72 results about "Map quality" patented technology

The invention discloses a stereo image quality objective evaluation method based on visual perception. Firstly, a stereo image is divided into a strong edge block, a weak edge block , a flat block and a texture block, and characteristic information reflecting image quality and depth perception of different zone blocks is extracted through computation so as to obtain stereo image characteristic vectors; and then the characteristic vectors of distorted stereo images of the same distortion type in a distorted stereo image set are trained through support vector regression, and each distorted stereo image of the same distortion type is tested by a support vector regression training model to obtain the objective image quality evaluation forecast value of each distorted stereo image. The method has the advantages that the obtained characteristic vector information reflecting the image quality and the depth perception has stronger stability and can better reflect the quality change condition of the stereo images, and the relevance of an objective evaluation result and subjective perception is improved.

An assessment module for assessing data from a digital map for a vehicle. The assessment is made on the basis of a piece of dedicated map quality information and measurement data from an ambient sensor system in the vehicle. The connected driver assistance systems and safety systems in the vehicle use the digital map data on the basis of the assessment.

A navigation system is described which includes a navigation processor, an inertial navigation unit configured to provide a position solution to the navigation processor, and a digital elevation map. The described navigation system also includes a radar altimeter having a terrain correlation processor configured to receive map data from the digital elevation map and provide a position solution based on radar return data to the navigation processor. A map quality processor within the navigation system is configured to receive map data from the digital elevation map and provide a map quality factor to the navigation processor which weights the position solution from the terrain correlation processor according to the map quality factor and determines a position solution from the weighted terrain correlation processor position solution and the position solution from the inertial navigation unit.

The invention discloses an objective evaluation method of no-reference video quality weighted by key frame image quality. The method comprises the steps of: firstly, preliminarily selecting a key frame according to a movement intensity method weighed by a human eye interest, then dividing the preliminarily selected key frame into a scene switching type key frame and a non-scene switching type key frame by correlation analysis; evaluating image quality of the non-scene switching type key frame, subdividing the non-scene switching type key frame into a content change type key frame and a quality change type key frame according to an evaluation result; finally carrying out weighted summation calculation on single frame quality by using a duration factor and a deterioration frequency factor of the quality change type key frame, and obtaining the quality evaluation result of an entire video sequence. According to the objective evaluation method, the defect that then existing evaluating technology cannot be coincided with an actual subjective feeling; computation complexity can be reduced on the premise of ensuring the evaluating performance; the objective evaluation method is suitable for automatic evaluation of various video applications of an existing network, simple in operating steps and easy to integrate.

A mapping and quality control system and method for the distribution of flyers, circulars and other such hardcopy media is disclosed. For example, a Web based system for displaying location-specific market data for an area in view of targeting a delivery of hardcopy media via a carrier in the area is comprised of at least one database, comprising the market data and a number of distribution zones, a data processor for mapping the market data and the distribution zones on a map and, a communicatively linked Web server and client for displaying the map via a client output device. Further, an exemplary system for use in providing a mapped quality control of objects delivered at locations along a distribution route comprises a portable positioning device for acquiring a QC position of at least one object delivery location, a data processor adapted to assign a positive delivery status marker thereto and, an output device for displaying the marker on a map of an area encompassing the distribution route. Methods and computer readable programs for implementing such systems are also disclosed.

The invention discloses a binocular stereo matching method based on a joint up-sampling convolutional neural network. The method comprises the following steps: firstly, carrying out feature extractionon an input three-dimensional image pair by utilizing a two-dimensional convolutional neural network based on joint up-sampling; constructing an initial three-dimensional matching cost amount of thematching cost by splicing the features of the three-dimensional image pairs, further performing cost aggregation on the matching cost amount by adopting three cascaded three-dimensional convolutionalneural networks based on joint up-sampling, and finally obtaining a dense disparity map with sub-pixel precision by utilizing a regression method. Compared with an existing binocular stereo matching deep neural network, the convolutional neural network based on pyramid joint up-sampling is adopted in the decoding stage of the feature extraction and cost aggregation steps; and by fusing multi-leveland multi-scale context feature information, more detail textures can be effectively reserved in the up-sampling process, the calculation efficiency of the method is improved by adopting depth separable convolution with low calculation complexity, and the disparity map quality of binocular stereo matching is improved.

The invention provides a method and device for randomly detecting and evaluating navigation electronic map quality, which belong to the technical field of navigation. The method comprises the steps of randomly selecting navigation electronic map information of an area to be detected from a navigation electronic map; obtaining field data of the area to be detected; comparing the selected navigation electronic map information of the area to be detected with the obtained filed data of the area to be detected so as to obtain comparing results; counting the comparing results and calculating an average correct rate of the comparing results according to the count comparing results; and obtaining an integral quality distribution level of the navigation electronic map product through calculating a detecting and evaluating result of a sample area. Therefore, quality detecting efficiency of the navigation electronic map product is effectively increased and the timeliness of the detecting result is assured.

The invention discloses an adaptive image quality objective evaluation method. When the adaptive image quality objective evaluation method is adopted to obtain the structural similarity between two image blocks which have the same coordinate position in an original undistorted image and a distorted image to be evaluated, not only are the brightness mean value and standard deviation of all pixels in each image block in the original undistorted image and the distorted image to be evaluated, as well as the covariance among all the pixels in two image blocks in the original undistorted image and the distorted image to be evaluated utilized, but also the distortion types of the distorted image to be evaluated are utilized in a combined manner. Thus, the adaptive image quality objective evaluation method can determine the structural similarity between two image blocks according to the distortion types of the distorted image to be evaluated in the perspective of adaptive evaluation, and therefore, the consistency between an objective evaluation result and subjective perception about image quality can be improved.

The invention relates to a robot multi-sensor fusion positioning method and application thereof in an intelligent cleaning robot, and the method comprises the following steps: installing a laser scanner and an IMU on a robot, installing encoders on two wheel shafts of the robot, and installing a GPS if the robot is outdoor and needs to use the GPS; if the UWB is required to be used indoors, installing the UWB, and installing the RFID and the camera; constructing an environment map of the place by the robot through the SLAM; during positioning, firstly, giving an initial pose of the robot, in the subsequent positioning process, using a graph optimization method to calculate the moving distance of the robot, wherein the moving distance is made to most accord with the moving distance, observed by all sensors, of the robot, and obtaining accurate positioning of the current robot in combination with the pose at the last moment; according to the invention, the current position can be accurately identified, the positioning accuracy is greatly improved, and the phenomenon that the robot loses positioning in an environment with poor map quality is avoided.

The invention discloses an image optimization method by blending of ultrasound fundamental wave and harmonic wave. Based on a method of covariance coefficient of texture analysis and Yin Yang normalization, the invention combines the logarithmic compressed ultrasound fundamental wave and the texture harmonic wave, and then a blending image is obtained through scanning and transformation, so that the advantages of both can be utilized and the image quality can be improved.

The invention discloses a no-reference quality evaluation method for fuzzy images, comprising the steps of dividing a to-be-evaluated image into blocks, carrying out second fuzzy processing, measuringthe luminance, contrast and fuzziness, calculating a no-reference quality evaluation function, and obtaining average improved structure similarity. The second fuzzy theory and the structure similarity index (SSIM) algorithm are combined, and the SSIM algorithm is modified by making full use of the data information contained in the image and introducing the histogram information of the image. Thequality index of the image can be obtained by comparing the SSIM' values of the to-be-evaluated image and the second fuzzy image thereof. The image quality of the to-be-evaluated image can be accurately evaluated. The human visual characteristic is taken into account, and no reference image is needed. Therefore, the method has a wider application range.

When a memory card MC is inserted into a slot, a control circuit of a color printer acquires and analyzes graphics processing control information GC from the memory card MC. A CPU corrects standard values for image quality parameters indicating properties of graphics data, to reflect the graphics processing control information GC. The CPU corrects image quality parameters to bring them into approximation with the corrected standard values, and adjusts image quality of the graphics data to reflect the corrected image quality parameters.

The invention relates to the quality evaluation method without a reference image based on regional mutual information and belongs to the computer image analysis field. The method comprises the following steps: 1 carrying out 256*256 image blocking on an image so that image analysis is accurate; 2 acquiring regional mutual information RMI of an image block in each image block and dividing into 12 groups of RMI through distinguishing a size of a window, a motion direction and a distance size; 3 for each group of the RMI, describing a characteristic, carrying out wavelet decomposition processing on each group of the RMI, calculating a mean value of decomposed RMI low frequency information and calculating a variance of high frequency information; 4 using each dimension of RMI description characteristic to calculating a mean value of all the image block characteristics and taking the mean value as a characteristic of a whole image; 5 using a support vector machine and a method of combination of two step framework in image quality evaluation to calculating image quality. The method has advantages that subjective consistency is high; versatility is good and so on. The method can be used in an image processing correlation system and image equipment and possesses a good utility value.

The invention relates to a map optimization method and system for a mobile robot SLAM. The method comprises the steps: acquiring the environment information collected by a mobile robot, wherein the environment information is obtained by scanning in an environment of a to-be-constructed map by the mobile robot; obtaining a plurality of indexes representing the quality of the environment grid map, wherein the plurality of indexes comprise a grid occupancy rate, an angular point number and a closed area number; taking the plurality of indexes as targets, and performing multi-target iterative optimization according to the environment information and the ranges of the plurality of parameters to be optimized in the SLAM algorithm to obtain an optimal parameter combination in the SLAM algorithm,wherein the SLAM algorithm is used for generating an environment grid map according to the environment information; generating an optimal environment grid map by adopting an SLAM algorithm of the optimal parameter combination according to the environment information, wherein the optimal environment grid map is used for navigation and positioning of the mobile robot. According to the invention, themap construction efficiency and the map quality of the SLAM can be improved.

The invention discloses an image quality evaluation method and device, wherein the method includes: calculating the loss of an evaluated image relative to a reference image on a luminance component, and obtaining the luminance loss result of the evaluated image; calculating the edge loss of the evaluated image relative to the reference image, and obtaining the edge loss result of the evaluated image; obtaining the quality score of the evaluated image according to the luminance loss result and the edge loss result of the evaluated image. The luminance component loss and the edge loss are synthetically evaluated according to the characteristics of human visual system, thereby enhancing the consistency between objective evaluation and subjective evaluation results and improving the accuracy of the evaluation results.

The invention discloses a video monitoring service quality measuring method, a system and a quality measuring server. In the video monitoring service quality measuring method, the quality measuring server obtains current video information, corresponding to equipment information, collected by video monitoring equipment in current time from a video monitoring service server at preset time intervals. When indicators of image quality of the current video information are beyond a preset range, historical video information of the video monitoring equipment is acquired through the video monitoring service server, and a reference value is calculated on the base of the historical video information. Judging whether the indicators of the image quality of the current video information are within the allowable range of the reference value or not, if the indicators of the image quality of the current video information are beyond the allowable range, the quality measuring server sends warning information of abnormal video quality to a quality management server. Due to the fact that definite scenes are referred to when the image quality of the current video information is judged, probability of generating incorrect warning information is reduced, and cost of system maintenance is lowered.

The invention provides an unattended high-precision map quality inspection method and a device, and the method comprises the steps: extracting a first traffic element set through a first deep learningmodel based on the collected high-precision map data; extracting a second traffic element set from the high-precision map data through a second deep learning model, and performing quality inspectionon the first traffic element set by taking the second traffic element set as a standard; and error type marking is performed on the error targets which are unqualified in quality inspection, and the error targets are recorded in a database, so that an operator can modify the error targets according to the error type marks. According to the method, an artificial intelligence deep learning algorithmis used to replace quality inspection personnel to carry out quality inspection on the extracted traffic factor information. A traditional manual quality inspection mode is low in efficiency, and theinfluence of high subjectivity of manual quality inspection on the quality inspection accuracy is avoided. The labor cost is reduced, and the quality inspection efficiency of the high-precision map is improved.

The invention discloses a significance detection method based on background priors. The method comprises the steps that a color image and a depth image are taken as input; superpixel segmentation anddepth map quality evaluation are carried out on the input images through a preprocessing operation; based on depth selective difference and background priors, the initial significance value of each superpixel region is calculated; and finally an initial significance map is optimized through the minimization of a cost function to acquire the final significance detection result. According to the invention, the problem that a traditional significance detection method based on color images cannot detect objects with similar visual characteristics with the background is solved; the problem of misdetection, which is caused by the fact that a bottom background region cannot be ignored when significance detection is carried out simply by relying on depth information; is solved; and the method provided by the invention is suitable for significance detection with color images and depth images, has the advantages of good overall effect and high accuracy, and can effectively detect significant objects.

An ultrasound diagnostic apparatus is provided which includes an ultrasound probe performing ultrasound transmission and reception in different directions and a diagnostic apparatus body combining images different in the direction of transmission and reception to produce an ultrasound image. At least one of ultrasound images to be combined is changed depending on the set region of interest (ROI) to an image of the depth corresponding to the ROI. When an acoustic coupler is attached, the depth of at least one of ultrasound images to be combined is increased. The ultrasound diagnostic apparatus is capable of improving the image quality of the ROI and obtaining efficient high-definition ultrasound images reaching a predetermined depth when the acoustic coupler is attached.

The invention discloses an establishment method of evaluation criterion parameters, wherein the evaluation criterion parameters are used for evaluation on the image quality of a display screen; and the image quality is inversely proportional to the severity of mura and hotspots of images. The establishment method comprises the following steps of photographing a group of various-severity test images to obtain a sample photo group; selecting standard photos from the sample photo group by eyes; performing Fourier transformation on the brightness of the photos in the sample photo group to obtain frequency distribution functions; convolving the frequency distribution functions and human-eye contrast sensitivity functions in a frequency domain to obtain convolution functions; normalizing the convolution functions to obtain evaluation parameters; and selecting the evaluation parameters of the standard photos from the evaluation parameters of all the photos in the sample photo group as the evaluation criterion parameters. The invention also discloses a method for evaluating the image quality of the display screen. With the adoption of the establishment method of the evaluation criterion parameters and the method for evaluating the image quality of the display screen, the evaluation criterion parameters which are more objective and more systematic can be obtained.

The invention provides a robotic map-building and positioning system and a robot. The robotic map-building and positioning system comprises: an obstacle detection module for acquiring information of obstacles around a robot; an inertial measuring module for acquiring acceleration and angular velocity data of the robot; a correcting module for correcting measured information of the obstacles; a map-building and positioning module for building an E-amp for an environment where the robot locates according to the information of the obstacles around the robot, and acquiring current attitude of therobot. The robotic map-building and positioning system and the robot have the advantages that the measured information of the obstacles around the robot can be corrected, the information of the obstacles is more reliable, a map-building and positioning algorithm is more accurate and robust, and map quality and robot positioning precision are improved accordingly.

The invention provides a method, a device and a system for map quality inspection, and a storage medium. The method comprises: obtaining thermodynamic values of all regions on the map; wherein the thermodynamic value of each area is used for representing the reliability of the corresponding area; wherein the thermodynamic value and a reliable value of an area corresponding to the thermodynamic value are in a negative correlation mapping relation; determining an area of which the thermal value is greater than a preset threshold as a target area; and displaying the target area on the map. Therefore, map quality inspection personnel can quickly determine a problematic area, the area is corrected, the manual detection time is shortened, and the labor cost is reduced. The map production time isshortened, and the map production efficiency is improved.

The invention provides a prediction method and a device for air quality, a computer device and a readable storage medium. The prediction method for air quality comprises steps of obtaining historicalpollutant data at a pollutant collection spot and historical meteorological data at a meteorological collection spot; weighting the historical pollutant data according to location information of the pollutant collection spot and the meteorological collection spot to obtain historical pollutant mapping data at the meteorological collection spot; training the historical pollutant mapping data and the historical meteorological data as a training set with an extreme gradient ascent model to obtain a prediction model; using the prediction model to obtain predicted air quality data based on the current pollutant data and the current meteorological data. The prediction method for air quality proposed by the invention does not need to preprocess missing data, so that the whole deep learning process is faster, and the historical pollutant data collected at the pollutant collection spot is mapped to the meteorological collection spot, so as to obtain more accurate sample data, improving prediction accuracy.

An assembly, system, and method particularly adapted for a magnetic resonator apparatus in a magnetic resonance imaging (MRI) scanning system. Provided is a restraining assembly supportable by the supporting assembly and including at least a system that is selectively operable for positioning and restraining at least a portion of the body to be imaged, thereby optimizing image quality.

The invention provides a map quality inspection method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to an electronic map technology. According to the specific implementation scheme, the method comprises the steps of obtaining a road map of a current version and a road map of a previous version; comparing the road map of the current version with the road map of the previous version to obtain road network differential data of the road map of the current version relative to the road map of the previous version; and carrying out quality inspection on the road map of the current version based on the road network differential data of the road map of the current version relative to the road map of the previous version. The embodiment of the invention not only can reduce the labor cost and improve the quality inspection efficiency, but also can improve the accuracy of map quality inspection, so that the map quality inspection is more targeted.

The invention discloses a map quality detection processing method and device, electronic equipment and a storage medium, and relates to the field of automatic driving. According to the specific implementation scheme, the method comprises the steps that obtaining a map to be detected, obtaining traffic indication information marked in the map, obtaining actual road test data of each detection areain the map, obtaining vehicle driving behaviors of each detection area according to the actual road test data of each detection area, and generating a detection result of the map according to the traffic indication information and the vehicle driving behaviors corresponding to each detection area. Therefore, automatic detection of the map is realized through the vehicle driving behaviors and thetraffic indication information marked in the map, and the high-precision map detection efficiency and accuracy can be improved.

The invention discloses a no-reference image quality evaluation method based on deep forest classification. The method comprises the following steps: step 1, image classification; step 2, extracting color quality characteristics of the image; step 3, extracting texture quality characteristics of the image; step 4, simulating the difference of different people on image quality cognition by utilizing the difference of decision tree extraction features in the deep forest classification model, and constructing the deep forest classification model to classify the image quality, including a multi-granularity scanning forest and a cascade forest; step 5, training the deep forest classification model based on the image quality features and the category labels thereof to obtain the probability thatthe test image belongs to different categories, i.e., statistical information of subjective evaluation results of different people on the image quality; step 6, setting a quality anchor, and fully considering the difference in the subjective evaluation process in combination with the probability that the image belongs to different categories to obtain a final image quality score. According to thenon-reference image quality evaluation method, the difference of different people for image quality cognition is simulated by using the deep forest, so that an image quality evaluation result is given. The method has important theoretical significance and practical value.

An assessment module for assessing data from a digital map for a vehicle. The assessment is made on the basis of a piece of dedicated map quality information and measurement data from an ambient sensor system in the vehicle. The connected driver assistance systems and safety systems in the vehicle use the digital map data on the basis of the assessment.