Abstract: A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One general aspect includes an intelligent irrigation system including one or more of a sprinkler and one or more processors configured to: obtain imagery data regarding a plant; determine a current life cycle stage of the plant based on the imagery data, determine a plant crop coefficient for the plant based on the current life cycle stage of the plant, determine irrigation water output for the plant based on the plant crop coefficient, potential evapotranspiration calculated using solar radiation, temperature, wind speed and/or any other factors, and control the sprinkler to irrigate the plant based on the irrigation water output determined for the plant.

Abstract: A method and apparatus for depth estimation of a monocular image, and a storage medium are provided. The method includes: obtaining, through a depth estimation neural network, a global feature of a monocular image according to absolute features of preset regions and relative features among the preset regions in the monocular image; and obtaining a predicted depth map of the monocular image according to the global feature, and the absolute features of preset regions and relative features among the preset regions in the monocular image.

Abstract: A method for image processing comprises: acquiring first, second, third, and fourth optical flow maps of t-th to (t?1)-th frame images, t-th to (t+1)-th frame images, (t+1)-th to t-th frame images, and (t+1)-th to (t+2)-th frame images, respectively, wherein t is an integer; determining first and second interpolation optical flow maps according to the first and second optical flow maps, and the third and fourth optical flow maps, respectively; determining a first interpolation frame image according to the first interpolation optical flow map and the t-th frame image, and a second interpolation frame image according to the second interpolation optical flow map and the (t+1)-th frame image; and fusing the first and second interpolation frame images to obtain an interpolation frame image to be interpolated between the t-th and (t+1)-th frame images. The embodiment of the present disclosure is capable of improving the accuracy of the obtained interpolation frame image.

Abstract: An image processing method and apparatus, an electronic device, and a storage medium are provided. The method includes: obtaining an infrared image and a color image for the same object in a predetermined scenario, and a first depth map corresponding to the color image; obtaining a first optical flow between the color image and the infrared image; and performing first optimization processing on the first depth map by using the first optical flow to obtain an optimized second depth map corresponding to the color image.

Abstract: A dual-view angle image calibration method includes: performing feature matching on a first image pair to obtain a first feature point pair set, the first image pair including two images respectively photographed corresponding to two different view angles of a same scene; obtaining multiple different first fundamental matrices of the first image pair at least according to the first feature point pair set, and obtaining first image deformation information indicating relative deformation between the first image pair after mapping transformation is performed through the first fundamental matrices and the first image pair before the mapping transformation is performed; determining a first optimization fundamental matrix from the multiple first fundamental matrices at least according to the first image deformation information; and calibrating the first image pair according to the first optimization fundamental matrix.

Abstract: A method and apparatus for estimating the depth of a binocular image, a device, a program, and a medium are provided. The method includes: inputting a to-be-processed binocular image into a predetermined neural network to obtain a parallax image and a confidence score image thereof, where a discrete parameter of each pixel in a preset distribution in the parallax image has a negative correlation with a confidence score of the pixel; and obtaining a depth image of the to-be-processed binocular image according to the parallax image.

Abstract: In various embodiments, information regarding weather sensing devices in an observation network can be collected. Value differences such as root-mean-square errors (RMSEs) can then be calculated for values of a weather variable based on the collected information. A relationship between the value differences and distances among the weather sensing devices can be determined. A pre-determined value difference for the weather variable such as a pre-determined RMSE for the weather variable can be obtained. A distance for positioning two weather sensing devices in the observation network can be determined based on the relationship and the pre-determined value difference. One or more instructions may be issued based on the determined distance for a configuration of the observation network.

Abstract: A video processing method includes: a convolution parameter corresponding to a frame to be processed in a video sequence is acquired, the convolution parameter including a sampling point of a deformable convolution kernel and a weight of the sampling point; and denoising processing is performed on the frame to be processed based on the sampling point of the deformable convolution kernel and the weight of the sampling point to obtain a denoised video frame.

Abstract: An image processing method includes: obtaining an original photographed image and original depth information of at least one pixel point in the original photographed image; determining blurring degree data of each of the at least one pixel point in the original photographed image according to blurring parameter data and the original depth information of the at least one pixel point; and performing blurring processing on the original photographed image according to the blurring degree data of the at least one pixel point to generate a post-processing result image.

Abstract: The present disclosure relates to image processing method and apparatus, and storage medium. The method includes: obtaining multiple original images which are collected by a Time of Flight (TOF) sensor in the same exposure process and have a signal-noise rate lower than a first numerical value, where phase parameter values corresponding to same pixel points in the multiple original images are different; and performing optimization processing on the multiple original images by means of a neural network to obtain depth maps corresponding to the multiple original images, where the processing includes at least one convolution processing and at least one nonlinear function mapping processing. Embodiments of the present disclosure may effectively recover high-quality depth information from the original images.

Abstract: Image blurring methods and apparatuses, storage media, and electronic devices can include: obtaining a main image and a secondary image obtained by photographing the same object with a dual-lens camera; obtaining depth data and depth confidence degree data according to the main image and the secondary image, the depth data indicating depth values of corresponding pixel points in the main image and the secondary image, and the depth confidence degree data indicating confidence degrees of the depth values in the depth data; correcting at least one depth value in the depth data according to the depth confidence degree data; and blurring the main image according to corrected depth data.

Abstract: An image processing method and apparatus, an electronic device, and a storage medium are provided. The method includes: obtaining an infrared image and a color image for the same object in a predetermined scenario, and a first depth map corresponding to the color image; obtaining a first optical flow between the color image and the infrared image; and performing first optimization processing on the first depth map by using the first optical flow to obtain an optimized second depth map corresponding to the color image.

Abstract: A method and apparatus for estimating the depth of a binocular image, a device, a program, and a medium are provided. The method includes: inputting a to-be-processed binocular image into a predetermined neural network to obtain a parallax image and a confidence score image thereof, where a discrete parameter of each pixel in a preset distribution in the parallax image has a negative correlation with a confidence score of the pixel; and obtaining a depth image of the to-be-processed binocular image according to the parallax image.

Abstract: A method and apparatus for depth estimation of a monocular image, and a storage medium are provided. The method includes: obtaining, through a depth estimation neural network, a global feature of a monocular image according to absolute features of preset regions and relative features among the preset regions in the monocular image; and obtaining a predicted depth map of the monocular image according to the global feature, and the absolute features of preset regions and relative features among the preset regions in the monocular image.

Abstract: A dual-view angle image calibration method includes: performing feature matching on a first image pair to obtain a first feature point pair set, the first image pair including two images respectively photographed corresponding to two different view angles of a same scene; obtaining multiple different first fundamental matrices of the first image pair at least according to the first feature point pair set, and obtaining first image deformation information indicating relative deformation between the first image pair after mapping transformation is performed through the first fundamental matrices and the first image pair before the mapping transformation is performed; determining a first optimization fundamental matrix from the multiple first fundamental matrices at least according to the first image deformation information; and calibrating the first image pair according to the first optimization fundamental matrix.

Abstract: Image blurring methods and apparatuses, storage media, and electronic devices can include: obtaining a main image and a secondary image obtained by photographing the same object with a dual-lens camera; obtaining depth data and depth confidence degree data according to the main image and the secondary image, the depth data indicating depth values of corresponding pixel points in the main image and the secondary image, and the depth confidence degree data indicating confidence degrees of the depth values in the depth data; correcting at least one depth value in the depth data according to the depth confidence degree data; and blurring the main image according to corrected depth data.

Abstract: An image processing method includes: obtaining an original photographed image and original depth information of at least one pixel point in the original photographed image; determining blurring degree data of each of the at least one pixel point in the original photographed image according to blurring parameter data and the original depth information of the at least one pixel point; and performing blurring processing on the original photographed image according to the blurring degree data of the at least one pixel point to generate a post-processing result image.