Abstract: This disclosure relates generally to method and system to calculate net carbon sequestration for agriculture using remote sensing data. Climate change is one of the factor in sustainable development on the earth and has sparked numerous initiatives to reduce earth's carbon footprint. The disclosed method processes remote sensing data comprising one or more input images indicating one or more characteristics of at least one agriculture crop of a geographical region. The method calculates a carbon footprint value of at least one agriculture crop by obtaining a plurality of carbon values associated with the geographical region. A net carbon flux of least one agriculture crop is calculated based on the carbon footprint value, a data maturity index, and a difficulty level. The method enables growers and carbon credit purchasers to correctly determine the carbon footprint of a geographical region to accurately predict the reduction of greenhouse gas emissions reducing environmental degradation.

Abstract: A remote location monitoring system identifies a place of interest and a subset of artificial satellites that can capture images of the place of interest within a threshold period. The next available artificial satellite with image sensors that can capture images of the place of interest earliest is selected and the cache heating signal is transmitted to be stored in a cache associated with the next available image sensor. The cache heating signal activates particular image sensors for image capture and enables the transmission of meaningful images that enable monitoring the place of interest.

Abstract: A method for electronically removing a lesion from a three-dimensional (3D) medical image includes segmenting each two-dimensional (2D) slice of a sequence of 2D slices of the 3D medical image to identify the lesion within any one or more of the 2D slices. The method includes deleting the lesion from each 2D slice in which the lesion was identified to create a sequence of lesion-deleted slices. The method includes constructing, based on the sequence of lesion-deleted slices, a lesion-deleted intensity-based projection image, such as a lesion-deleted maximum-intensity projection image. The method includes processing the lesion-deleted intensity-based projection image to obtain a background parenchymal enhancement (BPE) score. The method excludes high-intensity voxels that indicate the presence of a lesion, and thus are not indicative of BPE.

Abstract: An unmanned parking space detection method based on a panoramic surround view is provided. The unmanned parking space detection method adopts a Laplace fusion algorithm to splice images to obtain the panoramic surround view, which effectively realize fusion of the images. By using an image quality evaluation function, complexity of the unmanned parking space detection method is reduced while ensuring a quality of the splicing. The panoramic surround view generated by the present disclosure has a more natural transition in the areas being spliced and fused, and the panoramic surround view display is more complete and beautiful. Compared with conventional machine vision detection technologies, the unmanned parking space detection method adopts a target detection technology for detecting parking space entrance lines and adopts a more lightweight network design to ensure real-time performance of a detection algorithm thereof, which has higher detection accuracy and stronger robustness.

Abstract: Provided are a method and apparatus for detecting cargo in a container image using container wall background removal. The method of detecting cargo in a container image according to an aspect of the present invention includes: receiving a backscatter X-ray target container image captured on a target container; acquiring a backscatter X-ray empty target container wall image of an empty container that corresponds to the target container and a capturing condition of the backscatter X-ray target container image; generating a difference image in which a wall background of the target container is removed based on the backscatter X-ray target container image and the backscatter X-ray empty target container wall image; and detecting cargo included in the target container based on the difference image.

Abstract: The present invention relates to an activity recognition apparatus and method in sports videos using CGAM.

Abstract: An image capturing apparatus acquires successive images in a time-series manner and performs image processing thereon. The image capturing apparatus detects a first region (for example, a face) of a subject from an image, and detects a second region (for example, a torso) of a subject from an image. The image capturing apparatus performs processing for searching for a detection result which is obtained from a current image with use of a detection result obtained from a previously acquired image and classifying detection results each satisfying a condition according to subject. In the search processing, identical-region search, which uses detection results of an identical region between the previously acquired image and the current image, is performed in preference to different-region search, which uses detection results of different regions between the previously acquired image and the current image.

Abstract: Certain aspects of the present disclosure provide techniques for feature-based image matching. A method generally includes obtaining a first image frame and a second image frame; generating a modified first image frame and a modified second image frame, wherein to generate the modified first image frame and the modified second image frame comprises to convert a first color space of the first image frame and a second color space of the second image frame to a third color space; extracting a first plurality of features from the modified first image frame; extracting a second plurality of features from the modified second image frame; and determining at least one first matching cost based on the first plurality of features and the second plurality of features.

Abstract: A blockchain-based and human characteristics intelligence recognition for appointment-based elderly care system includes a cloud server, a smartwatch and a management terminal, the cloud server is connected to the smartwatch and the management terminal, the smartwatch is worn on the elderly user, the management terminal includes a PC terminal, a mobile terminal and a display screen; the invention provides effective first time identification and can use the wearing device efficiently for a long time based on blockchain.

Abstract: In one aspect, there is provided a method that includes receiving, by a control system having (i) a first camera configured to obtain an image of a scene, (ii) a winch controller, and (iii) a feeding system configured to deliver a feed to aquaculture, instructions to initiate a calibration of the first camera, determining a calibration state of the first camera, determining a sequence of calibration steps based on the calibration state of the first camera, and executing the sequence of calibration steps to calibrate the first camera.

Abstract: A color matching control apparatus obtains a second image picked up by a second image pickup apparatus to include a color chart area, extracts pixel values of the color chart area included in a first image as first values, extracts pixel values of the color chart area in the second image as second values, calculates a dynamic range of the color chart area in the first image based on the first pixel values, sets a predetermined pixel in an area excluding the color chart area in the first image as at least one first singular point based on the dynamic range, extracts a pixel value of the at least one first singular point, sets at least one second singular point in the second image based on the at least one first point, extracts a pixel value of the at least one second singular point, and generates a color matching parameter.

Abstract: A safety belt wearing detection method includes: acquiring a target image collected from a traffic device; determining a first image region and/or a second image region in the target image, the first image region being a region where a human torso of a target object in the target image is located, the second image region being a region where a face of the target object in the target image is located; determining a third image region according to the first image region and/or the second image region, the third image region being a safety belt wearing region in the target image; and determining whether wearing of a safety belt in the target image meets a preset wearing requirement according to image information in the third image region.

Abstract: Methods and systems directed to processing of a polarized image are disclosed. A method may involve determining a polarization characterization for a polarized image. The polarization characterization is indicative of polarization data associated with a plurality of polarization directions of incident light in the polarized image. The method may also involve extracting, from the polarized image, a first collection of global features and a second collection of local features. The method may further involve performing, based on the polarization characterization, a global feature fusion to fuse global features in the first collection, and a local feature fusion to fuse local features in the second collection. The method may involve compositing the polarization characterization with the fused global features and the fused local features to generate a reconstructed image. The method may also involve providing the reconstructed image to an image processing resource to perform one or more image processing tasks.

Abstract: The present disclosure relates to a method and apparatus for object identification using a hierarchical model. An object identification method using a hierarchical model according to one embodiment of the present disclosure comprises detecting an object area from an object image in which an object is located based on a trained object area extraction model and cropping the detected object area by an object identification apparatus; and identifying object type information of an object located in the object area cropped by the object identification apparatus based on an object type inference model by an object management server.

Abstract: The techniques described herein relate to computerized methods and apparatus for grouping images of objects based on semantic and visual information associated with the objects. The techniques described herein further relate to computerized methods and apparatus for training a machine learning model for object recognition.

Abstract: This application describes examples of an information processing method and apparatus. In one example, the method includes: obtaining an image; inputting the image into a feature extraction model; obtaining, from the feature extraction model, a first feature map; inputting the first feature map into a first semantic recognition model; and obtaining, from the first semantic recognition model, first target semantic information.

Abstract: In a method and apparatus for generating a depth map, the method includes obtaining a LiDAR point cloud generated by a LiDAR, obtaining a 3D bounding box for at least a portion of the LiDAR point cloud, generating a first vehicle mask corresponding to the 3D bounding box from an image point cloud obtained by projecting coordinates of points of the LiDAR point cloud into an image coordinate system, and generating the depth map by removing distant points that are not included in the 3D bounding box among points included in the first vehicle mask.

Abstract: A handheld object recognition method includes obtaining location information of each of one or more detected objects in a to-be-recognized image; obtaining a first label of each detected object indicating a type of the detected object, wherein the type of the detected object is used to represent a handheld relationship of the detected object; obtaining a handheld object from the one or more detected objects based on the first label; and obtaining a recognition result of the handheld object in the to-be-recognized image based on the location information of the handheld object.

Abstract: Devices, systems, and methods for extraction of routing card information of an event system are described herein. In some examples, one or more embodiments include a computing device comprising a memory and a processor to execute instructions stored in the memory to receive a file of a routing card associated with a facility including the event system, convert the file to an image, extract facility information, map and routing information, and event device information from the image of the routing card, and generate an output file having the extracted data as metadata in the output file.

Abstract: This disclosure provides a deep learning method, apparatus, device, and medium for task joint optimization based on vascular centerline guidance, including: in a process of extracting a global image feature by an encoder, extracting a local image feature after performing data augmentation on a local image with morphological specificity based on the vascular centerline guidance, and fusing the local image feature with the global image feature to form a fused image feature; and forming a bi-directional feature correction process between decoders of different decoding branches. The vascular centerline guidance is used to provide gated feature optimization and computational efficiency adjustment for an encoding process. Specific morphological data of the vascular centerline guidance is used to form a control mechanism for modulating efficiency of local feature extraction. A scarcity problem of hemodynamic annotations has been alleviated and generalization ability of a model has been enhanced.