue. The number of SOCS data located outside the gamut is used as a criterion to judge the coverage of object colors. Also it is set the hues of primaries to be close to those of sRGB. The simulation results showed that the optimal primaries for 85% of NTSC area have similar points with sRGB for red and blue, and green primary is located in between sRGB and NTSC. For 100% of NTSC area, the optimal chromaticities are located near those of NTSC for red and green and that of sRGB for blue.
A new approach to image segmentation is presented. Novelty consists in combining multiple image feature information together -- color feature, texture feature and pixel’s geometric location in spatial domain to separate the regions with homogeneous color, texture, and similar spatiality --, as well as grouping the homogeneous clusters in the feature space with unique manner. The proposed segmentation algorithm contains two main stages. First, the mode finding and multi-link clustering algorithm converts an image into a map of small primary regions - region graph representation. The nodes of the graph correspond to distinguished regions, and the lines correspond to relations between neighbor regions. The region map is further simplified by the secondary graph analysis and merging of neighbor regions. The performance of developed algorithm was tested by using various images obtained by a real camera.
The term "color temperature" usually represents the color of light source or the white point of image displaying devices. The color temperature can be an effective bridge between images' characteristic and human's perceptual temperature feeling against the images. It can capture human's high-level perception to improve image browsing. In this paper, our goal is to demonstrate how well the color temperature is connected to such human's perception. We demonstrate the method of subjective experiment, a color temperature mapping range for each perceptual category, and browsing accuracy of the color temperature ranges obtained from the experiment. The compact representation for the color temperature and some of usage scenarios are explained as presented in the amendment of MPEG-7 standard.
The term "color temperature" represents the color of light source or the white point of image displaying devices such as TV and PC monitor. By controlling the color temperature, we can convert the reference white color of images. This is equivalent to the illuminant change, which alters all colors in the scene. In this paper, our goal is to find an appropriate method of converting the color temperature in order to reproduce the user-preferred color temperature in video displaying devices. It is essential that the relative difference of color temperature between successive image frames should be well preserved as well as the appearance of images should seem natural after applying the user-preferred color temperature. In order to satisfy these conditions, we propose an adaptive color temperature conversion method that estimates the color temperature of an input image and determines the output color temperature in accordance with the value of the estimated one.
In this paper, the method to calculate the illuminant chromaticity of an image is proposed by combine the perceived illumination and highlight approach. The hybrid approach is more stable and accurate compared to each approach. The application for this algorithm is two-fold. For simple and quick implementation, perceived illumination is enough, and for more accurate case, hybrid approach can be used. And conversion of image illuminant chromaticity is also proposed. This can be applied into special effect for the images.
Two error diffusion algorithms, based on Pappas's
printer model accounting for dot-overlapping and ink distortion, are presented to achieve good color reproduction. The basic idea is to combine printer and color models on the perceptually uniform Commission Internationale de I'Eclairge (CIE) L*a*b* (CIE 1976) color space. The models, derived from the Neugebauer equations and color matching theories, are designed to achieve the minimization of
the human visual color distortions between the colors of original pixels and those of a halftoned image. The effectiveness of our approaches is shown by comparison and examination of two error diffusion algorithms with previous methods: the error diffusion based models and the window based minimization algorithm. Experimental results of the error clipping technique, focused on the real application of the nonseparable algorithm, and the desired range of error
clipping, where an image produced by the nonseparable algorithm can be stable without additional color distortion, are reported.
In color reproduction research, a linear model designed to minimize the error between original surface reflectance spectra and reproduced spectra is useful in the process of producing an accurate color match between the original image and reproduction under a variety of illuminants, but it is inappropriate in efficiency. We propose an efficient linear model based on surface reflectance spectra and a unified wavelength function of CIE 1931 standard observer representing human perceptual property. The surface spectra weighted with the unified wavelength function were introduced to minimize the human perceptual error between original reflectance spectra and reproduced spectra and to reduce the number of the spectral basis functions. The performance of reflectance spectra-to-CIELAB transformation on our proposed linear model is tested and compared with a conventional model based on reflectance spectra under a variety of illuminants. The results of our linear model is superior to that of the conventional model. With Munsell 400 color patches, D65 illuminant and 4-dimensional linear model, the mean color difference of our model is 1.28 CIELAB unit. And an algorithm for color scanner characterization using our model is made and tested, and the results are shown.