Fast mosaicing method based on image resizing pre-processing

In the paper, an image mosaic algorithm based on SIFT feature matching is proposed. For an image mosaic method based on feature matching, feature detection is needed to perform in each image. Thus a rapid detection operator is essential to the efficiency of the whole algorithm. In this paper, we use SIFT to extract features. The extracted features are matched by k-d tree and bidirectional matching strategy to enhance the accuracy of matching. Then, a RANSAC algorithm is applied to eliminate outliers to ensure effectiveness of the matching. Finally images are stitched by weighted average blending algorithm. The presented algorithm overcomes the disadvantages of the traditional image mosaic methods which are susceptible to different scale and moving objects, and can achieve sub-pixel accuracy of matching and algorithm is still available to the images at different scale. Experimental results show that the method with strong robustness performs effectively.

In virtual microscopy, multiple overlapping fields of view are acquired from a large slide using a motorized microscope stage that moves and focuses the slide automatically. A virtual slide is reconstructed by combining digitally saved fields of view into an image mosaic. A seamless reconstruction requires the correction of unknown positioning errors of the stage. This is usually done by automatically estimating alignment parameters of the tiles in the image mosaic. But finding accurate alignment parameters can be inhibited by the presence of tiles that lack information content in the areas of overlap. In this work we propose a new mosaicing method that accesses information content of each overlap and performs pairwise registrations of adjacent tiles only if the content of their overlap is deemed sufficient for successful registration. For global positioning of tiles a stitching path is found by tracing such content-rich overlaps. We tested the proposed algorithm on bright field and fluorescence microscope images and compared the results with those of an existing algorithm based on simultaneous estimation of global alignment parameters. It is shown that the new algorithm improves perceived image quality at boundaries between tiles. Our method is also computationally efficient since it performs no more than one pairwise registration per tile on average.

In this paper, an automatic image mosaic algorithm based on feature matching is presented. In the algorithm, a scale invariant feature transform (SIFT) is used to extract feature points. The initial matches are divided into inliers and outliers by robust RANSAC algorithm. Then LM algorithm is used to estimate the point transformation matrix between two images accurately. In the end, weighted average method is used to blend images. Experimental results show the image mosaic effect is wonderful and the algorithm is adaptive very much. It is high valuable in practice.

Osteoporosis is a systemic skeletal disease characterized by low bone mineral density (BMD) and micro-architectural deterioration of bone tissue, leading to bone fragility and increased fracture risk. Since Panoramic image is a feasible and relatively routine imaging technique in dentistry; it could provide an opportunistic chance for screening osteoporosis. In this regard, numerous panoramic derived indices have been developed and suggested for osteoporosis screening. Jaw trabecular pattern is one of the main bone strength factors and trabecular bone pattern assessment is important factor in bone quality analysis. Texture analysis applied to trabecular bone images offers an ability to exploit the information present on conventional radiographs. The purpose of this study was to evaluate the relationship between Jaw trabecular pattern in panoramic image and osteoporosis based on image texture analyzing using local binary pattern. An experiment is evaluated in this paper based on a real hand-captured database of panoramic radiograph images from osteoporosis and non-osteoporosis person in Namazi Hospital, Shiraz, Iran .An approach is proposed for osteoporosis diagnosis consisting of two steps. First, modified version of local binary patterns is used to extract discriminative features from jaw panoramic radiograph images. Then, classification is done using different classifiers. Comparative results show that the proposed approach provides classification accuracy about 99.6%, which is higher than many state-of-the-art methods. High classification accuracy, low computational complexity, multi-resolution and rotation invariant are among advantages of our proposed approach.

The use of fisheye image stitching is a popular method to produce panoramic images rapidly. A fisheye lens with a large angle can reduce the number of times of image acquisition, but distortion makes the registration of images difficult. As a key determinant of the effect of fisheye image mosaic quality, the estimation of external parameters of images has become a core issue in panorama generation. Previously established methods require additional measurement devices or are only suitable for specific scenarios. In this study, we develop an image mosaic method. A large-angle fisheye lens is adopted for conformal projection and spherical matching. First, to facilitate the search for corresponding corners, the source images are rotated by 90°. Then, conformal projection transformation is utilized to present the feature structures of the overlap area of two images in a consistent manner. Second, the searched corner points are projected onto the unit sphere, and the motion parameters are estimated with the direct linear transform method. Experimental results show that our method can efficiently complete image registration with a large angle between fisheye images. Hence, a full-view panoramic image of 360°×180° can be obtained with the use of two fisheye cameras with an angle of more than 180°.

Current remote video monitoring system in substation can not fully play the role it is supposed to have in real-time active supervision. In order to improve the effect of remote video monitoring system, image mosaic technology is introduced firstly. Then an automatic image mosaic SIFT-based method for remote video monitoring system is proposed. The steps of this method are described in detail, and the implementation process of image mosaic is demonstrated by an example. The experimental results show that the proposed method can perform well, and meet the requirement of real project, which is beneficial to improve the safety and reliability of substation operation.

The mosaic of sonar images is more difficult than the mosaic of traditional optical images due to their poor quality and the difficulty in extracting feature points. The existing mosaic methods of sonar images have a series of problems, such as low correct matching rate, large cumulative errors and high requirements for the quality of collected sonar images. In this paper, we proposed a high precision method to implement the mosaic of the underwater sonar video image sequence. Firstly, Accelerated Unsharp Masking (AUSM) algorithm is proposed to preprocess the original image. Then we extract KAZE feature points from preprocessed sonar images. A matching method combining multiple matching strategies with Progressive Sample Consensus (PROSAC) algorithm is followed to complete the image registration. Weighted fusion method and a region of interest (ROI) acquisition method based on the slope of right border is used to optimize the mosaicked image. Finally, we can obtain a high-quality panoramic image of underwater sonar video by a global mosaic strategy. Mosaic experiments on the sonar video image sequence collected by multi-beam sonar demonstrate that the proposed method in this paper can increase the number of feature points by about 70% and make the correct matching rate higher than 70%. The proposed method also has good robustness and the cumulative error during multi-image mosaic is less.

Images are integral part in our daily lives. With a normal camera it is not possible to get a wide angle panorama with high resolution. Image Mosaicing is one of the novel techniques, for combining two or more images of the same scene taken in different views into one image. In the dark areas, the obtained image is a panoramic image with high resolution without mask. But in the case of lighting areas, the resultant image is generating mask. In order to gets wide angle panorama, in the existing system, extracting feature points, finding the best stitching line, Cluster Analysis (CA) and Dynamic Programming (DP) methods are used. Also used Weighted Average (WA) method for smooth stitching results and also eliminate intensity seam effectively. In the proposed system, to get feature extraction and feature matching SIFT (Scaled Invariant Feature Transform) algorithm used. In this process, outliers can be generated. RANSAC (Random Sample Consensus) is used for detecting the outliers from the resultant image. Masking is significantly reduced by using Algebraic Reconstruction Techniques (ART).

Image mosaicing/stitching is considered as an active research area in computer vision and computer graphics. Image mosaicing is concerned with combining two or more images of the same scene into one panoramic image with high resolution. There are two main types of techniques used for creating image stitching: direct methods and feature-based methods. The greatest advantages of feature-based methods over the other methods are their speed, robustness, and the availability of creating panoramic image of a non-planar scene with unrestricted camera motion. In this paper, we propose a real time image stitching system based on ORB feature-based technique. We compared the performance of our proposed system with SIFT and SURF feature-based techniques. The experiment results show that the ORB algorithm is the fastest, the highest performance, and it needs very low memory requirements. In addition, we make a comparison between different feature-based detectors. The experimental result shows that SIFT is a robust algorithm but it takes more time for computations. MSER and FAST techniques have better performance with respect to speed and accuracy.

Mosaic imaging is a computer vision process that is used for merging multiple overlapping imaging patches into a wide-field-of-view image. To achieve a wide-field-of-view photoacoustic microscopy (PAM) image, the limitations of the scan range of PAM require a merging process, such as marking the location of patches or merging overlapping areas between adjacent images. By using the mosaic imaging process, PAM shows a larger field view of targets and preserves the quality of the spatial resolution. As an essential process in mosaic imaging, various feature generation methods have been used to estimate pairs of image locations. In this study, various feature generation algorithms were applied and analyzed using a high-resolution mouse ear PAM image dataset to achieve and optimize a mosaic imaging process for wide-field PAM imaging. We compared the performance of traditional and deep learning feature generation algorithms by estimating the processing time, the number of matches, good matching ratio, and matching efficiency. The analytic results indicate the successful implementation of wide-field PAM images, realized by applying suitable methods to the mosaic PAM imaging process.

Providing a wider movement range of virtual camera is an unsolved problem for state-of-the-art image-based rendering system. In this paper, we present a new image based rendering technology called panoramic mosaics of slit images with depth that can provide large virtual camera motion region for some scenes. By limiting camera motion to a horizontal plane only, a slit image with united depth value is used as the rendering element. The panoramic mosaics of slit images with depth are easy to capture, and the data size is as small as that of panorama. We present here the capturing, construction as well as rendering process of panoramic slit images mosaic with depth. In addition, we present the join up process of multiple panoramic slit images mosaic with depth.

Making panoramic images has gradually become an essential function inside personal intelligent devices because panoramic images can provide broader and richer content than typical images. However, the techniques to classify the types of panoramic images are still deficient. This paper presents novel approaches for classifying the photographic composition of panoramic images into five types using fuzzy rules. A test database with 168 panoramic images was collected from the Internet. After analyzing the panoramic image database, the proposed feature model defined a set of photographic compositions. Then, the panoramic image was identified by using the proposed feature vector. An algorithm based on fuzzy rules is also proposed to match the identification results with that of human experts. The experimental results show that the proposed methods have demonstrated performance with high accuracy and this can be used for related applications in the future.

Interpreting endomicroscopic images is still a significant challenge, especially since one single still image may not always contain enough information to make a robust diagnosis. To aid the physicians, we investigated some local feature-based retrieval methods that provide, given a query image, similar annotated images from a database of endomicroscopic images combined with high-level diagnosis represented as textual information. Local feature-based methods may be limited by the small field of view (FOV) of endomicroscopy and the fact that they do not take into account the spatial relationship between the local features, and the time relationship between successive images of the video sequences. To extract discriminative information over the entire image field, our proposed method collects local features in a dense manner instead of using a standard salient region detector. After the retrieval process, we introduce a verification step driven by the textual information in the database and in which spatial relationship between the local features is used. A spatial criterion is built from the co-occurence matrix of local features and used to remove outliers by thresholding on this criterion. To overcome the small-FOV problem and take advantage of the video sequence, we propose to combine image retrieval and mosaicing. Mosaicing essentially projects the temporal dimension onto a large field of view image. In this framework, videos, represented by mosaics, and single images can be retrieved with the same tools. With a leave-n-out cross-validation, our results show that taking into account the spatial relationship between local features and the temporal information of endomicroscopic videos by image mosaicing improves the retrieval accuracy.

Image mosaicing is an effective means of constructing a single panoramic image from a series of snapshots taken in different viewing angles. However, in the case of congested traffic scenes with a cluttered environment including vehicles or pedestrians, there are severe difficulties in aligning a pair of snapshots. In such cases, some objects would be taken only in one of the image pair, thereby resulting in failure in stitching the pair of images. This paper deals with three types of techniques for performing an image mosaicing: Homography estimation for determining geometrical relationships between the image pair, expectation-maximization algorithm for removing inconsistent overlapping region, and graph cuts for seamless stitching and background estimation. Experimental results indicate that the proposed technique is effective to synthesize a panoramic image from a series of narrow-field-of-view snapshots.

When the size of an image such as a picture or a painting is beyond the scope of a camera, it is difficult to take the whole picture which is a high resolution image all at once. In this case, each part of the image is taken by a camera one by one. A part of the image is an image patch. Using image patches, the whole picture is reproduced. This method is called image mosaicing. However the method has disadvantages for the reproduction of a high resolution image as follows. 1. When there are regions that do not have features or intensity gradients, it is hard to reproduce the original image because they are used to register each part of the image. 2. The higher the number of image patches, the more the image distortion is cumulated because errors on the borders between image patches are added. To solve these problems, the layered image mosaicing method is proposed. In this paper, the concept, implementation and an experiment are described. The experiment showed that the method was effective for image mosaicing.