1161 results about "Objective evaluation" patented technology

Devices, systems, and methods measure, diagnose, and/or treat one or both eyes of a patient. Adaptive optics systems (such as those having a deformable mirror) may be configured to an aspherical or multi-spherical presbyopia-mitigating prescriptive shape to allow objective and/or subjective measurements of a candidate prescription. A plurality of viewing distances allow subjective and/or objective evaluations of performance using a light spot or a test viewing image. Measurements of aberrations at selected viewing conditions (including distances and/or brightness) with correlating pupil sizes may also be provided.

A video quality evaluation method based on artificial neural network, which belongs to the field of computer digital video processing. The evaluation algorithm computes damage extent of an image by analyzing spatial characters (blur, entropy, blocking effect, frequency-domain energy analysis, saturation) and time behaviors (frame-to-frame differences). Evaluation effect of the algorithm can be effectively modified by using colour space saturation as one of the parameters for non-reference evaluation algorithm. The system is designed based on artificial neural network, therefore, training process and testing process of the network are included in realization of the algorithm. To the selected training sample (video image sequence), firstly extracts six parameters of the sample, then obtains expected output (subjective evaluation result) in training through subjective evaluation. Characteristic parameters of the training sample and the corresponding subjective evaluation result are inputted to the artificial neural network as training parameters. The experiment shows that evaluation result obtained by the objective evaluation system is highly-consistent with visual sense of human eyes.

The invention discloses a method for detecting lane change of a vehicle based on a vehicle-mounted camera. The method comprises the following steps: firstly, initializing a read-in image, and converting the read-in image to a gray space; secondly, segmenting an sky region and a ground region of the image, and acquiring the image of the ground region; thirdly, carrying out edge detection by utilizing the sobel operator; fourthly, carrying out binaryzation by utilizing an Otsu's method; fifthly, restricting a fitting range, wherein hough transformation is restricted by the minimum fitting points, and extracting a lane line equation; sixthly, determining the type of a lane line; seventhly, classifying lane line treatment results; and eighthly, determining the current lane change situation ofthe vehicle. According to the invention, the current lane change situation of the vehicle is detected by adopting the mode of images, the existing vehicle-mounted camera of a driving school is utilized, has low cost, high practicability, diversity detection data, and has accurate and objective evaluations of lane change level for students, the equipment is simple to install, and has wide applications.

The invention discloses a method for evaluating the objective quality of a full-reference image based on structure similarity, comprising the following steps: firstly, utilizing the space domain visual characteristic to obtain a visual perceptual map of an original image and solve for a position at which visual perception is remarkable; secondly, utilizing an evaluating method based on the structure similarity to solve for a structure similarity drawing SSIM (i, j) between the original image and a distorted image, calculating relative quality of the distorted image, and solving for a position of severe distortion; thirdly, defining the principle of visual attention focus transfer, ensuring a new visual attention focus, and producing a new visual perceptual map after the visual attention focus transfer; and fourthly, obtaining the objective evaluation of the image quality by using the weighted structure similarity of the produced visual perceptual map. The method is suitable for design of various image coding and treating algorithm as well as comparison of different algorithm effects, more accords with a human subjective evaluation on the aspect of an evaluating result of the image, and has a wide application prospect.

The present invention relates to an automatic cloth inspection grade objective evaluation system. It mainly includes CCD camera, fabric holding device, light source, computer and image-collecting card. Said objective evaluation method includes the following steps: utilizing image-collecting card to convert the fabric image into digital image, using fabric defect detection DSP to detect fabric defect, using defect image separation DSP module to separate fabric image with defect, using defect characterization DSP module to make automatic characterization of defect, inputting the characterized result into fabric grade evaluation DSP module to make automatic evaluation of fabric grade.

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.

The present invention provides a method for evaluating compression quality of satellite-loading optical remote sensing image. The method comprises the following steps: obtaining an analog original image; obtaining the image which is decompressed after compression; respectively analyzing the dynamic range of analog original image and dynamic range of compressed image; respectively analyzing the texture detail of analog original image and texture detail of compressed image; analyzing the application effect of compression to image; analyzing the interrelationship between the analog original picture and compressed image; and evaluating the compression quality of image after synthesizing each item of analytical result to analog original image and compressed image. The invention aims at the characteristic of satellite sensor for generating analog image, adopts a comprehensive method which mainly uses objective evaluation and uses subjective evaluation in auxiliary for evaluating compression quality of image fully, systemtically and purposely. A whole system for evaluating the compression quality of satellite-loading optical remote sensing image is constructed.

The invention discloses a method for evaluating quality of streaming video without reference. The method is realized in a simulation environment of simulating network loss on MPEG-TS stream; network damage is simulated through man-made loss, simultaneously loss data is recorded, attribute of the data is analyzed, an image of the loss is stored, and impact to visual effect after the data loss is observed by eye, thereby establishing a one-to-one correspondence. In practical application, a QOE evaluation value which is in accord with subjective feeling of a user can be acquired through sampling and analyzing a transmission media and analyzing the network damage. In the method, video flowing is directly analyzed without source file information, analysis process is simple and complex algorithm is not needed, quality of user experience is reflected in real time and an accurate evaluation value is given, and an objective evaluation method and a subjective evaluation method are combined to ensure the evaluation value is closer to the real feeling of the user.

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.

The invention discloses a single image super-resolution reconstruction method combining depth learning and gradient transformation. The method comprises the steps that a super-resolution method based on depth learning is used to carry out upsampling on an input low-resolution image to acquire an upsampling image; a gradient operator is used to carry out gradient-extracting on the upsampling image; a depth convolutional neural network is used to convert extracted gradient; a cost function is reconstructed by using the input low-resolution image and the converted gradient as constraints; a gradient descent method is used to optimize the reconstructed cost function to acquire a final output high-resolution image. According to the single image super-resolution reconstruction method provided by the invention, the reconstructed image has a fine structure in the subjective visual effect, is free of artificial effect, and has a high objective evaluation parameter value. The invention provides the effective single image super-resolution reconstruction method.

The invention discloses a quality objective evaluation method with no reference images based on multi-scale generative adversarial network. Similar quality images corresponding to distorted images canbe generated through the multi-scale generative adversarial network, and similar quality images in different scales can undergo regression to obtain the image quality scores through convolution nervenetwork. The multi-scale generative adversarial network is trained, and similar quality images are generated for the distorted images through the full-reference image quality evaluation method, and the similar quality images are considered as a real data set for determining network. Three groups of similar quality images in different scales are considered as the data set, and the subjective evaluation score is used as the label, and the image quality score regression network is trained. The distorted image generates a plurality of similar quality images in different scales through the generative network, and generates the image quality score through the image quality score regression network. The invention is advantageous in that the integral distortion degree and the local distortion details are combined, and the quality score of the distorted images can be further determined, and the quality of distorted images can be embodied in a more comprehensive and more accurate manner.

The invention discloses a three-dimensional image objective quality evaluation method, which comprises the following steps: respectively carrying out wavelet transformation on left view point images and right view point images of undistorted three-dimensional images and distorted three-dimensional images; extracting characteristic values of each sub band; carrying out similarity measurement on the characteristic values; determining the weight factors of each sub band by contrast sensitive functions; then, realizing the evaluation on the three-dimensional sensing quality through evaluating the absolute difference image similarity of left images and right images of the undistorted three-dimensional images and the distorted three-dimensional images; and finally, combining the quality of the left images and the right images and the three-dimensional sensing quality to obtain the final evaluation results of the three-dimensional image quality. The invention has the advantages that the human eye visual characteristics such as human visual sensitivity bandpass, the multi-channel effect, the three-dimensional sensation and the like are utilized, the three-dimensional sensation is simultaneously evaluated on the basis of the evaluation on the quality of the left view point images and the right view point images, the influence of the three-dimensional sensation in the three-dimensional images on the final three-dimensional image quality is merged into an evaluation model, and the correlativity of the objective evaluation results and the subjective sensation is improved.

A method and system for evaluating quality service though the use of anonymous shoppers. The method and system provide executives and managers of retail operations with an essentially objective evaluation of the performance of its business operations through the use of data obtained by anonymous shoppers. The method formulates essentially objective evaluation parameters for a business operation and composes essentially objective questions based on parameters. Anonymous shoppers are trained to monitor quality service and provide essentially objective responses. Key areas of quality service are evaluated by responses to questions. Numerical ratings are assigned to the responses using a weighted point system; these ratings are combined for each key area of business operation. The resulting numerical ratings are further combined for a single overall rating. The numerical ratings are entered into a computer to generate one or more managerial reports.

The invention discloses a non-reference image quality objective evaluation method based on deep learning. Multi-resolution pyramid and Gaussian difference decomposition is performed on distortion images to be evaluated and then natural statistical characteristics can be extracted by performing simple local normalization on sub-band images without extracting characteristics from a transform domain so that complexity is greatly reduced. Degree of distortion of the images is measured by degree of loss of the natural statistical characteristics with no requirement for reference images or distortion types. The change condition of visual quality of the images under the influence of various image processing and compression methods can be objectively reflected by the method, and evaluation performance of the method is not influenced by the image content or the distortion types and is consistent with subjective perception of human eyes. An existing L moment estimation method is adopted to estimate the distribution parameters of the envelope curve of a gray level histogram, and the distribution parameters obtained through estimation are more accurate and have higher generalization capability.

A computer-implemented method for providing an objective evaluation to a customer service representative regarding his performance during an interaction with a customer may include receiving a digitized data stream corresponding to a spoken conversation between a customer and a representative; converting the data stream to a text stream; generating a representative transcript that includes the words from the text stream that are spoken by the representative; comparing the representative transcript with a plurality of positive words and a plurality of negative words; and generating a score that varies according to the occurrence of each word spoken by the representative that matches one of the positive words, and/or the occurrence of each word spoken by the representative that matches one of the negative words. Tone of voice, as well as response time, during the interaction may also be monitored and analyzed to adjust the score, or generate a separate score.

The invention discloses an all-reference three-dimensional image quality objective evaluation method based on visual salient feature extraction. According to the method, a left view and a right view of a three-dimensional image pair are processed to obtain a corresponding disparity map; image fusion is performed on the left view and the right view of the three-dimensional image pair to obtain an intermediate reference image and an intermediate distortion image; a spectral residual visual saliency model is utilized to obtain a reference saliency map and a distortion saliency map, and a visual saliency map is obtained through integration; visual information features are extracted from the intermediate reference image and the intermediate distortion image, and depth information features are extracted from the disparity map of the three-dimensional image pair; similarity measurement is performed to obtain measurement indexes of all the visual information features of vision saliency enhancement; and support vector machine training prediction is performed, an objective quality score is obtained, mapping of three-dimensional image quality is realized, and measurement and evaluation of three-dimensional image quality are completed. Through the method, image quality objective evaluation and subjective evaluation have good consistency, and the performance is superior to that of existingthree-dimensional image quality evaluation methods.

The invention discloses a method for objectively evaluating image quality. The method comprises the following steps of: dividing a reference image and a distorted image into an edge region, a texture region and a planar region in a combined way according to gradient amplitude information of each pixel point in the reference image and the distorted image; performing phase quantization coding on the reference image and the distorted image according to a gradient phase and a gradient amplitude of each pixel point in each region; calculating hamming distances for the phase quantization coding of different regions of the reference image and the distorted image; and giving different weights to the hamming distances of the different regions according to a human visual system so as to acquire a final object score of image quality evaluation. In the method, since the influence of variation of the distorted image relative to the gradient amplitude and the gradient phase of the reference image on the quality of the distorted image is comprehensively considered, an objective evaluation result obtained by the method has high consistency with a human eye subjective perception result; in addition, the method has low calculation complexity and high running speed.

A feature amount extracting unit extracts a block distortion degree of an evaluated video y relative to an original video x, a PSNR overall temporal fluctuation degree, and a PSNR local temporal fluctuation degree as feature amounts. A weighted sum calculating unit calculates a weighted sum of these feature amounts, and calculates an objective video quality index. Frame rate detecting unit detects frame rate of the evaluated video y. A correcting unit corrects the objective video quality index based on the frame rate detected by the frame rate detecting unit. An objective evaluation index-subjective video quality mapping unit applies the corrected objective video quality index Q_obj to a correlation between an objective video quality index and a subjective video quality given in advance, thereby deriving a subjective video quality estimated value DMOS.

The invention discloses a method for evaluating a stereo image vision comfort level based on machine learning. According to the method, the important vision area mask of stereo images is extracted through the saliency map of a right viewpoint image and a right parallax image, a characteristic vector used for reflecting parallax range characteristics and parallax gradient characteristics and a characteristic vector used for reflecting space frequency characteristics are extracted through the important vision area mask, the characteristic vectors of the stereo images are obtained, training is carried out on all the stereo images in a stereo image set through support vector regression, and finally each stereo image in the stereo image set is tested through a support vector regression training model obtained in training to obtain the vision comfort level evaluation predicted value of each stereo image. The method has the advantages that the obtained characteristic vector information of the stereo images has strong stability and can reflect the vision comfort level changing conditions of the stereo images well, and therefore the correlation of objective evaluation conditions and subjective perception is effectively improved.