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This paper presents a novel automatic white balance (AWB) algorithm for consumer imaging devices. While existing AWB methods require reference white patches to correct color, the proposed method performs the AWB function using only an input image in two steps: i) white point detection, and ii) color constancy gain computation. Based on the dark channel prior assumption, a white point or region can be accurately extracted, because the intensity of a sufficiently bright achromatic region is higher than that of other regions in all color channels. In order to finally correct the color, the proposed method computes color constancy gain values based on the Y component in the XYZ color space. Experimental results show that the proposed method gives better color-corrected images than recent existing methods. Moreover, the proposed method is suitable for real-time implementation, since it does not need a frame memory for iterative optimization. As a result, it can be applied to various consumer imaging devices, including mobile phone cameras, compact digital cameras, and computational cameras with coded color.

In this paper, a novel dynamic range extension method is presented by detecting under-exposed regions and performing spatially adaptive contrast enhancement. More specifically, a combined color-channels transmission (CCT) map is proposed to detect low-contrast regions, and spatially adaptive gain computation is used to enhance the contrast of the under-exposed regions. As a result, the proposed method can extend the dynamic range of a single input image by changing the brightness of under-exposed regions with preserving the brightness of background. Moreover, the proposed method requires less computational load than existing multiple frames-based high dynamic range (HDR) imaging methods without color distortion. For these reasons, the proposed method can be applied to various imaging devices, such as mobile phone cameras, compact digital cameras, and video surveillance systems.

This paper presents a novel wide dynamic range system to reduce flickering artifact produced by a high speed electronic rolling shutter in a CMOS image sensor. The proposed algorithm removes flicker artifact in the shortexposure image using a correlation between the pair of two differently exposed images to extend the dynamic range. Since the proposed method overcomes the electronic shutter problem in a conventional wide dynamic range imaging system, it can remove the flicker artifact under an alternating current light source such as a fluorescent bulb.

In this paper, we present a novel high dynamic range (HDR) imaging method using a single image. The existing multiple image-based HDR methods work only on condition that there is no camera and object movement when acquiring multiple, differently exposed low dynamic range (LDR) images. To overcome such an unrealistic restriction, we make three LDR images from a single input image using local histogram stretching. An edge-preserving spatially adaptive denoising method is also proposed to suppress the noise that is amplified in the histogram stretching process. Because the proposed method self-generates three histogram-stretched LDR images from a single input image, ghost artifacts that are the result of the relative motion between the camera and objects during exposure time, are inherently removed. Therefore, the proposed method can be applied to mobile imaging devices such as a mobile phone camera and a consumer compact camera to provide the ghost artifacts free HDR function in the form of either embedded or post-processing software.

In this paper, we present a ghost artifacts removing method for obtaining artifact-free high dynamic range (HDR) images in the presence of camera movement. The existing HDR methods work on condition that there is no camera movement when acquiring multiple low dynamic range (LDR) images. For overcoming such unrealistic restriction, we register the multiple LDR images by using a novel target frame finding method. Ghost artifacts are removed in the resulting HDR images since the registration process of LDR images compensates undesired camera motion. Since the proposed method can embed the self compensating function into an imaging device, Ghost-free HDR imaging application can be extended to most consumer cameras, such as mobile phone cameras, point-and-shoot cameras, and digital single lens reflected (DSLR) cameras without using additional stabilizing equipments.

This paper present a novel object tracking algorithm that can efficiently overcome the object occlusion problem by combining depth and color probability distribution information. The proposed algorithm consists of; i the depth estimation step using a color shift model CSM-based single camera, and ii the combination of depth and color probability distribution step using continuous adaptive mean shift CAMSHIFT algorithm, which is an adaptive version of the existing mean shift algorithm. In spite of the optimum object segmentation ability, the CAMSHIFT algorithm may fail in tracking if multiple occluded objects have similar colors. In order to overcome this limitation, the proposed algorithm combines depth and color probability distribution information. The experimental results show that the proposed algorithm is real time for well tracking the occluded object which cannot be tracked by the traditional CAMSHIFT algorithm, and the accuracy of depth estimation of the proposed algorithm is about 97.5i?ź%.

This paper presents a defocus-invariant image registration method for measuring the shifting value between two differently located patterns in an imaging sensor. Existing registration methods fail with unfocused images since features or regions of interest are degraded by defocus. In order to solve this problem, the proposed method consists of three stages: i) pre-generation of the set of point spread functions (PSFs) estimated in different focusing positions, ii) the geometric transformation estimation using estimated PSF data, and iii) registration using estimated transformation matrix. The proposed method improves out-of-focus degradation through estimation of PSF. For this reason, the proposed method can accurately estimate the difference of phase between two out-of-focus images. Furthermore, it can be applied to phase-difference detection auto focusing, and provide accurate auto focusing performance.

Carpal tunnel syndrome, a median nerve entrapment neuropathy, is characterized by sensorimotor deficits. Recent reports have shown that this syndrome is also characterized by functional and structural neuroplasticity in the primary somatosensory cortex of the brain. However, the linkage between this neuroplasticity and the functional deficits in carpal tunnel syndrome is unknown. Sixty-three subjects with carpal tunnel syndrome aged 20–60 years and 28 age- and sex-matched healthy control subjects were evaluated with event-related functional magnetic resonance imaging at 3 T while vibrotactile stimulation was delivered to median nerve innervated (second and third) and ulnar nerve innervated (fifth) digits. For each subject, the interdigit cortical separation distance for each digit’s contralateral primary somatosensory cortex representation was assessed. We also evaluated fine motor skill performance using a previously validated psychomotor performance test (maximum voluntary contraction and visuomotor pinch/release testing) and tactile discrimination capacity using a four-finger forced choice response test. These biobehavioural and clinical metrics were evaluated and correlated with the second/third interdigit cortical separation distance. Compared with healthy control subjects, subjects with carpal tunnel syndrome demonstrated reduced second/third interdigit cortical separation distance (P < 0.05) in contralateral primary somatosensory cortex, corroborating our previous preliminary multi-modal neuroimaging findings. For psychomotor performance testing, subjects with carpal tunnel syndrome demonstrated reduced maximum voluntary contraction pinch strength (P < 0.01) and a reduced number of pinch/release cycles per second (P < 0.05). Additionally, for four-finger forced-choice testing, subjects with carpal tunnel syndrome demonstrated greater response time (P < 0.05), and reduced sensory discrimination accuracy (P < 0.001) for median nerve, but not ulnar nerve, innervated digits. Moreover, the second/third interdigit cortical separation distance was negatively correlated with paraesthesia severity (r = −0.31, P < 0.05), and number of pinch/release cycles (r = −0.31, P < 0.05), and positively correlated with the second and third digit sensory discrimination accuracy (r = 0.50, P < 0.05). Therefore, reduced second/third interdigit cortical separation distance in contralateral primary somatosensory cortex was associated with worse symptomatology (particularly paraesthesia), reduced fine motor skill performance, and worse sensory discrimination accuracy for median nerve innervated digits. In conclusion, primary somatosensory cortex neuroplasticity for median nerve innervated digits in carpal tunnel syndrome is indeed maladaptive and underlies the functional deficits seen in these patients.

A novel white point estimation method is presented for automatic white balance (AWB). The proposed method first detects white points by estimating dark channel prior information in the input image, and then adjusts the white balance on the detected white points. Experimental results indicate that the proposed method produces visually-correct color representation in most test images.

In this paper, we present a novel contrast enhancement method for backlit images that consists of three steps: i) computation of the transmission coefficients using the dark channel prior, ii) generation of multiple images having different exposures based on the transmission coefficients, and iii) image fusion. Compared to global intensity transformation methods and spatially invariant contrast enhancement algorithms, our approach first extracts under-exposed regions using the dark channel prior map, and then performs spatially adaptive contrast enhancement. As a result, the contrast of the image is increased, especially for backlit scenes and those with very wide dynamic range, while still preserving image details and color.

Detecting and identifying targets in aerial images has been a challenging problem due to various types of image distortion factors, such as motion of a sensing device, weather variation, scale changes, and dynamic viewpoint. For accurate, robust recognition of objects in unmanned aerial vehicle (UAV) videos, we present a novel target detection and identification algorithm using part-template matching. The proposed method for target detection partitions the target into part-templates by efficient extraction method based on target part regions. We also propose distribution distance measurement-based target identification using the target part-template.

In this paper, we present a novel high dynamic range (HDR) imaging method using a single input image. Conventional multiple image-based HDR methods are successful only on condition that there is no motion in the scene during the acquisition of multiple, differently exposed low dynamic range (LDR) images. If these constraints are not satisfied, a ghost artifact is produced in the resulting HDR image. In order to overcome these limitations, we generate multiple, differently exposed LDR images from a single input image. We call these multiple images a set of layered exposed (LE) images. In order to generate an appropriate set of LE images, the proposed method divides input image into 9 subregions and computes local mean in each subregion to estimate the minimum and maximum local mean. The estimated local means become ranges of histogram equalization (HE). HDR image is generated by fusing differently exposed LE images. More specifically, given a set of LE images, we perform weighted fusion to produce the resulting HDR image, which is inherently free from ghost artifacts since all LE images are geometrically identical. Experimental results show that the proposed method outperforms the existing algorithms in the sense of both removing ghost artifacts and enhancement of image contrast.

We present a spatially adaptive defogging algorithm for enhancement of color and visibility of unmanned aerial vehicle (UAV) images. It is hard to identify an object of interest from airborne image acquired by a satellite, an airplane, and a UAV because of various atmospheric distortions. For overcoming this problem, the proposed algorithm decomposes the input foggy image into the original fog-free component and the atmospherically distorted component, and then estimates the original image based on the image degradation model. We first generate a normalized image using the maximum value among RGB color channels of a foggy Image. We estimate the atmospheric light in the labeled image. We also generate a modified transmission map using the labeled image and a guided filter. A major contribution of the proposed work is the enhancement of details using a guided filter as well as defogging. We can significantly enhance the visibility of a foggy image by using the estimated atmospheric light and the transmission map. The proposed algorithm can remove foggy components better than existing defogging techniques because the specular component and the labeled image are used.

In this paper, we propose a ghost artifacts removing algorithm for obtaining artifact-free high dynamic range (HDR) images in the presence of camera movement. Existing HDR methods work on condition that there is no camera movement when acquiring multiple low dynamic range (LDR) images. For overcoming such unrealistic restriction, the proposed algorithm first specifies the target and the source images in the set of acquired LDR images, and then the source image is transformed to fit the target image by estimating translation and rotation components in the affine matrix. Therefore, the proposed algorithm can reconstruct the HDR image without ghost artifacts because camera movement is completely removed in transformed images. Experimental results show that the proposed algorithm successfully removes ghost artifacts. For this reason, the proposed algorithm can extend application areas of HDR imaging to various mobile imaging devices, such as a handheld camcorder and a mobile phone camera.

This study proposes a novel sensor device adopted with a nematic liquid crystal Fabry-Perot (NLC FP) resonator. The NLC FP filter can be a good solution due to its simplicity, compactness, easy fabrication, and high wavelength selectivity. Moreover, the director of the LCs can be controlled by applying an electric field because LCs have high dielectric anisotropy. A sensor system can be constructed which detects temperature variations only by measuring an applied voltage necessary for compensating the thermal effects.

Freezing behavior is a widely used parameter that represents the level of fear. A number of studies on emotional learning have used this behavior for quantification of fear that results from a cue or a context. Even though the expression of freezing behavior is based on the dynamic interaction of mice with the environment, the effect of environmental stimuli on freezing behavior has not been studied extensively because of its minority compared to the effect of conditioning-stimuli. In this study, we found that the auditory environment of a context affects the freezing behavior of a mouse in it. This effect was not observed when the mouse was exposed to the context for the first time. However, during the second exposure, the level of freezing behavior increased significantly in an intensity-dependent manner, while the type, pitch, and rhythm of additional sounds involved in the context did not induce notable effects. This intensity-dependent effect was unrelated to the level of fear and anxiety, reflecting another aspect of the freezing behavior as a parameter for recognizing the pattern of normal behaviors.

Video object segmentation often fails when the background and foreground contain a similar distribution of colours. Proposed is a novel image segmentation algorithm to detect salient motion under a complex environment by combining temporal difference and background generation. Experimental results show that the proposed algorithm provides a twice higher matching ratio than the conventional Gaussian mixture-based approaches under various conditions.

The thermal effect on the optical properties of liquid crystal (LC) devices was investigated using the Fabry–Perot (FP) Interferometry From thermal variations of the resonant modes in the LC-filled FP cell, the temperature-dependence of the phase retardation and the cell gap variations were precisely measured. Experimental results showed that both the thermal variations of the cell gap and the refractive index variations of the LC with temperature play a critical role in the optical performances of the LC devices.