An image processing method is provided. An image including a target object is obtained. Image segmentation is performed on the image. A mask image of the target object is determined based on the image segmentation performed on the image. A first feature extraction is performed on the image. A first predicted value associated with the target object is determined based on a first feature extraction result of the first feature extraction performed on the image. A second feature extraction is performed on the mask image. A second predicted value associated with the target object is determined based on a second feature extraction result of the second feature extraction performed on the mask image. A target predicted value associated with the target object is determined according to the first predicted value and the second predicted value.

A water area object detection system includes an imager to capture an image around a hull, and a controller configured or programmed to perform a control to detect a feature point corresponding to an object in the image together with a distance to the feature point based on the image captured by the imager to create a water area map in which an object presence range including a likelihood that the object is present is set around the feature point. The controller is configured or programmed to reduce the object presence range as the distance from the imager to the feature point decreases, and set a size of the object presence range to a lower limit when the distance from the imager to the feature point is equal to or less than a predetermined distance.

A water area object detection system includes an imager to capture an image around a hull, and a controller configured or programmed to perform a control to detect a feature point corresponding to an object in the image together with a distance to the feature point based on the image captured by the imager to create a water area map in which an object presence range including a likelihood that the object is present is set around the feature point. The controller is configured or programmed to reduce the object presence range as the distance from the imager to the feature point decreases, and set a size of the object presence range to a lower limit when the distance from the imager to the feature point is equal to or less than a predetermined distance.

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

A LiDAR-based object detection method includes clustering a point cloud acquired from LiDAR, selecting a to-be-divided cluster among clusters generated in the clustering, and selecting division points according to a geometrical feature formed with adjacent points from among points belonging to the to-be-divided cluster, and dividing the to-be-divided cluster based on a representative point determined by at least some of the division points.

A LiDAR-based object detection method includes clustering a point cloud acquired from LiDAR, selecting a to-be-divided cluster among clusters generated in the clustering, and selecting division points according to a geometrical feature formed with adjacent points from among points belonging to the to-be-divided cluster, and dividing the to-be-divided cluster based on a representative point determined by at least some of the division points.

Methods, systems, and computer programs are presented for adding new features to a network service. A method includes receiving an image depicting an object of interest. A category set is determined for the object of interest and an image signature is generated for the image. Using the category set and the image signature, the method identifies a set of publications within a publication database and assigns a rank to each publication. The method causes presentation of the ranked list of publications at a computing device from which the image was received.

A signal processing device and an image display apparatus including the same are disclosed. The signal processing device includes a scaler configured to scale input images of various resolutions to a first resolution, a resolution enhancement processor configured to perform learning on the input images and to output a residual image of the first resolution, and an image output interface configured to output an output image of the first resolution based on a scaling image from the scaler and the residual image from the resolution enhancement processor, and the image output interface changes a weight and an application strength of the residual image according to the area of the input image.

A signal processing device and an image display apparatus including the same are disclosed. The signal processing device includes a scaler configured to scale input images of various resolutions to a first resolution, a resolution enhancement processor configured to perform learning on the input images and to output a residual image of the first resolution, and an image output interface configured to output an output image of the first resolution based on a scaling image from the scaler and the residual image from the resolution enhancement processor, and the image output interface changes a weight and an application strength of the residual image according to the area of the input image.