Provided herein are systems and methods for identifying a recommended treatment to land. An example method comprises: identifying a plurality of sites of interest in the land; segmenting the land into a plurality of areas based on ownership information and ecological information; identifying, for a particular area of the plurality of areas, a plurality of potential treatments; calculating a performance metric for each of the plurality of potential treatments to obtain a plurality of performance metrics for the particular area, wherein each performance metric of the plurality of performance metrics is calculated based on one or more sites of interest located in the particular area; and selecting the recommended treatment for the particular area of the land from the plurality of potential treatments based on the plurality of performance metrics.

Systems and methods for determining groundwater levels based upon crop classification are provided. A set of aerial images are collected via satellite, manned aircraft or drones. They are filtered by a time domain, and a sufficiently high-resolution image is selected. If there isn't an image with sufficient resolution, a series of lower resolution images may be combined to generate a ‘fused’ image suitable for analysis. The image is then subjected to pre-processing. Crop boundaries within the image are determined, and areas outside of the crop boundary are masked off. The resulting image is subjected to a sliding window algorithm to generate discrete “patches” of the image suitable for analysis by a trained neural network. The neural network generated a classification for the crop. This data may be combined with surface water data, precipitation data, and weather pattern data to determine groundwater levels.

An image processing method and apparatus, a device, and a storage medium. The image processing method includes: performing preliminary segmentation recognition on a raw image by using a first segmentation model to obtain a candidate foreground image region and a candidate background image region of the raw image; recombining the candidate foreground image region, the candidate background image region, and the raw image to obtain a recombined image, pixels in the recombined image being in a one-to-one correspondence with pixels in the raw image; and performing region segmentation recognition on the recombined image by using a second segmentation model to obtain a target foreground image region and a target background image region of the raw image.

A microscope comprises a microscope stand, a camera for recording microscope images and a computing device, which is configured to carry out image processing of the recorded microscope images. The computing device is configured to: define relevant image structures; localize relevant image structures in the microscope images; derive stitching parameters from locations of the relevant image structures; and create a result image with the aid of the microscope images, with the stitching parameters being taken into account. Moreover, a corresponding method is described.

An image processing apparatus includes a unit (input unit) configured to acquire image data and depth information corresponding to the image data, a unit (layer division image generation unit) configured to generate layer division image data based on the depth information by dividing the image data into a plurality of layers depending on a subject distance, and a unit (output unit) configured to output the layer division image data. The layer division image data includes image data of a first layer including image data corresponding to a subject at a subject distance less than a first distance, and image data of a second layer including image data corresponding to a subject at a subject distance larger than or equal to the first distance. The first distance changes based on the depth information.

Systems and methods for scheduling environment perception-based data offloading for numerous connected vehicles are disclosed. In one embodiment, a method for offloading data includes capturing an image of a view of interest from a vehicle, segmenting the image into a plurality of blocks, and determining a scheduling priority for each of one or more blocks among the plurality of blocks based on block values, wherein the block values relate to one or more objects of interest contained in each of the one or more blocks. The method further includes offloading, from the vehicle to a server, one or more blocks based on the scheduling priority of the one or more blocks.

The present disclosure provides a method and a device for retina image recognition, an electronic equipment, and a storage medium, the method including: acquiring a retinal image; classifying the retinal image by using a first neural network model to obtain an image classification result; if the image classification result meets a preset condition, segmenting the retinal image by using a second neural network model to obtain an image segmentation result; generating a recognition result of the retinal image according to the image segmentation result and in combination with a decision tree model.

A lane line recognition method, a device and a storage medium are provided. The position information of lane lines is determined by first detecting a current frame image collected by a vehicle and determining a plurality of detection frames where the lane lines in the current frame image are located, determining a connection area according to the position information of the plurality of detection frames where the connection area includes the lane lines, and then performing edge detection on the connection area and determining the position information of the lane lines in the connection area. That is to say, the position information of the lane lines is obtained by first dividing the current frame image into a plurality of detection frames, then connecting the detection frames to obtain the connection area including the lane lines, and then performing edge detection on the connection area.

An image inpainting method is provided. The image inpainting method includes determining a missing region in an original image, generating an input image to be reconstructed from the original image, based on the missing region, obtaining a mask image indicating the missing region, determining whether to extract a structural feature of the missing region, based on an attribute of the missing region, obtaining structure vectors each consisting of one or more lines and one or more junctions by applying the input image and the mask image to a first model for extracting a structural feature of the input image, and obtaining an inpainted image in which the missing region in the input image is reconstructed by applying the input image, the mask image, and a structure vector image converted from the structure vectors to a second model for reconstructing the input image.

Described herein is a method and system for automated, context sensitive and non-intrusive insertion of consumer-adaptive content in video. It assesses ‘context’ in the video that a consumer is viewing through multiple modalities and metadata about the video. The method and system described herein analyzes relevance for a consumer based on multiple factors such as the profile information of the end-user, history of the content, social media and consumer interests and professional or educational background, through patterns from multiple sources. The system also implements local-context through search techniques for localizing sufficiently large, homogenous regions in the image that do not obfuscate protagonists or objects in focus but are viable candidate regions for insertion for the intended content. This makes relevant, curated content available to a user in the most effortless manner without hampering the viewing experience of the main video.