Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for extracting material properties from a single digital image portraying one or more materials by utilizing a neural network encoder, a neural network material classifier, and one or more neural network material property decoders. In particular, in one or more embodiments, the disclosed systems and methods train the neural network encoder, the neural network material classifier, and one or more neural network material property decoders to accurately extract material properties from a single digital image portraying one or more materials. Furthermore, in one or more embodiments, the disclosed systems and methods train and utilize a rendering layer to generate model images from the extracted material properties.

Abstract: A distance-based score approximation having improved computational efficiency is provided. Responsive to receiving a score request, an observation point is identified based the score request. A geographic database is queried for map information corresponding to geometry elements located within a predetermined maximum distance of the observation point. A plurality of sections of the geometry elements are defined. Each section is associated with one of a plurality of groups of sections based on map information corresponding to a geometry element corresponding to the section. For each of the plurality of sections, an impact curtain area is determined. For each group of sections, the impact curtain area for each section associated with the group is aggregated to define a contribution for the group. The contribution for each group of sections is aggregated to determine the distance-based score. The distance-based score is provided for display via a display of a user computing entity.

Abstract: An image processing device includes a memory configured to store layout data of a layout of a format, and a processor coupled to the memory, wherein the processor is configured to execute first acquiring a captured image that includes a format region, detecting edges that are included in the captured image, second acquiring edge straight lines based on the edges, calculating a correction amount of the format region with reference to the layout based on the edges, selecting, as a selected straight line, an edge straight line that is outermost among the edge straight lines that form a rectangle, third acquiring a reference straight line that forms an outline of the format region based on the correction amount and the selected straight line, and specifying a format coordinate of the format region based on the reference straight line.

Abstract: A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.

Abstract: The disclosure provides a method for dynamically adjusting image clarity and an image processing device using the same method. The method includes: retrieving an image frame and searching for a predetermined object therein; evaluating a first sharpness of the predetermined object; if the first sharpness of the predetermined object is lower than a sharpness threshold, calculating a difference value between the first sharpness and the sharpness threshold; dividing the image frame into blocks and evaluating a risk value and an effect value of increasing a second sharpness of each block; inputting the difference value, the risk value, and the effect value to a classifying model to generate a clarity setting value; and adjusting a clarity of a display on showing the image frame based on the clarity setting value.

Abstract: The present disclosure provides a QR code graphic manufacturing device, which comprises a gray-scaled adjusting module, a data-hiding module, and an error-diffusion module. The gray-scaled adjusting module is used to adjust a gray-scaled value of an original image to generate a modified gray-scaled image. The data-hiding module and the error-diffusion module are respectively used to process the modified gray-scaled image and a QR code, and to generate a QR code graphic with a better hidden effect.

Abstract: Artistic styles extracted from one or more source images may be applied to one or more target images, e.g., in the form of stylized images and/or stylized video sequences. The extracted artistic style may be stored as a plurality of layers in a neural network, which neural network may be further optimized, e.g., via the fusion of various elements of the network's architectures. An optimized network architecture may be determined for each processing environment in which the network will be applied. The artistic style may be applied to the obtained images and/or video sequence of images using various optimization methods, such as the use of scalars to control the resolution of the unstylized and stylized images, temporal consistency constraints, as well as the use of dynamically adjustable or selectable versions of Deep Neural Networks (DNN) that are responsive to system performance parameters, such as available processing resources and thermal capacity.

Abstract: Supervised machine learning using neural networks is applied to denoising images rendered by MC path tracing. Specialization of neural networks may be achieved by using a modular design that allows reusing trained components in different networks and facilitates easy debugging and incremental building of complex structures. Specialization may also be achieved by using progressive neural networks. In some embodiments, training of a neural-network based denoiser may use importance sampling, where more challenging patches or patches including areas of particular interests within a training dataset are selected with higher probabilities than others. In some other embodiments, generative adversarial networks (GANs) may be used for training a machine-learning based denoiser as an alternative to using pre-defined loss functions.

Abstract: A computing device for performing image pre-processing for blending images receives a first image depicting a head. The computing device detects a portion of the head in the first image and calculates an image attribute of the portion in the first image. The computing device receives a second image and generates an adjusted second image by adjusting color pixels in the second image based on the calculated image attribute of the portion in the first image. The computing device blends the adjusted second image with the first image.

Abstract: The technology relates to navigating imagery that is organized into clusters based on common patterns exhibited when imagery is captured. For example, a set of captured images which satisfy a predetermined pattern may be determined. The images in the set of set of captured images may be grouped into one or more clusters according to the predetermined pattern. A request to display a first cluster of the one or more clusters may be received and, in response, a first captured image from the requested first cluster may be selected. The selected first captured image may then be displayed.

Abstract: To accurately classify a form without using form layout information, the image processing apparatus utilizes a classifier that accepts a filled-in form whose image has been reduced into a specific size as an input and specifies the form type of the filled-in form. Machine learning has been performed to the classifier by a form type learning system and the form type learning system reduces an image of a filled-in form as an original document image, adds a noise to the original document image, which has not been reduced or has been reduced, to generate multiple images for machine learning, associates the form type of the original document image with the multiple images for machine learning as a label, and performs machine learning of the classifier using the multiple images for machine learning and the label as training data.

Abstract: A method and apparatus for detecting text are provided. The method includes: extracting features of a to-be-detected image; predicting using a character detection network a probability of each pixel point in the to-be-detected image being a character pixel point, and position information of each pixel point relative to a bounding box of a character including the pixel point when the pixel point is the character pixel point; determining position information of bounding boxes of candidate characters based on the prediction result of the character detection network; inputting the extracted features into a character map network, converting a feature map outputted by the character map network, and generating character vectors; determining a neighboring candidate character of each candidate character, and connecting each candidate character with an associated neighboring candidate character to form a character set; and determining a character area of the to-be-detected image.

Abstract: A method for extracting a surface deformation feature of an object based on linear scanning three-dimensional point cloud is disclosed comprising: performing data acquisition by using a three-dimensional measurement sensor based on line structured light, and obtaining a three-dimensional point cloud data of a surface of the object after data pre-processing; eliminating influences of abnormal data and textures on extraction of sectional main profiles, to accurately obtain the sectional main profiles of the object; obtaining a binary image based on deformation feature points extracted from sections, in conjunction with a deformation feature knowledge base, and preliminarily positioning a deformation region based on sub-block images to obtain a set of target morphology sub-blocks; then performing a morphological operation on deformation feature points in the set of target morphology sub-blocks, and generating a deformation region of confidence ROC, and then performing a region growth to extract a target using ge

Abstract: A system for automated geospatial image analysis comprising a deep learning model that receives orthorectified geospatial images, pre-labeled to demarcate objects of interest. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to a convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives orthorectified geospatial images from one or more geospatial image caches. Using a multi-scale sliding window submodule, image analysis software scans geospatial images, detects objects present and geospatially locates them.

Abstract: The present disclosure provides a computer-implemented method and system for performing real time supervised detection of televised video ads in a media content of a broadcasted channel. The method includes reception of the media content and selection of a set of frames per second from the media content. The method includes extraction of keypoints from each selected frame and derivation of binary descriptors from extracted keypoints. The method includes assignment of weight value to each binary descriptor and creation of a special pyramid of the binary descriptors. The method includes obtaining a first vocabulary of binary descriptors for each selected frame and accessing a second vocabulary of binary descriptors. The method includes comparison of each binary descriptor in the first vocabulary with binary descriptors in second vocabulary and progressively scoring each selected frame of the media content. The method includes detection of a first ad in the media content.

Abstract: The present disclosure provides a computer-implemented method and system for adaptively reducing detection time in real time supervised detection of televised ads in media content of a channel. The method includes reception of the media content and selection of a set of frames per second from the media content. The method includes extraction of keypoints from each selected frame and derivation of binary descriptors from extracted keypoints. The method includes assignment of weight value to each binary descriptor and creation of a special pyramid of the binary descriptors. The method includes obtaining a first vocabulary of binary descriptors for each selected frame and accessing a second vocabulary of binary descriptors. The method includes comparison of each binary descriptor in the first vocabulary with binary descriptors in second vocabulary. The method includes progressively scoring each selected frame of the media content and detecting a first ad in the media content.

Abstract: The present disclosure provides a computer-implemented method and system for adaptively switching supervised detection strategy for watermarked and non-watermarked real time televised video advertisements. The televised video advertisements are present in a live stream of a media content of a broadcasted channel. The method includes selection of a set of frames per second from the media content. The method includes checking for one or more watermarked features in each selected frame of the media content. The method includes switching to a first detection strategy. The first detection strategy is associated with detection of a first ad in the live stream of the media content when the one or more watermarked feature are present in each checked frame in the selected set of frames.

Abstract: The present disclosure provides a computer-implemented method and system for hardware, channel, language and ad length agnostic detection of multi-lingual televised advertisements. The detection is performed across live streams of media content of one or more broadcasted channels. The method includes selection of a set of frames per second from a pre-defined set of frames. The method includes extraction of a pre-defined number of keypoints from each selected frame and derivation of a pre-defined number of binary descriptors from the extracted keypoints. The method includes creation of a special pyramid of the binary descriptors and accessing a second vocabulary of binary descriptors. The method includes comparison of each spatially identifiable binary descriptor from the first vocabulary with spatially identifiable binary descriptors in clusters of the second vocabulary. The method includes progressively scoring each selected frame and detection of the first ad in the live streams of the media content.

Abstract: An object recognition system is provided that includes a device configured to capture a video sequence formed from unlabeled testing video frames. The system includes a processor configured to pre-train a recognition engine formed from a reference set of CNNs on a still image domain that includes labeled training still image frames. The processor adapts the recognition engine to a video domain to form an adapted recognition engine, by applying a non-reference set of CNNs to a set of domains that include the still image and video domains and a degraded image domain. The degraded image domain includes labeled synthetically degraded versions of the labeled training still image frames included in the still image domain. The video domain includes random unlabeled training video frames. The processor recognizes, using the adapted engine, a set of objects in the video sequence. A display device displays the set of recognized objects.

Abstract: An image processing device includes a memory, and a processor coupled to the memory, wherein the processor is configured to execute acquiring a captured image including an original document region, detecting a color component value of a predetermined area in the captured image, detecting an edge in the predetermined area to acquire an edge amount indicating any one or both of a density of the edge and an edge intensity, and identifying, based on the color component value and the edge amount, a difference between a background of the original document region and a background of a background region obtained by removing the original document region from the captured image.