Abstract: An assembly change detection method based on attention mechanism, including: establishing a three-dimensional model of an assembly body, adding a tag to each part in the three-dimensional model, setting several assembly nodes, obtaining depth images of the three-dimensional model under each assembly node in different viewing angles, and obtaining a change tag image of a added part at each assembly node; selecting two depth images at front and back moments in different viewing angles as training samples; performing semantic fusion, feature extraction, attention mechanism processing and metric learning sequentially on the training samples, training a detection model, continuously selecting training samples to train the detection model, saving model parameters with optimal similarity during training, completing training; and obtaining depth images of successive assembly nodes during assembling the assembly body, inputting depth images into trained detection model, and outputting change image of added part of the

Abstract: Disclosed a crop yield prediction method and system based on low-altitude remote sensing information from an unmanned aerial vehicle (UAV). Obtaining a plurality of images taken by the UAV; stitching the plurality of images to obtain a stitched image; performing spectral calibration on the stitched image to obtain the reflectivity of each pixel in the stitched image; using a threshold segmentation method to segment the stitched image, to obtain a target area for crop yield prediction; using a Pearson correlation analysis method to analyze a correlation between the reflectivity of each band and the growth status and yield of the crop to obtain feature bands; constructing yield prediction factors based on the feature bands; and determining a predicted crop yield value of the target area for crop yield prediction based on the yield prediction factors and a crop planting area of the target area for crop yield prediction.

Abstract: There is provided a system for customized application of herbicides, comprising: a processor(s) executing a code for: feeding test images corresponding to a target agricultural field into a machine learning model trained on a training dataset of sample images of sample agricultural field(s) labelled with ground truth of weed parameters, selecting specific weed parameter(s) of according to performance metric(s) of the model, setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the model indicating likelihood of the specific weed parameter(s) being depicted in an input image of the portion of the target agricultural field, and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the model indicating non-likelihood of the specific weed parameter(s) being depicted in the input image.

Abstract: Among other things, the techniques described herein include a method for receiving a plurality of images of one or more containers while the one or more containers are being emptied, the plurality of images comprising a training set of images and a validation set of images; labeling each image of the plurality of images as including either an overfilled container or a not-overfilled container; processing each image of the plurality of images to reduce bias of a machine learning model; training, and based on the labeling, the machine learning model using the plurality of images; and optimizing the machine learning model by performing learning against the validation set, the optimized machine learning model being used to generate a prediction for a new image of a container, the prediction indicating whether the container in the new image was overfilled prior to the new container being emptied.

Abstract: For a wireless communications system, scalable orthogonal frequency division multiplexing (OFDM) numerology is incorporated in a manner that can apply to radio link transmissions in future wireless network for frequency division duplex (FDD) and time division duplex (TDD) communications.

Abstract: One variation of a method for interpreting pressures in plants includes: accessing a first image of a first set of sentinel plants in a field; accessing a second image of a second set of sentinel plants in the field, recorded during a first period; interpreting a first pressure of a stressor in the first set based on features extracted from the first image, captured during the first period; interpreting a second pressure in the second set based on features extracted from the second image; deriving a model associating pressure at the first set and pressure at the second set based on the first pressure and the second pressure; interpreting a third pressure in the first set based on features extracted from a third image captured during a second period; and predicting a fourth pressure in the second set during the second period based on the third pressure and the model.

Abstract: A method for decoding an image by a decoding device, according to the present document, comprises the steps of: acquiring image information; and generating a reconstructed picture on the basis of the image information.

Abstract: In an embodiment, an image-capture system, includes an image-capture device and computing circuitry. The image-capture device is configured to capture an image of a region of space that includes an object. And the computing circuitry is coupled to the image-capture device and is configured to detect a representation of the object in the image, to determine a representation of a boundary of the detected representation, to provide image information corresponding to the detected representation to an image-analysis system, to receive, from the image-analysis system, an identifier of a category to which the object belongs, and a descriptor of the object, and to generate a representation of a list that includes the identifier and the descriptor.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.

Abstract: A search method for a neural network model structure, includes: generating an initial generation population of network model structure based on multi-objective optimization hyper parameters, as a current generation population of network model structure; performing selection and crossover on the current generation population of network model structure; generating a part of network model structure based on reinforcement learning mutation, and generating a remaining part of network model structure based on random mutation on the selected and crossed network model structure; generating a new population of network model structure based on the part of network model structure generated by reinforcement learning mutation and the remaining part of network model structure generated by random mutation; and searching a next generation population of network model structure based on the current generation population of network model structure and the new population of network model structure.

Abstract: In an embodiment, digital images of agricultural fields are received at an agricultural intelligence processing system. Each digital image includes a set of pixels having pixel values, and each pixel value of a pixel includes a plurality of spectral band intensity values. Each spectral band intensity value describes a spectral band intensity of one band among several bands of electromagnetic radiation. For each of the agricultural fields, spectral band intensity values of each band are preprocessed at a field level using the digital images for that agricultural field resulting in preprocessed intensity values. The preprocessed intensity values are provided as input to a machine learning model. The model generates a predicted yield value for each field. The predicted yield value is used to update field yield maps of agricultural fields for forecasting and can be displayed via a graphical user interface (GUI) of a client computing device.

Abstract: A method includes receiving manual input from a human operator, where the manual input includes human observation data measurements associated with plants in a growing area. The method also includes, for each human observation data measurement, identifying a height of a movable portion of a mobile platform on which the human operator rides, identifying a location of the mobile platform in the growing area, identifying a time at which the human observation data measurement is received, and associating the human observation data measurement with a three-dimensional position within the growing area and the time in order to generate a spatio-temporal data measurement. The three-dimensional position includes the height and the location. The method further includes storing, processing, or transmitting at least some of the spatio-temporal data measurements or one or more results based on at least some of the spatio-temporal data measurements.

Abstract: Methods and systems estimate crop coefficients of a crop. At least one image sensor system captures a plurality of multispectral images of the crop and image data is derived from the multispectral images. At least one vegetation index of the crop is determined based on image data in at least a first spectral band. The reflectance of the crop monotonically increases and reaches a reflectance of at least 20% for at least one wavelength in the first spectral band. A crop coefficient of the crop is estimated based on the determined at least one vegetation index.

Abstract: A facial verification method includes separating a query face image into color channel images of different color channels, obtaining a multi-color channel target face image with a reduced shading of the query face image based on a smoothed image and a gradient image of each of the color channel images, extracting a face feature from the multi-color channel target face image, and determining whether face verification is successful based on the extracted face feature.

Abstract: The present invention relates to a computer implemented method for aligning a three-dimensional model (6) of a patient's dentition to an image of the face of the patient recorded by a camera (3), the image including the mouth opening, comprising: estimating the positioning of the camera (3) relative to the face of the patient during recording of the image to obtain an estimated positioning, retrieving the three-dimensional model (6) of the dentition of the patient, rendering a two-dimensional image (7) of the dentition of the patient using the virtual camera (8) processing the three-dimensional model (6) of the dentition at the estimated positioning, carrying out feature detection in a dentition area in the mouth opening of the image (1) of the patient recorded by the camera (3) and in the rendered image (7) by performing edge detection and/or a color-based tooth likelihood determination in the respective images and forming a detected feature image for the or each detected feature, calculating a measure of

Abstract: The present invention provides image processing apparatus, an image processing system and an image processing method, whereby the accuracy of evaluation can be improved.

Abstract: Provided herein are systems and methods for scoring a post-treatment tooth position of a patient's teeth. A patient's dentition may be scanned and/or segmented. Raw dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A classifier can identify and/or output the post-treatment tooth position of the patient's teeth.

Abstract: A global multi-label generating system is operable to receive a plurality of medical scans and a corresponding plurality of global labels that each correspond to one of a set of abnormality classes. A computer vision model is generated by training on the medical scans and the global labels. Probability matrix data, which includes a set of image patch probability values that each indicate a probability that a corresponding one of the set of abnormality classes is present in each of a set of image patches, is generated by performing an inference function that utilizes the computer vision model on a new medical scan. Global probability data that includes a set of global probability values each indicating a probability that a corresponding one of the set of abnormality classes is present in the new medical scan is generated based on the probability matrix data for transmission to a client device.

Abstract: An apparatus and method for generating learning data for combustion optimization is provided. The apparatus includes a data pre-processor to collect raw data including currently measured real-time data for boiler combustion and previously measured past data for the boiler combustion, and to perform pre-processing for the collected raw data, and a data analyzer to derive learning data from the raw data by analyzing the raw data. An apparatus for combustion optimization includes a management layer to collect currently measured real-time data for boiler combustion, to determine whether to perform combustion optimization, and to determine whether to tune a combustion model and a combustion controller; a data layer to derive learning data from raw data; a model layer to generate the combustion model/controller through the learning data; and an optimal layer to calculate a target value for combustion optimization and to output a control signal according to the calculated target value.

Abstract: Provided is a learning device that can generate a feature deriving device capable of deriving, for an identical object, feature amounts which respectively express a feature of the object in different forms and which are mutually related. This learning device comprises: an acquisition unit that acquires first data and second data, with different forms of the object recorded therein; an encoder that derives a first feature amount from the first data; a conversion unit that converts the first feature amount to a second feature amount; a decoder that generates third data from the second feature amount; and a parameter updating unit that updates, on the basis of a comparison between the second data and the third data, the value of a parameter used in the derivation of the first feature amount, and the value of a parameter used in the generation of the third data.