A shooting score identifying method and device, and an electronic device are provided. The method includes the steps of capturing a picture of a shooting operation on target paper to acquire a shooting image formed on the target paper; forming a thermal imaging image of the target paper after the shooting image is acquired; performing target identification on the shooting image to obtain a target image related to a shooting point, and performing direction correction on the shooting point present in the target image to obtain a correction image; and performing interference removal processing on the correction image through the thermal imaging image to obtain an identification image, thereby identifying a shooting score based on the identification image.

A shooting score identifying method and device, and an electronic device are provided. The method includes the steps of capturing a picture of a shooting operation on target paper to acquire a shooting image formed on the target paper; forming a thermal imaging image of the target paper after the shooting image is acquired; performing target identification on the shooting image to obtain a target image related to a shooting point, and performing direction correction on the shooting point present in the target image to obtain a correction image; and performing interference removal processing on the correction image through the thermal imaging image to obtain an identification image, thereby identifying a shooting score based on the identification image.

The present invention discloses a clustering algorithm-based multi-parameter cumulative calculation method for lower limb vascular calcification indexes, including the following steps: firstly carrying out super-pixel segmentation of a CT image, and enabling calcified spots in the CT image to be segmented in each super-pixel region; after the super-pixel segmentation is accomplished, extracting a brightness characteristic value of a super-pixel region where the calcified spots are located by using a Lab color space, and performing edge detection and contour extraction on the calcified spots in the image; and after edge detection and contour extraction, fitting the calcified spots in the image by using a segmented ellipse, and extracting the area of the calcified spots after optimizing an ellipse contour.

The present invention discloses a clustering algorithm-based multi-parameter cumulative calculation method for lower limb vascular calcification indexes, including the following steps: firstly carrying out super-pixel segmentation of a CT image, and enabling calcified spots in the CT image to be segmented in each super-pixel region; after the super-pixel segmentation is accomplished, extracting a brightness characteristic value of a super-pixel region where the calcified spots are located by using a Lab color space, and performing edge detection and contour extraction on the calcified spots in the image; and after edge detection and contour extraction, fitting the calcified spots in the image by using a segmented ellipse, and extracting the area of the calcified spots after optimizing an ellipse contour.

Visual localization method and related apparatus are disclosed. In the method, a first candidate image sequence is determined from image library, the image library being configured to construct electronic map, image frames in the first candidate image sequence being sequentially arranged according to degrees of matching with first image, and the first image being an image collected by a camera; an order of the image frames in the first candidate image sequence is adjusted according to target window to obtain second candidate image sequence, the target window being multiple successive image frames including target image frame and determined from the image library, the target image frame being an image matching with second image, which is collected by the camera before the first image is collected, in the image library; and target posture of the camera when the first image is collected is determined according to the second candidate image sequence.

Visual localization method and related apparatus are disclosed. In the method, a first candidate image sequence is determined from image library, the image library being configured to construct electronic map, image frames in the first candidate image sequence being sequentially arranged according to degrees of matching with first image, and the first image being an image collected by a camera; an order of the image frames in the first candidate image sequence is adjusted according to target window to obtain second candidate image sequence, the target window being multiple successive image frames including target image frame and determined from the image library, the target image frame being an image matching with second image, which is collected by the camera before the first image is collected, in the image library; and target posture of the camera when the first image is collected is determined according to the second candidate image sequence.

An intelligent recognition method of hyperspectral image of parasites in raw fish relates to optical detection technology, and includes step 1: obtaining a hyperspectral image of the raw fish in a wavelength range from 300 to 1100 nm; step 2: extracting a grayscale image of the hyperspectral image at a wavelength value of 437 nm, and obtaining a position range of fish meat in the grayscale image by performing a median filtering process and a binarization process on the grayscale image; step 3: extracting spectral signals of pixel points in the position range of the hyperspectral image, performing a first-order derivative process on the spectral signals, and import the spectral signals after the first-order derivative process into a preset first model, a second model, and a third model for analysis. The method can accurately distinguish the parasite body in the raw fish.

An intelligent recognition method of hyperspectral image of parasites in raw fish relates to optical detection technology, and includes step 1: obtaining a hyperspectral image of the raw fish in a wavelength range from 300 to 1100 nm; step 2: extracting a grayscale image of the hyperspectral image at a wavelength value of 437 nm, and obtaining a position range of fish meat in the grayscale image by performing a median filtering process and a binarization process on the grayscale image; step 3: extracting spectral signals of pixel points in the position range of the hyperspectral image, performing a first-order derivative process on the spectral signals, and import the spectral signals after the first-order derivative process into a preset first model, a second model, and a third model for analysis. The method can accurately distinguish the parasite body in the raw fish.

The present invention relates to a method for acquiring an object information, the method comprising: obtaining an input image acquired by capturing a sea; obtaining a noise level of the input image; when the noise level indicates a noise lower than a predetermined level, acquiring an object information related to an obstacle included in the input image from the input image by using a first artificial neural network, and when the noise level indicates a noise higher than the predetermined level, obtaining a noise-reduced image of which the environmental noise is reduced from the input image by using a second artificial neural network, and acquiring an object information related to an obstacle included in the sea from the noise-reduced image by using the first artificial neural network.

An image processing apparatus includes a light source, an image sensor, and an invisible component remover. The light source emits visible component light and invisible component light to a target object. The image sensor receives reflected light from the target object to detect a visible invisible mixture image including an invisible component and a visible component and an invisible image including an invisible component. The invisible component remover, based on the visible invisible mixture image and the invisible image which have been detected, removes the invisible component from the visible invisible mixture image to generate a visible image. The invisible component remover includes a removal calculator performing a removal calculation process of an invisible component with respect to the visible invisible mixture image, and a noise reducer performing a noise reduction process on at least one of images to be input to and output from the removal calculator.