Abstract: A method, non-transitory computer readable medium, and system that manage agricultural analysis in dynamic environments includes detecting a location of one or more agricultural objects of interest in image data of an environment captured by a sensor device during active navigation of the environment. An orientation and position of the sensor device with respect to the image data is determined. Each of the one or more agricultural objects of interest is analyzed based on the image data, the detected location of the one or more agricultural objects of interest, and the determined orientation and position of the sensor device to determine one or more characteristics about the one or more agricultural objects of interest. At least one action is initiated based on the determined one or more characteristics about the one or more agricultural objects of interest.

Abstract: Disclosed herein is a method for an image analysis server to detect a change to a structure by using a drone. The method for an image analysis server to detect a change to a structure by using a drone includes: receiving images of a specific inspection target structure taken at different time points by a drone; detecting the difference between an image taken at a first time point and an image taken at a second time point based on the received images; and detecting a change to the inspection target structure via the detected difference, and generating a risk signal and then transmitting it to an administrator terminal.

Abstract: Systems and methods of detecting a vortex made by a travelling object is disclosed. Techniques include positioning a media collector to capture a visual media file of the vortex. In some configurations, a graphic recognition algorithm and vortex similarity engine are used to determine whether a visual media file captured by a media collector contains a vortex. In some configurations, a computer may trigger an alert if a travelling object vortex is not expected to be in the visual media file.

Abstract: A system and a method for counting aquatic creatures are provided. The system includes an image capture device and a computer system configured to perform the method. The method includes: using the image capture device to capture images of a flow channel; defining a scan line in the images; performing a binarization process on pixels of the scan line in each of the images; determining aquatic creature range data according to each of the binarized pixel data sets; determining aquatic creature range data sets according to the binarized pixel data sets; determining identification and direction information of aquatic creature corresponding to each of the aquatic creature range data sets according to the aquatic creature range data sets; and determining a number of aquatic creatures passing through the scan line according to the identification and direction information of aquatic creature corresponding to each of the aquatic creature range data sets.

Abstract: A facial verification method and apparatus is disclosed. The facial verification method includes detecting a face region in an input image, determining whether the detected face region represents a partial face, in response to a determination that the detected face region represents the partial face, generating a synthesized image by combining image information of the detected face region and reference image information, performing a verification operation with respect to the synthesized image and predetermined first registration information, and indicating whether facial verification of the input image is successful based on a result of the performed verification operation.

Abstract: Systems and methods for optimizing asset maintenance protocols by predicting vegetation-driven outages are disclosed. An example method includes determining, by one or more processors, a failure probability for each asset in a set of assets within a designated area. The example method further includes defining, by the one or more processors, an asset risk for each asset in the set of assets based on the failure probability, and clustering, by the one or more processors, vegetation within the designated area to determine a predicted vegetation-driven outage. The example method further includes optimizing, by the one or more processors, a set of asset maintenance protocols corresponding to the set of assets based on the asset risk and the predicted vegetation-driven outage.

Abstract: A system and method of organically generating a camera-pose map is disclosed. A target image is obtained of a location deemed suitable for augmenting with a virtual augmentation or graphic. An initial camera-pose map is created having a limited number of calibrated camera-pose images having calculated camera-pose locations and homographies to the target image. Then, during the event, the system automatically obtains current images of the event venue and determines homographies to the nearest calibration camera-pose image in the camera-pose map. The separation in camera-pose space between the current images and the camera-pose images are calculated. If this separation is less than a predetermined threshold, that current image is fully calibrated and added to the camera-pose map, thereby growing the map organically.

Abstract: The invention generally relates to methods for manually calibrating imaging systems such as optical coherence tomography systems. In certain aspects, an imaging system displays an image showing a target and a reference item. A user looks at the image and indicates a point within the image near the reference item. A processer detects an actual location of the reference item within an area around the indicated point. The processer can use an expected location of the reference item with the detected actual location to calculate a calibration value and provide a calibrated image. In this way, a user can identify the actual location of the reference point and a processing algorithm can give precision to the actual location.

Abstract: A neural network system, includes: a processor configured to detect a plurality of object candidates included in a first image, generate metadata corresponding to the plurality of object candidates based on the first image, and set data processing orders of the plurality of object candidates based on the metadata; and at least one resource configured to perform data processing with respect to the plurality of object candidates. The processor is configured to sequentially provide pieces of information related to data processing of the plurality of object candidates to the at least one resource according to the set data processing orders, and the at least one resource is configured to sequentially perform data processing with respect to the plurality of object candidates according to an order in which a piece of information related to data processing of each of the plurality of object candidates is received.

Abstract: Implementations are described herein for predicting soil organic carbon (“SOC”) content for agricultural fields detected in digital imagery. In various implementations, one or more digital images depicting portion(s) of one or more agricultural fields may be processed. The one or more digital images may have been acquired by a vision sensor carried through the field(s) by a ground-based vehicle. Based on the processing, one or more agricultural inferences indicating agricultural practices or conditions predicted to affect SOC content may be determined. Based on the agricultural inferences, one or more predicted SOC measurements for the field(s) may be determined.

Abstract: Provided are a method of providing carbon emission management information, the method comprising extracting, by a carbon emission management information providing server, an area corresponding to a company to be evaluated from satellite image data of the company to be evaluated, calculating, by the carbon emission management information providing server, a greenhouse gas concentration of the area corresponding to the company to be evaluated from the satellite image data, calculating, by the carbon emission management information providing server, a change in vegetation index around the company to be evaluated from the satellite image data, analyzing, by the carbon emission management information providing server, a relationship between carbon emission management factors input in relation to the company to be evaluated, a change in the calculated greenhouse gas concentration, and the calculated change in vegetation index and generating, by the carbon emission management information providing server, carbon emi

Abstract: Systems and methods are disclosed herein for detecting impurities of harvested plants in a receptacle of a harvester. In an embodiment, a harvester controller receives, from a camera facing the contents of the receptacle, an image of the contents. The harvester controller applies the image as input to a machine learning model. The harvester controller receives, as output from the machine learning model, an identification of an impurity of the harvested plants. The harvester controller transmits a control signal based on the impurity.

Abstract: The facial verification apparatus is a mobile computing apparatus, including a camera to capture an image, a display, and one or more processors. While in a lock state, the image is captured and facial verification performed using a face image, or using a detected face and in response to the face being detected. The facial verification includes a matching with respect to the detected face, or obtained face image, and a registered face information. If the verification is successful, the lock state of the apparatus may be canceled and the user allowed access to the apparatus. The lock state may be cancelled when the verification is successful and the user has been determined to have been attempting to gain access to the apparatus. Face image feedback to the user may not be displayed during the detecting for, or obtaining of, the face and/or performing of the facial verification.

Abstract: A method for determining a localization pose of an at least partially automated mobile platform, the mobile platform being equipped to generate ground images of an area surrounding the mobile platform, and being equipped to receive aerial images of the area surrounding the mobile platform from an aerial-image system. The method includes: providing a digital ground image of the area surrounding the mobile platform; receiving an aerial image of the area surrounding the mobile platform; generating the localization pose of the mobile platform with the aid of a trained convolutional neural network, which has a first trained encoder convolutional-neural-network part and a second trained encoder convolutional-neural-network part.

Abstract: Existing currency validation (CVAL) devices, systems, and methods are too slow, costly, intrusive, and/or bulky to be routinely used in common transaction locations (e.g., at checkout, at an automatic teller machine, etc.). Presented herein are devices, systems, and methods to facilitate optical validation of documents, merchandise, or currency at common transaction locations and to do so in an obtrusive and convenient way. More specifically, the present invention embraces a validation device that may be used alone or integrated within a larger system (e.g., point of sale system, kiosk, etc.). The present invention also embraces methods for currency validation using the validation device, as well as methods for improving the quality and consistency of data captured by the validation device for validation.

Abstract: In one aspect, a method for detecting terrain variations within a field includes receiving one or more images depicting an imaged portion of an agricultural field. The method also includes classifying a portion of the plurality of pixels that are associated with soil within the imaged portion of the agricultural field as soil pixels with each soil pixel being associated with a respective pixel height. Additionally, the method includes identifying each soil pixel having a pixel height that exceeds a height threshold as a candidate ridge pixel and each soil pixel having a pixel height that is less than a depth threshold as a candidate valley pixel. The method further includes determining whether a ridge or a valley is present within the imaged portion of the agricultural field based at least in part on the candidate ridge pixels or the candidate valley pixels.

Abstract: The disclosure extends to methods, systems, and apparatuses for automated fixation generation and more particularly relates to generation of synthetic saliency maps. A method for generating saliency information includes receiving a first image and an indication of one or more sub-regions within the first image corresponding to one or more objects of interest. The method includes generating and storing a label image by creating an intermediate image having one or more random points. The random points have a first color in regions corresponding to the sub-regions and a remainder of the intermediate image having a second color. Generating and storing the label image further includes applying a Gaussian blur to the intermediate image.

Abstract: A system may be configured to collect geospatial features (in vector form) such that a software application is operable to edit an object represented by at least one vector. Some embodiments may: generate, via a trained machine learning model, a pixel map based on an aerial or satellite image; convert the pixel map into vector form; and store the vectors. This conversion may include a raster phase and a vector phase. A system may be configured to obtain another image, generate another pixel map based on the other image, convert the other pixel map into vector form, and compare the vectors to identify changes between the images. Some implementations may cause identification, based on a similarity with converted vectors, of a more trustworthy set of vectors for subsequent data source conflation.

Abstract: A farming machine moves through a field and includes an image sensor that captures an image of a plant in the field. A control system accesses the captured image and applies the image to a machine learned plant identification model. The plant identification model identifies pixels representing the plant and categorizes the plant into a plant group (e.g., plant species). The identified pixels are labeled as the plant group and a location of the pixels is determined. The control system actuates a treatment mechanism based on the identified plant group and location. Additionally, the images from the image sensor and the plant identification model may be used to generate a plant identification map. The plant identification map is a map of the field that indicates the locations of the plant groups identified by the plant identification model.

Abstract: A deep learning (DL) convolution neural network (CNN) reduces noise in positron emission tomography (PET) images, and is trained using a range of noise levels for the low-quality images having high noise in the training dataset to produce uniform high-quality images having low noise, independently of the noise level of the input image. The DL-CNN network can be implemented by slicing a three-dimensional (3D) PET image into 2D slices along transaxial, coronal, and sagittal planes, using three separate 2D CNN networks for each respective plane, and averaging the outputs from these three separate 2D CNN networks. Feature-oriented training can be implemented by segmenting each training image into lesion and background regions, and, in the loss function, applying greater weights to voxels in the lesion region. Other medical images (e.g. MRI and CT) can be used to enhance resolution of the PET images and provide partial volume corrections.