Real-time Flow-based Image Abstraction implements an automatic image and video abstraction technique based on the orientation-aligned bilateral and separable flow-based difference of Gaussians filters. You can load your image or video file and adjust the default settings of the filters t match your liking and then save the result.
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The filter calculates the joint orientation-aligned bilateral and separable flow-based difference of Gaussians for each pixel in the input image. In contrast to existing methods which perform a global, single sequence B-DGDF (e.g. in the form of optical flow) without adapting it to the local appearance of a certain pixel, we compute a local, sequence-specific flow field in the form of orientation-aligned bilateral and separable B-DGDF at each pixel. Afterward, the filter calculates a two-dimensional binary vector which contains all orientation-aligned bilateral and separable flow field Similarly as in the image pyramid, we apply these vectors on a pyramid pyramid with various resolution levels (hence the name of the filter). In contrast to existing image pyramids where the feature are in a pure visual sense, we use the computed orientation-aligned bilateral and separable flow field, which is generated by the orientation-aligned bilateral and separable flow-based difference of Gaussians filters and thus contains more information about the shape (e.g. the local texture) of the object or person. This way we can increase the level of abstraction beyond the single pixel and include it into the feature pyramid. The filter offers a number of options that can be fine-tuned using the sliders and check boxes in the user interface to adapt the filter to your needs. How to use Real-time Flow-based Image Abstraction? Expectation: You can save the settings of the filter and load the features of your image or video file again and adjust to the user interface of the filter to match your liking. Real-time Flow-based Image Abstraction Video Example: Concept & Technology Features How does it work? How can you use it? For More Detail Ref: Update: 4K UPConvert 4K Videos to HD and HD UPConvert HD Videos to 4K UPSizeThis enables you to up- or downscale any file in your disk drive with any ratio in either direction, preserving the original aspect ratio, up to a factor of 4K. UPSizeThis works by splitting the source file up into smaller and smaller chunks and effectively resizing the file by increasing the number of chunks. Update: Mac OS X 10.10 Yosemite & SSD Drive 1. Download the APP
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Real-time Flow-based Image Abstraction Product Key is based on the OAIBis, for Open Source Image and Video Abstraction Based on Orientation-Aware Bilateral Similarity Features. Real-time Flow-based Image Abstraction can be used for a wide range of video editing and visual enhancement tasks such as video removal, redaction, cutting, image stitching, real-time removal of stop motion, hue shift, sharpening, color balancing and pan and zoom. Additionally Real-time Flow-based Image Abstraction provides an innovative approach to the detection of red-eye in videos, highly suitable for video post-processing and video repair. Usage: – Open your image or video file. The default image size is 500 x 500. – Select the configuration settings: – 1) “a. ori_frame”: The target frame, you can select a frame to use it as input for the filters. – 2) “b. ori_frames_list”: List of target frames – 3) “c. trim_edges”: Remove image frames. Default is trim. Keep the frames with minimum size and remove the rest. – 4) “d. trim_sizes”: The frame size range. Only keep the frames that have a size between “a” and “b” – 5) “e. trim_size”: The trim frame size – 6) “f. trim_sizes_list”: List of trim frame sizes, only keep the frames that have a size between “a” and “b” – 7) “g. trim_type”: Only keep frames: “1”: Only keep frames. Keep all frames: “0”: Keep all frames – 8) “i. dissolve_alpha”: Set the dissolve alpha. The alpha channel with a value larger than 1 will be set to 1. The alpha channel with a value smaller than 0 will be set to 0. The default value is 1. – 9) “j. dissolve_thresh”: Set the dissolve alpha threshold. Alpha channel with value bigger than this threshold will be set to 1. Alpha channel with value smaller than this threshold will be set to 0. The default value is 10 2f7fe94e24
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The proposed algorithm is based on orientation-aligned bilateral and separable flow-based difference of Gaussian filters. The orientation-aligned bilateral part of the filter process the image in the vertical and horizontal directions using a least squares optimization technique. The vertical (horizontal) part is computed using the horizontal (vertical) orientation-aligned bilateral filter. Separable part of the filter captures the main direction in the image and suppress the noise in that direction. At the end of the whole process, oriented in the same direction blurs the noise in the rest of the image. The image is obtained by averaging all the filtered regions. The geometric flow method is used for registration of the feature points and vectors. An initial registration of the feature points and vectors using optical flow is also included in the method. The proposed method is quite effective in removing noise and blurring the edges in real-time. It is very robust against change in contrast and color of the images. From the result of some experiments, we can say that real-time flow-based image abstraction is much better than the standard run-time oriented Canny algorithm and Canny operator. This paper is published as the preliminary version in IEEE Int’l Conf. on Image Processing (ICIP-2007) and IEEE Int’l Conf. on Multimedia and Expo (ICME), 2007. Real-time flow-based image abstraction algorithm is based on the orientation-aligned bilateral and separable flow-based difference of Gaussians filters, which are comprised of two sub-parts: an orientation-aligned bilateral filter and a separable filter. The orientation-aligned bilateral filter is applied to local regions of the image to reduce the local noise and preserve the edges. These regions are computed by minimizing the squared error of the intensity and orientation of the image. The orientation-aligned bilateral filter is computed using the normalized cross-correlation between the images filtered in the horizontal and vertical directions. It is relatively straightforward to compute this filter because the optimal filtering matrix for the bilateral filter can be deduced using the normalized cross-correlation matrix. The separable filter is used to estimate the direction of the edges. This filter is based on the concept that the image content is more concentrated in a certain direction.
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