A hardware environment-based data quantization method includes: parsing a model file under a current deep learning framework to obtain intermediate computational graph data and weight data that are independent of a hardware environment; performing calculation on image data in an input data set through a process indicated by an intermediate computational graph to obtain feature map data; separately performing uniform quantization on the weight data and the feature map data of each layer according to a preset linear quantization method, and calculating a weight quantization factor and a feature map quantization factor (S103); combining the weight quantization factor and the feature map quantization factor to obtain a quantization parameter that makes hardware use shift instead of division; and finally, writing the quantization parameter and the quantized weight data to a bin file according to a hardware requirement so as to generate quantized file data (S105).

A developed impact mark analysis apparatus includes: an image acquisition unit configured to obtain at least one first image by photographing impact marks that are developed, and to obtain a second image of impact marks at a crime scene that are developed from evidence at the crime scene; an outliner configured to outline the at least one first image to obtain at least one first outline image, and to outline the second image to obtain a second outline image; a database configured to store the first outline image corresponding to related tool characteristic information; a matching unit configured to search the database for the first outline image determined to be similar to the second outline image and match them with each other; a display unit; and a user input unit.

A method for quantifying disorder and extracting a corresponding numerical value of an order parameter from contrast analysis applied to optical images acquired of the solar photosphere. Temporal variation of the order parameter may be utilized to predict events such as solar flares, which have the ability to disrupt both communication systems and satellite orbits. The degree of order of the photosphere may be monitored to predict solar flares and other solar events. The method may utilize a spin-based (Ising/Potts) model of disorder.

A method comprising the steps of obtaining at least one frame of input data from at least one sensor, the frame of input data representative of a real-world environment at a given time. The frame is analysed to determine at least a foveal region within the frame, and at least a method for generating depth information associated with the real-world environment based on the frame of input data is selected. The method is applied to the foveal region to generate depth information associated with the foveal region, and at least the depth information associated with the foveal region is outputted.

Systems and methods for acquiring and inspecting lens images of ophthalmic lenses using one or more cameras to acquire the images of the lenses in a dry state or a wet state. The images are preprocessed and then inputted into an artificial intelligence network, such as a convolutional neural network (CNN), to analyze and characterize for type of lens defects. The artificial intelligence network identifies defect regions on the images and output defect categories or classifications for each of the images based in part on the defect regions.

An image processing method according to the invention includes obtaining a ground truth image teaching a cell region occupied by a cell in an original image for each of a plurality of the original images obtained by bright-field imaging of the cell, generating a reverse image by reversing luminance of the original image at least for the cell region based on each original image, and constructing a classification model by performing machine learning using a set of the original image and the ground truth image corresponding to the original image and a set of the reverse image and the ground truth image corresponding to the original image as a basis of the reverse image respectively as training data.

An electronic apparatus and an object information recognition method by using touch data thereof are provided. Touch sensing is performed in the case where no object touches a touch panel to obtain a specific background frame through the touch panel. A current touch sensing frame is obtained through the touch panel. Touch background data of a plurality of first frame cells in the specific background frame is respectively subtracted from touch raw data of a plurality of second frame cells in the current touch sensing frame to obtain a background removal frame including a plurality of cell values. The background removal frame is transformed into a touch sensing image. The touch sensing image is inputted to a trained neural network model to recognize object information of a touch object.

The present invention discloses a method and an apparatus for extracting mountain landscape buildings based on high-resolution remote sensing images. The method comprises: segmenting a remote sensing image, and extracting non-vegetation areas from the remote sensing image by using NDVI; segmenting the non-vegetation areas, and extracting building areas by using NDBI; segmenting the building areas again, and calculating a normalized difference build shadow index NSBI of each patch; calculating NSBI separator of each patch in the non-vegetation areas and setting a separator threshold, and extracting landscape building areas based on the threshold. In the present invention, by introducing a near infrared band in the remote sensing image spectrum, in which there is a significant difference between shadows and non-shadows, the influence of large shadow areas in mountainous shady areas in the remote sensing image on the result of extraction is reduced.

Embodiments of the present disclosure disclose a method and apparatus for positioning an autonomous vehicle. The method includes: matching a current point cloud projected image of a first resolution with a map of the first resolution to generate a first histogram filter based on the matching result; determining at least two first response areas in the first histogram filter based on a probability value of an element in the first histogram filter; generating a second histogram filter based on a result of matching a current point cloud projected image of a second resolution with a map of the second resolution and the at least two first response areas, the first resolution being less than the second resolution; and calculating a weighted average of probability values of target elements in the second histogram filter to determine a positioning result of the autonomous vehicle in the map of the second resolution.

A gauze detection system is provided that is capable of effectively detecting a gauze pad in the patient's body during surgery without applying special processing to the gauze pad. A gauze detection system 100, includes: an image input section 1 to input a taken image of an operative field; a determination section 2 to determine whether a determination target region contains a feature of a gauze image by image processing, the region having a predetermined size in an input image; and a determination result output section 3 to report detection of a gauze pad in the input image in a case of the determination target region being determined by the determination section 2 to contain the feature of the gauze image.