Abstract: The disclosure relates to methods and systems for detecting the orientation of an oocyte and its polar body. Examples include an automated method for detection of an orientation of an oocyte, the method including: i) acquiring an image of the oocyte; ii) defining first and second elliptical features in the image based on edges detected in the image; iii) calculating an orientation of the second elliptical feature relative to the first elliptical feature; and iv) outputting a relative orientation of a polar body of the oocyte based on the orientation of the second elliptical feature relative to the first elliptical feature.

Abstract: Certain aspects involve video inpainting in which content is propagated from a user-provided reference frame to other video frames depicting a scene. For example, a computing system accesses a set of video frames with annotations identifying a target region to be modified. The computing system determines a motion of the target region's boundary across the set of video frames, and also interpolates pixel motion within the target region across the set of video frames. The computing system also inserts, responsive to user input, a reference frame into the set of video frames. The reference frame can include reference color data from a user-specified modification to the target region. The computing system can use the reference color data and the interpolated motion to update color data in the target region across set of video frames.

Abstract: A pose determination method for a subject includes identifying a plurality of candidate regions from depth data representing an environment based on a depth connectivity criterion, determining a first region including a first subset of the plurality of candidate regions based on an estimation regarding a first pose component of the subject, determining a second region including a second subset of the plurality of candidate regions that are disconnected from the first subset of the plurality of candidate regions based on relative locations of the first region and the second region, generating a collective region by associating the first region with the second region, identifying the first pose component and a second pose component of the subject from the collective region, determining a spatial relationship between the first pose component and the second pose component, and generating a controlling command based on the spatial relationship.

Abstract: Dual-modality relation networks for audio-visual event localization can be provided. A video feed for audio-visual event localization can be received. Based on a combination of extracted audio features and video features of the video feed, informative features and regions in the video feed can be determined by running a first neural network. Based on the informative features and regions in the video feed determined by the first neural network, relation-aware video features can be determined by running a second neural network. Based on the informative features and regions in the video feed, relation-aware audio features can be determined by running a third neural network. A dual-modality representation can be obtained based on the relation-aware video features and the relation-aware audio features by running a fourth neural network. The dual-modality representation can be input to a classifier to identity an audio-visual event in the video feed.

Abstract: Variation in received documents types and templates used for each document type poses challenge in developing a generic background noise removal approach for automatic text information extraction technique. Embodiments herein provide a method and a system for document classification and text information extraction. Time efficient and accurate text detection engine-based Region of Interest (ROI) technique is provided to accurately identify text region followed by a multi-layered neural network based architecture for enhanced classification accuracy to identify the type of document. A multistage image pre-processing approach is provided for efficient, effective, and accurate background noise removal from the classified document, which includes unsupervised clustering, identification, segmentation, masking, contour approximation, selective subtraction, and dynamic thresholding.

Abstract: Systems and methods for generating synthesized medical images for training a machine learning based network. An input medical image in a first modality is received comprising a nodule region for each of one or more nodules, a remaining region and an annotation for each of the nodules. A synthesized medical image in a second modality is generated from the input medical image comprising the annotation for each of the nodules. A synthesized nodule image of each of the nodule regions and synthesized remaining image of the remaining region are generated in the second modality. It is determined whether a particular nodule is visible in the synthesized medical image based on the synthesized nodule image for the particular nodule and the synthesized remaining image. If at least one nodule is not visible in the synthesized medical image, the annotation for the not visible nodule is removed from the synthesized nodule image.

Abstract: A system and method for generating a bounding box for an object in proximity to a vehicle are disclosed. The method includes: receiving a three-dimensional (3D) point cloud representative of an environment; receiving a two-dimensional (2D) image of the environment; processing the 3D point cloud to identify an object cluster of 3D data points for a 3D object in the 3D point cloud; processing the 2D image to detect a 2D object in the 2D image and generate information regarding the 2D object from the 2D image; and when the 3D object and the 2D object correspond to the same object in the environment: generating a bird's eye view (BEV) bounding box for the object based on the object cluster of 3D data points and the information from the 2D image.

Abstract: A PET image reconstruction method, including: 1) injecting a PET radioactive tracer into a biological tissue, scanning by a PET device, and detecting and counting coincidence photons to obtain an original protection data matrix; 2) establishing a measurement equation model; 3) splitting the reconstruction problem into a first sub-problem and a second sub-problem; 4) solving the first sub-problem by a filtered back-projection layer, solving the second sub-problem by an improved denoising convolutional neural network, where the filtered back-projection layer and the improved denoising convolutional neural network are connected in series to form a filtered back-projection network (FBP-Net); 5) inputting original projection data into the FBP-Net, and using an image as a tag to adjust parameters of the FBP-Net to reduce an error between an output of the FBP-Net and the tag; and 6) inputting projection data to be reconstructed into the trained FBP-Net to obtain a desired reconstructed image.

Abstract: This invention provides a video analyzing apparatus which comprises an acquisition unit configured to acquire an image captured by an image capturing unit, a determining unit configured to determine a degree of congestion of persons in the image captured by the image capturing unit, and a deciding unit configured to decide a threshold to be used to determine a moving state of a person in the image based on the degree of congestion determined by the determining unit.

Abstract: A method for identifying mineral pore types in mud shale includes: determining an inorganic mineral pore image and a kerogen region image of a mud shale Scanning Electron Microscopy (SEM) gray-scale image; performing an expansion operation on the inorganic mineral pore image to obtain an expanded inorganic mineral pore image; comparing the inorganic mineral pore image with the expanded inorganic mineral pore image, and determining an extra region in the expanded inorganic mineral pore image as an expansion region; collecting statistics about the number of pixel points of a siliceous mineral, a calcareous mineral, and a clay mineral; calculating the proportion of each mineral according to the number of pixel points of the minerals; drawing a mineral pore triangular image chart according to the proportions of minerals; and determining the mineral type corresponding to the pores in the inorganic mineral pore image according to the mineral pore triangular image chart.

Abstract: A classification device for classifying targets using a neural network on the basis of a target image that captures each target and at least one attribute parameter associated with the target. The classification device is equipped with a receiver, a neural network unit, and a classifier. The receiver receives a target image that captures a target and at least one attribute parameter associated with the target. The classifier classifies targets using the neural network unit. In the neural network unit, a convolution operator convolves individual elements of a provided feature map and the received at least one attribute parameter.