243 results about "Background suppression" patented technology

The present invention relates to the use of fluorescently labeled nucleic acid probes to identify and image analytes in a biological sample. In the preferred embodiments, a probe is provided that comprises a target region able to specifically bind an analyte of interest and an initiator region that is able to initiate polymerization of nucleic acid monomers. After contacting a sample with the probe, labeled monomers are provided that form a tethered polymer. Triggered probes and self-quenching monomers can be used to provide active background suppression.

The invention provides a method for the outline automatic extraction of a level set medical ultrasonic image area based on an edge and a statistical characteristic, aiming at the characteristics of low contrast degree and small signal-to-noise ratio of medical ultrasonic images and designing a method for the outline extraction of the level set area based on the edge and the statistical characteristic. The method comprises three steps of: 1, carrying out area outline coarse extraction, completing image inverse extraction and self-adapting Gaussian function background suppression, applying otsu image automatic threshold algorithm to convert the image into a binary image, and carrying out image degreasing interference operation and closed area outline extraction work; 2, adopting a selectivity aeolotropy medical ultrasonic image smoothing algorithm to pre-treat an original image; and 3, carrying out the precise extraction of the level set image area based on the edge and the statistical characteristic. Experimental results indicate that compared with the prior method, the method of the invention can obtain a more precise partition result.

A demodulation image sensor, such as used in time of flight (TOF) cameras, extracts all storage- and post-processing-related steps from the pixels to another array of storage and processing elements (proxels) on the chip. The pixel array has the task of photo-detection, first processing and intermediate storage, while the array of storage and processing elements provides further processing and enhanced storage capabilities for each pixel individually. The architecture can be used to address problems due to the down-scaling of the pixel size. Typically, either the photo-sensitivity or the signal storage capacitance suffers significantly. Both a lower sensitivity and smaller storage capacitances have negative influence on the image quality. The disclosed architecture allows for keeping the storage capacitance unaffected by the pixel down-scaling. In addition to that, it provides a high degree of flexibility in integrating more intelligence into the image sensor design already on the level of the pixel array. In particular, if applied to demodulation pixels, the flexibility of the architecture allows for integrating on sensor-level concepts for multi-tap sampling, mismatch compensation, background suppression and so on, without any requirement to adjust the particular demodulation pixel architecture.

The invention discloses an in-orbit moving target detecting method with space-based radar and infrared data fusion. The method includes the steps that when an infrared detecting system is used for detection, background suppression is carried out on each frame in an infrared image sequence by adopting a filtering method based on the morphology, self-adaptive threshold segmentation is carried out on images obtained after background suppression, a single frame detection result is extracted, a multi-frame target is determined through a tracking algorithm, and whether the detection result is a real target or not is judged to obtain the detection result of the multi-frame target; during radar detection, pulse doppler (PD) is adopted for processing. After time calibration and space calibration are carried out on the obtained infrared and radar target information, data fusion is carried out on the information by adopting a measurement fusion method, and track prediction is carried out on fused data to obtain the estimated position of the target.

The invention discloses a welding defect extraction method, which belongs to the technical field that a welding technology and a digital image processing technology are crossed. According to the method, an original welding image is dissected by NSST (non-subsampled shearlet transform); for a roughly approximate low-frequency component capable of embodying a defect, the rough region of the detect is extracted by a PCNN (pulse coupled neural network); then, inverse NSST is carried out to the low-frequency component and a high-frequency component subjected to background suppression to obtain a high-frequency characteristic image; after the high-frequency characteristic image is subjected to coarse segmentation, the outline of the defect is optimized by an improved CV (Chan-Vese) model to obtain the fine edge of the defect; and finally extracted results are blended to obtain a finally-extracted defect. The invention also discloses a welding defect detection method adopting the welding defect extraction method. A welding defect structure obtained by the method disclosed by the invention has the advantages of more integral structure and clearer detail and outline, and a more reasonable and accurate reference can be provided for further processing the defect.

The invention belongs to the field of infrared image processing, and mainly relates to an infrared weak and small target detection method based on time-space domain background suppression. The infrared weak and small target detection method is used for achieving the aim of infrared movement weak and small target detection in a complicated background and includes the steps that firstly, stable background noise waves in a space domain are suppressed through guiding filtering; secondly, slowly-changed backgrounds in a time domain are suppressed with a gradient weight filtering method on the time domain through target movement information in an infrared image sequence; thirdly, the time domain background suppression result and the space domain background suppression result are fused to obtain a background-suppressed weak and small target image; finally, the image is split through a self-adaptation threshold value, and a weak and small target is detected. By means of the infrared weak and small target detection method, during target detection, space grey information of the infrared weak and small target is used, time domain movement information of the target is further sufficiently used, the background noise waves are suppressed in the time domain and the space domain, and therefore the movement weak and small target detection performance in the complex background is greatly improved.

The invention provides a small infrared aerial target detection method based on non-downsampling contourlet transformation. The method includes the following steps of 1, non-downsampling contourlet transformation, wherein non-downsampling contourlet transformation first-level decomposition is performed on a small infrared target image, and a band pass sub-band is discomposed into four-direction high-frequency sub-bands; 2, background suppression, wherein low-frequency influences are removed, and thresholding processing is performed on a high-frequency portion; 3, coefficient mapping, wherein coefficients left by the four-direction high-frequency sub-bands are mapped to a gray level space in a linear mode; 4, high-frequency image segmentation, wherein four-direction high-frequency sub-band images are segmented into binaryzation images; 5, binary high-frequency image noise reduction, wherein small bright noise points in the binary high-frequency images are eliminated; 6, detection of related small targets in dimension, wherein the four-direction high-frequency sub-band images get along with each other to obtain a small target single-frame detection result; 7, small target sequence detection, wherein comprehensive vote is performed on multi-frame images to intercept and capture small targets. According to the method, the problem of small aerial target detection under the complicated infrared background is solved.

The invention discloses a background suppression method for an infrared reading circuit and a circuit thereof. The circuit is provided with a negative charge packet generator, a detector, an injection circuit, a sampling hold circuit and a buffer, wherein an output end of the negative charge packet generator is connected with one input end of the injection circuit; and an output end of the detector is connected with the other input end of the injection circuit, an output end of the injection circuit is connected with an input end of the sampling hold circuit, an output end of the sampling hold circuit is connected with an input end of the buffer, and an output end of the buffer is connected with a subsequent signal processing circuit. The method and the circuit adopt the negative charge packet generator to provide a plurality of negative charge packets by stage to reduce charges of background current accumulated on an integrating capacitor, do not need a background current memory of the prior art to generate background reduction current to realize background suppression, have extremely low background suppression non-uniformity, and do not introduce extra noise and power consumption.

Disclosed are embodiments of systems and methods for suppressing the background of an image. In embodiments, the number of foreground pixels or background pixels within a neighborhood of an identified background pixel may be compared against an aggressiveness threshold. Responsive to the number of foreground pixels within a neighborhood of an identified background pixel not exceeding an aggressiveness threshold, the color of the identified background pixel may be changed to an average local background color. Alternatively, responsive to the number of background pixels within a neighborhood of an identified background pixel exceeding an aggressiveness threshold, the color of the identified background pixel may be changed to an average local background color. In embodiments, additional processes may be performed on the image including, but not limited to, color adjusting, filtering, image enhancing, compression, format conversion, watermarking, special effects, video editing, etc.

The invention discloses an improved two-dimensional maximum entropy division night vision image fusion target detection algorithm which comprises the steps that a two-dimensional histogram is improved, that is the two-dimensional histogram is established according to the maximum gray scale of a gray scale-weighted area, a weight is selected, the maximum entropy is calculated by using the histogram, and infrared and low light images are divided. Compared with the traditional maximum entropy division algorithm, an effect in the aspect of target detection is obvious, and background suppression and target extraction functions are provided; and then a phase of the multi-dimensional characteristic and the validation of operation are verified, and the divided infrared and low light images are subjected to characteristic level fusion target detection. The detection algorithm has a better effect and better applicability in the aspects of target detection under a complicated background and multi-target detection.

The invention discloses a guidance infrared small target tracking method based on self-adaption manifold particle filters. The method comprises a image background suppression proceeding which is achieved by using a bi-direction diffusion filter background suppression algorithm of a Facet image model, a small target tracking based on a self-adaption manifold particle filter algorithm and target detecting. The guidance infrared small target tracking method based on the self-adaption manifold particle filters has the advantages that real-time, robustness and filtering precision of the algorithm are improved.

The invention discloses an optical remote sensing image region-of-interest detection method which is applied to high-resolution optical remote sensing image region-of-interest detection. A super-pixel-level image is processed, super pixel-to-pixel saliency mapping is performed to get a pixel-level saliency map, and thus, a region of interest is quickly located from coarse-scale description to fine-scale precise description. The method comprises the following steps: firstly, down-sampling an original image, performing super pixel segmentation, and converting the high-resolution image into a coarse-scale image based on the operations; secondly, getting a super-pixel-level texture feature map and a super-pixel-level color feature map through a structure tensor and a color space background suppression technology based on the generated coarse-scale image; thirdly, getting a super-pixel-level saliency map under the original resolution scale through feature map fusion and up-sampling interpolation; and finally, getting a pixel-level saliency map through super pixel-to-pixel saliency mapping, thus completing pixel-level precise description of the region of interest.

Method and system for background suppression in magneto-motive photoacoustic imaging of labeled target objects.

The invention provides a weighted local entropy infrared small target detection method based on multi-scale morphological image fusion, and the method comprises the steps: firstly, converting an infrared image into a gray domain, and carrying out the processing; secondly, performing multi-scale morphology Top-Hat image segmentation processing on the infrared image; solving image difference on thebasis of adjacent scale Top-Hat and obtaining minimum difference graph is obtained, and then comparing the minimum difference graph with a minimum mean value image of the image subjected to Hat transformation to obtain an image subjected to background suppression; then, obtaining a local entropy information graph by calculating the local entropy of the initial image; then, carrying out dot multiplication on the image subjected to background suppression and the local entropy information graph, and carrying out normalization to obtain a saliency map of the infrared small target; and finally, filtering and binarizing the infrared small target saliency map by using a threshold segmentation technology to obtain a processed image, the region with the binarized value of 1 being the infrared smalltarget. The method is suitable for the field of infrared small target detection, can effectively improve the accuracy of infrared small target detection, and effectively reduces the false alarm rate.

The invention relates to an infrared target detection method based on a space-time cooperation framework. The method comprises the following steps: 1. acquiring a background frame Bg and a current frame Ft of a video, combining the background frame Bg and the current frame Ft to carry out background clutter suppression and acquiring a background suppression graph Gt after the background clutter suppression is performed; 2. for the background suppression graph Gt obtained in the step 1, firstly establishing a space-time background model, and then carrying out target positioning aiming at space-time background model information after the model is established; 3. according to an imaging mechanism of the infrared target, analyzing a space difference of the infrared target and the surrounding background, using a fuzzy adaptive resonance nerve network to carry out local classification aiming at the target which is positioned in the step 2 and then extracting the infrared target. The method has the following advantages that: the method does not depend on any target shapes and motion information priori knowledge; the method is suitable for a complex outdoor scene; a signal to noise ratio can be increased; a target detection rate can be increased and a calculated amount can be reduced; false targets can be effectively removed and a false alarm rate can be reduced; the method is beneficial to follow-up target identification.

The invention discloses an intelligent infrared small target detection method. The method comprises: firstly, dividing an infrared image into sub images on the basis of statistical characteristics, and determining a candidate target area; then, determining the size of a structural element based on the size of the candidate target region, and computing to realize the estimation on an infrared complicate background by using a grayscale morphology; making a difference image between an infrared original and a background estimation image, so as to realize the complicate infrared background suppression and giving prominence to a to-be-detected small target; taking six variables subjected to background suppression as infrared small target characteristics; taking the infrared small target characteristics as input, and taking a pixel category as output to form a three-layer BP (back propagation) neural network; forming a nonlinear input-output relation between an image pixel characteristic and a target or a background after the training on a large sample, and building a BP neural network detection model. After an actual infrared image is subjected to the background suppression, a pixel characteristic vector is extracted and then is fed into the trained BP neutral network, so as to realize the small target online intelligent detection under an infrared complicate background.

The invention discloses a transparent liquid impurity detection system. The transparent liquid impurity detection system comprises an operation panel and a host computer, a DSP (digital signal processor), a plurality of CCD (charge coupled device) cameras and a photoelectric switch which are sequentially connected, an encoder, an exclusion device and an exclusion confirmation switch which are connected in parallel, and a PLC (programmable logic controller) station. A detection method of the detection system comprises the following steps of: collecting sequence images of a product to be detected by utilizing the plurality of the CCD cameras and storing the sequence images; performing image background suppression by image pretreatment; performing treatment on the images after background suppression to realize detection and tracking of an object; and extracting features of the object, performing impurity identification according to the features and judging whether impurities exist or not. According to the transparent liquid impurity detection system disclosed by the invention, the acquisition speed and treatment speed of the image sequences can be increased to the greatest extent; and an impurity identification algorithm which is adopted can effectively increase the identification speed and further achieve the purposes of completely replacing artificial detection, increasing detection quality and speed, saving production cost and improving product quality and production benefits on the premise of performing good segmentation, tracking and identification on the visible object.

The invention relates to a colloidal gold detecting method based on digital image processing, comprising the following steps of: collecting RGB (Red, Green, Blue) coloured images by an area array CCD (Charge Coupled Device) detector, carrying out gray level conversion, edge extraction, background suppression and the like on the collected coloured images, and finally quantitatively calculating the corresponding concentration result of a sample. The invention also relates to a colloidal gold detecting device based on the digital image processing; and the device can be used for detecting the colloidal gold by the method provided by the invention so that the device not only realizes the negative and positive qualitative judgement of the sample but also realizes the quantitative application on the lower end.

The invention provides a robot target part saliency detecting method based on vision, which belongs to the technical field of industrial robot target part detection and recognition. The method comprises the steps that a binocular vision system is calibrated; area segmentation is carried out on an original image; image processing is carried out in units of regions, and a binocular vision model is used for depth perception; the perceived depth and a color feature fusion clustering result are collaboratively processed to acquire regional depth saliency; and finally, the weighted fusion result ofglobal saliency and depth saliency is used for background suppression to complete target part detection. According to the invention, the method has the image detection effects of clear edge, completebackground rejection and complete target shape segmentation, has a good effect in the aspect of robot target part detection, makes a robot more intelligent, and can be applied to an intelligent robotsorting system.

A recognizing and positioning method of ground buildings belongs to the imaging automatic target recognition field, which aims at solving the problem of recognition and positioning from different viewpoints and from different scales, and different heights, and to be used in forward looking ground buildings. The invention constructs a ground building standard feature library in advance. The sequence includes: an enhanced image procedure, a background suppression processing procedure, a gray level merge procedure, a feedback and segmentation procedure, a vertical bar feature detection procedure, and a quadratic character matching procedure. The invention further extracts characteristic quantity to match with the standard characteristic, considers recognizing the veins and scene information of the buildings, and recognizes and positions the forward looking ground buildings, aiming at the characteristic of ground buildings, and making use of mathematical morphology to extract structure information of image. The novel method has high precision of recognition, good reliability, and is used in fields such as urban planning, supervision, aircraft contact navigation, collision-avoidance to recognize the forward looking ground buildings of different viewpoints, different scales and different heights.

The invention provides a traffic light detection method and system based on an adaptive background suppression filter and a combined direction gradient histogram, which is used for detecting and identifying various traffic signal lights appearing in a real road traffic environment. In this method, the color of the image is normalized by Gamma correction method, and the RGB space image is convertedinto gray image by gray processing. By extracting HOG and local RGB color histogram, the HOG-RGB combinatorial feature is used as the basic feature descriptor for detecting traffic lights, The adaptive background suppression filtering method proposed by the invention is used for detecting candidate areas of traffic lights. After a series of candidate windows are obtained, the candidate areas arefurther verified and recognized as different semantic types by using a linear SVM classifier obtained by training, and the specific types of traffic lights are confirmed. The invention utilizes the adaptive background filtering method to extract and detect the traffic signal lamp features, and has good robustness to different weather and illumination in actual driving environment.