Abstract: The present invention relates to a method for comparing three-dimensional dental structures, comprising the steps of acquiring (S101) a target data set representing the shape and optical properties of a natural tooth; rendering (S102) a digital tooth model (200) to generate an actual data set representing the shape and optical properties of the digital tooth model; determining (S104) a first comparison value from the actual data set; determining (S105) a second comparison value from the target data set; and determining (S106) a deviation based on the first and second comparison values.

Abstract: Method and system for managing multiple impressions of a patient's jaw for an orthodontic treatment are provided. The method includes scanning at least a first impression and a second impression of the same jaw for the orthodontic treatment; determining if the first jaw impression and the second jaw impression have distortion in different areas; selecting the first jaw impression or the second jaw impression as a base impression; and replacing distorted tooth data from the base impression with data for the same tooth from a non-base impression. The method also includes scanning at least a first jaw impression for the orthodontic treatment; scanning a bite impression for the orthodontic treatment; matching the scanned first jaw impression with the scanned bite impression; comparing bite information with a tooth occlusal surface; and determining if reconstruction is to be performed on the tooth occlusal surface.

Abstract: In various example embodiments, machine vision-based techniques for non- contact SHM are provided that may integrate both phase-based algorithms and template matching algorithms to enable selection of one or more machine vision algorithms that are effective at measuring responses (e.g., displacement, strain, acceleration, velocity, etc.) under present conditions. Results of a single algorithm or a combination of results from multiple algorithms may be returned. In such techniques, improved template matching algorithms may be employed that provide sub-pixel accuracy. Responses may be adjusted to cancel out camera vibration and video noise. Defects or damage may be determined by tracking changes in displacement within an area of interest.

Abstract: A computer-implemented method for training a convolutional neural network includes receiving a captured image. A denoised image is generated by applying the convolutional neural network to the captured image. The convolutional neural network is trained based on a high frequency loss function, as well as the captured image and the denoised image.

Abstract: Methods and system for training a neural network for depth estimation in a vehicle. The methods and systems receive respective training image data from at least two cameras. Fields of view of adjacent cameras of the at least two cameras partially overlap. The respective training image data is processed through a neural network providing depth data and semantic segmentation data as outputs. The neural network is trained based on a loss function. The loss function combines a plurality of loss terms including at least a semantic segmentation loss term and a panoramic loss term. The panoramic loss term includes a similarity measure regarding overlapping image patches of the respective image data that each correspond to a region of overlapping fields of view of the adjacent cameras. The semantic segmentation loss term quantifies a difference between ground truth semantic segmentation data and the semantic segmentation data output from the neural network.

Abstract: Techniques for processing images to be used for more accurate measurement of biological processes such as cell migration, as well as techniques for measuring cell migration. A method for processing microscopic images includes generating a smoothed image for a raw image by applying a smoothing filter to the raw image, wherein the raw image shows a plurality of cells and a background; generating a high pass filter image by dividing the raw image by the smoothed image; and transforming the high pass filter image into a transformed image by augmenting the spatial frequency of the plurality of cells shown in the high pass filter image with respect to the background.

Abstract: An image processing method of the present disclosure may include receiving a scanned image, and processing the received image through an octave convolution-based neural network to output a high-quality image and an edge image for the received image. The octave convolution-based neural network may include a plurality of octave encoder blocks and a plurality of octave decoder blocks. Each octave encoder block may include an octave convolutional layer, and may be configured to output a high-frequency feature map and a low-frequency feature map for the image.

Abstract: Systems and methods are described herein for implementing a hybrid codec to compress and decompress image data using both lossy and lossless compression. In one example encoding process, it may be determined whether a first block of pixels of a frame of image data contains an edge. A type of compression by which to encode the first block may be selected based on that determination. The first block may be compressed using the selected type of compression. At least one second value associated with the first block of pixels may be set to indicate at least oof the compressed value or the type of compression used to compress the first block.

Abstract: A device may receive frames of a video capturing an analog meter with a dial and a needle, may process the frames to identify a center, a radius, and a perimeter of the dial, and may determine calibrated values for the dial. The device may apply a model to one of the frames to create a base mask, may apply thresholding for a dynamic HSV bounding value, to the base mask and the frames, to create masked frames, and may identify contours for the masked frames. The device may identify a quantity of points for each of the contours, may estimate angles of the needle of the analog meter based on the quantity of points, and may average the estimated angles to determine an averaged needle angle. The device may determine a needle direction based on the averaged needle angle and may calculate a meter reading.

Abstract: Examples are provided relating to recovering depth data from noisy phase data of low-signal pixels. One example provides a computing system, comprising a logic machine, and a storage machine holding instructions executable by the logic machine to process depth data by obtaining depth image data and active brightness image data for a plurality of pixels, the depth image data comprising phase data for a plurality of frequencies, and identifying low-signal pixels based at least on the active brightness image data. The instructions are further executable to apply a denoising filter to phase data of the low-signal pixels to obtain denoised phase data and not applying the denoising filter to phase data of other pixels. The instructions are further executable to, after applying the denoising filter, perform phase unwrapping on the phase data for the plurality of frequencies to obtain a depth image, and output the depth image.

Abstract: An authentication device includes: a wearing position determination unit that determines a wearing position, the wearing position being a position at which a wearable article comprising a sensor is being worn on a body; and an authentication unit that performs authentication by using biometric information of the body, the biometric information being detected by the sensor at the wearing position.

Abstract: An image processing apparatus includes at least one memory and at least one processor that executes instructions stored in the memory to receive an input image based on image data, execute noise reduction processing on the image data, and output noise-reduced output data based on a result of the noise reduction processing, wherein the noise reduction processing calculates a value using reference pixels based on a first frequency range, and subtracts a value using pixels based on a second frequency range.

Abstract: An apparatus for content based anti-aliasing is described herein. The apparatus comprises a detector, corrector, and downscaler. The detector is to detect potential aliased content in an input image, wherein the potentially aliased content occurs at a downscaled version of the input image. The corrector is to apply a correction to a single component of the input image. A downscaler may downscale the corrected input image to an output image according to a scaling factor.

Abstract: In one embodiment, a first device identifies a region of interest in video in which a light source of a second device is present by: using a current frame of the video and a prior frame of the video to compute a difference frame, performing thresholding on the current frame to form a threshold frame, and performing pixel-wise conjunction operations between the difference frame and the threshold frame, to identify a centroid of the light source of the second device. The first device detects a message within the region of interest transmitted by the second device via its light source. The device provides the message for review by a user.

Abstract: Encoding can involve correcting chroma components representing the chroma of an input image according to a first component representing a mapping of the luminance component of said input image used for reducing or increasing the dynamic range of said luminance component, and a reconstructed component representing an inverse mapping of said first component. At least one correction factor according to said at least one scaled chroma components can also be obtained and transmitted. Decoding can involve scaled chroma components being obtained by multiplying chroma components of an image by at least one corrected chroma correction function depending on said at least one correction factor. Components of a reconstructed image can then be derived as a function of said scaled chroma components and a corrected matrix that depends on an inverse of a theoretical color space matrix conversion and said at least one correction factor.

Abstract: An information processing method includes: acquiring a first object detection result obtained by use of an object detection model to which sensing data from a first sensor is input, and a second object detection result obtained by use of a second sensor; determining a degree of agreement between the first object detection result and the second object detection result in a specific region in a sensing space of the first sensor and the second sensor; and selecting the sensing data as learning data for the object detection model, according to the degree of agreement obtained in the determining.

Abstract: The present application relates to an occluded pedestrian re-identification method, including steps of obtaining global features and local features of occluded pedestrians, and recombining the local features into a local feature map; obtaining a heat map of key-points of pedestrian images and a group of key-point confidences, obtaining a group of features of the pedestrian key-points by using the local feature map and the heat map; obtaining a local feature group by using the global features to enhance each key-point feature in the group of features of pedestrian key-points according to Conv, and an adjacency matrix of key-points is obtained through the key-points, the local feature group and the adjacency matrix of key-points are used as the input of GCN to obtain the final features of pedestrian key-points.

Abstract: A method includes obtaining a fisheye image of a scene and identifying multiple regions of interest in the fisheye image. The method also includes applying one or more transformations to transform and rotate one or more of the regions of interest in the fisheye image to produce one or more transformed regions. The method further includes generating a collage image having at least one portion based on the fisheye image and one or more portions containing the one or more transformed regions. In addition, the method includes performing object detection to identify one or more objects captured in the collage image.

Abstract: A system is provided that includes a mobile user device that executes an application that determines a skintone of a user, and determines and transmits a recipe for generating a target foundation that is based on a combination of a plurality of separate foundation ingredients that are associated with the skintone of the user. The system includes a dispensing device configured to receive the transmitted recipe from the mobile user device and dispense each of the plurality of separate foundation ingredients onto a common dispensing surface such that when the dispensed amounts of each of the plurality of separate foundation ingredients is blended on the dispensing surface, the target foundation is achieved. The mobile user device is configured to determine a skintone of the user based on features in a detected face of the user in a self-taken image of the user that is captured by a camera of the mobile user device.

Abstract: A security camera with a motion detection function, which comprises: an image sensor, configured to capture original images; a variation level computation circuit, configured to compute image variation levels of the original images; a long term computation circuit, configured to calculate a first average level for the image variation levels corresponding to M of the original images; a short term computation circuit, configured to calculate a second average level for the image variation levels corresponding to N of the original images, wherein M>N; and a motion determining circuit, configured to determine whether a motion of an object appears in a detection range of the image sensor according to a relation between the first average level and the second average level. By such security camera, the interference caused by noise or small object can be avoided. Accordingly, the motion detection of the security camera can be more accurate.