35 results about "Image histogram" patented technology

The invention relates to the technical field of parking lot positioning, in particular to a vehicle positioning management system in a parking lot based on dynamic weight multi-sensor fusion. Comprising a multi-source sensor data acquisition unit; an environment entropy perception and weight pre-adjustment unit; a reinforcement learning weight optimization unit; and an edge end vehicle positioning management unit. According to the method, an image histogram entropy value and a UWB signal RSSI fluctuation variance are calculated in real time through a heterogeneous sensor array in combination with an environment entropy perception and weight pre-adjustment unit, meanwhile, a space correlation matrix is constructed through sensor plane coordinate mapping, and interference space correlation characteristics are extracted. Based on reinforcement learning, a causal correlation model of an environmental entropy change rate, interference characteristics and sensor failure time is established, a sensor failure window can be predicted in advance, the weight of each sensor accessing a fusion filter is dynamically adjusted, and the problems of environmental influence perception deficiency, sensor correlation analysis and failure pre-judgment are solved.

The invention provides a light-SAR image conversion method based on coordinate attention and histogram loss, and relates to the technical field of SAR image data enhancement, and the method comprises the steps: obtaining a to-be-converted optical image; inputting the optical image to be converted into a pre-trained SAR image generator, and converting to obtain a target SAR image; a coordinate attention module is arranged in the pre-trained SAR image generator, and the pre-trained SAR image generator is obtained based on a pre-trained bidirectional conversion framework; the coordinate attention module is used for performing global pooling from the horizontal direction and the vertical direction respectively, and constructing a global feature vector corresponding to the optical image to be converted based on a global pooling result; histogram loss is set in an overall loss function of the pre-training bidirectional conversion framework; histogram losses are constructed based on kernel density estimates and differences between image histograms are determined using square earth motion distances. In combination with the method, the realistic degree of the finally generated target SAR image is improved.

The present invention discloses a lane region segmentation method, system, electronic device, and storage medium, relating to the field of intelligent transportation. The method comprises the following steps: 1) inputting a road image captured by a road monitoring probe and extracting straight lines from the road image using the Hough method; 2) clustering the extracted lines and sorting them by angle and distance to the origin, taking the first and last straight lines l1 and l2 after the sorting; 3) using l1 and l2 to segment the road image to obtain an image of the road surface area; 4) calculating the histogram of the segmented image, using grayscale features to find two grayscale thresholds, and using dual-threshold binarization to segment the lane region; 5) using morphological methods to remove interference from small image holes, and using connected domain detection to remove the median strip, thereby completing the lane region.

The present invention discloses a digital image threshold self-determination algorithm based on pixel neighborhood information, comprising the following steps: (1) selecting a Laplace template of an image, performing convolution calculation to obtain an L‑P grayscale value; (2) marking the L‑P grayscale value obtained in step (1) and replacing the original image grayscale, and performing grayscale value cumulative statistics; (3) drawing a grayscale distribution histogram after replacement and comparing it with the original grayscale image histogram; (4) marking two intersection points generated by the grayscale histogram of the L‑P value after replacement and the grayscale histogram of the original image, obtaining the grayscale value interval of the pixel points corresponding to the two intersection points by calculation, using this interval to cover the original grayscale values ​​of all segmentable boundary pixels, and calculating the maximum value of inter-class variance within this interval; (5) taking the grayscale corresponding to the maximum value of the inter-class variance as the optimal segmentation threshold, which can more accurately and effectively segment the grayscale image of the geotechnical material microscopic image.

Systems and methods of detecting and recognizing products on product storage structures of a product storage facility include an image capture device that moves about and captures images of the product storage structures at the product storage facility. A computing device processes the obtained images to detect and identify the products on the product storage structure, crops each of the identified individual products from the image to generate a plurality of cropped images and generates an image histogram template, feature vector template and location information template for each of the cropped images. The cropped images are stored in an electronic database and represent a reference model for each of the identified individual products and are stored in association with the generated image histogram template, feature vector template and location information template to facilitate recognition of products subsequently captured on the product storage structure by the image capture device.

The invention provides a multi-scale adaptive short wave infrared image enhancement method and device. The method comprises the following steps: calculating a cumulative probability density function of an original image histogram; based on the cumulative probability density function and the golden section value, determining a segmentation gray threshold value used for segmenting the original image histogram; segmenting the original image histogram according to the segmentation gray threshold to obtain a first sub-image histogram and a second sub-image histogram; performing multi-scale adaptive correction on the first subimage histogram and the second subimage histogram to obtain a first corrected histogram corresponding to the first subimage histogram and a second corrected histogram corresponding to the second subimage histogram; determining an enhanced histogram of the original image histogram according to the first corrected histogram and the second corrected histogram; and determining an enhanced image of the original image according to the enhanced histogram. The image enhancement effect is improved when image enhancement is carried out.

The invention discloses a rapid adaptive SAR image correction method based on a human vision system. The method comprises the following steps: S1, normalizing an original SAR image and calculating a pixel concentration ratio; s2, judging whether the pixel concentration ratio exceeds a threshold value or not: if the pixel concentration ratio does not exceed the threshold value, assigning the normalized original SAR image to the low-concentration-ratio SAR image, and entering S4; otherwise, entering S3; s3, mapping the histogram peak value of the normalized original SAR image to a set histogram peak value position through a power function, and calculating a low-concentration SAR image; s4, constructing a reference histogram based on a human visual system theory, and mapping the histogram of the low-concentration SAR image into the reference histogram to obtain a mapped SAR image; and S5, obtaining a detail enhanced image based on partition control. The multi-source SAR image self-adaptive correction method has wide applicability and self-adaptive processing capacity, after parameter setting is completed, multi-source SAR images can be corrected in a self-adaptive mode, and histograms of the corrected images are basically consistent.

The invention discloses a medical X-ray perspective grayscale adaptive control method based on histogram features, and relates to the technical field of medical X-ray imaging and medical image processing, and the method comprises the steps: extracting image histogram features, and adjusting a high-voltage generator and parameters in combination with an ABC curve to maintain the image grayscale stable; the method further comprises the following steps: constructing a multi-dimensional correction-real-time calibration contrast agent photon attenuation-histogram feature nonlinear mapping model, performing synchronous coupling control on real-time photon counting-histogram features, and completing parameter self-calibration by using a contrast agent attenuation critical threshold effect; according to the invention, through a nonlinear mapping model, an FPGA parallel architecture, physiological signal correction and one-frame pre-injection calibration, a contrast agent gray abrupt change time sequence difference is eliminated, overexposure and underexposure are avoided, and high-frequency clinical real-time accurate requirements are met; meanwhile, contrast agent concentration quantification and flow evaluation are achieved, trinity diagnosis and treatment assistance is formed, the missed diagnosis rate is reduced, and the clinical value of medical X-ray fluoroscopy equipment is remarkably improved.

The present invention is a video contrast enhancement method and system, especially a video contrast enhancement algorithm based on self-supervised learning. The unsupervised image quality evaluation model is used to guide the training of the video contrast enhancement algorithm model. The present invention can achieve real-time video contrast enhancement on the CPU. Inspired by the image histogram equalization algorithm, the present invention designs an adaptive gray mapping mechanism, uses the unsupervised image quality evaluation model as a guide, and regresses to obtain the Map mapping matrix; different Map mapping matrices will be obtained for different images for video contrast enhancement, without human intervention, which is particularly user-friendly in the use of the algorithm.

The application discloses a low-distortion reversible contrast enhancement method based on RDH, which comprises the following steps: 1, a contrast-enhanced gray level mapping is obtained by histogram specification processing of an original image; a globally enhanced image is obtained by transformation according to the gray level mapping; data structures A and B of one-to-one and one-to-many gray mapping inversion are respectively calculated according to the gray level mapping; global recovery data information is extracted from the original image; the global recovery data information is encoded as embedded data; the embedded data is embedded into the globally enhanced image to obtain a reversible globally contrast-enhanced image; a locally contrast-enhanced image is obtained by using a fuzzy mean clustering algorithm for local contrast enhancement; and local recovery data information is extracted from the reversible globally contrast-enhanced image and embedded into the locally contrast-enhanced image to obtain a reversible locally contrast-enhanced image. The application introduces local contrast enhancement on the basis of global contrast enhancement, thereby reducing image distortion.

The invention relates to the technical field of image processing and data acquisition, and discloses a financial bill intelligent identification and archiving method of an embedded hardware terminal, and the method comprises the steps: firstly obtaining a global low-resolution image, and generating an acquisition instruction queue containing heterogeneous ISP parameters; then controlling the image sensor to execute hardware-level asynchronous serialization acquisition by utilizing vertical blanking interval configuration to obtain heterogeneous local image slices; and eliminating view field displacement between the slice and the global image by using gradient domain registration, and executing logic consistency verification on the identified and extracted service data. And if the verification is passed, generating a layered archived file, otherwise, based on the image histogram, generating a correction parameter and triggering hardware feedback recollection. According to the invention, through a heterogeneous acquisition strategy and a business logic closed loop, the problems of high delay of high-resolution acquisition of a mobile terminal and difficult alignment of handheld shooting are effectively solved, and the bill identification accuracy and filing efficiency are significantly improved.

The invention relates to a method and system for screening crop growth images, and the method comprises the steps: obtaining a plurality of continuous growth images of crops, and carrying out the preprocessing of the growth images through a bilateral filtering method and a contrast-limited adaptive histogram equalization method; the preprocessing comprises denoising, contrast enhancement, brightness enhancement and saturation enhancement; on the basis of feature point matching and an image histogram, comparing the preprocessed continuous growth images, and screening and storing a plurality of images of which the image difference values are greater than a preset value within a preset time; based on a Laplacian operator and an image entropy, performing quality evaluation on the plurality of screened images; and filtering the image with the quality lower than the preset quality according to the evaluation result. Through the image comparison and screening method, the storage efficiency, the monitoring accuracy and the image quality of the crop growth image are improved, and the adaptability is high.

The application provides a histogram equalization method for protecting details, comprising the following steps: S1, dividing an input luminance image into blocks; S2, counting a histogram of the image; calculating an average gray level of a current block; S3, histogram redistribution, comprising the following steps: calculating a clipping threshold; histogram clipping and redistribution; S4, calculating a mapping function; S5, adjusting the mapping function; and S6, enhancing the input image. The histogram equalization method solves the problem of over-enhancement in bright areas and dark areas, and ensures that low-contrast images will not lose the details of bright areas and dark areas after being enhanced.

The invention discloses a remote sensing image nonlinear simulation method and system and a readable storage medium. The method comprises the following steps: inputting a panchromatic image and a multispectral band image; registering the multispectral image and the panchromatic image, and resampling according to a high-spatial-resolution wave band; counting the mean value, mean square error and correlation coefficient matrix of the panchromatic image and the multispectral image; constructing an intermediate wave band I by using the low-resolution multispectral image; constructing an image simulation operator by utilizing remote sensing image histogram feature matching; and solving a feature fusion coefficient kE and a feature extraction coefficient kI, determining an image simulation scheme, and carrying out image simulation on wave bands one by one. The method is not limited by the number of image wavebands, and the remote sensing image simulation result can be predicted. According to the method, two image simulation modes of injecting the panchromatic wave band image into the multispectral wave band image and injecting the multispectral wave band image into the panchromatic wave band image are provided, and high-fidelity simulation of ground object spectrum and space information of the remote sensing image can be realized.

The application provides an image histogram smoothing device, method, electronic equipment and storage medium, relates to the technical field of image processing, and the device comprises an initialization module, a weight curve determination module, a smoothing module and a construction module, wherein: the initialization module is used for determining an initial image histogram corresponding to a target image and a reference brightness; the weight curve determination module is used for determining a weight curve corresponding to each bar in the initial image histogram based on the reference brightness; the smoothing module is used for traversing each bar, determining a target statistical value of the current bar based on each target weight curve extending to the current bar, and the target statistical value is used for representing the mutual influence relationship between each bar; and the construction module is used for determining a smoothed image histogram based on the target statistical value of each bar. The application can realize the smoothing of the image histogram, and improve the anti-interference and stability of the image histogram.

The invention relates to a vegetation identification method and device based on an unmanned aerial vehicle image, a readable medium and a program, and the method comprises the steps: obtaining the unmanned aerial vehicle image of a to-be-identified region, and calculating a constructed RGBSI vegetation index based on the brightness values or reflectivity of the red light wave band, the green light wave band and the blue light wave band of the image, and extracting a vegetation region from the image of which the RGBSI vegetation index is calculated by using a threshold segmentation method. The RGBSI vegetation index constructed by the method combines the strong reflection of green vegetation in a green light wave band and the absorption characteristics of the green vegetation in red and blue wave bands, when the method is used for vegetation identification, an image histogram of a vegetation index grey-scale map image calculated by RGBSI has a good double-peak form under the condition of medium and high vegetation coverage, a threshold can be rapidly determined by using a maximum entropy threshold method, and the method can be applied to vegetation identification. And the threshold value is generally close to 0. The overall extraction precision of identifying the vegetation-containing image by using the RGBSI index is more than 90%, the extraction precision is high, and the extraction effect of vegetation with medium and high vegetation coverage is better.

This invention belongs to the field of digital image preprocessing technology and discloses an adaptive enhancement method for underground utility tunnel fire images, comprising: Step 1, receiving a fire image and preprocessing it to obtain a grayscale histogram; Step 2, calculating a grayscale histogram segmentation threshold; Step 3, using the grayscale histogram threshold, segmenting the original image histogram into three sub-block arrays: a low grayscale area, a middle grayscale area, and a high grayscale area, and marking the pixel range corresponding to each sub-historical array; Step 4, performing enhancement processing on the three sub-block arrays to obtain an enhanced and reconstructed fire image, and transmitting the enhanced fire image data to a remote control center. This invention can effectively improve the clarity and contrast of fire images in complex environments such as low light and low contrast, thereby achieving rapid and accurate identification of fire features.

The invention relates to a visual SLAM front-end processing method based on fuzzy clustering contrast enhancement, and belongs to the technical field of visual synchronous positioning and mapping, and the method comprises the following steps: S1, obtaining an original image collected by a camera, and carrying out the preprocessing of the original image; s2, performing image quality evaluation on the preprocessed image, making a decision according to an evaluation result, and if enhancement is skipped, directly turning to a front-end processing flow; if enhancement is started, executing a fuzzy C-means clustering enhancement algorithm; s3, analyzing the intensity distribution of the image through fuzzy C-means clustering, and adaptively stretching an image histogram according to a membership relationship to enhance the image; and S4, carrying out feature extraction, feature matching and pose estimation on the enhanced image and the preprocessed unenhanced image to realize visual SLAM front-end processing.

The invention provides a method and system for enhancing a Mura defect image in an OLED display panel, and relates to the field of defect detection and quality control, and the method comprises the steps: obtaining an original image of the OLED display panel, the original image having a Mura defect; performing histogram image conversion and pseudo-infrared image conversion on the original image by adopting a dual-channel preprocessing strategy to obtain two complementary histogram enhanced images and pseudo-infrared images; wherein the histogram image conversion process comprises color image graying, local histogram statistics, equalization mapping and smooth fusion, and the pseudo infrared image conversion process comprises contrast and brightness adjustment and pseudo infrared color mapping; and performing three-mode feature fusion on the original image, the histogram enhanced image and the pseudo-infrared image to obtain a defect integrated image. According to the method, a complementary information fusion image preprocessing mode is adopted, histogram equalization and pseudo-thermal imaging transformation are combined, and the visibility of low-contrast defects can be enhanced.

The application relates to an infrared aerial video environment perception algorithm based on scene classification, which comprises the following steps: (1) region segmentation of an infrared image; (2) input of the image of each region into a scene classification model respectively to obtain the scene classification probability of each region; (3) histogram statistics of each region respectively to obtain the histogram statistical information of each region; (4) calculation of the histogram weighted statistical result of each category of each region and the histogram statistical information of each category of a frame image; (5) setting of the weight of each category, weighted summation of the histogram statistical results of all categories to obtain a full image histogram; and weighted updating of the full image histogram in the time domain according to the set updating rate to obtain the environment histogram statistical result. The application realizes histogram statistics of the aerial environment and provides good and comprehensive environment information for subsequent image processing.

The present application provides an image histogram generation method, apparatus, electronic device, and storage medium. The method includes: when target image data is acquired, performing separated sampling on each image frame in the target image data to obtain at least one first pixel point and at least one second pixel point corresponding to the image frame; the first pixel point is a pixel point with an odd number of bits on the image frame, and the second pixel point is a pixel point with an even number of bits on the image frame; writing a first statistical value of each first pixel point into a first memory based on a first read / write operation, and writing a second statistical value of each second pixel point into a second memory based on a second read / write operation; both the first read / write operation and the second read / write operation are time-division multiplexed read / write operations, and both the first memory and the second memory are single-port random access memories; obtaining an image histogram corresponding to the target image data based on the first statistical value and the second statistical value.

The present invention discloses a frequency domain-based image global manipulation forensics method, which relates to technical fields such as digital image processing, computer vision, signal processing, and machine learning. The specific steps are: 1) collecting and organizing public image samples; 2) performing one or more typical image operations on the public images to construct an image data set and obtain labels of the manipulated images through annotation. 3) performing forensic feature extraction based on the frequency domain of the image histogram and detail image; 4) using the trained model to test the forensic frequency features of global manipulation of multiple images to obtain the final classification results. This method utilizes the proposed forensic frequency features, combined with the model obtained by training the KNN classifier, to detect global manipulation of multiple images in reality, and can also detect images generated by the network. The invention has practical significance and shows good detection performance in subsequent forensic tasks.

The invention discloses a bus shelter LED show window screen energy-saving display system and a control method thereof, and a computer storage medium, the display system is provided with a control module and a data storage unit, and the method comprises the following steps: obtaining a media file uploaded by a user; performing image processing on the media file to obtain image histogram information; obtaining a brightness threshold value of the image information according to the image histogram information; acquiring a playing instruction of a user; acquiring electric quantity information of the LED display terminal; acquiring image histogram information and a brightness threshold value corresponding to the media file according to the playing instruction; and dynamically adjusting a display picture according to the electric quantity information, the image histogram information and the brightness threshold. Through the mode, the brightness characteristics of the media image can be quantified, a data basis is provided for dynamic adjustment, the energy supply state can be perceived in real time, image histogram data are associated with the battery state, the brightness of the output image is dynamically adjusted according to needs, and'content-energy consumption 'linkage control closed loop and energy self-adaptive image output are constructed; the problem that in the prior art, due to the lack of content perception, high-energy-consumption elements in a picture cannot be analyzed, and targeted consumption reduction cannot be achieved is solved.

An image processing circuit, a circuit device, and an electronic device are provided. The image processing circuit includes: a gamma correction circuit that performs gamma correction on input image data using a lookup table and outputs output image data; an output image histogram calculation circuit that calculates an output image histogram; an input image histogram calculation circuit that calculates an input image histogram; a reference histogram calculation circuit that calculates a reference histogram based on the input image histogram according to the lookup table; and a histogram comparison circuit that compares the output image histogram with the reference histogram.

The present invention provides a dual-gain HDR synthesis method and system for an sCMOS sensor, relating to the field of image processing. The method utilizes an sCMOS sensor to simultaneously acquire two images, one high-gain and one low-gain; performs a linear grayscale value analysis on the high-gain and low-gain images; performs image histogram statistics to determine gain and exposure time settings; and performs grayscale stretching on the high-gain and low-gain images to synthesize an HDR image based on the grayscale value relationship. By performing the grayscale value linear relationship analysis and grayscale stretching on the high-gain and low-gain images, a high dynamic range image can be rapidly synthesized, eliminating image registration issues and simplifying the HDR image synthesis process. By performing the grayscale value linear relationship analysis and grayscale stretching on the high-gain and low-gain images, a high dynamic range image can be rapidly synthesized.

The application discloses a histogram statistical data access method and device, electronic equipment and a computer readable medium, which are applied to electronic equipment, the electronic equipment comprises a static random access memory, the static random access memory has a plurality of storage areas, the method comprises the following steps: dividing a to-be-processed image into a plurality of image blocks, each image block corresponds to a storage area, and the image blocks corresponding to the storage areas are different; determining a target image block corresponding to a target storage area; acquiring histogram statistical data of the target image block, and writing the histogram statistical data into the target storage area. Therefore, when storing image histogram data of the same size, the method needs smaller storage space, reduces the chip volume, can read the histogram data statistical results of four image blocks from four storage areas at a time, and ensures the data reading efficiency under the condition of avoiding data redundancy.

The application discloses a kind of based on the infringement image identification method and device of improved perceptual hashing algorithm, comprising: according to image histogram, the image to be verified obtained is divided into solid color background image and non-solid color background image;The image fingerprint of non-solid color background image is calculated using perceptual hashing algorithm;The image fingerprint of solid color background image is calculated using improved perceptual hashing algorithm;According to the image fingerprint of each image to be verified, the hash value of each image to be verified is obtained;According to the hash value of all images to be verified, the similarity of any two images to be verified is calculated;The similarity of any two images to be verified is compared with the preset similarity threshold, and the infringement image identification result is obtained.The application can identify whether solid color background image is infringing by retaining the high frequency part of image matrix, improve the universality of infringement image identification.

The invention relates to a scanning image histogram conjoint analysis method and system. The method comprises the following steps: acquiring a user scanning image; determining a target scanning image; expanding the marked contour of the object of interest to obtain an expanded object; generating a first gray histogram of the interested object and a second gray histogram of the extended object, and obtaining quantitative parameters; and comparing the quantitative parameter with the quantitative parameter template corresponding to each classification to determine the classification, and sending the classification as a conjoint analysis result to the user terminal. According to the method, hierarchical scanning images are fully utilized, auxiliary information contained in each layer of scanning images is fully utilized in a weight decreasing mode, and the conjoint analysis efficiency is greatly improved.

Systems and methods of detecting and recognizing products on product storage structures of a product storage facility include an image capture device that moves about and captures images of the product storage structures at the product storage facility. A computing device processes the obtained images to detect and identify the products on the product storage structure, crops each of the identified individual products from the image to generate a plurality of cropped images and generates an image histogram template, feature vector template and location information template for each of the cropped images. The cropped images are stored in an electronic database and represent a reference model for each of the identified individual products and are stored in association with the generated image histogram template, feature vector template and location information template to facilitate recognition of products subsequently captured on the product storage structure by the image capture device.

Some of embodiments of the present application provide an image histogram-based target object edge chipping detection method, a device, a medium, and a product. In the embodiments of the present application, an image of a target object is collected, and a central position of the target object in the image is acquired; at least two detection regions of the target object are determined on the basis of the central position; image histograms of the detection regions are acquired; and the image histograms of the detection regions are analyzed, and whether there are clippings at the edge of the target object is determined on the basis of the analysis result.