Video Mosaicing Research Articles

An Improved Video Mosaic Block Detection Method on the Statistics of Pixel Difference across the Boundary of Macroblock and Suspected Mosaic Blocks

This passage proposes a new method to detect mosaic not only using the Y (luminance) component in YUV color space of videos, but also using the U (chrominance) and V component. The mosaic effect is measured by the boundary pixel difference from the neighbor macroblock. Instead of detecting the existence and position of mosaic blocks as traditional methods do, this method focuses on the statistics of the number of suspected mosaic blocks so that the quality of the whole frame affected by mosaic can be assessed. Experimental results show that the new method has good performance on fallout ratio, omission factor and computational complexity.

The Application of Normalized Covariance in Video Mosaicing

Considering the mismatch and the high computational complexity of video mosaicing, a fast stitching method based on the normalized covariance is proposed. Firstly, the image corner features are extracted using the SURF algorithm and the corresponding matching points are found using the nearest neighbor method. The least square method is used to filter the matching points and accurately match the point-pair. And then, the homography matrix is got and can be used in finding the overlapping area of two images. The optimal homography matrix is obtained according to the normalization covariance and the bidirectional matching by which the video mosaicing is finished. Finally, the bilinear interpolation algorithm and the weighted average method are used to get a realistic and seamless image that retains high image resolution. The experimental results show that this algorithm can obtain better parameters estimation values and has smaller calculations as well as good robustness so as to stitch the video fast and seamlessly. DOI: http://dx.doi.org/10.11591/telkomnika.v10i7.1453 Full Text: PDF

Real-time video mosaicing with a high-resolution microendoscope

Microendoscopes allow clinicians to view subcellular features in vivo and in real-time, but their field-of-view is inherently limited by the small size of the probe’s distal end. Video mosaicing has emerged as an effective technique to increase the acquired image size. Current implementations are performed post-procedure, which removes the benefits of live imaging. In this manuscript we present an algorithm for real-time video mosaicing using a low-cost high-resolution microendoscope. We present algorithm execution times and show image results obtained from in vivo tissue.

Video motion stitching using trajectory and position similarities

Stitching motions in multiple videos into a single video scene is a challenging task in current video fusion and mosaicing research and film production. In this paper, we present a novel method of video motion stitching based on the similarities of trajectory and position of foreground objects. First, multiple video sequences are registered in a common reference frame, whereby we estimate the static and dynamic backgrounds, with the former responsible for distinguishing the foreground from the background and the static region from the dynamic region, and the latter functioning in mosaicing the warped input video sequences into a panoramic video. Accordingly, the motion similarity is calculated by reference to trajectory and position similarity, whereby the corresponding motion parts are extracted from multiple video sequences. Finally, using the corresponding motion parts, the foregrounds of different videos and dynamic backgrounds are fused into a single video scene through Poisson editing, with the motions involved being stitched together. Our major contributions are a framework of multiple video mosaicing based on motion similarity and a method of calculating motion similarity from the trajectory similarity and the position similarity. Experiments on everyday videos show that the agreement of trajectory and position similarities with the real motion similarity plays a decisive role in determining whether two motions can be stitched. We acquire satisfactory results for motion stitching and video mosaicing.

A Self-adaptive and Real-time panoramic video mosaicing system

In real-time monitoring process, resolution must be set against field vision. There are two problems in the real-time monitoring procedures, which are: the field vision is too small to capture the target completely and a larger field vision with low resolution. In order to solve these problems, a new type of real-time panoramic monitoring systems becomes necessary. In this paper, we proposed a new adaptive video mosaicing algorithm based on SIFT algorithm and template matching method. By real-time adjusting and updating the mosaicing position of adjacent two images to establish the optimal mosaic line, we can both made full use of the accuracy of feature matching and the simplicity of template matching. Experimental results show our algorithm not only has good matching rate but also reduces the time complexity, which make it very suitable for the application which requests high real-time performance.

DLIG: Direct Local Indirect Global Alignment for Video Mosaicing

In this paper, we present a framework for real-time mosaicing from video sequences recorded from an uncalibrated pan tilt zoom camera based on multiframe registration. To this end, a new frame alignment algorithm, the direct local indirect global (DLIG), is presented. The key idea of the DLIG alignment is to divide the frame alignment problem into the problem of registering a set of spatially related image patches. The registration is iteratively computed by sequentially imposing a good local match and global spatial coherence. The patch registration is performed using a tracking algorithm, so a very efficient local matching can be achieved. We use the patch-based registration to obtain multiframe registration, using the mosaic coordinates to relate the current frame to patches from different frames that partially share the current field of view. Multiframe registration prevents the error accumulation problem, one of the most important problems in mosaicing. We also show how to embed a kernel tracking algorithm in order to obtain a precise and extremely efficient mosaicing algorithm. Finally, we perform a quantitative evaluation of our algorithm, including a comparison with other alignment approaches, and studying its performance against interlaced videos and illumination changes.

Toward Dynamic Image Mosaic Generation With Robustness to Parallax

Mosaicing is largely dependent on the quality of registration among the constituent input images. Parallax and object motion present challenges to image registration, leading to artifacts in the result. To reduce the impact of these artifacts, traditional image mosaicing approaches often impose planar scene constraints or rely on purely rotational camera motion or dense sampling. However, these requirements are often impractical or fail to address the needs of all applications. Instead, taking advantage of depth cues and a smooth transition criterion, we achieve significantly improved mosaicing results for static scenes, coping effectively with nontrivial parallax in the input. We extend this approach to the synthesis of dynamic video mosaics, incorporating foreground/background segmentation and a consistent motion perception criterion. Although further additions are required to cope with unconstrained object motion, our algorithm can synthesize a perceptually convincing output, conveying the same appearance of motion as seen in the input sequences.

Development of a machine learning technique for automatic analysis of seafloor image data: Case example, Pogonophora coverage at mud volcanoes

Digital image processing provides powerful tools for fast and precise analysis of large image data sets in marine and geoscientific applications. Because of the increasing volume of georeferenced image and video data acquired by underwater platforms such as remotely operated vehicles, means of automatic analysis of the acquired image data are required. A new and fast-developing application is the combination of video imagery and mosaicking techniques for seafloor habitat mapping. In this article we introduce an approach to fully automatic detection and quantification of Pogonophora coverage in seafloor video mosaics from mud volcanoes. The automatic recognition is based on textural image features extracted from the raw image data and classification using machine learning techniques. Classification rates of up to 98.86% were achieved on the training data. The approach was extensively validated on a data set of more than 4000 seafloor video mosaics from the Håkon Mosby Mud Volcano.

An Algorithm Based on SURF for Surveillance Video Mosaicing

Using the SIFT algorithm for image mosaicing is the study hotspot in recent years, which is in a wide range of applications. SIFT algorithm of large amount of data and the time-consuming calculation method is not applicable in higher real-time video mosaicing. Firstly using SURF extracts feature points, secondly using the nearest matching method, RANSAC and least-square method solve the homography matrix between images, and finally using normalized covariance related function for obtaining the best effect of the homography matrix. The algorithm not only meets the accuracy requirement of parameter estimation, but also has smaller computation and faster speed than SIFT. It has proved that the algorithm used in this paper has good real-time performance, high accuracy and the ideal effect, which can satisfy the requirement of real-time mosaicking.

Consistent image alignment for video mosaicing

Image mosaicing is widely used in computer vision applications. In this paper, a new globally consistent image alignment method for video mosaicing is presented. Due to various uncertainties on noise, illumination, and modeling, the problem of global image alignment is considered as a stochastic estimation problem. The augmented system state consists of the transformation parameters of video image sequences. The system motion model, the augmentation model, and the observation model are constructed. The homography parameters of image sequences are augmented and estimated recursively with augmented Kalman filter in a common state vector and covariance matrix. Some experimental results are provided to validate the performance of the proposed method. The proposed image alignment method can handle the uncertainty efficiently. Image alignment is globally consistent and accurate.

Collusion, MPEG4 compression and frame dropping resistant video watermarking

We present in this article a new video watermarking which resists collusion, MPEG4 compression and frame dropping attacks. This scheme is based on video mosaicing. For that, we are going to start by describing the mosaicing technique in order to illustrate the contribution of this technique in video watermarking. In fact, mosaicing allows to select an interesting area where the mark should be embedded. The idea is to insert the same mark into the same pixels which represent the same physical point. This is exactly the information which can be provided by a mosaic image at least for the located points in the scene background. Next, we present extensive experimental simulations which prove the watermark imperceptibility and robustness against several video attacks.

Community composition and temporal change at deep Gulf of Mexico cold seeps

Specialized cold-seep communities have been known to exist in the Gulf of Mexico (GOM) since the mid-1980s, but only recently has extensive research been carried out on sites at depths >1000m. This study uses a combination of imagery and analyses within a Geographic Information Systems (GIS) framework to examine the composition of mussel and tubeworm communities at depths between 2200 and 2800m in the Gulf of Mexico, spatial relations among the fauna, and changes in these communities over time. Photomosaics at three discrete seep communities were obtained in 2006 and a video mosaic of another community was obtained in 1992. Each of these communities was re-imaged in 2007. In addition, quantitative physical collections were made within two of the photomosaic sites and used to confirm the identification of megafauna, quantify the occurrence of smaller and cryptic macrofauna, and allow first-order calculations of biomass within the sites. Substrate type had a significant effect on community composition. Significant associations were identified between live mussels with anemones, shrimp, and sea cucumbers, and between tubeworm aggregations and Munidopsis sp. crabs and encrusting fauna, indicating differences in the composition of megafauna associated with adjacent mussel and tubeworm aggregations. Little change was seen in the total area colonized by foundation fauna (tubeworms and mussels) between years at any site. However, significant changes occurred in the positions of mussels, even over periods of a single year, at all sites, and evidence for the establishment of new tubeworm aggregations between 1992 and 2007 was noted at one site. These photomosaics provide data suggesting that environmental conditions can change over small spatial and temporal scales and mussels move in response to these changes. The successional trends are examined and compared to the patterns that have been documented in shallow (<1000-m depth) Gulf of Mexico seep communities.

Damage and recovery assessment of vessel grounding injuries on coral reef habitats by use of georeferenced landscape video mosaics

Vessel groundings are a major source of disturbance to coral reefs worldwide. Documenting the extent of damage caused by groundings is a crucial first step in the reef restoration process. Here, we describe the application of a novel survey methodology, landscape video mosaics, to assessment of the damage caused by vessel groundings. Video mosaics, created by merging thousands of video frames, combine quantitative and qualitative aspects of damage assessment and provide a georeferenced, landscape, high‐resolution, spatially accurate permanent record of an injury. The scar in a Florida reef impacted by a 49‐foot vessel, imaged in 2005 and 2006, covered an area of 150 m2 (total imaged area was &gt;600 m2). The impacted coral community showed limited signs of coral recovery more than 3 years after the initial impact; the cover of corals was still significantly higher in the undamaged areas compared to the scar. However, seagrass colonization of the scar was observed. Finally, no evidence of further physical impacts was documented even when four hurricanes passed near the grounding site in 2005. The video mosaics developed in this study proved to be ideal tools to survey the grounding scars. Mosaics provide a means to collect information on the size of the damage area and the status and trends of the impacted biological communities and provide a permanent visual record of the damage, thereby expanding the quality and diversity of information that can be collected during field surveys.

Automatic video mosaicing for surveillance using vanishing points

Video surveillance typically relies on various methods of 3D scene reconstruction, such as people counting, and object detection and tracking. Video mosaicing is a related technology that enables an expanded view of a scene by pasting together frames from the video stream (superimages) of a moving single camera in real time. This technique provides better resolution than can be achieved with a conventional video camera. Well-known related applications in other fields include composition of panoramic pictures, alignment of satellite images, and generation of synthetic 360°-angle views. Given two frames of a scene, a general approach consists of finding a number of points in both images that correspond to the same objects in the scene. This makes it possible to compute a ‘transform’ that describes the geometrical relation between the views. Known as a planar homography, this transform can be obtained using robust estimation techniques on the point correspondences.

Landscape video mosaic from a mesophotic coral reef

Landscape video mosaic from a mesophotic coral reef

Augmented Panoramic Video

AbstractMany video sequences consist of a locally dynamic background containing moving foreground subjects. In this paper we propose a novel way of re‐displaying these sequences, by giving the user control over a virtual camera frame. Based on video mosaicing, we first compute a static high quality background panorama. After segmenting and removing the foreground subjects from the original video, the remaining elements are merged into a dynamic background panorama, which seamlessly extends the original video footage. We then re‐display this augmented video by warping and cropping the panorama. The virtual camera can have an enlarged field‐of‐view and a controlled camera motion. Our technique is able to process videos with complex camera motions, reconstructing high quality panoramas without parallax artefacts, visible seams or blurring, while retaining repetitive dynamic elements.

Techniques for Video Mosaicing

Video Mosaicing is the stitching of selected frames of a video by estimating the camera motion between the frames and thereby registering successive frames of the video to arrive at the mosaic. Different techniques have been proposed in the literature for video mosaicing. Despite of the large number of papers dealing with techniques to generate mosaic, only a few authors have investigated conditions under which these techniques generate good estimate of motion parameters. In this paper, these techniques are studied under different videos, and the reasons for failures are found. We propose algorithms with incorporation of outlier removal algorithms for better estimation of motion parameters.

High-resolution miniprobe-based confocal microscopy in combination with video mosaicing (with video)

A new portable confocal laser microscopy system has recently been developed. Our purpose was to evaluate the feasibility of performing real-time microscopic imaging with a prototype of a new high-resolution miniprobe in conjunction with a video mosaicing algorithm. Feasibility study. Tertiary referral center at a large university hospital. Seven patients referred for endoscopy for various indications. High-resolution laser microscopy of the upper and lower GI tract was performed with standard endoscopes. Seven to 10 mL of 1% fluorescein was injected intravenously a few seconds before the procedure. No additional preparation was required. The prototype used has a lateral resolution of 1.2 microm and an axial resolution of 3 mum with a total field of 240 x 200 microm. From all stored video sequences a video mosaicing algorithm was used to combine the successive individual images, cancel motion artifacts, and reconstitute panoramas of the tissues. Cell-to-cell borders, single cell structures, and mucosal inflammation was readily detectable. By the use of the mosaicing algorithm, the image area could be increased 2- to 4-fold, and image definition could be further enhanced to allow finer detail visualization. Low number of patients, early feasibility study. Our preliminary data show that high-resolution miniprobe-based confocal fluorescence microscopy in conjunction with video mosaicing has the potential to provide images similar to standard histopathologic studies. Dynamic images with a smaller field of view can be combined to reconstruct still images of high resolution covering a fairly large area.

Mosaicing-by-recognition for video-based text recognition

Text recognition captured in multiple frames by a hand-held video camera is a challenging task because it is possible to capture and recognize a longer line of text while improving the quality of the text image by utilizing the redundancy of the overlapping areas between the frames. For this task, the video frames should be registered, i.e., mosaiced, after compensating for their distortions due to camera shakes. In this paper, a mosaicing-by-recognition technique is proposed where the problems of video mosaicing and text recognition are formulated as a unified optimization problem and solved by a dynamic programming-based optimization algorithm simultaneously and collaboratively. Experimental results indicate that, even if the frames undergo various distortions such as rotation, scaling, translation, and nonlinear speed fluctuation of camera movement, the proposed technique provides fine mosaic image by accurate distortion estimation (around 90% of perfect estimation) and character recognition accuracy (over 95%).

Automated Feature Points Management for Video Mosaic Construction

Automated Feature Points Management for Video Mosaic Construction