The invention provides a multi-attribute image aesthetics evaluation system based on attention mechanism. Using machinelearning methods, A composite neural network model is trained by using large-scale photograph dataset and corresponding comment information, this model can extract the multi-attribute aesthetic features of image effectively by convolution operation, the image features are extracted from the multi-attribute feature extraction network of the model, Features are further processed in the channel and spatial attention network, and finally the final comments are generated in the language generation network through the long-short memory network unit. The model can automatically output comments of different attributes according to the image characteristics. When an image is inputted, the generated model considers the characteristics of the image from different attributes and evaluates the aesthetic quality of the image in natural language. The method is easily realized by software, and the invention can be widely applied to computer vision, image evaluation and the like.
A method of determining a distance to be walked by a delivery vehicle driver including providing a satellite image that has an image of a building to which an item is to be delivered and an image of a street adjacent to the building. The method further includes defining a path, within the image, that corresponds to a path that the delivery vehicle driver will walk when delivering the item to the building. The method also includes the step of determining a length of the path.
A method is described to obtain a binary image from the print-and-scan process to best match the known original. A point-spread function (PSF) of the PAS process is first obtained from its knife-edge responses, and deblurring is carried out on the scanned images using deconvolution. After image deskewing and preliminary registration, a supervised adaptive thresholding procedure is utilized to binarize the scanned image such that a measure of difference (e.g. the Euclidean distance) between the original and binarized images is minimized. The supervised adaptive thresholding procedure divides the scanned images into many rectangular sub-images. Otsu's method is used to find a starting threshold for each scanned sub-image. An optimal threshold is found around the Otsu's threshold via iterative search to minimize the measure of difference between the original sub-image and scanned sub-image. The sub-images are binarized using the optimal threshold. This method may be used in document authentication.
For efficient detection or observation of a skindisease, especially skincancer, methods for examining the skin (2) of a subject (3) and an associated device (1) are specified. Accordingly a camera element (20) is provided, by means of which an image (B, B′) of an area of skin (35) is recorded. The image (B, B′) is fed to an image evaluation unit (30) which uses electronic pattern recognition based on at least one prespecified selection rule (A) to analyze the image (B, B′) for the occurrence of suspect skin marks (37a, 37b) with, on detection of a suspect skin mark (37a, 37b), its location being determined and displayed.