In order to reduce the errors in depth estimation, a credible depth estimation method based on superpixel constraint matching is proposed. It consists of normalized binocular image disparity optimization, credible granularity region segmentation and similarity measure of granularity region. This method segments the normalized binocular images finely by using the superpixel granulation method, and divides the binocular image into a large number of excellent granularity regions. To get the best match for each granularity partitioned, the correlative matching area is obtained by polar line constraint matching. And then the matching similarity measure function is used to achieve the best superpixel granularity regional matching results in binocular images, so as to find the two-dimensional correspondence of each granularity region. Finally, realize the depth information estimation of binocular parallax images. The experimental results show that this method can obviously reduce the errors of depth estimation in traditional methods.
Kinect is a motion sensing input device which is widely used in computer vision and other related fields. However, there are many inaccurate depth data in Kinect depth images even Kinect v2. In this paper, an algorithm is proposed to enhance Kinect v2 depth images. According to the principle of its depth measuring, the foreground and the background are considered separately. As to the background, the holes are filled according to the depth data in the neighborhood. And as to the foreground, a filling algorithm, based on the color image concerning about both space and color information, is proposed. An adaptive joint bilateral filtering method is used to reduce noise. Experimental results show that the processed depth images have clean background and clear edges. The results are better than ones of traditional Strategies. It can be applied in 3D reconstruction fields to pretreat depth image in real time and obtain accurate results.
There are many disadvantages such as lower timeliness, greater manual intervention in multi-channel projection system, in order to solve the above problems, this paper proposes a multi-projector correction technology based on color coding grid array. Firstly, a color structured light stripe is generated by using the De Bruijn sequences, then meshing the feature information of the color structured light stripe image. We put the meshing colored grid intersection as the center of the circle, and build a white solid circle as the feature sample set of projected images. It makes the constructed feature sample set not only has the perceptual localization, but also has good noise immunity. Secondly, we establish the subpixel geometric mapping relationship between the projection screen and the individual projectors by using the structure of light encoding and decoding based on the color array, and the geometrical mapping relation is used to solve the homography matrix of each projector. Lastly the brightness inconsistency of the multi-channel projection overlap area is seriously interfered, it leads to the corrected image doesn’t fit well with the observer's visual needs, and we obtain the projection display image of visual consistency by using the luminance fusion correction algorithm. The experimental results show that this method not only effectively solved the problem of distortion of multi-projection screen and the issue of luminance interference in overlapping region, but also improved the calibration efficient of multi-channel projective system and reduced the maintenance cost of intelligent multi-projection system.
One of the challenges of augmented reality is a seamless combination of objects of the real and virtual worlds, for example light sources. We suggest a measurement and computation models for reconstruction of light source position. The model is based on the dependence of luminance of the small size diffuse surface directly illuminated by point like source placed at a short distance from the observer or camera. The advantage of the computational model is the ability to eliminate the effects of indirect illumination. The paper presents a number of examples to illustrate the efficiency and accuracy of the proposed method.
A number of the modern applications such as medical imaging, remote sensing satellites imaging, virtual prototyping etc use the High Dynamic Range Image (HDRI). Generally to obtain HDRI from ordinary digital image the camera is calibrated. The article proposes the camera calibration method based on the clear sky as the standard light source and takes sky luminance from CIE sky model for the corresponding geographical coordinates and time. The article considers base algorithms for getting real luminance values from ordinary digital image and corresponding programmed implementation of the algorithms. Moreover, examples of HDRI reconstructed from ordinary images illustrate the article.
The digital matting problem is a classical problem of imaging. It aims at separating non-rectangular foreground objects from a background image, and compositing with a new background image. Accurate matting determines the quality of the compositing image. A Bayesian matting Algorithm Based on Gaussian Mixture Model is proposed to solve this matting problem. Firstly, the traditional Bayesian framework is improved by introducing Gaussian mixture model. Then, a weighting factor is added in order to suppress the noises of the compositing images. Finally, the effect is further improved by regulating the user's input. This algorithm is applied to matting jobs of classical images. The results are compared to the traditional Bayesian method. It is shown that our algorithm has better performance in detail such as hair. Our algorithm eliminates the noise well. And it is very effectively in dealing with the kind of work, such as interested objects with intricate boundaries.