Optimal Stitching Research Articles

Application of Virtual Reality Technology in Historical and Cultural Landscape Reproduction

Abstract Virtual reality panoramic technology transcends the constraints of time and space, offering audiences immersive visual and auditory experiences that diminish the costs associated with exploring historical and cultural landscapes and enhance communication effectiveness. This study employs fisheye cameras to capture panoramic images of such landscapes. The collected images undergo correction via spherical projection and reflective planar unfolding mapping. Subsequently, an enhanced method for optimal stitching line selection is applied to ensure seamless transitions between spliced images. The process is completed by integrating image interpolation and projection algorithms to virtually recreate historical landscapes. Analytical assessment of the model’s reproduction effects focuses on point error and accuracy. A practical application involving a real historical and cultural landscape is examined, revealing a maximum control point deviation from the optical scan of 0.02m and a minimum of 0.0014m, which negligibly affects the landscape’s virtual representation. Reproducing an actual landscape image yielded a maximum measurement error of 0.02m, a minimum of 0.01m, and an average of 0.0175m, affirming the capability for precise replication. This paper introduces innovative approaches and methodologies for leveraging virtual reality technology in the accurate reproduction of historical and cultural landscapes, illustrating its practical utility and effectiveness.

Enhancement of video condensation with video browsing and retrieval for surveillance videos using heuristic-derived deep learning algorithm

Video condensation or synopsis is an effective solution for problems regarding video storage and video browsing. The proposed model contributed to developing the video condensation framework for efficient video browsing and video retrieval. In the first stage, the videos are gathered from the surveillance videos. Here, the frames are generated, and then the video backgrounds are extracted. The objects from the frames are acquired through the support of Yolov3. Next, the optimal stitching is done based on the time and object activity of video frames using the Improved Blue Monkey Optimization (IBMO) algorithm. Moreover, video condensation is performed to get the compact video for making better browsing and retrieval of video. The video browsing and retrieval are performed under two phases such as training and testing phases and both phases are done by gathering the videos and followed by the feature extraction using VGG16, where the heuristic improvement is made by the same IBMO algorithm. Then, the extracted deep features from video segments are clustered based on Fuzzy C-means (FCM) clustering for combining the extracted features. These features are stored in the feature database in the training phase. Next, in the testing phase, video browsing and retrieval are performed by considering the queries gathered from the standard dataset. The features of query videos are extracted, which are compared based on Multi-Similarity Function (MSF) with the features in the database for retrieving the video segments. Experimental results show that the developed IBMO-VGG-MSF-based video condensation saves computational loads compared to the previous methods without compromising the condensation ratio and visual quality.

An Upscaling–Downscaling Optimal Seamline Detection Algorithm for Very Large Remote Sensing Image Mosaicking

For the mosaicking of multiple remote sensing images, obtaining the optimal stitching line in the overlapping region is a key step in creating a seamless mosaic image. However, for very large remote sensing images, the computation of finding seamlines involves a huge amount of image pixels. To handle this issue, we propose a stepwise strategy to obtain pixel-level optimal stitching lines for large remote sensing images via an upscaling–downscaling image sampling procedure. First, the resolution of the image is reduced and the graph cut algorithm is applied to find an energy-optimal seamline in the reduced image. Then, a stripe along the preliminary seamline is identified from the overlap area to remove the other inefficient nodes. Finally, the graph cut algorithm is applied nested within the identified stripe to seek the pixel-level optimal seamline of the original image. Compared to the existing algorithms, the proposed method produces fewer spectral differences between stitching lines and less-crossed features in the experiments. For a wide range of remote sensing images involving large data, the new method uses less than 10 percent of the time needed by the SLIC+ graph cut method.

Finding Hamiltonian and Longest (s,t)-Paths of C-Shaped Supergrid Graphs in Linear Time

A graph is called Hamiltonian connected if it contains a Hamiltonian path between any two distinct vertices. In the past, we proved the Hamiltonian path and cycle problems for general supergrid graphs to be NP-complete. However, they are still open for solid supergrid graphs. In this paper, first we will verify the Hamiltonian cycle property of C-shaped supergrid graphs, which are a special case of solid supergrid graphs. Next, we show that C-shaped supergrid graphs are Hamiltonian connected except in a few conditions. For these excluding conditions of Hamiltonian connectivity, we compute their longest paths. Then, we design a linear-time algorithm to solve the longest path problem in these graphs. The Hamiltonian connectivity of C-shaped supergrid graphs can be applied to compute the optimal stitching trace of computer embroidery machines, and construct the minimum printing trace of 3D printers with a C-like component being printed.

Farmland Aerial Images Fast-Stitching Method and Application Based on Improved SIFT Algorithm

The main black land conservation measure in China is the straw return to the fields. The processing of high-resolution images collected by aerial photography of UAVs through image stitching technology can provide image information for achieving fast and accurate detection of straw cover over large areas. The classical SIFT algorithm has many drawbacks, such as high dimensionality of feature descriptors, high computational effort, and low matching efficiency. To solve the problems above, this study proposes an improved algorithm. First, the method down sampled the high-resolution images before detecting the features to reduce the number of feature points and improve the efficiency of feature detection. Then, matching among feature points is achieved by gradient normalization-based feature descriptors to improve the matching accuracy. Next, the Progressive Sample Consistency algorithm eliminates the mismatch points and optimizes the transformation model. Finally, the images are fused with optimal stitching combined with fade-in and fade-out to achieve high-quality stitching. The comparative experimental results show that compared with the traditional SIFT and the speed-up robust feature algorithms, the algorithm has the advantage of the speed and good robustness to angle rotation, and makes full use of the texture information and the detail information, so it has higher accuracy. Compared with the traditional methods, the panoramic stitching image quality herein is excellent and can be applied to subsequent straw cover detection, the straw cover error is ≤3%, meeting the demand for large-area straw cover detection. Overall, the method proposed herein achieves an ideal balance between accuracy and efficiency; and outperforms other widely used and superior methods.

Smooth Stitching Method for the Texture Seams of Remote Sensing Images Based on Gradient Structure Information

Traditional smooth stitching method for the texture seams of remote sensing images is affected by gradient structure information, leading to poor stitching effect. Therefore, a smooth stitching method for the texture seams of remote sensing images based on gradient structure information is proposed in this research. By matching the feature points of remote sensing images and introducing a block link constraint and shape distortion constraint, the modified stitching image is obtained. By using remote sensing image fusion, the smooth stitching image of texture seams is obtained, and the local overlapping area of the texture is optimized. The main direction of texture seams is determined by calculating the gradient structure information of texture seams in horizontal and vertical directions. By selecting the initial point, the optimal stitching line is extracted by using the minimum mean value of the cumulative error of the smooth stitching line. By using the method of boundary correlation constraints, matching the feature points of the texture seams of remote sensing images and selecting the best matching pair, a smooth stitching algorithm for the texture seams of remote sensing image is designed, which realizes the smooth stitching of the texture seams of remote sensing images. Experimental results show that the design method has good performance in stitching accuracy and efficiency in the smooth stitching of the texture seams of remote sensing images. Specifically, the Liu et al. and the Zhang et al. methods that are the benchmark studies in the literature are introduced as a comparison, and the stitching experiment is carried out. The test is carried out according to accuracy and time and the proposed method achieves better results by almost 25%.

Stitching error compensation for large-area microstructures based on digital oblique scanning exposure mode

We proposed an efficient method for improving the stitching precision of large-area microstructures. A clever triangular region compensation strategy based on a digital micromirror device oblique scanning exposure mode was developed, which can effectively enhance lithographic resolution and complete seamless stitching. In order to ensure the optimal stitching effect, the stage self-calibration technology was also introduced into the exposure system. The experimental results showed that the stitching region traces can be effectively eliminated and smoothed by the above strategy. The presented method will have important applications in the printed circuit board and flat panel display fields.

A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention

Stitching gaps and misalignments in mosaic images can severely degrade the human visual perception of mosaic effects. Image stitching plays a key role in eliminating these unpleasant defects. In this paper, an image-stitching method for mosaic images with invisible seams is proposed, according to the research on the human visual system (HVS). By quantifying the human visual attention of images and visual discrimination about luminance difference and fine dislocations, each pixel in the stitching region is given a priority value for tracing a stitching line. Coupled with the processing of an optimal stitching line locating method and the multi-band blending algorithm, the pixels of discontinuous items in mosaic images decrease significantly and the stitching line is almost invisible. This study provides a new insight into the image-stitching field, and the experiments show that the results of the proposed method are more consistent with the human visual system in creating high-quality image mosaics.

Fast Color Blending for Seamless Image Stitching

In this letter, we propose a fast and robust method for stitching overlapped images captured by the unmanned aerial vehicle. First, we apply the shape-preserving half-projective method to precisely and stably align a pair of partially overlapped input images. Then, an optimal stitching line is searched to remove ghosts caused by the moving objects in the overlapped area. We subsequently propose a color blending method to eliminate all the color inconsistencies in the prealigned image. In accordance with the color differences of the pixels on the optimal stitching seam, we utilize weighted value coordinate interpolation algorithms to compute accurate color changes for all the pixels in the target image. The calculated color changes are then added to the target image to remove the color inconsistency. Furthermore, we introduce the superpixel segmentation to divide the target image into a reduced number of superpixels, and we assign each superpixel the same color change value. Such a superpixel level operation can greatly reduce the computational complexity. Experiments show that our method is promising to achieve effective and efficient stitching results.

The algorithm of seamless image mosaic based on A‐KAZE features extraction and reducing the inclination of image

The traditional feature point detection algorithm is based on the linear scale decomposition. In the SIFT (Scale Invariant Feature Transform) algorithm, features are obtained through building the image pyramid by the Gaussian filter. SIFT has good robustness but has some flaws as well. Gaussian filter neither preserve object boundaries nor smooth the same level details and noise at all scales, which impair the accuracy and distinctiveness of the feature point positioning. Nonlinear scale decomposition can solve these problems. In this paper, a new image mosaic algorithm based on A‐KAZE feature is proposed to take advantages of the A‐KAZE algorithm in terms of rotation invariance, illumination invariance, speed, and stability. The optimal stitching line is obtained and the multi‐resolution fusion algorithm is used to fuse the image in order to achieve a satisfactory seamless image of high resolution. The whole straightening method is applied in the image mosaic to solve the problem of the tilt of multiple image mosaic. Experimental results show that stitching algorithm in this paper is faster and more robust compared to the traditional SIFT algorithm. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A profile measurement method of large aspheric optical surface based on optimal stitching planning

This paper proposes an optimal stitching planning for two-dimensional profile measurement of large aspheric surface by applying small range profiler as the measuring machine. The originality of this paper reflects on two aspects: (1) According to the applied range of probe, the amount of overlap region and the geometric characteristic of large aspheric surface, a segmentation planning algorithm is presented to determine the segment parameters during the measuring process. Considering the measurement efficiency and stitching accuracy synthetically, the applied range of probe with ±4mm and the amount of overlap region with 20% of segment length are regarded as the optimal parameters. (2) Based on the invariance of curvature radius of overlap region, a data matching algorithm is developed to match the adjacent segments. Through the simulation analysis, the deviation of adjacent segments is reduced to 0.03mm with the sampling interval of 0.01mm. Then on the basis of the simulation settings with the translation error of 1μm and the rotation error of 30′, the accumulative stitching errors of three different curves are predicted within sub-micron level. Finally the optimal stitching method is verified by experiments of two groups of 110-mm aspheric curves. The PV (Peak-to-valley) values of the stitching errors of the two curves are in the range of ±1μm and the standard deviations are less than 0.2μm.

A study on fast SIFT image mosaic algorithm based on compressed sensing and wavelet transform

Considering the disadvantages of massive calculation and slow speed of traditional Scale Invariant Feature Transform (SIFT) algorithm, we propose an improved image mosaic method which combines Wavelet Transform (WT) and Compressed Sensing (CS) algorithm. The method works as follows. Firstly, images are transformed with wavelet and compressed using compressed sensing technology. Then, image feature points are extracted in combination with SIFT algorithm. Finally, Sequential Similarity Detection Algorithm (SSDA) with adaptive threshold is used to fast search of image matching to find out an optimal stitching line, and a panoramic image is obtained. Experimental results demonstrate that the method realizes fast image matching, efficiently overcomes the shortcomings of heavy computation and low efficiency in the process of extracting image features, and guarantees matching accuracy and stitching efficiency, which meets the real-time requestments in machine vision system. This algorithm can be applied to image matching and stitching in the field of digital image security.

Multi-planar Image Stitching Method Based on Structural Feature Matching

ABSTRACTIn the paper, an optimal-seam-based multi-planar image stitching method using the feature matching of geometric structure is proposed. Based on the analysis of the brightness inconsistency caused by the exposure-duration difference and lens vignetting, the value of curvature is utilized as the matching structural feature in the calculation for the accumulated error on the stitching seam. Also, after the process of multi-plane detection, the searched optimal stitching seam is restricted to only pass through its corresponding planar structure constructed by the triangulation and homographic matrix, which can avoid the structural errors on the stitching seam and achieve satisfactory stitched results. Experimental results demonstrate the effectiveness of the proposed method.

Research of Image Stitching Method Based on Graph Cuts and Poisson Fusion

This paper considers the problem of ghosting artifacts and stitching seam caused by image stitching and fusion directly, which influenced by the registration and geometric transformation, and the differences between the two images overlap region. This paper proposed maximum flow / minimum cut in graph cut algorithm to obtain the optimal stitching line. Considering the relationship between color difference and structural changes of pixels in the neighborhood, achieving costs or energy division minimize, thereby eliminating the stitching seams. And using the Poisson fusion technology, further eliminate ghosting artifacts caused by the brightness and color differences or moving objects in the image fusion process. The experiment proved that the algorithm of searching optimal stitching line and Poisson fusion algorithm in this paper, can eliminate ghosting artifacts and also be able to maximize avoid stitching seams appears, it presents good performance in applications.

An intelligent computer method for automatic mosaic and segmentation of tracer fiber images for yarn structure analysis

Tracer fiber measurement is a popular and effective method to study yarn internal structure. In this method, a series of consecutive yarn images are stitched into panorama, and yarn boundaries and tracer fiber are extracted for further analysis. Currently, the image mosaic and segmentation of tracer fiber images largely involve manual operation because the existing image processing methods are generally not capable in most cases. Therefore, this study aims to develop an intelligent computer method for automatic mosaic and segmentation of tracer fiber images. In this study, an extended QRS complex detection method is developed for tracer fiber detection. In the image mosaic, a decision function, integrating several matching functions extracted from the tracer fiber and gradient image, is proposed to identify the optimal stitching position. The QRS complex is a name for the combination of Q wave, R wave and S wave on an electrocardiogram (ECG), which reflects the rapid depolarization of the right and left ventricles. In image segmentation, a baseline fitting method is used to eliminate the effect of uneven background for identifying yarn boundaries from the binary image. Finally, an objective method is proposed to evaluate the qualities of the image mosaic and segmentation of the proposed method.

Globally optimal stitching of tiled 3D microscopic image acquisitions

Motivation: Modern anatomical and developmental studies often require high-resolution imaging of large specimens in three dimensions (3D). Confocal microscopy produces high-resolution 3D images, but is limited by a relatively small field of view compared with the size of large biological specimens. Therefore, motorized stages that move the sample are used to create a tiled scan of the whole specimen. The physical coordinates provided by the microscope stage are not precise enough to allow direct reconstruction (Stitching) of the whole image from individual image stacks.Results: To optimally stitch a large collection of 3D confocal images, we developed a method that, based on the Fourier Shift Theorem, computes all possible translations between pairs of 3D images, yielding the best overlap in terms of the cross-correlation measure and subsequently finds the globally optimal configuration of the whole group of 3D images. This method avoids the propagation of errors by consecutive registration steps. Additionally, to compensate the brightness differences between tiles, we apply a smooth, non-linear intensity transition between the overlapping images. Our stitching approach is fast, works on 2D and 3D images, and for small image sets does not require prior knowledge about the tile configuration.Availability: The implementation of this method is available as an ImageJ plugin distributed as a part of the Fiji project (Fiji is just ImageJ: http://pacific.mpi-cbg.de/).Contact: tomancak@mpi-cbg.de