Abstract: An inspection system includes an acquisition unit and a determination unit. The acquisition unit acquires an image representing a surface of an object. The determination unit performs color determination processing. The color determination processing is performed to determine a color of the surface of the object based on a plurality of conditions of reflection. The plurality of conditions of reflection are obtained from the image representing the surface of the object as acquired by the acquisition unit, and have a specular reflection component and a diffuse reflection component at respectively different ratios on the surface of the object.

Abstract: A method for generating training data for hand-printed text recognition includes obtaining a structured document, obtaining a set of hand-printed character images and database metadata from a database, generating a modified document page image, and outputting a training file. The structured document includes a document page image that includes text characters and document metadata that associates each of the text characters to a document character label. The database metadata associates each of the set of hand-printed character images to a database character label. The modified document page image is generated by iteratively processing each of the text characters.

Abstract: A method for segmenting an image includes receiving one or more object seeds associated with one or more first object portions represented by a first image and receiving one or more background seeds associated with one or more first background portions represented by the first image. A second image representing one or more second object portions and one or more second background portions is received, wherein the first image comprises a plurality of subportions and the second image is a subportion of the plurality of subportions of the first image. The second image is processed based on the one or more object seeds and the one or more background seeds to determine the one or more second object portions and the one or more second background portions.

Abstract: Embodiments allow live action images from an image capture device to be composited with computer generated images in real-time or near real-time. The two types of images (live action and computer generated) are composited accurately by using a depth map. In an embodiment, the depth map includes a “depth value” for each pixel in the live action image. In an embodiment, steps of one or more of feature extraction, matching, filtering or refinement can be implemented, at least in part, with an artificial intelligence (AI) computing approach using a deep neural network with training. A combination of computer-generated (“synthetic”) and live-action (“recorded”) training data is created and used to train the network so that it can improve the accuracy or usefulness of a depth map so that compositing can be improved.

Abstract: The application discloses a method and system for segmenting a lung image. The method may include obtaining a target image relating to a lung region. The target image may include a plurality of image slices. The method may also include segmenting the lung region from the target image, identifying an airway structure relating to the lung region, and identifying one or more fissures in the lung region. The method may further include determining one or more pulmonary lobes in the lung region.

Abstract: A depth estimation system is described capable of determining depth information using two images from two cameras. A first camera captures a first image and a second camera captures a second image, both images including a plurality of light channels. A scan direction is selected from a plurality of scan directions. For the selected scan direction, along each of a plurality of scanlines, the system compares pixels from the first image to pixels from the second image. The comparison is based on calculating a census transform for each pixel in the first image and a census transform for each pixel in the second image. This comparison is used to determine a stereo correspondence between the pixels in the first image and the pixels in the second image. The system generates a depth map based on the stereo correspondence.

Abstract: An image processing apparatus includes an input unit that inputs an image, and a processor configured to read out a program stored in a memory, and executes the program. The processor is configured to detect an intended subject from the input image by a first detection method, set an intended subject region for the detected intended subject, detect the intended subject from the input image by a second detection method different from the first detection method, and update the set intended subject region by using a detection result of the second detection method.

Abstract: Techniques are disclosed for implementing a neural network that outputs embeddings. Furthermore, techniques are disclosed for using sensor data to train a neural network to learn such embeddings. In some examples, the neural network may be trained to learn embeddings for instance segmentation of an object based on an embedding for a bounding box associated with the object being trained to match pixel embeddings for pixels associated with the object. The embeddings may be used for object identification, object matching, object classification, and/or object tracking in various examples.

Abstract: Automated detection of features and/or parameters within an ocean environment using image data. In an embodiment, captured image data is received from ocean-facing camera(s) that are positioned to capture a region of an ocean environment. Feature(s) are identified within the captured image data, and parameter(s) are measured based on the identified feature(s). Then, when a request for data is received from a user system, the requested data is generated based on the parameter(s) and sent to the user system.

Abstract: A segmentation neural network is extended to provide classification at the segment level. An input image of a document is received and processed, utilizing a segmentation neural network, to detect pixels having a signature feature type. A signature heatmap of the input image can be generated based on the pixels in the input image having the signature feature type. The segmentation neural network is extended from here to further process the signature heatmap by morphing it to include noise surrounding an object of interest. This creates a signature region that can have no defined shape or size. The morphed heatmap acts as a mask so that each signature region or object in the input image can be detected as a segment. Based on this segment-level detection, the input image is classified. The classification result can be provided as feedback to a machine learning framework to refine training.

Abstract: An image enhancement system and method based on a generative adversarial network (GAN) model. The image enhancement system includes an acquiring unit, a training unit and an enhancement unit. The acquiring unit is configured to acquire a first image of a driving environment captured by a camera of a first vehicle and a second image of the driving environment captured by a camera of a second vehicle. The training unit is configured to train a GAN by using the first training image to obtain an image enhancement model. The enhancement unit is configured to enhance the second image by inputting the second image into the image enhancement model.

Abstract: Apparatuses, systems, methods, and medium are disclosed for precise geospatial structure geometry extraction from multi-view imagery, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: receive an image of a structure having an outline, the image having pixels with first pixel values depicting the structure and second pixel values outside of the structure depicting a background of a geographic area surrounding the structure, and image metadata including first geolocation data; and generate a synthetic shape image of the structure from the image using a machine learning algorithm, the synthetic shape image including pixels having pixel values forming a synthetic shape of the outline, the synthetic shape image having second geolocation data derived from the first geolocation data.

Abstract: Devices, systems, and methods of wirelessly controlling appliances and electronic devices, such as ceiling fans, air conditioners, garage doors, or the like. A receive-only garage door system is wirelessly controlled by a proprietary remote control unit. A cloning unit is able to clone or duplicate the proprietary wireless signal, and to replay it or re-generate it in response to a triggering command that a user submitted via a smartphone or tablet; thereby enabling to control such garage door system via mobile electronic devices. The cloning unit utilizes recording of the wireless signal payload and carrier frequency; wireless signal analysis; image analysis of the appliance or of the remote control unit; queries to a remote server to obtain properties of the proprietary wireless signal; or other techniques of signal analysis or duplication.

Abstract: Machine vision devices may be configured to automatically connect to a remote management server (e.g., a “cloud”-based management server), and may offload and/or communicate images and analyses to the remote management server via wired or wireless communications. The machine vision devices may further communicate with the management server, user computing devices, and/or human machine interface devices, e.g., to provide remote access to the machine vision device, provide real-time information from the machine vision device, receive configurations/updates, provide interactive graphical user interfaces, and/or the like.

Abstract: A computer vision system is provided that includes an image generation device configured to capture consecutive two dimensional (2D) images of a scene, a first memory configured to store the consecutive 2D images, a second memory configured to store a growing window of consecutive rows of a reference image and a growing window of consecutive rows of a current image, wherein the reference image and the current image are a pair of consecutive 2D images stored in the first memory, a third memory configured to store a sliding window of pixels fetched from the growing window of the reference image, wherein the pixels in the sliding window are stored in tiles, and a dense optical flow engine (DOFE) configured to determine a dense optical flow map for the pair of consecutive 2D images, wherein the DOFE uses the sliding window as a search window for pixel correspondence searches.

Abstract: The present invention is related to correct the errors in instruments, operation, and others using intelligent monitoring structures and machine learning, and others.

Abstract: A plurality of temporally successive vehicle sensor images are received as input to a variational autoencoder neural network that outputs an averaged semantic birds-eye view image that includes respective pixels determined by averaging semantic class values of corresponding pixels in respective images in the plurality of temporally successive vehicle sensor images. From a plurality of topological nodes that each specify respective real-world locations, a topological node closest to the vehicle, and a three degree-of-freedom pose for the vehicle relative to the topological node closest to the vehicle, is determined based on the averaged semantic birds-eye view image. A real-world three degree-of-freedom pose for the vehicle is determined by combining the three degree-of-freedom pose for the vehicle relative to the topological node and the real-world location of the topological node closest to the vehicle.

Abstract: Techniques for automatic measurement using structured lights are provided. A computer system uses structured lights to acquire skin information, the skin information being associated with at least one affected area of the skin. The computer system determines parameters related to the at least one affected area of the skin, at least partly, on the skin information and generates an assessment for the at least one affected area of the skin.

Abstract: An object of the present invention is to extract an area of a foreground object with high accuracy. The present invention is an image processing apparatus including: a target image acquisition unit configured to acquire a target image that is a target of extraction of a foreground area; a reference image acquisition unit configured to acquire a plurality of reference images including an image whose viewpoint is different from that of the target image; a conversion unit configured to convert a plurality of reference images acquired by the reference image acquisition unit based on a viewpoint corresponding to the target image; and an extraction unit configured to extract a foreground area of the target image by using data relating to a degree of coincidence of a plurality of reference images converted by the conversion unit.

Abstract: Systems and methods for determining modified fractional flow reserve values of vascular lesions are provided. Patient physiologic data, including coronary vascular information, is measured. According to the physiologic data, a coronary vascular model is generated. Lesions of interest within the coronary vascular system of the patient are identified for modified fractional flow reserve value determination. The coronary vascular model is modified to generate modified blood flow information for determining the modified fractional flow reserve value.