Abstract: Systems and methods determining velocity of an object associated with a three-dimensional (3D) scene may include: a LIDAR system generating two sets of 3D point cloud data of the scene from two consecutive point cloud sweeps; a pillar feature network encoding data of the point cloud data to extract two-dimensional (2D) bird's-eye-view embeddings for each of the point cloud data sets in the form of pseudo images, wherein the 2D bird's-eye-view embeddings for a first of the two point cloud data sets comprises pillar features for the first point cloud data set and the 2D bird's-eye-view embeddings for a second of the two point cloud data sets comprises pillar features for the second point cloud data set; and a feature pyramid network encoding the pillar features and performing a 2D optical flow estimation to estimate the velocity of the object.

Abstract: A target detection method based on the fusion of prior positioning of a millimeter-wave radar and a visual feature includes: simultaneously obtaining, based on the millimeter-wave radar and a vehicle-mounted camera after being calibrated, point cloud data of the millimeter-wave radar and a camera image; performing spatial 3D coordinate transformation on the point cloud data to project transformed point cloud data onto a camera plane; generating a plurality of anchor samples based on projected point cloud data according to a preset anchor strategy, and obtaining a final anchor sample based on a velocity-distance weight of each candidate region; fusing RGB information of the camera image and intensity information of an RCS in the point cloud data to obtain a feature of the final sample; and inputting the feature of the final anchor sample into a detection network to generate category and position information of a target in a scenario.

Abstract: The present disclosure relates to method and system for assessing quality of commodities. An image of bulk commodity is captured and segmented into one or more segmented images for classification into one of predefined categories of commodities. The method and system classify the commodities based on generalized features created from training images. One or more features in the training images are determined and grouped to obtain the generalized features. A feature score and corresponding weightage score of the generalized feature is then determined to estimate a predetermined regression score. Based on the generalized features and predetermined regression score, a likelihood score of the segmented image is determined that enables the classification of the input image to one of the predefined categories of commodities.

Abstract: Systems and methods for generating depth models and depth maps from images obtained from an imaging system are presented. A self-supervised neural network may be capable of regularizing depth information from surface normals. Rather than rely on separate depth and surface normal networks, surface normal information is extracted from the depth information and a smoothness function is applied to the surface normals instead of a depth gradient. Smoothing the surface normal may provide improved representation of environmental structures by both smoothing texture-less areas while preserving sharp boundaries between structures.

Abstract: Computer-implemented methods and systems for image analysis of multiband images of geographic regions are described, including a method by one or more computer executing executable instructions stored in one or more non-transitory, tangible, computer readable media, the method comprising: receiving one or more multiband image of a geographic region, the one or more multiband image having pixels; generating a grey level co-occurrence matrix for the pixels in the one or more multiband image; generating a surface index for the one or more multiband image containing information indicative of a surface type represented by one or more of the pixels in the one or more multiband image; and classifying the pixels of the one or more multiband image into one of a group of predefined land cover classes, based on the surface index in combination with the grey level co-occurrence matrix.

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: Systems and methods for image classification include receiving imaging data of in-vivo or excised tissue of a patient during a surgical procedure. Local image features are extracted from the imaging data. A vocabulary histogram for the imaging data is computed based on the extracted local image features. A classification of the in-vivo or excised tissue of the patient in the imaging data is determined based on the vocabulary histogram using a trained classifier, which is trained based on a set of sample images with confirmed tissue types.

Abstract: Methods, systems and computer program products for improving ground truth quality for modeling are provided. Aspects include receiving a plurality of data inputs, wherein each of the plurality of data inputs has an associated label. Aspects also include training a model based on the plurality of data inputs. Aspects also include generating a plurality of vector representations corresponding to the plurality of data inputs based on the model. Aspects also include clustering the plurality of vector representations into one or more clusters. Aspects also include identifying at least one anomalous data input based on the one or more clusters. The at least one anomalous data input can be a data input of the plurality of data inputs that is mislabeled, contributes to an ambiguous class structure or is an outlier. Aspects also include outputting a notification that provides an indication of the at least one anomalous data input.

Abstract: Systems and methods are described, and an example method includes a training an artificial intelligence (AI) classifier of electromagnetic (EM) scanned items, including obtaining a training set of sample raw EM scans. The set includes a population of sample in-class raw EM scans, which include blocks of EM sensor data from EM scans of regions having in-class objects, and the set includes a population of sample not-in-class raw EM scans, which include blocks of EM sensor data from EM scan of regions without in-class objects. The example includes applying the AI classifier to sample raw EM scans in the training set, measuring errors in the results, and updating classifier parameters based on the errors, until detecting a training completion state.

Abstract: The disclosure provides a method for training generative adversarial network (GAN), a method for generating images by using GAN, and a computer readable storage medium. The method may train the first generator of the GAN with available training samples belonging to the first type category and share the knowledge learnt by the first generator to the second generator. Accordingly, the second generator may learn to generate (fake) images belonging to the second type category even if there are no available training data during training the second generator.

Abstract: A printed circuit board includes a mounting surface, a transceiver mounted on the mounting surface and configured to process radio-frequency signals, and a radio-frequency module mounted on the mounting surface in communication with the transceiver, the radio-frequency module including an interface including M inputs and N outputs, each of the quantities M and N greater than 1, the interface configured so that each of at least two of the M inputs is coupled to a separate output through a separate switch.

Abstract: An image processing method executed by an image includes acquiring image data, acquiring region of interest data indicating a targeted region in recognition processing for an object included in the image data, acquiring information of a candidate region based on a degree of being similar to an object included in the image data, estimating an object region in the acquired image data based on the region of interest data and the information of a candidate, performing a preview display of a plurality of candidate images to be cropped on a display unit by using information of the estimate object region, and receiving a selection by a user from among the plurality of displayed candidate images. Cropping is performed on the image data that corresponds to the candidate image, for a received selection.

Abstract: A monitoring system implements a method for versatile and efficient training of a machine learning-based model for subsequent detection and grading of deviations in packaging containers for liquid food in a manufacturing plant. The method comprises creating a virtual model of a packaging container or of a starting material for use in producing the packaging container; obtaining probability distributions for features that are characteristic of a deviation type; producing reproductions of the virtual model with deviations included among the reproductions in correspondence with the probability distributions; associating gradings with the reproductions; and inputting the reproductions and the associated gradings for training of the machine learning-based model for subsequent detection and grading of an actual deviation in image data acquired in the manufacturing plant.

Abstract: In order to provide monocular depth prediction, a trained neural network may be used. To train the neural network, edge detection on a digital image may be performed to determine at least one edge of the digital image, and then a first point and a second point of the digital image may be sampled, based on the at least one edge. A relative depth between the first point and the second point may be predicted, and the neural network may be trained to perform monocular depth prediction using a loss function that compares the predicted relative depth with a ground truth relative depth between the first point and the second point.

Abstract: This neural network-type image processing device is provided with an input layer which comprises one unit where an input image is inputted, an output layer which comprises one unit where an output image is outputted, and multiple intermediate layers which are arranged between the input layer and the output layer and each of which comprises multiple units, the unit of the input layer, the units of the intermediate layers, and the unit of the output layer are fully connected with connection coefficients. The units of the intermediate layers are image processing modules which perform image processing on the image inputted to said units. The input image is inputted from the unit of the input layer, passes through the units of the intermediate layers, and is then outputted as an output image from the unit of the output layer; the connection coefficients are updated with learning based on a backpropagation algorithm.

Abstract: An artificial intelligence (AI) based system for detecting defects in infrastructure uses an image recognizer and image data. A set of annotated training and validation data is generated to train and validate the image recognizer. The image data is annotated with classification data such as defect type and severity of the defect. Once trained and validated, the image recognizer can analyze inspection images to identify detects therein and generate an output report including the identification and classification of the defect, and remediation recommendations.

Abstract: A cascaded system for classifying an image includes a first cascade layer including a first analysis module coupled to a first input terminal, and a first pooling module coupled to the first analysis module; a second cascade layer including a second analysis module coupled to a second input terminal, and a second pooling module coupled to the first pooling module and the second analysis module; a synthesis layer coupled to the second pooling module, and an activation layer coupled to the synthesis layer.

Abstract: Systems and methods are provided for estimating the 3D joint location of skeleton joints from an image segment of an object and a 2D joint heatmaps comprising 2D locations of skeleton joints on the image segment. This includes applying the image segment and 2D joint heatmaps to a convolutional neural network containing at least one 3D convolutional layer block, wherein the 2D resolution is reduced at each 3D convolutional layer and the depth resolution is expanded to produce an estimated depth for each joint. Combining the 2D location of each kind of joint with the estimated depth of the kind of joint generates an estimated 3D joint position of the skeleton joint.

Abstract: The present specification discloses a method, apparatus, and device for video frame interpolation.

Abstract: A computer system for verifying hail damage and/or detecting hail fraud includes a processor and a non-transitory, tangible, computer-readable storage medium having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations including: (i) receiving at least one image of at least a portion of a rooftop; (ii) analyzing the at least one image to identify a plurality of damaged locations; (iii) analyzing damaged locations to determine a shape and a size of each of the damaged locations; and (vi) determining, based upon the analyzing, whether the damaged locations are a result of hail damage by comparing the shape and the size of at least one damaged location to the shape and the size of at least one other damaged location.