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Lighting consumes the largest amount of energy in buildings. Recently, many studies of energy-efficient lighting systems with a variety of sensor and communication technologies have been conducted as a way to increase the cost efficiency of lighting. However, earlier studies have mostly focused on energy efficiency, whereas they have not significantly considered the occupant’s satisfaction. Therefore, this paper proposes an energy-saving lighting control system considering the occupant’s satisfaction. The proposed system improves the energy efficiency and the occupant’s satisfaction by controlling lighting control parameters considering the characteristics of space and the occupant’s behavior patterns. Moreover, this paper deployed the proposed lighting systems in a building and operated them in a real work environment to evaluate the performance. The results showed that the proposed system reduced energy consumption up to 43% by replacing the existing fluorescent lights with the proposed lighting control systems. [ABSTRACT FROM PUBLISHER]

Images captured in low lighting environment suffer from both low luminance contrast and noise corruption. However, most existing contrast enhancement algorithms only consider contrast boosting, which tends to reveal or amplify noise that is originally not visible in the dark areas. In this paper, we propose a joint contrast enhancement and denoising algorithm, which is based on structure/texture layer decomposition via minimization of dual forms of graph total variation (GTV). Specifically, the structure layer is expected to be generally smoothing but with sharp edges at the foreground background boundaries, for which we propose a quadratic form of GTV (QGTV) as the prior that promotes signal smoothness along graph structure. For the texture layer, a re-weighted GTV (RGTV) is tailored to noise removal while preserving true image details. We provide theoretical analysis about the filtering behavior of these two priors. Furthermore, a boost factor is derived per patch via optimal contrast-tone mapping to improve the overall brightness level of the patch. Finally, an optimization objective function is formulated, which casts image decomposition, brightness boosting, and noise reduction into a unified optimization framework. We further propose a fast approach to efficiently solve the optimization and provide analysis about the convergency. The experimental results show that the proposed method outperforms the state-of-the-art works in subjective, objective, and statistical quality evaluation. [ABSTRACT FROM AUTHOR]

This paper discusses options for testing correspondence algorithms in stereo or motion analysis that are designed or considered for vision-based driver assistance. It introduces a globally available database, with a main focus on testing on video sequences of real-world data. We suggest the classification of recorded video data into situations defined by a cooccurrence of some events in recorded traffic scenes. About 100–400 stereo frames (or 4–16 s of recording) are considered a basic sequence, which will be identified with one particular situation. Future testing is expected to be on data that report on hours of driving, and multiple hours of long video data may be segmented into basic sequences and classified into situations. This paper prepares for this expected development. This paper uses three different evaluation approaches (prediction error, synthesized sequences, and labeled sequences) for demonstrating ideas, difficulties, and possible ways in this future field of extensive performance tests in vision-based driver assistance, particularly for cases where the ground truth is not available. This paper shows that the complexity of real-world data does not support the identification of general rankings of correspondence techniques on sets of basic sequences that show different situations. It is suggested that correspondence techniques should adaptively be chosen in real time using some type of statistical situation classifiers. [ABSTRACT FROM PUBLISHER]

This paper presents a Modified Stripe-RGBW (MS-RGBW) color filter structure to keep the same high resolution, and obtain a higher brightness in comparison with conventional RGB color filters. An image-processing engine is also designed to achieve sharp text image for thin-film-transistor (TFT) LCD with the MS-RGBW color filter. In MS-RGBW color filter structure, each pixel with three sub-pixels is the same area to that in the conventional RGB stripe color filter, and each row shifts two sub-pixels. The image-processing engine consists of two new.algorithms: RGB-RGBW mapping algorithm and sub-pixel rendering algorithm. The RGB-RGBW mapping algorithm obtains a new RGBW image data without distortion in hue and saturation. The sub-pixel rendering algorithm transfers RGBW data into a MS-RGBW color filter structure in order to achieve sharp text image. Numerous simulation results are provided to illustrate the merits and performance of the proposed techniques. The usefulness of the proposed techniques for mobile phone displays is exemplified by conducting several experimental results on a 2.2-inch TFT LCD. [ABSTRACT FROM AUTHOR]

An optimized brightness-compensated contrast enhancement (BCCE) algorithm for transmissive liquid crystal displays (LCDs) is proposed in this paper. We first develop a global contrast enhancement scheme to compensate for the reduced brightness when the backlight of an LCD device is dimmed for power reduction. We also derive a distortion model to describe the information loss due to the brightness compensation. Then, we formulate an objective function that consists of the contrast enhancement term and the distortion term. By minimizing the objective function, we maximize the backlight-scaled image contrast, subject to the constraint on the distortion. Simulation results show that the proposed BCCE algorithm provides high-quality images, even when the backlight intensity is reduced by up to 50–70% to save power. [ABSTRACT FROM AUTHOR]

We present a novel iterative feedback approach for the simultaneous estimation of depth and all-in-focus (AIF) videos from a defocused video pair by joint spatiotemporal optimization. Depth and AIF videos benefit each other in the iterative optimization. First, for the recovery of AIF video, the sparse prior of natural video is incorporated to ensure a high-quality defocus blur removal even under inaccurate depth estimation. Second, in depth estimation step, we feed back the spatial and temporal constraints from the high-quality AIF video and adopt a numerical solution, which is robust to the inaccuracy of AIF recovery to further boost the performance of depth from the defocus algorithm. Benefitting from the incorporation of AIF video priors and the temporal consistency constraint, the proposed framework can effectively reconstruct the depth of the textureless region and is insensitive to camera parameter changes. Our approach provides better temporal consistency and higher depth accuracy than the conventional method that applies postsmoothing to the sequential frame estimation. We not only demonstrate the feasibility of our approach via real experimentation but also provide visual and quantitative evaluation on synthetic data. [ABSTRACT FROM AUTHOR]

Stereo correspondence is challenging under realistic conditions due to uncontrolled factors that affect input images, including illumination inconsistencies and radiometric variations. Many local and global models have been suggested to address these problems; however, their performance is often degraded due to the assumption of color consistency between the left and right images. Therefore, we present a new local pattern, local density encoding, for stereo matching measurements to improve the performance of existing stereo methods. Our experimental results indicate that the proposed method is less sensitive to illumination changes and radiometric variations. Moreover, in the cases with normal and severe illumination changes, the proposed method is more robust than state-of-the-art data costs. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels' gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types. [ABSTRACT FROM PUBLISHER]

Because of the outstanding contribution in improving compression efficiency, block-based quantization has been widely accepted in state-of-the-art image/video coding standards. However, false contour artifacts are introduced, which result in reducing the fidelity of the decoded image/video especially in terms of subjective quality. In this paper, a block-based decontouring method is proposed to reduce the false contour artifacts in the decoded image/video by automatically dithering its direct current (DC) value according to a composite model established between gradient smoothness and block-edge smoothness. Feature points on the model with the corresponding criteria in suppressing contour artifacts are compared to show a good consistency between the model and the actual processing effects. Discrete cosine transform (DCT)-based block level contour artifacts detection mechanism ensures the blocks within the texture region are not affected by the DC dithering. Both the implementation method and the algorithm complexity are analyzed to present the feasibility in integrating the proposed method into an existing video decoder on an embedded platform or system-on-chip (SoC). Experimental results demonstrate the effectiveness of the proposed method both in terms of subjective quality and processing complexity in comparison with the previous methods. [ABSTRACT FROM PUBLISHER]

This paper proposes an algorithm that enhances the contrast of an input image using interpixel contextual information. The algorithm uses a 2-D histogram of the input image constructed using a mutual relationship between each pixel and its neighboring pixels. A smooth 2-D target histogram is obtained by minimizing the sum of Frobenius norms of the differences from the input histogram and the uniformly distributed histogram. The enhancement is achieved by mapping the diagonal elements of the input histogram to the diagonal elements of the target histogram. Experimental results show that the algorithm produces better or comparable enhanced images than four state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Impulse noise detection is important to the restoration of color images contaminated by impulse noise in switching vector median filters. To increase detection accuracy, an effective color-impulse detector is presented. A new color distance metric based on quaternion theory is proposed. The proposed color distance metric is used to calculate the local density of a color pixel. A hard thresholding strategy is used to determine whether a color pixel is corrupted by impulse noise or not (i.e., an outlier). The noisy pixels detected will be restored by a weighted vector median filter, while the noise-free pixels remain unchanged. The experimental comparisons show that the proposed algorithm can obtain lower false and miss detection rate, and produces better performance in terms of peak signal-to-noise ratio and feature similarity measures, compared to other well-known color image filtering methods. [ABSTRACT FROM AUTHOR]

We present a novel approach using distributed source coding for image authentication. The key idea is to provide a Slepian–Wolf encoded quantized image projection as authentication data. This version can be correctly decoded with the help of an authentic image as side information. Distributed source coding provides the desired robustness against legitimate variations while detecting illegitimate modification. The decoder incorporating expectation maximization algorithms can authenticate images which have undergone contrast, brightness, and affine warping adjustments. Our authentication system also offers tampering localization by using the sum-product algorithm. [ABSTRACT FROM PUBLISHER]

In the work presented here, a new star identification strategy is proposed. In the proposed method the pattern of nearest $k$ neighbor stars of each star is coded into a translation, rotation, and scale-invariant integer. If the code words of any two stars have equal integer values, it implies that the patterns of their nearest $k$ neighbors are similar. Therefore, a database search for a $(k+1)$-polygonal pattern can be equated to a simple search for an integer value. This simplified method significantly reduces the complexities associated with star identification. By using the code words as indexes for the lookup table, the time required to solve the star identification problem is determined based on the number of stars within the field of view (FOV) and not the number of catalog stars. Simulations show that the proposed pattern-coding algorithm is robust and efficient. The developed algorithm has successfully identified stars from night sky photographs of $0.21$?$\times0.21$? FOV. [ABSTRACT FROM AUTHOR]

Restructuring of power system stresses the need for economic and reliable generation of power. Therefore generating units should be committed considering fuel cost and reliability level of the system. This necessitates the need for multi-objectives to be met in a unit commitment problem (UCP). Since the above objectives are conflicting in nature, a novel methodology employing optimal deviation based firefly algorithm tuned fuzzy membership function is applied to multi-objective unit commitment problem (MOUCP). The ON/OFF status of the generating units is obtained by binary coded FF whereas the sub-problem economic dispatch (ED) is obtained by real coded FF. Here the conflicting functions are formulated as a single objective function using fuzzy weighted optimal deviation. The fuzzy membership design variables are tuned using real coded FF; thereby the requirement of expertise for setting these variables are eliminated. The proposed methodology is validated on 100-unit system, IEEE RTS 24-bus system, IEEE 118-bus system and a practical Taiwan Power (Taipower) 38-unit system over a 24-h period. Effective strategy on scheduling spinning reserve is demonstrated by comparing its performance with other methods reported in the literature. [ABSTRACT FROM PUBLISHER]

In this paper, we propose skin tone enhancement and background change algorithms for mobile phones. It is important to obtain natural results in skin tone enhancement and background change since only parts of an image, skin tone and background, are changed artificially. Moreover, since our target is a mobile application, the algorithm should be fast to satisfy mobile users and require small memory space to meet limited mobile conditions. From the experiments, we verified that the proposed algorithm generates the result images of people whose skin tone is enhanced to the preferred skin color and the original background is changed to other ones such that the foreground and the background are harmonized well. Furthermore, the proposed algorithm works fast, so it takes about 0.15 seconds with 360?480 images in our test conditions1. [ABSTRACT FROM AUTHOR]

A local dimming algorithm is proposed to reduce the power consumption of LCD TVs using edge-type LED backlight. The proposed local dimming algorithm is implemented by matrix driving of orthogonally located LEDs at the edge of the backlight. The dimming rate of LED string is determined according to target luminance of local blocks and modified to reduce image distortion. The backlight power saving ratios of four typical images are 23.2-53.2 % and 30.2- 54.3% by using proposed division and rate algorithms, respectively. PSNR is greater than 48 dB using proposed local dimming algorithms1. [ABSTRACT FROM AUTHOR]

A novel vision-based navigation algorithm is proposed. The gray levels of two images, together with a Digital Terrain Map (DTM), are directly utilized to define constraints on the navigation parameters. The feasibility of the algorithm is examined both under a simulated environment and using real flight data. [ABSTRACT FROM AUTHOR]