Abstract: The invention relates to a method and a device for producing a thermal map of an area, wherein the thermal map is generated by a combination of two thermal images of different properties, and both thermal images comprise pixels associated with the area and have been recorded by satellites. The two thermal images are recorded at different times using different recording devices. Furthermore, a radiometric precision of the first thermal image is higher than that of the second thermal image, and a spatial resolution of the second thermal image is higher than that of the first thermal image. The two thermal images are used to determine a measurement value offset of a first pixel group belonging to the second thermal image and spatially associated with the area, and then corrected absolute measurement values of the pixels belonging to the second thermal image and spatially associated with the area are determined. A precise thermal map of the area is then created on this basis.

Abstract: An image processing apparatus includes: an obtaining unit configured to obtain album information forming an album; a display control unit configured to display an editing screen for editing the album on a display based on the album information; and a recommendation unit configured to make a removal recommendation in the editing screen based on the album information, the removal recommendation being a recommendation to remove an image from an image group used in the album.

Abstract: A computer implemented method of automatically controlling an enclosed crop growing device, comprising: feeding an image of the enclosed crop growing device into a machine learning model, wherein the image simultaneously depicts a plurality of crops of a plurality of different types at a plurality of different growth stages arranged at a plurality of predefined crop growing locations within the enclosed crop growing device, obtaining as an outcome of the ML model, a plurality of parameters for setting a plurality of environmental control components that control an environment of the enclosed crop growing device, and automatically adjusting the plurality of environmental control components according to the plurality of parameters.

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.

Abstract: A farming machine includes one or more image sensors for capturing an image as the farming machine moves through the field. A control system accesses an image captured by the one or more sensors and identifies a distance value associated with each pixel of the image. The distance value corresponds to a distance between a point and an object that the pixel represents. The control system classifies pixels in the image as crop, plant, ground, etc. based on depth information in in the pixels. The control system generates a labelled point cloud using the labels and depth information, and identifies features about the crops, plants, ground, etc. in the point cloud. The control system generates treatment actions based on any of the depth information, visual information, point cloud, and feature values. The control system actuates a treatment mechanism based on the classified pixels.

Abstract: The invention relates to the technical field of image processing, and in particular to a method, a system and a device for acquiring a seaweed bed area, and a storage medium. In the acquisition method, firstly, an initial seaweed bed image with fog is firstly subjected to the fog thickness removal which is convenient for quick calculation, checking whether the quality of the fog thickness removal chart meets the requirements. If the quality of fog thickness removal chart is not up to the standard, a more detailed fog concentration removal method is used to improve the image quality of the initial seaweed bed image until the fog concentration removal chart is up to the standard and then feature extraction is performed. If the quality of fog thickness removal chart is up to the standard, the feature extraction is performed directly.

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: Techniques are described herein for using artificial intelligence to predict crop yields based on observational crop data. A method includes: obtaining a first digital image of at least one plant; segmenting the first digital image of the at least one plant to identify at least one seedpod in the first digital image; for each of the at least one seedpod in the first digital image: determining a color of the seedpod; determining a number of seeds in the seedpod; inferring, using one or more machine learning models, a moisture content of the seedpod based on the color of the seedpod; and estimating, based on the moisture content of the seedpod and the number of seeds in the seedpod, a weight of the seedpod; and predicting a crop yield based on the moisture content and the weight of each of the at least one seedpod.

Abstract: A method for characterization of coronary plaque tissue data and perivascular tissue data using image data gathered from a computed tomography (CT) scan along a blood vessel, the image information including radiodensity values of coronary plaque and perivascular tissue located adjacent to the coronary plaque, the method comprising quantifying radiodensity in regions of coronary plaque, quantifying, radiodensity in at least one region of corresponding perivascular tissue adjacent to the coronary plaque, determining gradients of the quantified radiodensity values within the coronary plaque and the quantified radiodensity values within the corresponding perivascular tissue, and determining a ratio of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue; and characterizing the coronary plaque by analyzing a gradient of the quantified radiodensity values in the coronary plaque and the corresponding perivascular, and/or the ratio of the coronary plaque radiodensity

Abstract: A system includes: a training dataset including first objects of a first modality and second objects of a second modality different than the first modality, where the second objects include text that is descriptive of the first objects; a first matrix including first relevance values indicative of relevance between the first objects and the second objects, respectively; a second matrix including second relevance values indicative of relevance between the second objects and the first objects, respectively; and a training module configured to: assign ones of the second objects to bins based on distances between the ones of the objects and a query; determine a ranking measure based on a number of the ones of the second objects assigned to the bins; determine losses based on the ranking measure and the first and second matrices; determine a final loss based on the losses; train embedding functions based on the final loss.

Abstract: The present art applies a unique conceptual model that uses image statistics of top-of-atmosphere reflectance to assess and then reverse atmospheric effects. The workflow first calibrates the method for an Earth observation satellite pre-operationally. Then, for operational conversion of each image of the satellite, the degree of atmospheric effect is mapped across an image to be converted and then reversed to deliver surface reflectance data for each pixel of the image. The resulting surface reflectance images have haze and thin clouds removed, are clarified for viewing and appropriate for automated analyses.

Abstract: Disclosed are a machine model update method and apparatus, a medium, and a device. The method includes: obtaining first hard example samples of a machine model and attribute information of the first hard example samples; determining category-based data distribution information of the first hard example samples according to the attribute information of the first hard example samples; determining second hard example samples of the machine model in current machine learning according to learning requirement information of the machine model for categories of the first hard example samples and the data distribution information; performing, according to learning operation information corresponding to the machine model, a learning operation on the machine model by using the second hard example samples; and updating the machine model based on a learning result of the machine model. The present disclosure helps efficiently implement a machine learning lifecycle, and reduce the cost of machine learning lifecycle.

Abstract: An image generating apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to successively generate Magnetic Resonance (MR) images in a plurality of temporal phases by using a first reconstruction method. The processing circuitry is configured to determine a first temporal phase before MR images in all the temporal phases during a predetermined time period are generated. The processing circuitry is configured to generate an MR image in the first temporal phase determined by the determining unit, by using a second reconstruction method having a larger processing load than the first reconstruction method.

Abstract: A vehicular driving assistance system includes a camera and a LIDAR sensor disposed at a vehicle. An electronic control unit (ECU) includes at least one data processor. The system, responsive to processing of image data captured by the camera, determines a lane marker ahead of the vehicle. The system, responsive to processing of sensor data captured by the LIDAR sensor, determines a first portion of the lane marker forward of the vehicle and determines a second portion of the lane marker rearward of the vehicle. The system interpolates the lane marker between the first portion and the second portion. The system, responsive to the determined lane marker and the interpolated lane marker, estimates location of a front wheel of the vehicle relative to the lane marker.

Abstract: Systems/techniques that facilitate multi-layer image registration are provided. In various embodiments, a system can access a first image and a second image. In various aspects, the system can generate, via execution of a machine learning model on the first image and the second image, a plurality of registration fields and a plurality of weight matrices that respectively correspond to the plurality of registration fields. In various instances, the system can register the first image with the second image based on the plurality of registration fields and the plurality of weight matrices.

Abstract: An information processing apparatus comprises an image processing unit configured to acquire a plurality of images representing results of executing predetermined image processing for target data using a plurality of candidate values of a parameter associated with the predetermined image processing; a selection unit configured to select a plurality of representative images from the plurality of images; a display control unit configured to display the plurality of selected representative images on a display unit; an operation unit configured to accept an operation input to the plurality of displayed representative images; and a statistical processing unit configured to decide at least one value from the plurality of candidate values based on a result of statistical processing of information obtained by the accepted operation.

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: Systems and methods for visuo-tactile object pose estimation are provided. In one embodiment, a computer implemented method includes receiving image data, depth data, and tactile data about the object in the environment. The computer implemented method also includes generating a visual estimate of the object that includes an object point cloud. The computer implemented method further includes generating a tactile estimate of the object that includes a surface point cloud based on the tactile data. The computer implemented method yet further includes estimating a pose of the object based on the visual estimate and the tactile estimate by fusing the object point cloud and the surface point cloud in a 3D space. The pose is a six-dimensional pose.

Abstract: The present invention relates to a method by which a computing means monitors a harbor, and a harbor monitoring method, according to one aspect of the present invention, comprises the steps of: acquiring a harbor image; generating a segmentation image corresponding to the harbor image; generating a display image corresponding to the harbor image and having a first view attribute; generating a conversion segmentation image, which corresponds to the segmentation image and has a second view attribute different from the first view attribute; matching the display image so as to generate a panoramic image; matching the conversion segmentation image so as to generate a matching segmentation image; calculating ship mooring guide information on the basis of the matching segmentation image; and outputting the mooring guide information together with the panoramic image.

Abstract: Systems and methods for acquiring and inspecting lens images of ophthalmic lenses using one or more cameras to acquire the images of the lenses in a dry state or a wet state. The images are preprocessed and then inputted into an artificial intelligence network, such as a convolutional neural network (CNN), to analyze and characterize for type of lens defects. The artificial intelligence network identifies defect regions on the images and output defect categories or classifications for each of the images based in part on the defect regions.