With the rapid development of technologies based on virtual reality, image stitching is widely used in various fields such as broadcasting, games, education, and architecture. Image stitching is a method for connectin...
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With the rapid development of technologies based on virtual reality, image stitching is widely used in various fields such as broadcasting, games, education, and architecture. Image stitching is a method for connecting multiple images to produce a high-resolution image and a wide field of view image. It is common for most of the stitching methods to find and match the feature in the image. However, these stitching methods have the disadvantage that they cannot create a perfect 360-degree panoramic image because the depth of the projected area varies depending on the position and direction between adjacent cameras. Therefore, we propose an advanced stitching method to improve the deviation due to the difference in the depth of each area using the pixel value of the input image after the feature-based stitching. After the feature-based stitching method has been performed, the pixel values of overlapping areas in the image are calculated as an optical flow algorithm, then finely distorted, and then corrected to ensure that the image overlaps correctly. Through experiments, it was confirmed that the problem that was deviated from the feature-based stitching was solved. Besides, as a result of performance evaluation, it was proved that the proposed stitching method using an optical flow algorithm is capable of real-time and fast service.
This paper presents the design and implementation of a motion detection algorithm for processing of video sequences captured by a purpose-built camera trap, removing of unwanted frames, and reducing of the size of the...
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This paper presents the design and implementation of a motion detection algorithm for processing of video sequences captured by a purpose-built camera trap, removing of unwanted frames, and reducing of the size of the video sequences. The camera trap is used to create a dataset containing H.264 video sequences of the critically endangered Baw Baw Frog Philoria frosti. A motion detection method based on the optical flow farneback algorithm is then developed to reduce the size of the video sequences by detecting the frames that contain moving frogs, keeping and storing the detected frames, and discarding the frames that do not contain moving frogs. Twenty video sequences containing a total of 13770 frames are used in this study. The frames with moving frogs are detected with sensitivity of 0.993, specificity of 0.992, and accuracy of 0.992. The original video sequences are then replaced by the newly created video sequences that are much smaller in size with an average reduction of 80.4% for the test video sequences used in this study.
We evaluated a software based application for cell proliferation determination. Cells were cultured in different set-ups with and without cell-inhabitant agent. A scratch wound assay was performed to stimulate cell pr...
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We evaluated a software based application for cell proliferation determination. Cells were cultured in different set-ups with and without cell-inhabitant agent. A scratch wound assay was performed to stimulate cell proliferation. The scratch wounds were photographed light-microscopically for 66.5 h. The images were software based evaluated in timely order by comparing each image to its predecessor using a farneback algorithm. Compared to the manual evaluation, this application is timesaving, accurate, reproducible and of objective nature independent from the observer.
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