Methods, systems, and articles of manufacture, including computer program products, are provided for image cleanup. In some embodiments, there is provide a method which may include subsampling a first image to a first level image of a multiscale transform; performing, based on a machine learning model, an identification of a foreground portion of the first level image and a background portion of the first level image; generating, based on the identification of the foreground portion and the background portion, a first mask; upscaling the first mask to a resolution corresponding to the first image depicting the foreground item; applying the upscaled first mask to the first image to form a second image depicting the foreground item; and providing the second image depicting the foreground item to a publication system. Related systems and articles of manufacture, including computer program products, are also provided.

A method of processing an image of a scene including a road acquired by a vehicle-mounted camera to generate boundary data indicative of a boundary of an image region which represents an unoccupied area of the road, comprising: generating an LL sub-band image of an N.sup.th level of an (N+1)-level discrete wavelet transform, DWT, decomposition of the image by iteratively low-pass filtering and down-sampling the image N times, where N is an integer equal to or greater than one; generating a sub-band image of an (N+1).sup.th level by high-pass filtering the LL sub-band image of the N.sup.th level, and down-sampling a result of the high-pass filtering, such that the sub-band image of the (N+1).sup.th level has a pixel region having substantially equal pixel values representing the unoccupied area of the road in the image; and generating the boundary data by determining a boundary of the pixel region.

A method of processing an image of a scene including a road acquired by a vehicle-mounted camera to generate boundary data indicative of a boundary of an image region which represents an unoccupied area of the road, comprising: generating an LL sub-band image of an N.sup.th level of an (N+1)-level discrete wavelet transform, DWT, decomposition of the image by iteratively low-pass filtering and down-sampling the image N times, where N is an integer equal to or greater than one; generating a sub-band image of an (N+1).sup.th level by high-pass filtering the LL sub-band image of the N.sup.th level, and down-sampling a result of the high-pass filtering, such that the sub-band image of the (N+1).sup.th level has a pixel region having substantially equal pixel values representing the unoccupied area of the road in the image; and generating the boundary data by determining a boundary of the pixel region.

The embodiments of the present disclosure provide a visual positioning method, the method may include obtaining a positioning image collected by an imaging device; obtaining a three-dimensional (3D) point cloud map associated with an area where the imaging device is located; determining a target area associated with the positioning image from the 3D point cloud map based on the positioning image; and

determining positioning information of the imaging device based on the positioning image and the target area.

The embodiments of the present disclosure provide a visual positioning method, the method may include obtaining a positioning image collected by an imaging device; obtaining a three-dimensional (3D) point cloud map associated with an area where the imaging device is located; determining a target area associated with the positioning image from the 3D point cloud map based on the positioning image; and

determining positioning information of the imaging device based on the positioning image and the target area.

The present disclosure refers to methods and system for classifying rocks according to their minerals from the processing of color images and hyperspectral images. The methods and systems of this disclosure achieve an efficient and low-cost classification of rocks with minerals. In particular, the method uses classification methods that take color images and hyperspectral images and give as a result a probability that the rock or rocks present in said images are suitable or waste.

Methods, systems, and articles of manufacture, including computer program products, are provided for image cleanup. In some embodiments, there is provide a method which may include subsampling a first image to a first level image of a multiscale transform; performing, based on a machine learning model, an identification of a foreground portion of the first level image and a background portion of the first level image; generating, based on the identification of the foreground portion and the background portion, a first mask; upscaling the first mask to a resolution corresponding to the first image depicting the foreground item; applying the upscaled first mask to the first image to form a second image depicting the foreground item; and providing the second image depicting the foreground item to a publication system. Related systems and articles of manufacture, including computer program products, are also provided.

An image recognition method is provided, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. An implementation includes: performing five-sense-organ recognition on a preprocessed human face image and marking positions of the human facial five sense organs in the human face image, to obtain the marked human face image; determining human face images at multiple scales of the marked human face image, inputting the human face images of multiple scales into a backbone network model, and performing feature extraction, to obtain a wrinkle feature of the human face image at each of the multiple scales; and fusing the wrinkle feature at each scale that is located in a same area of the human face image, to obtain a wrinkle recognition result of the human face image.

An image recognition method is provided, which is related to a technical field of artificial intelligence, and in particular, to a technical field of image processing. An implementation includes: performing five-sense-organ recognition on a preprocessed human face image and marking positions of the human facial five sense organs in the human face image, to obtain the marked human face image; determining human face images at multiple scales of the marked human face image, inputting the human face images of multiple scales into a backbone network model, and performing feature extraction, to obtain a wrinkle feature of the human face image at each of the multiple scales; and fusing the wrinkle feature at each scale that is located in a same area of the human face image, to obtain a wrinkle recognition result of the human face image.

This application discloses a method and apparatus for improving video target detection performance in surveillance edge computing. This application relates to the technical field of digital image processing. The method includes: determine the size of multiple rectangular sliding windows for scanning according to the input size of the object detection neural network algorithm and the size of the original input image; when each frame is detected, the original input image and the sub-images in each rectangular sliding window are scaled in different proportions; the resolution of the scaled original input image is lower than that of the scaled sliding window sub-images; stitching the scaled images into a rectangular image and using it as a detection input image; the detection is performed by an object detection neural network algorithm corresponding to the size of the detection input image.