This paper presents Probabilistic Bi-polar Radial Reach Correlation (PrBPRRC), a change detection method that is robust against illumination changes and background movements. Most of the traditional change detection methods are robust against either illumination changes or background movements; BPRRC is one of the illumination-robust change detection methods. We introduce a probabilistic background texture model into BPRRC and add the robustness against background movements including foreground invasions such as moving cars, walking people, swaying trees, and falling snow. We show the superiority of PrBPRRC in the environment with illumination changes and background movements by using three public datasets and one private dataset: ATON Highway data, Karlsruhe traffic sequence data, PETS 2007 data, and Walking-in-a-room data.

Image sequence registration has attracted increasing attention due to its significance in image processing and computer vision. In this paper, we put forward a new kernel based image registration approach, combining both feature-based and intensity-based methods. The proposed algorithm consists of two steps. The first step utilizes feature points to roughly estimate a motion parameter between successive frames; the second step applies our kernel based idea to align all the frames to the reference frame (typically the first frame). Experimental results using both synthetic and real image sequences demonstrate that our approach can automatically register all the image frames and be robust against illumination change, occlusion and image noise.

We investigated the relationship between ambient illumination and psychological effect by applying a modified color harmony model. We verified the proposed model by analyzing correlation between psychological value and modified color harmony score. Experimental results showed the possibility to obtain the best color for illumination using this model.

In this paper, a novel illumination invariant face recognition algorithm is proposed for face recognition. This algorithm is composed of two phases. In the first phase, we reduce the effect of illumination changes using a nonlinear mapping of image intensities. Then, we modify the distribution of the coefficients of wavelet transform in certain sub-bands. In this step, the recognition performance is more important than image quality. In the second phase, we used the unitary factor of polar decomposition of enhanced image as a feature vector. In the recognition phase, the correlation-based nearest neighbor rule is applied for the matching. We have performed some experiments on several databases and have evaluated the proposed method in different aspects. Experimental results in recognition show that this approach provides a suitable representation for overcoming illumination effects.

Background modeling is one of the most challenging and time consuming tasks in motion detection from video sequence. This paper presents a background independent moving object segmentation algorithm utilizing the spatio-temporal information of the last three frames. Existing three-frame based methods face challenges due to the insignificant gradient information in the overlapping region of difference images and edge localization errors. These methods extract scattered moving edges and experience poor detection rate especially when objects with slow movement exist in the scene. Moreover, they are not much suitable for moving object segmentation and tracking. The proposed method solves these problems by representing edges as segments and applying a novel segment based flexible edge matching algorithm which makes use of gradient accumulation through distance transformation. Due to working with three most recent frames, the proposed method can adapt to changes in the environment. Segment based representation facilitates local geometric transformation and thus it can make proper use of flexible matching to provide an effective solution for tracking. To segment the moving object region from the detected moving edges, we introduce a watershed based algorithm followed by an iterative background removal procedure. Watershed based segmentation algorithm helps to extract moving object with more accurate boundary which eventually achieves higher coding efficiency in content based applications and ensures a good visual quality even in the limited bit rate multimedia communication.

A novel bimodal method for face recognition under low-level lighting conditions is proposed. It fuses an enhanced gray level image and an illumination-invariant geometric image at the feature-level. To further improve the recognition performance under large variations in attributions such as poses and expressions, discriminant features are extracted from source images using the wavelet transform-based method. Features are adaptively fused to reconstruct the final face sample. Then FLD is used to generate a supervised discriminant space for the classification task. Experiments show that the bimodal method outperforms conventional methods under complex conditions.

Image enhancement plays an important role in many machine vision applications on images captured in low contrast and low illumination conditions. In this study, we propose a new method for image enhancement based on analysis on embedded surfaces of images. The proposed method gives an insight into the relationship between the image intensity and image enhancement. In our method, scaled surface area and the surface volume are proposed and used to reconstruct the image iteratively for contrast enhancement, and the illumination of the reconstructed image can also be adjusted simultaneously. On the other hand, the most common methods for measuring the quality of enhanced images are Mean Square Error (MSE) or Peak Signal-to-Noise-Ratio (PSNR) in conventional works. The two measures have been recognized as inadequate ones because they do not evaluate the result in the way that the human vision system does. This paper also presents a new framework for evaluating image enhancement using both objective and subjective measures. This framework can also be used for other image quality evaluations such as denoising evaluation. We compare our enhancement method with some well-known enhancement algorithms, including wavelet and curvelet methods, using the new evaluation framework. The results show that our method can give better performance in most objective and subjective criteria than the conventional methods.

None of the existing color constancy algorithms can be considered universal. Furthermore, they use all the image pixels, although actually not all of the pixels are effective in illumination estimation. Consequently, how to select a proper color constancy algorithm from existing algorithms and how to select effective(or useful) pixels from an image are two most important problems for natural images color constancy. In this paper, a novel Color Constancy method using Effective Regions (CCER) is proposed, which consists of the proper algorithm selection and effective regions selection. For a given image, the most proper algorithm is selected according to its Weilbull distribution while its effective regions are chosen based on image similarity. The experiments show promising results compared with the state-of-the-art methods.

This paper presents an automatic edge segment based algorithm for the detection of moving objects that has been specially developed to deal with the variations in illumination and contents of background. We investigated the suitability of the proposed edge segment based moving object detection algorithm in comparison with the traditional intensity based as well as edge pixel based detection methods. In our method, edges are extracted from video frames and are represented as segments using an efficiently designed edge class. This representation helps to obtain the geometric information of edge in the case of edge matching and shape retrieval; and creates effective means to incorporate knowledge into edge segment during background modeling and motion tracking. An efficient approach for background edge generation and a robust method of edge matching are presented to effectively reduce the risk of false alarm due to illumination change and camera motion while maintaining the high sensitivity to the presence of moving object. The proposed method can be successfully realized in video surveillance applications in home networking environment as well as various monitoring systems. As, video coding standard MPEG-4 enables content based functionality, it can successfully utilize the shape information of the detected moving objects to achieve high coding efficiency. Experiments with real image sequences, along with comparisons with some other existing methods are presented, illustrating the robustness of the proposed algorithm.

Skin tone detection in conditions where illuminate intensity and/or chromaticity can vary often comes with high computational time or low accuracy. Here a technique is presented integrating chromaticity and intensity normalization combined with a neural skin tone classification network to achieve robust classification faster than other approaches.

We propose a method of face identification under various illumination conditions. Because we use image based method for identification, the accurate position of the face is required. First, face features are detected, and the face region is determined using the features. Then, by registering the face region to the average face, the horizontal position of the face is adjusted. Finally, the size of the face region is adjusted based on the distance of two eyes determined from all input frames. If the sizes of images for all faces are normalized into one size, the face length feature is lost in the normalized face image. The face is classified into three categories according to the face length, and the subspace is generated in each category so that the face length feature is preserved. We demonstrate the effectiveness of the proposed method by experiments.

This investigation of the temperature and illumination effects on the AlGaN/GaN HFET threshold voltage shows that it shifts about -1 V under incandescent lamp or blue LED illumination, while almost no shift takes place under red LED illumination. The temperature coefficient for the threshold voltage shift is +3.44 mV/deg under the illuminations and +0.28 mV/deg in darkness. The threshold voltage variation can be attributed to a virtual back-gate effect caused by light-generated buffer layer potential variations. The expressions for the potential variation are derived using Shockley-Read-Hall (SRH) statistics and the Maxwell-Boltzmann distribution for the carriers and deep traps in the buffer layer. The expressions indicate that large photoresponses will occur when the electron concentration in the buffer layer is extremely small, that is, highly resistive. In semi-insulating substrates, the substrate potential varies so as to keep the trap occupation function constant. The sign and the magnitude of the threshold voltage variation are explained by the shift of the pinning energy calculated from the Fermi-Dirac distribution function.

White-light emitting diode lamps for general illumination can be realized by a combination of a blue light-emitting diode semiconductor die and phosphors. Newly developed oxynitride and nitride phosphors are promising candidates for this application because they have suitable excitation and emission wavelengths and stable optical properties in a high temperature environment. High brightness warm-white LED lamps have been realized using a yellowish-orange α-SiAlON oxynitride phosphor. High color-rendering index white LED lamps have been also realized using three color oxynitride/nitride phosphors.

In this paper, we propose a technique for detection and real-time tracking of moving targets. This uses a color segmentation algorithm robust to irregular illumination variation and a line-based tracker. The former is based on statistical representation of a color. And, we can obtain a real-time property for detection and tracking of moving targets from the latter.

Caustics are patterns of light focused by reflective or refractive objects. Because of their visually fascinating patterns, several methods have been developed to render caustics. We propose a method for the quick rendering of caustics formed by refracted and converged light through transparent objects. First, in the preprocess, we calculate sampling rays incident on each vertex of the object, and trace the rays until they leave the object taking refraction into account. The position and direction of each ray that finally transmits the transparent object are obtained and stored in a lookup table. Next, in the rendering process, when the object is illuminated, the positions and directions of the rays leaving the object are calculated using the lookup table. This makes it possible to render refractive caustics due to transparent objects at interactive frame rates, allowing us to change the light position and direction, and translate and rotate the object.

A double surface light emission backlight that uses single light-guide plate, has been developed for illumination of two liquid-crystal displays (LCD) on its front and rear, to be used in a cellular phone. The light-guide plate has a trapezoid cross-section with arrays of optical micro deflector and micro prism on the front and the rear surfaces, respectively. Propagated light, forward and backward, inside the light-guide plate are controlled and directed toward LCDs using only two prism sheets with internal reflection characteristic, each for the front and the rear. Only three optical components and four light-emitting diodes (LEDs) are used in the new structure compared with ten components and six LEDs of the current type. Comparing with the current type, the thickness and power consumption of the new backlight are reduced by a factor of 0.59 and 0.67, respectively.

In this paper we present a new type of CMOS Time-of-Flight (TOF) range image sensor based on CMOS Active Pixel Sensor (APS) techniques. The TOF sensor features high-speed and efficient photo-charge transfer that is essential in range imaging. The rapid and efficient charge transfer is made possible by the use of a high-gain inverting amplifier and capacitors connected alternatively to the feedback path. This leads to the cost-effective implementation of the system. The analysis of simulation results suggests that the proposed technique can achieve a sufficient range resolution of millimeters to centimeters depending on the maximum measured range, if the noise is dominated by photon shot noise.

We propose a method to determine accurate motion fields and illumination conditions such as non-uniform or non-stationary illuminations. The method extends a stabilization method using reliability indices of optical flow to combine with a gradient-based approach that determines a motion field and illumination conditions simultaneously. We applied the proposed method to two synthetic image sequences and a standard image sequence. The method is effective for image sequences including poorly textured areas, edges of brightness variation, and almost dark objects.

A post-wall waveguide-fed parallel plate slotted array is an attractive candidate for high efficiency and mass producible planar array antennas for millimeter wave applications. For the slot design of this large sized array, a periodic boundary wall model based on the assumption of infinite array size and a parallel waveguide is used. In fact, the aperture is large but still finite (10-40 wavelength) and the TEM-like wave is perturbed due to the narrow walls at the periphery of the aperture as well as the slot coupling; antenna efficiency is affected by the size and the aspect ratio of the aperture. All these observations imply the unique defects of oversized waveguide arrays. In this paper, the aperture efficiency of post-wall waveguide arrays is assessed as a function of size and aspect ratio of the aperture for the first time, both in theory and measurement. An effective field analysis for an electrically large oversized waveguide array, developed by the author, is utilized for determining the slot excitation coefficients and aperture illumination. It is predicted that the oversized waveguide array has a potential efficiency of 80-90% if the aperture is larger than 18 wavelength on a side and the gain is more than 30 dBi. A transversely wide aperture generally provides higher efficiency than a longitudinally long aperture, provided a perfectly uniform TEM wave would be launched from the feed waveguide.

Among ITS applications, it is very important to acquire detailed statistics of traffic flows. For that purpose, vision sensors have an advantage because of their rich information compared to such spot sensors such as loop detectors or supersonic wave sensors. However, for many years, vehicle tracking in traffic images has suffered from the problems of occlusion effect and illumination effect. In order to resolve occlusion problems, we have been proposing the Spatio-Temporal Markov Random Field model(S-T MRF) for segmentation of Spatio-Temporal images. This S-T MRF model optimizes the segmentation boundaries of occluded vehicles and their motion vectors simultaneously by referring to textures and segment labeling correlations along the temporal axis as well as the spatial axis. Consequently, S-T MRF has been proven to be successful for vehicle tracking even against severe occlusions found in low-angle traffic images with complicated motions, such at highway junctions. In addition, in this paper, we define a method for obtaining illumination-invariant images by estimating MRF energy among neighbor pixel intensities. These illumination-invariant images are very stable even when sudden variations in illumination or shading effect are occurred in the original images. We then succeeded in seamlessly integrating the method for MRF energy images into our S-T MRF model. Thus, vehicle tracking was performed successfully by S-T MRF, even against sudden variations in illumination and against shading effects . Finally, in order to verify the effectiveness of our tracking algorithm based on the S-T MRF for practical uses, we developed an automated system for acquiring traffic statistics out of a flow of traffic images. This system has been operating continuously for ten months, and thus effectiveness of the tracking algorithm based on S-T MRF model was proven.