121 results about "Imaging condition" patented technology

This invention discloses a computer vision-based method for ship traffic situational awareness, comprising: collecting ship traffic data and preprocessing it to obtain a video frame sequence; performing ship target detection and outputting a set of ship detection results; constructing an improved ByteTrack model and outputting a set of trajectory segments; performing a Poisson-Dobernoli hybrid filter to obtain a set of ship tracks; performing coordinate mapping and track element calculation, calculating speed, heading, and position; performing risk event determination and outputting risk event results; and performing graded early warning and interpretable processing to generate early warning information. This invention, through the collaborative processing of the improved ByteTrack model and the Poisson-Dobernoli hybrid filter, achieves graded early warning and interpretable output of risk events for ship targets under complex sea surface imaging conditions.

This invention discloses a method for screening the risk of congenital scoliosis. The method includes: acquiring a back surface image of the subject, which reflects the surface morphological distribution of the back region; preprocessing the back surface image to obtain a standardized surface image to reduce the influence of differences in acquisition posture, imaging conditions, or subject body shape; based on the standardized surface image, constructing surface morphological feature information for congenital scoliosis risk screening, which includes at least the left-right symmetry of the back, the state of spinal midline deviation, or the trend of surface morphological changes; inputting the surface morphological feature information into a pre-trained congenital scoliosis risk screening model to generate congenital scoliosis risk screening results. This invention can achieve objective and consistent congenital scoliosis risk screening.

The application discloses a kind of intelligent detection systems of metal material surface defects, it is related to defect detection technical field, including state curve module, for obtaining the position of metal material along the direction of movement and the change curve of surface state variation, and the position interval of key detection area in the direction of movement;The key detection area at least includes oxide skin thick area, oxide skin thin area and surface state variation intense mutation area of imaging condition conflict;The application makes conflict area into imaging field by time sequence decoupling mode in time, and makes non-target area be in the physical position that imaging interference is less, cooperates special imaging parameter, special analysis rule and closed loop feedback optimization mechanism, overcomes the defect that fixed parameter detection mode is difficult to compatible multi-area conflict imaging demand, improves the reliability and adaptability of metal material surface defect detection under complex surface state.

The optical information reading method can appropriately adjust imaging conditions when analyzing captured images and reading the optical information contained in the images, without considering the environment in which the information to be read is placed. In the case of illuminating a aiming light that serves as a reference for pointing the imaging unit at the optical information to be read, and simultaneously periodically capturing images and analyzing the images to read the optical information contained in the images, the amount of movement of the imaging unit within a certain time range is estimated based on the images captured within that time range, and the imaging conditions are adjusted based on the pixel values ​​of the pixels in the captured images through imaging adjustment processing (212). When it is determined that the target has been determined based on the estimated amount of movement ("Yes" in S101), the imaging conditions are adjusted based on the pixel values ​​of pixels within a predetermined range near the position of the aiming light in the captured images (S102~S106, S113).

This invention provides a dark level correction method and system based on joint constraints of brightness and temperature, belonging to the field of image signal processing technology. The method includes: acquiring a current image frame in the field environment and obtaining the corresponding operating temperature information; determining dark level correction parameters corresponding to the operating temperature information based on a pre-set dark level reference value in the brightness dimension and a pre-set dark level correction component in the temperature dimension; generating a dark level correction value for the current image frame based on the dark level correction parameters; and applying the dark level correction value to the pixel data of the current image frame to complete the dark level correction process. This invention achieves stable suppression of dark level drift under different operating temperatures and continuous imaging conditions, thereby improving the brightness consistency of dark areas and reducing the probability of color cast in dark areas.

The present application provides a technology that can perform monitoring processing on a plurality of monitoring targets based on more appropriate image data. A substrate processing method of the present application includes: a holding step of moving a substrate into an inside of a chamber and holding the substrate; a supplying step of supplying a fluid to the substrate in the inside of the chamber; a photographing step (S12) of sequentially photographing the inside of the chamber by a camera to acquire image data; a condition setting step (S11) of determining a monitoring target and changing an image condition based on the monitoring target; and a monitoring step (S13) of performing monitoring processing on the monitoring target based on the image data having the image condition corresponding to the monitoring target.

The application discloses a remote sensing image binary change detection network based on frequency domain feature interaction. The network is aimed at the pseudo change problem caused by light, season, sensor noise and complex background in high-resolution remote sensing images, and a binary change detection network composed of a hybrid encoder, a frequency domain feature interaction module, a difference feature enhancement module and a lightweight decoder is constructed. The hybrid encoder extracts multi-scale spatial detail features through a spatial encoder and extracts global semantic context through a semantic encoder, so as to give consideration to local boundary texture and advanced semantic information. The frequency domain feature interaction module converts the double-time-phase features into the frequency domain, generates a content-aware filter according to the semantic context, and applies asymmetric frequency domain filtering to the two time phases respectively, so as to suppress the pseudo change caused by the difference in imaging conditions. The difference feature enhancement module further extracts robust change difference features through multi-scale spatial interaction, frequency-aware double-gating and lightweight refinement, and finally outputs a binary change mask by the decoder. The application can improve the change region recognition accuracy, boundary positioning ability and robustness in complex remote sensing scenes.

A depth is estimated regardless of an imaging target and an imaging condition. An imaging device with a three-dimensional information measurement function includes: a pattern projection unit including a light source and a pattern forming unit that forms a projection pattern of a light beam; an imaging unit including a lens group including at least one lens, a coded aperture that limits external light passing through the lens group in a predetermined coded pattern, a shutter, and an image sensor; and a depth calculation unit configured to obtain, based on a pattern image imaged by the imaging unit and a known point spread function related to the coded pattern, a depth distribution of an image imaged by the imaging unit.

This invention provides a machine vision recognition method and system for commercially available frozen mutton slices adulterated with other meats, relating to the fields of food quality authenticity detection and computer vision technology. The invention acquires images of frozen mutton slices under standardized visible light imaging conditions, and obtains a preprocessed image set through unified resolution, normalization, circular region of interest extraction, and data augmentation. It constructs color matrix features in RGB, HSV, and Lab color spaces and generates five texture features from gray-level co-occurrence matrices, and extracts principal component features with a cumulative contribution rate exceeding 99% through principal component analysis. A random forest model is trained based on original-cut, conditioned, and reconstituted mutton slice samples to classify and identify the mutton slices to be tested. This invention achieves rapid, stable, and high-precision identification of frozen mutton slices adulterated with foreign meat and meat products under standardized illumination conditions.

This invention discloses a microscopic denoising method based on multi-expert judgment, relating to the field of computer image enhancement technology. The method includes: acquiring original microscopic image data; preprocessing the image data; calculating initial expert weights through an expert weight threshold discrimination network; inputting the preprocessed data into multiple expert denoising networks for denoising; calculating result weights based on the denoising results and performing weighted fusion; and generating a denoised microscopic image. By introducing a multi-expert structure and threshold discrimination mechanism, this invention can adaptively denoise under various observation objects and imaging conditions, significantly improving the generalization ability of the denoising method. This invention, through a multi-expert mechanism, overcomes the shortcomings of current denoising methods, such as weak generalization and applicability limited to specific observation sample types or imaging conditions.

The specification discloses a method, apparatus, device, and storage medium for detecting wafer edge structure offset. The method includes: generating candidate rings corresponding to the wafer edge and at least one edge ring structure in the wafer edge region based on image data of the wafer under test; dividing each candidate ring into multiple ring regions along its radial direction, and constructing multi-channel radial profile features of the candidate rings based on pixel features within each ring region; determining the matching score and matching position information corresponding to each candidate ring; determining the center position based on the matching score, and determining a radius parameter based on the matching score and matching position information; and calculating the relative position between the wafer edge and at least one edge ring structure based on the center position and radius parameter. This enables accurate identification of ring structures and calculation of their offset information under complex imaging conditions, achieving effective monitoring and closed-loop control of edge processes in semiconductor manufacturing.

This disclosure pertains to the field of exploration geophysics and presents a method and system for combined active and passive source reverse time migration imaging of metallic minerals. The method includes: performing time difference correction on active and passive source seismic data from the same subsurface structure; performing wavefield separation on the time-aligned active and passive source seismic data to reconstruct the P-wave and S-wave fields of the active source and the passive source; performing active and passive source wavefield extrapolation based on the two-way wave equation; obtaining active and passive source PS-converted wave imaging results using cross-correlation imaging conditions; and performing weighted superposition and fusion to obtain the combined active and passive source imaging results. This disclosure leverages the high resolution advantage of active sources combined with the deep detection capability of passive sources, replacing the limitations of single-source detection and achieving high-precision deep reverse time migration imaging.

The application provides a low-bubble adhesive label quality monitoring method and system based on image analysis, and belongs to the technical field of adhesive labels. The low-bubble adhesive label quality monitoring method based on image analysis comprises the following steps: acquiring image acquisition data of a low-bubble adhesive label, wherein the image acquisition data is digital image data obtained by imaging the low-bubble adhesive label containing a pattern area under preset imaging conditions; performing image processing on the image acquisition data to generate processed image data for representing label region brightness distribution, gray scale distribution and texture distribution; analyzing image abnormalities formed by bubble points in an adhesive layer at corresponding apparent positions of a face material layer according to the processed image data to generate bubble point candidate region data; and performing quality determination on the low-bubble adhesive label according to the bubble point candidate region data to obtain a quality monitoring result.

This invention discloses an apparatus for performing user authentication, comprising: a controller; and a display device. The controller is configured to display a user authentication image comprising a plurality of images on the display device. The plurality of images includes candidate images for user authentication. The conditions for user authentication include conditions for selecting a candidate image. The candidate image satisfies predetermined candidate image conditions. The candidate image conditions specify the positional relationship between the candidate image and adjacent images that are different from the candidate image and satisfy predetermined adjacent image conditions.

This disclosure describes a substrate processing apparatus and a substrate processing method capable of detecting the position of a notch in a substrate with high precision. [Solution] The substrate processing apparatus comprises a rotating holding unit configured to hold and rotate a substrate having a notch, an imaging unit configured to image the substrate held by the rotating holding unit, and a control unit. The control unit is configured to perform a first process in which the imaging unit controls the imaging unit to acquire multiple images including the entire substrate by imaging the entire substrate multiple times from the same imaging position while the substrate held by the rotating holding unit is stationary, while changing the imaging conditions; a second process in which the imaging unit sets different processing areas for each of the multiple images according to the imaging conditions; and a third process in which it processes each processing area of ​​the multiple images to detect the position of the notch on the substrate.

An imaging condition setting device (100) for an X-ray CT device (200) comprises an X-ray source (20), an X-ray detector (21), a radiation source holding mechanism (22), a detector holding mechanism (23), and a drive unit (24) for driving the radiation source holding mechanism and / or the detector holding mechanism, the imaging condition setting device (100) further comprising a control unit (1), the control unit having a memory (10) and a processor (11), and the processor executing a program (30) stored in a memory to execute a step for acquiring three-dimensional structure data (31) of a subject (90) on the basis of operation input by a user, and a step for setting, by using the acquired three-dimensional structure data (31), imaging conditions to perform X-ray CT imaging.

In a case where an images is periodically captured and optical information contained in the images is read by analyzing the image, a brightness index value of the captured image is calculated by an imaging adjustment process based on pixel values of pixels in the captured image, and an imaging condition is adjusted so that the brightness index value of an image to be obtained in subsequent imaging reaches a predetermined target level. In this case, the brightness index value is determined based on a threshold obtained by classifying pixels sampled from the captured image into a first class of dark pixels and a second class of bright pixels based on the variance of pixel values of pixels in each class.

This invention discloses a strong backlight imaging method based on a DMD (Digital Modulator-Demiller), belonging to the field of optical imaging technology. This method involves spatial pixel-level matching and synchronous triggering feedback between the camera and the DMD. Under strong backlight imaging conditions, the strong backlight passing through the target and the light reflected from the target together form the incident light, which sequentially passes through the imaging lens, the DMD target surface, the relay matching imaging lens, and the camera target surface. The overall grayscale value is reduced by the DMD mask to obtain an initial image containing target contour information. Based on this image, an initial mask is generated for differential grayscale modulation of the target and background areas. Further, local overexposed areas are detected and pixel-level masks are generated. Simultaneously, a high bit-depth grayscale modulation mode of the DMD is introduced to finely suppress brightness in locally bright areas, ultimately obtaining an image of the target without overexposure and with clear details. This invention can effectively suppress overexposure and improve imaging contrast under strong backlight conditions, and is suitable for high dynamic range backlight imaging scenarios.

The application discloses a complex ground surface ground penetrating radar amplitude-preserving imaging method based on an orthogonal skin mesh, and comprises the following steps: 1) constructing a ground surface model according to ground fluctuation data; constructing an electromagnetic wave underground propagation speed model according to geological exploration data; 2) generating an orthogonal skin mesh by using a Ryskin-Leal method; 3) discretizing the orthogonal skin mesh by using a finite difference time domain method; 4) performing reverse extrapolation on the received ground penetrating radar signal, and reversely propagating the electromagnetic wave field to an initial time; in the reverse extrapolation process, the spatial position of a reflection interface is calculated through a cross-correlation imaging condition, and an underground imaging profile is obtained; 5) calculating a target function gradient, and solving an inversion problem by using a conjugate gradient method to iteratively update the electromagnetic wave underground propagation speed model; and 6) generating an imaging profile of an underground structure according to the iteratively converged electromagnetic wave underground propagation speed model. The application significantly improves the precision, stability and anti-noise performance of underground imaging.

The application discloses a kind of methods and systems for spectral standardization under the influence of multi-dimensional environment, it is related to spectral data standardization field.The method comprises first to the target object is carried out single point spectral collection, for acquisition position, the apparent radiance spectral data of target object and the temperature data of target object surface are acquired, the geometric parameter when target object is carried out spectral collection is acquired;Target object is replaced by standard reference plate, for the center position of standard reference plate, the apparent radiance spectral data of standard reference plate is acquired;Then using the thermal radiation-reflectivity decoupling method based on iterative calculation, obtain the reflectivity spectral data of the surface of target object after preliminary correction;Finally, BRDF model is acquired and parameter is determined, using the model and reflectivity spectral data obtain geometric standardization reflectivity spectral data, complete spectral standardization.The application can cope with various temperature, illumination, zenith angle and so on imaging condition.

In a case where an image is periodically captured in each frame by an imager (110) and optical information contained in the image is read by analyzing (S81 to S87) the image, an amount of movement of the imager (110) per frame is estimated (S31 to S42) based on images captured in a certain time range (T), and an imaging condition including at least one of an exposure time and an illumination lighting time is adjusted (S113 or S115) by an imaging adjustment process (212). In this case, an upper limit of a value of the at least one to be set by the imaging adjustment process is determined (S112 or S114) based on the estimated amount of movement of the imager (110) per frame.

The application discloses a self-supervised learning network training method for remote sensing images and related devices. Without labeled information, the self-supervised training improves the representation ability of a basic model for real texture and structure information of ground objects, can realize accurate representation of remote sensing images, effectively reduces the probability of scale mismatch, texture statistical difference and geometric topological structure fracture during transfer training compared with related technologies, has more robust feature learning ability for remote sensing images of cross-resolution, cross-region and cross-imaging conditions, and effectively improves the generalization ability of the basic model.

Artifacts in a sequence of image frames can be reduced or eliminated by modifying an input image frame to match another image frame in the sequence, such as by geometrically warping to generate a corrected image frame having a field of view that matches another frame in the sequence of frames having that image frame. The warping can be performed based on a model generated from data about the multi-sensor device. Disparity between image frames can be modeled based on image capture from the first image sensor and the second image sensor for a scene at different depths. The model can be used to predict disparity values for captured images, and those predicted disparity values used to reduce artifacts resulting from image sensor switching. Predicted disparity values can be used in image conditions that result in erroneous actual disparity values.

A device for imaging multiple subjects is provided. The device includes a defocus range estimation part configured to estimate a defocus range for each of the subjects, a target determination part configured to set a combination of the defocus ranges, a defocus range setting part configured to set a control defocus range based on the set combination of the defocus ranges, and a control part configured to control image-taking conditions of the device based on the control defocus range.

The application provides an intelligent ship identity recognition method and system based on artificial intelligence, and the method comprises the following steps: training original samples through structure constraint enhancement, and constructing a model conforming to real maritime imaging conditions; generating stable target sequences by using joint segmentation and identification bearing area guide mechanism on input videos; continuously updating the frames by tracking feature extraction model in a global coordinate system to realize target tracking; detecting texts on the tracking sequences, and suppressing single-frame errors by using the probability fusion of the same character positions in multiple frames; and finally, matching the recognition results with an identity database index to complete verification. The application significantly improves the accuracy and stability of ship identity recognition in complex environments, and is mainly used in port monitoring, maritime supervision and ship automatic identification scenes.