948 results about "Histogram equalization" patented technology

A system and method for reliable content access using a cellular / wireless device with imaging capabilities, to use part of a printed or displayed medium for identifying and using a reference to access information, services, or content related to the reference, including capturing an image of the reference with an imaging device, sending the image via a communications network to a processing center, pre-processing to identify relevant frames within the image and to perform general purpose enhancement operations, detecting the most relevant frame within the image, and frame properties, applying geometric, illumination, and focus correction on the relevant frame, using color, aspect ration, and frame color, to perform a coarse recognition and thereby limit the number of possible identifications of a reference within the relevant frame, and using specific techniques of resealing, histogram equalization, block labeling, edge operation, and normalized cross correlation, to identify the reference within the image.

A system and method for reliable content access using a cellular / wireless device with imaging capabilities, to use part of a printed or displayed medium for identifying and using a reference to access information, services, or content related to the reference, including capturing an image of the reference with an imaging device, sending the image via a communications network to a processing center, pre-processing to identify relevant frames within the image and to perform general purpose enhancement operations, detecting the most relevant frame within the image, and frame properties, applying geometric, illumination, and focus correction on the relevant frame, using color, aspect ration, and frame color, to perform a coarse recognition and thereby limit the number of possible identifications of a reference within the relevant frame, and using specific techniques of resealing, histogram equalization, block labeling, edge operation, and normalized cross correlation, to identify the reference within the image.

Methods and apparatus for video contrast enhancement are disclosed. A pixel level threshold is set for an input video frame in a video sequence. For pixel levels in the input video frame that are below the threshold, an adaptive contrast-enhancing function is applied. For other pixel levels, a scene-stable mapping function is applied.This contrast enhancement method can improve the contrast in darker areas of a scene depicted in the video sequence, without destroying the intended light levels for a scene or causing temporal brightness fluctuations in the enhanced video sequence.

The invention discloses a panoramic parking assist system, which is a technique applied to the fields of automotive electronics and auto safety driving assistance. The system comprises a camera input module, an image correction module, a bird-eye perspective module, an image mosaicking module, a histogram equalization module and an image display module. The system is used for effectively solving the problem of car crash accidents caused by that the driver can not fully observe the situations around the car in the process of parking. By using the system, a 360-degree top view around a car bodyin real time can be showed for the drivers, so that the drivers can clearly see the relative positions of obstacles around the car body and the distances between the obstacles and the car body; and the blind visual areas around the car body can be eliminated so as to improve the security and stability of car in the process of parking. Through integrating a plurality of machine vision and digital image processing algorithms and fusing a plurality of advanced techniques in the field of machine vision, and based on the advantages of low cost, high performance, strong portability and the like, the system is convenient to be popularized and applied widely.

A vision-based hand movement recognition system and method thereof are disclosed. In embodiment, a hand posture is recognized according to consecutive hand images first. If the hand posture matches a start posture, the system then separates the consecutive hand images into multiple image groups and calculates motion vectors of these image groups. The distributions of these motion vectors are compared with multiple three-dimensional motion vector histogram equalizations to determine a corresponding movement for each image group. For example, the corresponding movement can be a left moving action, a right moving action, an up moving action or a down moving action. Finally, the combination of these corresponding movements is defined as a gesture, and an instruction mapped to this gesture is then executed.

The invention relates to a dynamic contrast enhancement device and a dynamic contrast enhancement method. The device comprises a color space switching module, a histogram statistical module, an enhanced mapping function module and a brightness transformation module, wherein the color space switching module is used for switching an input image data from a red, green and blue (RGB) color space to aluma and chroma (YUV) color space; the histogram statistical module is used for counting a gray histogram of an image according to a brightness component Y; the enhanced mapping function module is used for calculating weights of a plurality of brightness component intervals in the histogram and obtaining an adaptive mapping function f according to the designed mapping table of each brightness component interval and the weights; and the brightness transformation module is used for transforming the brightness Y of the image to a novel brightness Y' according to the adaptive mapping function f, and outputting the brightness in the RGB color space through the color space switching module. By the device, the image is divided into the plurality of brightness component intervals, the weights arecalculated respectively, and the adaptive mapping function is obtained by a weighting method to transform the image brightness; therefore, the processed image can keep a mean brightness, the contrastis effectively improved, and the problems of level reduction and details loss caused by the traditional histogram equalization are solved.

The invention discloses a light stream based vehicle motion state estimating method which is applicable to estimating motion of vehicles running of flat bituminous pavement at low speed in the road traffic environment. The light stream based vehicle motion state estimating method includes mounting a high-precision overlook monocular video camera at the center of a rear axle of a vehicle, and acquiring video camera parameters by means of calibration algorithm; preprocessing acquired image sequence by histogram equalization so as to highlight angular point characteristics of the bituminous pavement, and reducing adverse affection caused by pavement conditions and light variation; detecting the angular point characteristics of the pavement in real time by adopting efficient Harris angular point detection algorithm; performing angular point matching tracking of a front frame and a rear frame according to the Lucas-Kanade light stream algorithm, further optimizing matched angular points by RANSAC (random sample consensus) algorithm and acquiring more accurate light stream information; and finally, restructuring real-time motion parameters of the vehicle such as longitudinal velocity, transverse velocity and side slip angle under a vehicle carrier coordinate system, and accordingly, realizing high-precision vehicle ground motion state estimation.

The invention discloses a method for splicing a video in real time based on multiple cameras, which comprises the following steps of: acquiring synchronous multi-path video data; preprocessing frame images at the same moment; converting a color image into a grayscale image; enhancing the image, and expanding the dynamic range of grayscale of the image by a histogram equalization method; extracting the characteristic points of corresponding frames by using a speeded up robust features (SURF) algorithm; solving matched characteristic point pairs among corresponding frame images of the video by using a nearest neighbor matching method and a random sample consensus matching algorithm; solving an optimal homography matrix of initial k frames of the video; determining splicing overlapping regions according to the matched characteristic point pairs; taking a homography matrix corresponding to a frame with highest overlapping region similarity as the optimal homography matrix, and splicing subsequent video frame scenes; and outputting the spliced video. The method can reduce the calculated amount of splicing the video frame single-frame image, improves the splicing speed of traffic monitoring videos and achieves real-time processing effect.

The invention relates to an optimized human face recognition preprocessing method. The method comprises the following steps: firstly, converting a color human face image from a camera into a gray level image, then performing a scale normalization processing on the gray level image to cause the human face images to have the same size and posture, dividing the human face images into low-frequency components and high-frequency components by wavelet transformation, performing a histogram equalization processing on the low-frequency components only, executing wavelet reconstruction on the processed low-frequency components and the high-frequency components, and finally processing the reconstructed images by optimized median filtering. The method has the advantages of regulating the gray level range of the human face images, enhancing the contrast, better improving the human face gray level images with higher brightness, and enhancing the human face identification efficiency in a complicated illumination environment with different postures.

The invention relates to a face image gender recognition method based on stack type sparse self-coding, and belongs to the field of image recognition, machine learning, and computer vision. A training process of the method includes image graying, histogram equalization, geometric correction, image normalization, the training of a sparse self-coding model, logic regression classifier training, a fine tuning model, and model fusion of face standard databases FERET and CAS-PEAL-R1, and a prediction process comprises the capturing of natural scene images by a camera, image graying, histogram equalization, face detection, geometric correction, image normalization, the prediction by employing a stack type sparse self-coding model, and result marking. According to the method, the problem of face gender recognition is solved by employing the stack type sparse self-coding model, combination characteristics of the images can be learned layer by layer, original signals can be better represented in an abstract manner, characteristics extracted by a hiding unit are further adjusted by the adoption of fine tuning, and the recognition accuracy is higher.

An apparatus for and method of performing a most informative feature extraction (MIFE) method in which a facial image is separated into sub-regions, and each sub-region makes individual contribution for performing facial recognition. Specifically, each sub-region is subjected to a sub-region based adaptive gamma (SadaGamma) correction or sub-region based histogram equalization (SHE) in order to account for different illuminations and expressions. A set of reference images is also divided into sub-regions and subjected to the SadaGamma correction or SHE. A comparison is made between the each corrected sub-region and each corresponding sub-region of the reference images. Based upon the comparisons made individually for the sub-regions of the facial image, one of the stored reference images having the greatest correspondence is chosen. While usable individually, using the MIFE and / or SadaGamma correction or SHE together achieves a lower error ratio in face recognition under different expressions, illuminations and occlusions.

The invention belongs to the technical field of intelligent traffic and relates to a monocular vision based front vehicle detection and ranging method. The method is implemented by comprising: (1) converting a video image acquired by a vehicle-mounted camera into a gray image; (2) selecting a region of interest according to a camera imaging range to perform histogram equalization; (3) initially detecting an equalized image by using a classifier to obtain a rectangular region containing vehicle information; (4) verifying an initial detection result by using priori knowledge to remove false detections; (5) calculating an intersecting line of a tire and the ground by utilizing a vehicle bottom shadow to obtain an accurate vehicle position; and (6) performing vehicle ranging by taking the vehicle position and a lane line detection result as priori information. According to the method, the problems of illumination sensitivity due to single use of a priori knowledge based method and excessive false detections generated in a machine vision based method are solved; the vehicle can be accurately and effectively detected and the vehicle ranging is accurate; and the real-time requirement of intelligent vehicle control can be met.

The invention belongs to the technical field of vehicle active safety and relates to a lane line detection method based on monocular vision. The lane line detection method comprises the following steps: acquiring an original image of a road condition in front of a vehicle; intercepting subimages of boundary regions at the two sides, wherein the subimages include the original image; removing the high-frequency noise of the subimages and carrying out grey level histogram equalization and binaryzation; intercepting left and right lane line detection region images; filtering based on the fixed width range of a lane line according to the features of low grey value pavements at the two sides of the lane line; preliminarily determining feature points of the lane line in a sampling line scanning manner and screening; and carrying out lane detection. The lane line detection method has the characteristics of good real time and high reliability.

A data communications system and methods are disclosed. The system includes a transmitter for conveying a data signal filtered by a finite impulse response (FIR) filter to a receiver via a channel. The receiver equalizes the received data signal using a decision feedback equalizer (DFE) and the FIR. The receiver samples the data signal to determine an error signal and uses the error signal to adapt settings of a pre-cursor tap coefficient of the FIR, one or more post-cursor tap coefficients of the FIR, a phase of the recovered clock, and a coefficient of the DFE. To adapt the settings, the receiver determines the error signal based on an error sample taken from the data signal in a single clock cycle. To determine an error signal, the receiver samples the data signal at a phase estimated to correspond to a peak amplitude of a pulse response of the channel.

The invention provides a defocus blurred image definition detecting method based on edge strength weight. The defocus blurred image definition detecting method comprises the following steps: firstly, pre-processing an input image, correcting brightness and a contrast ratio through a histogram equalization method, performing Wiener filtering processing on constant power addition noises in a digital camera system, and respectively processing impulse noises and Gaussian noises through a median filter and a Gaussian filter; then, adopting four direction edge gradient operators to detect gradient of each pixel point, eliminating interferences of local bright dark points and isolated noise points according to the detected gradient size, and further processing the residual pixels; comparing direction gradient of the residual pixels with a set threshold value, distinguishing strong edge pixels with relatively large edge gradient values and weak edge pixels with relatively small boundary vicinity gradient values, and respectively endowing different weights; finally, adding up maximum gradient square of all pixels to obtain a definition detected value of the whole image.

The invention relates to an automatic detection method of particle size distribution, which is characterized by comprising the following steps: step one, preprocessing images, converting images to be detected into HSV space from RGB space, acquiring chroma, brightness and saturation components of the images to be detected, carrying out Gaussian smoothing filter and histogram equalization to all components and automatically enhancing the brightness, the color and the contrast of the images; step two, carrying out morphological smoothing of color images so as to avoid images caused by the effect of shadow and reflected light; step three, judging a particle area and a centroid thereof and eliminating pseudo-boundary points positioned on the surfaces of particles after completing the process of boundary extraction operation, wherein the pseudo-boundary points have the characteristics of boundary points but are not boundary points ; step four, expanding particles maximumly and carrying out maximum expansion operation of the particle area, i.e. dividing the whole material surface into a plurality of parts according to the principle of proximity to one of the particles; step five, calculating particle size by a double-circle method; and step six, calculating particle size distribution. Accordingly, the invention provides direct information reference of particle size distribution for follow-up decision operation of industrial process control.

An image processing method and system using gain-controllable clipped histogram equalization, in which the image processing method includes the steps of as obtaining a brightness histogram, computing a mean brightness of an image signal, determining a clipping rate based on the mean brightness, determining a clipping threshold based on the clipping rate, obtaining a clipped brightness histogram by clipping frequencies exceeding the clipping threshold in the brightness histogram, obtaining a corrected brightness histogram by correcting the clipped brightness as histogram using the clipping rate as a total gain, obtaining a cumulative histogram from the corrected brightness histogram, and correcting an input image using the cumulative histogram as a transformation function. Accordingly, the clipping rate is adaptively controlled so that image contrast is enhanced.

A user interface uses histogram equalization to allow easier manipulation and visualization of a data set, especially data sets having values that are not distributed uniformly. The user interface can show values of a data set in different colors or shades of colors, or a combination of these, or in grayscale. Additionally, values can also be represented according to a size or area relative to other values. A user may change views of the data by adjusting controls of the interface including slider bars, dials, tool bars, button bars, and others, and combinations of these.

The invention discloses a vision-based quick LED module brightness uniformity detection method. The method comprises the following steps: S1, a light emitting image of the LED module is acquired, gray processing is carried out on the image, the image is filtered after gray processing, the image after being filtered is subjected to morphological processing, and the image after morphological processing is subjected to histogram equalization processing; S2, each LED lamp point is positioned through block positioning algorithm, the LED lamp point is subjected to gray processing, the LED lamp point after gray processing is subjected to mathematical morphological open operation, and the brightness value of the LED lamp point and an average value are acquired; and S3, after each LED lamp point in the LED module is traversed, the brightness value of each LED lamp point is displayed, and the final processing result is displayed. The method plays an important role in yield detection during the LED lamp production process.

The invention discloses a method for automatically detecting the printing defects of a remote controller panel based on an SURF (Speed-Up Robust Feature) algorithm. The method disclosed by the invention comprises the following steps: carrying out histogram equalization on a to-be-detected sample image by making a remote controller template image, respectively acquiring feature points of the template image and the to-be-detected image through the SURF algorithm, matching the feature points by utilizing a partitioned and accelerated nearest neighbour matching method, acquiring a homography matrix according to a matching result, carrying out affine transformation on the to-be-detected image to obtain a correction image by utilizing the homography matrix, processing difference images of the template image and the correction image superposed with a mask, carrying out binaryzation and morphological processing on a difference image result, judging whether a to-be-detected sample is qualified, if the to-be-detected sample has a defect, locating the position of the defect; if the to-be-detected sample does not have a detect, judging that the to-be-detected sample is qualified, and completing detection. The method disclosed by the invention is capable of effectively detecting defects in the to-be-detected sample and accurately locating the positions of the defects.

The invention relates to a palm vein feature extracting and matching method based on eight neighborhood and secondary matching. The method comprises the following steps that 1) a palm vein image is collected; 2) the collected vein image is pre-processed, a palm is extracted in a binary manner, key points of the palm are positioned, and the vein image is enhanced by histogram equalization after normalization; 3) a vein contour is extracted from the enhanced vein image in a Niblack threshold segmentation method; and 4) feature points are extracted in the eight neighborhood algorithm, and a matching method in which local neighborhood matching is combined with European distance matching is used. The method provided by the invention is higher in the safety level.

The invention discloses an improved self-adaptive histogram equalization method. By finding a dividing gray scale which divides the total number of pixels of each sub-image block into a proximate half, and counting the the total number of the pixels in both high light areas and low light areas of the images, the adjusting parameters of the high light areas and low light areas of the images are calculated, and sub-image blocks are divided into high light areas and low light areas and then respectively subjected to histogram equalization, thereby effectively solving the problems of noise diffusion and texture merging.

The invention provides a self-adapting image enhancement method, belonging to the digital image processing technology field. The self-adapting image enhancement method comprises the following steps: setting a threshold according to a gray level scope of an image; carrying out automatic color interval enhancement if contrast is smaller than the threshold; carrying out method enhancement based on histogram equalization and anti-equalization transformation if the contrast is larger than the threshold. According to the self-adapting image enhancement method, an image with low contrast can be enhanced, images with part excessive dark and part excessive bright can be enhanced, robustness is strong, and the image after enhancement has a better visual effect.

The present invention provides an automatic cloud detection method and system for a multi-spectral remote sensing satellite image. The method comprises: preparing data; roughly extracting clouds; extracting texture information on an image intensity information channel by combining histogram equalization and bilateral filtering; segmenting a texture information map by using a two-dimensional Otsu threshold; removing an error from a rough detection result by using a binary detail map after segmentation; using intensity information of a raw image as a guide map; and based on the rough detection result with the error being removed, performing accurate extraction on the clouds through edge seed expansion. The technical scheme actually realizes automatic, fast, and accurate detection of cloud.

The invention discloses a gender identification method and system based on a face image. The method includes the following steps that firstly, the face image is preprocessed; the face image is converted into a grey level image firstly, then a face area image is cut from the whole image according to the positions of the eyes to obtain the face area image, and finally de-noising processing is performed on the face area based on histogram equalization; secondly, a composite LBP is used for performing feature extraction, a zoom LBP is used for conducting zoom and multiple setting on the image, then a multi-scale LBP is used for dividing the face image into a plurality of areas according to different sizes of LBP histogram areas, LBP feature extraction is performed on the image and each divided area, and all LBP histogram features are combined together to serve as multi-scale LBP features of the image; thirdly, gender identification is performed based on an SVM model. According to the gender identification method and system, the gender of the face can be identified according to the face image, and the identification accuracy is improved.

The present invention relates to digital image processing and high dynamic range image processing. In order to detect and correct ghosting generated in the image fusing process, the present invention adopts the technical scheme that a multi-exposure image fusion ghosting removing method based on background modeling comprises the following steps of: firstly, converting images into gray-scale images and carrying out histogram equalization on a series of images with different exposures, so that the gray scales of the images are normalized into a range of 0 to 255 and are uniformly distributed; then a moving object is used as a singular point and obtaining a modeling result of removing a background image of the moving object by utilizing median filtering; respectively subtracting the background image from the images comprising the moving object to obtain the images which only comprise the moving object after the background is removed; and by morphological operations comprising erosion and dilation, carrying out precision processing on the moving object images, using an obtained result as a weight map and then according to the weight map, carrying out fusion on the images. The multi-exposure image fusion ghosting removing method based on background modeling is mainly applied to digital image processing.

The invention relates to the field of computer vision and specifically discloses an improved histogram equalization low-illumination image enhancement algorithm. The algorithm is characterized by, to begin with, calculating contrast parameters p of an image to be processed H(x) according to a formula based on an original image, and calculating p' through a contrast mapping function f(x); obtaining a new gray value according to a formula, and obtaining an image H' (x) obtained after contrast enhancement processing; carrying out grayscale merging method improvement on the H' (x); and finally, carrying out histogram equalization through histogram grayscale spacing improvement to obtain an image G(i) obtained after final processing. The algorithm takes both overall effects and local details into consideration, so that image information is kept to a greater extent, and meanwhile, image brightness is improved; and a better visual effect is obtained.

The invention relates to a semi-supervised learning-based multi-gesture facial expression recognition algorithm which comprises the steps of acquiring n front expression images and n side expression images of n persons to form a training set X and a testing set S, segmenting face regions of the front expression images and the side expression images, and carrying out illumination compensation on the face regions by using a histogram equalization method; then extracting expression characteristics of the images by adopting a linear discriminant analysis method, carrying out expression recognition on samples in the testing set S; marking each unmarked sample in the training set X by using marked samples in the training set X by adopting an Euclidean distance nearest neighbour method; re-sampling the training set X by adopting a round-robin mode to obtain a new training set Xr; scheduling a basic classifying device to calculate a mark ht of each sample in the training set X at the tth circle by using the new training set Xr, and calculating a mark ft of each sample in the testing set S at the tth circle by using the new training set Xr; and finally, calculating a classifying error rate epsilon t of the basic classifying device to side samples in the training set, and updating weights of all training samples in the training set X until reaching the circle ending condition.