Embodiments provide systems and methods for three-dimensional building generation from machine learning and topological models. The method uses topology models that are converted into vertices and edges. A BGAN (Building generative adversarial network) is used to create fake vertices/edges. The BGAN is then used to generate random samples from seen sample of different structures of building based on relationship of vertices and edges. The embeddings are then fed into a machine trained network to create a digital structure from the image.

Systems and methods are directed to automatically creating customized whiteboard backgrounds. A network system accesses metadata associated with a virtual presentation (e.g., title, topic, tenant identifier). First image data is identified based on first data of the metadata and second image data is identified based on second data of the metadata. Using the first image data and the second image data, the network system generates a plurality of whiteboard backgrounds by combining a first object obtained from the first image data with a second object obtained from the second image data to form each whiteboard background. The network system then causes presentation of a representation of each of the plurality of whiteboard backgrounds on a user interface of a host, who can select one of the representations. In response to receiving a selection, a whiteboard background corresponding to the selected representation is displayed as background on a whiteboard canvas.

A global and local binary pattern image crack segmentation method based on robot vision comprises the following steps: enhancing a contrast of an acquired original image to obtain an enhanced map; using an improved local binary pattern detection algorithm to process the enhanced map and construct a saliency map; using the enhanced map and the saliency map to segment cracks and obtaining a global and local binary pattern automatic crack segmentation method; and evaluating performance of the obtained global and local binary pattern automatic crack segmentation method. The present application uses logarithmic transformation to enhance the contrast of a crack image, so that information of dark parts of the cracks is richer. Texture features of a rotation invariant local binary pattern are improved. Global information of four directions is integrated, and the law of universal gravitation and gray and roundness features are introduced to correct crack segmentation results, thereby improving segmentation accuracy. Crack regions can be segmented in the background of uneven illumination and complex textures. The method has good robustness and meets requirements of online detection.

A high efficiency method of processing images to provide perceptual high-contrast output. Pixel intensities are calculated by a weighted combination of a fixed number of static bounding rectangle sizes. This is more performant than incrementally growing the bounding rectangle size and performing expensive analysis on resultant histograms. To mitigate image artifacts and noise, blurring and down-sampling are applied to the image prior to processing.

Aspects of the disclosure provide a device for processing frames with aliasing artifacts. For example, the device can include a motion estimation circuit, a warping circuit coupled to the motion estimation circuit, and a temporal decision circuit coupled to the warping circuit. The motion estimation circuit can estimate a motion value between a current frame and a previous frame. The warping circuit can warp the previous frame based on the motion value such that the warped previous frame is aligned with the current frame and determine whether the current frame and the warped previous frame are consistent. The temporal decision circuit can generate an output frame, the output frame including either the current frame and the warped previous frame when the current frame and the warped previous frame are consistent, or the current frame when the current frame and the warped previous frame are not consistent.

Methods and systems for determining a plurality of sequences of nucleic acid (e.g., DNA) molecules in a sequencing-by-synthesis process are provided. In one embodiment, the method comprises obtaining images of fluorescent signals obtained in a plurality of synthesis cycles. The images of fluorescent signals are associated with a plurality of different fluorescence channels. The method further comprises preprocessing the images of fluorescent signals to obtain processed images. Based on a set of the processed images, the method further comprises detecting center positions of clusters of the fluorescent signals using a trained convolutional neural network (CNN) and extracting, based on the center positions of the clusters of fluorescent signals, features from the set of the processed images to generate feature embedding vectors. The method further comprises determining, in parallel, the plurality of sequences of DNA molecules using the extracted features based on a trained attention-based neural network.

A computer implemented method can decode a frame of video data comprising an array of pixels to obtain decoded luma values and decoded chroma values corresponding to the array of pixels, and extract a frame mask based on the decoded luma values. The frame mask can include an array of mask values respectively corresponding to the array of pixels. A mask value indicates whether a corresponding pixel is in foreground or background of the frame. The method can perform a morphological operation to the frame mask to change one or more mask values to indicate their corresponding pixels are removed from the foreground and added to the background of the frame. The method can also identify foreground pixels after performing the morphological operation to the frame mask, and render a foreground image for display based on the decoded luma values and decoded chroma values of the foreground pixels.

There is provided with an image processing apparatus. A noise reduction unit generates a noise-reduced image in which noise is reduced from an input image in which a plurality of types of pixels that represent mutually different types of color information are arranged in one plane. An extraction unit generates a high-frequency emphasized image in which a high-frequency component of the input image is emphasized. A demosaicing unit generates a demosaiced image having a plurality of planes that each represent one type of color information by demosaicing processing to the noise-reduced image. A generation unit generates an output image by correcting the demosaiced image by using the high-frequency emphasized image.

A system for image inpainting is provided, including an encoder, a decoder, and a sketch tensor space of a third-order tensor; wherein the encoder includes an improved wireframe parser and a canny detector, and a pyramid structure sub-encoder; the improved wireframe parser is used to extract line maps from an original image input to the encoder, the canny detector is used to extract edge maps from the original image, and the pyramid structure sub-encoder is used to generate the sketch tensor space based on the original image, the line maps and the edge maps; and the decoder outputs an inpainted image from the sketch tensor space. A method thereof is also provided.

The present invention relates to a method for size estimation by image recognition of a specific target using a given scale. First, a reference objected is recognized in an image and the corresponding scale is established. Then the specific target is searched and the size of the specific target is estimated according to the acquired scale.