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Multicamera systems are commonly applied in the large field-of-view (FOV) measurements. However, two cameras may have a non-overlapping FOV in some scenes, and traditional binocular camera calibration methods cannot be used to directly calibrate non-overlapping cameras. To solve this problem, our study proposes a calibration method for binocular cameras with non-overlapping FOVs on the basis of planar mirrors. According to the reflection characteristics of an optical planar mirror, the proposed method can use the same target to calibrate non-overlapping cameras. Hence, the method overcomes the limitation of non-overlapping cameras being unable to observe common targets. Experiments results show that the maximum RMS error does not exceed 0.53 mm. Hence, the proposed method is effective, and its measurement technique is simpler and more universal than those of other methods. In addition, it is applicable to a wide range of measurements.

The accurate estimation of camera internal and external parameters is one of the keys to high-precision camera calibration. At present, the mainstream Zhang's calibration method requires the use of a checkerboard to achieve high-precision calibration result. Therefore, the precise extraction of the pixel coordinates at the corner points on the checkerboard is one of the key technologies. The traditional methods are to improve the corner extraction algorithm, but ignore the imaging process of the calibration images. It is difficult to detect and eliminate the high-light regions in the process of calibrating image, and the existence of high-light regions will cause low accuracy of camera calibration. Therefore, a camera calibration method based on optimal polarization information is proposed in this paper. The method first uses a Stokes variable linear feedback through a CCD camera equipped with a polarizer to obtain a checkerboard calibration image at an optimal polarization angle in different spatial positions. Then use the sub-pixel level detection algorithm and Gaussian fitting method to locate the corner points in the image and to solve the sub-pixel coordinates of the corner points precisely. At last, the two-dimensional pixel coordinates of the corner points in each checkerboard image are extracted to perform camera calibration. The experimental results show that the coordinates of corner points of calibration plate image based on the optimal polarization angle have about 1 pixel difference compared with the coordinates of the calibration plate corner points under natural illumination, and the slopes between the parallel lines of the fitted are closer, meet the high precision calibration requirement of camera.

In the structured light three-dimensional measurement system, calibration is crucial for measurement accuracy. However, conventional projector calibration methods involve complicated procedures. Therefore, a novel method is developed for projector calibration. The key concept is to establish the relationship between projector coordinates and the world coordinates using the projection checkerboard, which enables the calibration of the projector to be the same as that of a camera. In addition, the edges of the checkerboard form different degrees of high-light regions when the projection checkerboard is collected. These high-light regions affect the extraction of corner points and cause low accuracy of the projector calibration. Therefore, the projection checkerboard is collected by a CCD camera equipped with a polarizer at the optimal polarization angle. Then the subpixel corner extraction algorithm based on the homography matrix mapping is used to extract the subpixel coordinates of the corners. Finally, the projector can be calibrated with the same method as the camera. In the experimental part, the validity of the method is verified by the reprojection system.

Polarized structured light is a novel method to measure shiny surface. However, the SNR of the captured image was affected by the additional polarizing filter. And the blurred influence of camera defocus was also strengthened. The accuracy of fringe edges detection was reduced. In this paper, a polarized-state-based structured light coding strategy and a phase image estimation method are proposed to improve the measurement robustness. To preserve the coding message in the complex environment, a special polarized-state-based coding strategy is adopted. To reduce the error which induced by additional polarizing filter and extracting the information from the saturated areas as much as possible, a phase image estimation method based on Stokes parameter is proposed. Compared with the traditional polarization-based structured light system, the experimental setup of proposed method is configured without any additional hardware. The experiment shows that the interference of camera defocus is remarkably reduced and the robustness of fringe edges detection is improved.

It is a challenge task to reconstruct images from compressed sensing measurement due to its implicit ill-posed property. In this paper, we propose an image reconstruction algorithm for compressed sensing image application based on the adaptive dictionary, which is learned from the reconstructed image itself. The sparsity level is enhanced since the sparse coding of overlapping image patches emphasizes the local image features; accordingly the quality of the reconstructed image is also improved. In addition, Batch-OMP algorithm, linearization technique and dynamic updating sparse coding algorithm are used to reduce the computational complexity of our proposed algorithm. Numerical experiments are conducted on several test images with a variety of sampling ratios. The results demonstrate that our proposed algorithm can efficiently reconstruct images from compressed sensing measurements and achieve more than 3 dB gain averagely over the current leading CS reconstruction algorithm.

Color measurement by the colorized vision system is a superior method to achieve the evaluation of color objectively and continuously. However, the accuracy of color measurement is influenced by the spectral responses of digital sensor and the spectral mismatch of illumination. In this paper, two-color vision system illuminated by digital sensor and LED array, respectively, is presented. The Polynomial-Based Regression method is applied to solve the problem of color calibration in the sRGB andCIE L⁎a⁎b⁎color spaces. By mapping the tristimulus values from RGB to sRGB color space, color difference between the estimated values and the reference values is less than3ΔE. Additionally, the mapping matrixΦRGB→sRGBhas proved a better performance in reducing the color difference, and it is introduced subsequently into the colorized vision system proposed for a better color measurement. Necessarily, the printed matter of clothes and the colored ceramic tile are chosen as the application experiment samples of our colorized vision system. As shown in the experimental data, the average color difference of images is less than6ΔE. It indicates that a better performance of color measurement is obtained via the colorized vision system proposed.

For scenes with complicated environments, the object is hard to discriminate from a background of various colors in color vision applications. This paper presents a partial least squares (PLS) method for improving discrimination of colored surfaces by selecting appropriate spectral intervals for illumination from the visible spectrum. First, the reflectance functions of all the surfaces are calibrated by multiple standard references. Second, the spectral intervals with high variables important in projection (VIP) scores of PLS analysis are selected for LED illumination. Afterwards, by using the selected wavelength intervals of LEDs for experiment, surfaces of the captured image can be clearly distinguished. Compared with the images obtained under illumination of unselected wavelength intervals of LEDs, the discriminations of most surfaces are more effective. The experiment result demonstrates the usefulness of this method.

Video transcoding is one of the key techniques to provide heterogeneous multimedia applications with Universal Multimedia Access (UMA) service. Recently, compressed domain (DCT, Discrete Cosine Transform) transcoding architectures have been proposed to achieve fast transcoding, in which DCT domain Inverse Motion Compensation (IMC) is the most important module to reduce computational complexity. The problem of IMC has been studied for integer-pixel precision motion vectors (MVs), and, subsequently, for half-pixel precision MVs in which extra filtering, hence, extra computation is introduced. Since most current video coding standards, such as H.263, MPEG-2, etc., use half-pixel precision MVs to achieve better quality, reducing computational complexity of IMC for half-pixel MVs is important for real time fast transcoding video streams encoded by these standards. In this paper, we propose a novel half-pixel filter for IMC, which simplifies the extract operations by integrating the interpolation and translation operations into one single step. Compared to other existing half-pixel filtering algorithms, the proposed filter does not introduce any distortion and drift errors. Experimental results demonstrate that the proposed filter achieves faster transcoding than other DCT domain transcoders, and almost the same video quality as that of pixel domain transcoders.

It is a challenging task to reconstruct images from compressed sensing measurement due to its implicit ill-posed property. In this paper, we propose an image reconstruction algorithm for compressed sensing image application based on the adaptive dictionary, which is learned from the reconstructed image itself. The sparsity level is enhanced since the sparse coding of overlapping image patches takes into account the local image features, and accordingly the quality of the reconstructed image is improved. In addition, linearization technique is also exploited to remove the computation of matrix inversion. Numerical experiments are conducted on several test images with a variety of sampling ratios. The results demonstrate that our proposed algorithm can efficiently reconstruct images from compressed sensing measurements and achieve more than 3dB gain averagely over the current state-of-art compressed sensing reconstruction algorithms.

Compressed Sensing (CS) has drawn quite an amount of attention as novel digital signal sampling theory in recent years when the signal is sparse in some domain. However, signal reconstruction from undersampled data has always been challenging due to its implicit ill-posed nature. This paper proposes an image compressed sensing reconstruction algorithm for image CS application, which consists of iteratively collaborative filtering of non local similar image patches in 3D transform domain and solving the least squares problems. In addition, the linearization technique is exploited to reduce the computation complexity. The results of various experiments on natural images and MRI images consistently demonstrate that the proposed algorithm can efficiently reconstruct images and gain more 2dB as compared to the current leading CS image reconstruction algorithm.

Color is often used to simplify object exaction and identification for many color-based machine vision systems. However, image colors produced by color vision system strongly depend on lighting geometry, illumination color and the spectral response function of digital camera. Either small variation in the illumination or the changes of digital camera can dramatically make the image color changed. In this paper, color correction is performed for our color vision measurement system. The mapping coefficient matrix is obtained by polynomial regression model under artificial D65 illumination and LED array illumination. The detailed correction accuracies are compared between the two common used device-independent color space (sRGB color space and CIEL*a*b*color space). sRGB color space is recommended due to the higher accuracy and simple algorithm. The correction images illustrate the usefulness of our method for color correction

In this paper, a classification-based adaptive compression method for computer screen image is presented. This method firstly divides the computer Screen Image into 16x16 non-overlapping blocks, and then every block is classified into three types: text/graphic, pictorial and hybrid blocks based on the characteristics of histogram distribution and the number of colors. For complex text/graphic block, - clustering method is used to reduce the number of colors to improve compression performance, finally the text/graphic block is coded by our proposed loss less coding method, hybrid block is coded by hybrid coding method and the pictorial block is coded by H.264-like intra coding method. Experiment results show that our proposed block classification method exactly distinguishes three block types, color clustering method can effectively reduce the number of colors for complex text/graphic. The compression performance and subjective image quality of our proposed method can outperform JPEG and JP2k.

The blind can only get perceptive and audio information, which bring them huge barriers to action. Mobile devices could provide the blind great help. However, because of limited computation capacity of mobile device, many existing visual augmentation scheme can't be migrated to embedded system. In this paper, we propose a kind of simple blind symbol and a visual aid system (VAS) for the visually impaired people. VAS is based on fast symbol recognition algorithm and RFID technology, which achieves good performance in embedded devices. VAS perceives all blind symbols in current scene and reports location information to blind by text-to-speech technology. We develop a prototype system, which consist of a mobile phone with camera and RFID reader. Test result in this paper shows that VAS gets good real-time effect under embedded platform, even though symbol database contains large number of symbol sample.

This paper presents a technique to choose appropriate light source for maximizing the contrast between the object and the background surfaces in color vision application. From the physics of color image formation, three parameters which affect generating signal of color digital camera are researched. An optimal color illumination for enhancing color contrast can be found by maximizing these surfaces spectral reflectance. The discrimination of these surfaces spectral reflectance was estimated by using average color difference in CIELab color space. A printed color patch which have seven several colored characters was used to demonstrate the approach. For each colored character, appropriate Light Emitting Diode (LED) illumination was selected to maximize the discriminability, which is more suitable than D65 illumination. These experiments illustrate the usefulness of properly chosen color illumination in color vision application.