12 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 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 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 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 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 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 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.

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.

An image enhancement processing method, device, equipment and medium, obtain the original histogram of the input image and the parameter information of the original histogram, the parameter information includes the number of effective gray levels; correct the original histogram to obtain a corrected histogram, and obtain the parameter information of the corrected histogram, which includes the number of effective gray levels of the corrected histogram; analyze the original histogram and the corrected histogram to determine the mapping range of the corrected histogram; determine the clipping coefficient of the corrected histogram according to the number of effective gray levels of the image histogram and the corrected histogram; clip the corrected histogram using the clipping coefficient, equalize the clipped histogram based on the mapping range, and use the mapping relationship matrix obtained after processing as the histogram mapping rule; perform image enhancement processing on the image to be processed based on the histogram mapping rule, and output the processed image.

The application discloses a kind of multi-aperture optical panoramic image view enhancement method, for under the condition of limited optical field of view, the purpose of realizing large scene target monitoring by multi-aperture optical array image splicing technology, specifically includes the following steps: 1) obtaining multi-aperture image;2) format conversion is carried out to multi-aperture image;3) statistics single-aperture image histogram and multi-aperture image histogram;4) the calibration base value of this aperture is calculated by single-aperture image histogram;5) the segmentation threshold of highlight and low light area is calculated by multi-aperture image histogram;6) the calibration parameter of panoramic image is calculated;7) real-time calibration is carried out to panoramic image using calibration parameter.