Abstract: An embodiment of the present disclosure provides a method of generating a tomography image and an X-ray imaging apparatus operating according to the method, whereby when a material with a high X-ray attenuation rate is inserted into an object, an image quality may be improved by reducing ripple artifacts and/or undershoot artifacts that may occur in a tomography image that is a captured X-ray image of the object.

Abstract: This invention provides a system and method that performs 3D imaging of a complex object, where image data is likely lost. Available 3D image data, in combination with an absence/loss of image data, allows computation of x, y and z dimensions. Absence/loss of data is assumed to be just another type of image data, and represents the presence of something that has prevented accurate data from being generated in the subject image. Segments of data can be connected to areas of absent data and generate a maximum bounding box. The shadow that this object generates can be represented as negative or missing data, but is not representative of the physical object. The height from the positive data, the object shadow size based on that height, the location in the FOV, and the ray angles that generate the images, are estimated and the object shadow size is removed from the result.

Abstract: A method for characterizing polarization image information and a method for computing characterization parameters are provided. The method includes: obtaining n polarized subimages of a polarization imaging target, wherein each polarized subimage corresponds to a different polarization angle and n?3; and computing a parameter matrix [ILP (x, y) INLP(x, y) ?(x, y)] of a polarization cosine characterization equation of all pixel points of the polarization imaging target according to all polarized subimages and the polarization cosine characterization equation of the polarization imaging target. The disclosure obtains a maximum polarization intensity image and a minimum polarization intensity image through computing multiple polarization intensity images, which improves the accuracy of polarization imaging, and the degree of accuracy is much higher than macroscopic accuracy.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include receiving a first image from a first image sensor; receiving a second image from a second image sensor; determining an electronic rolling shutter correction mapping for the first image and the second image; determining a parallax correction mapping based on the first image and the second image for stitching the first image and the second image; determining a warp mapping based on the parallax correction mapping and the electronic rolling shutter correction mapping, wherein the warp mapping applies the electronic rolling shutter correction mapping after the parallax correction mapping; applying the warp mapping to image data based on the first image and the second image to obtain a composite image; and storing, displaying, or transmitting an output image that is based on the composite image.

Abstract: Systems and methods for training models to predict dense correspondences across images such as human images. A model may be trained using synthetic training data created from one or more 3D computer models of a subject. In addition, one or more geodesic distances derived from the surfaces of one or more of the 3D models may be used to generate one or more loss values, which may in turn be used in modifying the model's parameters during training.

Abstract: An image processing apparatus determines, based on results of extracting a foreground area from a captured image by learning, a threshold value that is used for generation of a foreground image based on a difference between the captured image and a background image corresponding to the captured image. The image processing apparatus generates a foreground image based on a difference between the captured image and the background image by using the determined threshold value.

Abstract: Provided is a method for monitoring a manufacturing process of an agricultural product. The method utilizes hyperspectral imaging and comprises scanning at least one region along a sample of agricultural product using at least one light source of a single or different wavelengths; generating hyperspectral images from the at least one region; determining a spectral fingerprint for the sample of agricultural product from the hyperspectral images; and comparing the spectral fingerprint so obtained to a spectral fingerprint database containing a plurality of fingerprints obtained at various points of the manufacturing process, using a computer processor, to determine which point in the manufacturing process the sample has progressed to.

Abstract: An earthquake event classification method using an attention-based neural network includes: preprocessing input earthquake data by centering; extracting a feature map by nonlinearly converting the preprocessed earthquake data through a plurality of convolution layers having three or more layers; measuring importance of a learned feature of the nonlinear-converted earthquake data based on an attention technique in which interdependence of channels of the feature map is modeled; correcting a feature value of the measured importance value through element-wise multiply with the learned feature map; performing down-sampling through max-pooling based on the feature value; and classifying an earthquake event by regularizing the down-sampled feature value. Accordingly, main core features inherent in many/complex data are extracted through attention-based deep learning to overcome the limitations of the existing micro earthquake detection technology, thereby enabling earthquake detection even in low SNR environments.

Abstract: In various embodiments, a training application trains a convolutional neural network to downsample images in a video encoding pipeline. The convolution neural network includes at least two residual blocks and is associated with a downsampling factor. The training application executes the convolutional neural network on a source image to generate a downsampled image. The training application then executes an upsampling algorithm on the downsampled image to generate a reconstructed image having the same resolution as the source image. The training application computes a reconstruction error based on the reconstructed image and the source image. The training application updates at least one parameter of the convolutional neural network based on the reconstruction error to generate a trained convolutional neural network.

Abstract: A medical image processing apparatus includes processing circuitry configured to receive first volume data and second volume data; set a first symmetry plane for the first volume data and a second symmetry plane for the second volume data; and conduct a registration of the first volume data and the second volume data based on the first symmetry plane and the second symmetry plane.

Abstract: A technology capable of estimating information on a position and a size of a pupil or an iris using an image obtained by photographing eyes of a subject even when a part of the pupil or the iris is hidden in the image is provided.

Abstract: An appliance includes a camera for calibrating and determining whether the door of the appliance is in a closed position. A controller is operably coupled to the camera. The controller is configured for obtaining one or more images of the appliance chamber or door. An artificial intelligence image recognition process is used to perform image classification and establish a baseline image to determine whether subsequent closing of the appliance door is successful. In the event of a failure to obtain the baseline image or a determination that the door is not closed, operation of the appliance may be disabled.

Abstract: A camera parameter estimation apparatus: takes three sets of three-dimensional coordinates pertaining to an object and two-dimensional coordinates corresponding to the three-dimensional coordinates, and transforms a coordinate system of the three-dimensional coordinates from a world coordinate system to a local coordinate system; calculates a linear transformation matrix based on a projection transformation expression from the transformed three-dimensional coordinates to the two-dimensional coordinates, calculates a coefficient of a quartic equation pertaining to any one of depths from a camera center to each three-dimensional coordinate, and calculates each depth; calculates the rotation matrix in the local coordinate system using each depth and the linear transformation matrix; calculates a translation vector in the local coordinate system from each depth based on the projection transformation expression; and calculates a rotation matrix and a translation vector in the world coordinate system by performing

Abstract: A vision inspection system includes a sorting platform having an upper surface supporting parts for inspection. An inspection station is positioned adjacent the sorting platform including an imaging device to image the parts in a field of view. A vision inspection controller receives images from the imaging device. The vision inspection controller includes an image histogram tool to pre-process the images to improve contrast of the images by redistributing lightness values of the images based on adaptive histogram equalization processing to generate enhanced images. The vision inspection controller processes the enhanced images based on an image analysis model to determine inspection results for each of the parts. The vision inspection controller has an artificial intelligence learning module operated to customize and configure the image analysis model based on the enhanced images.

Abstract: Systems and methods for image-based item identification are disclosed. Image data corresponding to one or more images depicting an item may be sent to one or more remote systems for image-based item identification. The identification indications and/or identification confidence scores received from the remote systems may be aggregated and weighted based at least in part on one or more factors related to the remote systems, the results, domains, image capture timing, image capture angles, and/or events to more accurately identify an item depicted in the images.

Abstract: A method for generating an high-dynamic-range (HDR) color image from a dual-exposure-time single-shot HDR color image sensor includes obtaining pixel values generated by a local region of sensor pixels of the image sensor, determining a motion parameter for the local region from pixel values associated with a first color, and demosaicing the pixel values of the local region to determine, for each of three colors, an output value of the images pixel, wherein relative contributions of short-exposure-time pixels and long-exposure-time pixels to the output value are weighted according to the motion parameter.

Abstract: To accurately identify a region corresponding to a position where an object is preset in an image, an object region identification device acquires a first frame image and a second frame image that are temporally successive. The device inputs the first frame image to a model that identifies an object in an image and acquires position information, the position affecting identification of an object in the first frame image. The device acquires motion information indicating a motion of the object in the first frame image based on the first frame image and the second frame image. The device generates, based on the acquired position information and motion information, region information indicating a region in the first frame image, the region corresponding to a position where the object is present.

Abstract: Techniques for integrating sensor data into a scene or map based on statistical data of captured environmental data are discussed herein. The data may be stored as a multi-resolution voxel space and the techniques may comprise first applying a pre-alignment or localization technique prior to fully integrating the sensor data.

Abstract: An audio acquisition device positioning method is provided. In the method, a first image that includes an audio acquisition device is obtained. The audio acquisition device in the first image is identified. First coordinate data of the identified audio acquisition device in the first image is obtained. First displacement data is determined according to the first coordinate data and historical coordinate data of the audio acquisition device. First coordinates of the audio acquisition device are determined according to the first displacement data.

Abstract: An apriori guidance network for multitask medical image synthesis is provided. The apriori guidance network includes a generator and a discriminator, wherein the generator includes an apriori guidance module configured to convert an input feature map into a target modal image pointing to a target domain according to an apriori feature, and the apriori feature is a deep feature of the target modal image. The generator is configured to generate a corresponding target domain image by taking the apriori feature of the target modal image and source modal image data as an input. The discriminator is configured to discriminate an authenticity of the target domain image outputted by the generator.