116 results about "Human visual perception" patented technology

A document authentication system and method combine digital and non-electronic (or visual) authentication methodologies in an integrated, unified manner. As well as providing indicia of digital authentication, the invention generates a physical artifact that can be validated by unaided human visual perception. The present invention thus provides an opportunity to improve the level of trust in authentication of documents, while preserving the advantages of both traditional and digital authentication mechanisms.

Disclosed are an apparatus, a method and a computer-readable medium automatically generating a depth map corresponding to each two-dimensional (2D) image in a video. The apparatus includes an image acquiring unit to acquire a plurality of 2D images that are temporally consecutive in an input video, a saliency map generator to generate at least one saliency map corresponding to a current 2D image among the plurality of 2D images based on a Human Visual Perception (HVP) model, a saliency-based depth map generator, a three-dimensional (3D) structure matching unit to calculate matching scores between the current 2D image and a plurality of 3D typical structures that are stored in advance, and to determine a 3D typical structure having a highest matching score among the plurality of 3D typical structures to be a 3D structure of the current 2D image, a matching-based depth map generator; a combined depth map generator to combine the saliency-based depth map and the matching-based depth map and to generate a combined depth map, and a spatial and temporal smoothing unit to spatially and temporally smooth the combined depth map.

A method of measuring image-sticking of a display device based on a vision model is described. A display device is provided, and a test frame is displayed on the display device. The test frame has a pattern formed by a maximum gray level and a minimum gray level. After burning-in the test frame for a period of time, a gray frame at a medium gray level is displayed on the display device. The test frame, including portions of extreme black and extreme white, leaves image-sticking on the gray pattern at a medium gray level at the same time. Next, an image capture device is used to capture the image-sticking on the display device after burn-in. Finally, a vision model is employed to simulate the human visual perception on the image-sticking and to grade the image-sticking according to the human eye sensitivity for viewing the image-sticking on the display device.

A system and method for allowing users to view secure data while excluding others from viewing. The method includes generating a stream of first data frames including secure data content to be viewed on a display device; inserting second data frames within the stream in a manner to thereby render a displayed image of the secure data unreadable on the display device. Shutter glasses, to be worn by an authorized user, are provided to receive signals from the computer device and, in response to received signals, filter out the second data frames thus enabling the viewer to view the secure data to the exclusion of nearby observers. The second frames are generated and interspersed within the first video frames having secure data by software executing on a conventional computing device. The second frames may include random text interspersed according to a regular or irregular pattern, or may include color and intensities to exploit deficiencies of human visual perception.

The invention discloses a stereoscopic video objective quality evaluation method based on machine learning. When spatial domain quality of luminance component images of single-frame images is evaluated, each image block of luminance component images of each frame of image in original and distorted stereoscopic videos is subjected to singular value decomposition, and dot product of singular vectors obtained from singular value decomposition is adopted to evaluate distortion degree of each frame of image in the distorted stereoscopic video. Because the singular vectors can greatly reflect structural information of images, when the dot product of the singular vectors is adopted to evaluate the quality of the images, changes of the structural information are considered, and therefore, evaluation results can reflect changes of visual quality of the stereoscopic video more objectively when the stereoscopic video is under various kinds of distortion influences. According to the stereoscopic video objective quality evaluation method based on machine learning, a method of machine learning is adopted to process the relations between objective quality evaluation predicted values and the quality of a left-view point video and a right-view point video, and degree of difference among point views of the left-view point video and the right-view point video, and therefore, evaluation results which are more consistent with human visual perception can be effectively obtained.

The invention provides a colorful animal image retrieval method based on content and a colorful animal image retrieval system based on content. The method comprises the following steps: step 1, inputting an inquiry image which serves as a retrieval object; step 2, extracting the color, grain and framework characteristic descriptions of interested content in the inquiry image based on the guidance of a characteristic template library, and performing normalization processing, wherein the characteristic descriptions, which are different in animal color, grain and framework and are formed on the basis of sample statistics, are stored in the characteristic template library; step 3, performing retrieval based on an R-tree index structure on the characteristic descriptions extracted in the step 2, performing search calculation on image characteristic indexes in a characteristic index library, and extracting a corresponding image from an image database according to the matching result. The method and the system can be used for overcoming the influence of illumination changes on image retrieval judgment to a certain extent, describing the angle and posture semantic information of an inquiry animal according to the framework characteristic analysis during retrieval, and generating a retrieval result consistent with the human visual perception.

The invention discloses image interested object automatic retrieving method and system based on a complementary significant degree image. The method comprises the steps of: generating two complementary significant degree images for the same image to obtain two complementary crude retrieving results; taking the complementary crude retrieving results as priori knowledge, and retrieving the interested objects by adopting a pixel classification method; and optimizing the interested objects. In the invention, the interested object in the image is retrieved automatically and accurately under the condition of simulating human visual perception by giving any image, and the problem of the integrity of results based on the significant degree method is efficiently solved.

An image display device controls the video signal and the lighting circuit according to the input video signal, so as to improve contrast, so that flicker or “bright black display”, which may be caused when the display characteristics do not match the human visual characteristics, is eliminated. The image display device includes a filter circuitry, in which a filter selecting circuit changes the filter characteristic according to the difference of a video signal and memory data stored in a memory circuit. The filter circuitry selects different filter characteristics for the display change from dark to bright and for the display change from bright to dark. This prevents display flicker, which may be caused due to saturation of human visual perception with respect to changes in gradation and illuminance level.

A method of grading a level of damage to digital data provides a scan of the digital data for errors therein. Based on the detected errors and predetermined data based on at least one of audio human perception and human visual perception a grade of plain quality is determined. The grade of plain quality is then provided of an output from the system for interpretation by a user or a subsequent system.

A method for calibration of digital image capture devices is presented. This simplified method provides a calibration based on the human visual perception of the colors input into the device using simple test targets, measurement devices, and software with a minimum of labor and expertise. This analysis may be performed using the data analysis tools of a conventional electronic spreadsheet. The method normalizes the test target data to both black and white, and converts the normalized data into the color space of the capture device through white point adaptation. The raw captured image data is also normalized to both black and white, and is regressed with the converted, normalized target data to determine the expected measurement values. These values are used to compensate the device output to achieve a high level of color fidelity. To ensure that the level of fidelity is acceptable, the CIE color difference equations are used.

The invention discloses a bionic vision transformation-based image RSTN (rotation, scaling, translation and noise) invariant attributive feature extraction and recognition method. The method includes the following steps: 1) gray processing is performed on an original image, and the size of the image is reset by using a bilinear interpolation method; 2) the directional edge of the target image is detected based on a Gabor and bipolar filter F-based filter-filter filter, so that an edge image E can be obtained; 3) the spatial resolution pitch detection value of the edge image E is calculated, so that a first-stage output image S1 is obtained; and 4) directional edge detection mentioned in the step 2) and spatial resolution pitch detection mentioned in the step 3) are performed on the first-stage output image S1 again, so that a second-stage feature output image S2 can be obtained, and invariant attributive features can be obtained. According to the method of the invention, a human visual perception mechanism is simulated, and bionic vision transformation-based RSTN invariant attributive features are used in combination, and therefore, the accuracy of image recognition can be improved, and robustness to noises is enhanced.

The present invention is a method for presenting the fisheye-camera images. A calibration target with a concentric-and-symmetric pattern (PCT) is utilized to assist in parameterizing a fisheye camera (FIS) in order to ascertain the optical parameters comprising the principal point, the focal length constant and the projection function of the FIS. Hence, the position of an imaged point referring to the principal point on the image plane directly reflects its corresponding zenithal distance α and azimuthal distance β of the sight ray in space so as to normalize the imaged point onto a small sphere. Further according to the map projections in cartography capable of transforming the global geometry into flap maps, the interesting area in a field of view can be accordingly transformed by a proper projection method. The image transforming method disclosed in the invention is simple, low-cost, suitable to various FISs with different projection mechanisms and capable of transforming the fisheye-camera images for particular functions, such as the approach of normal human visual perception, video data encryption, and image compression/transformation with a high fidelity.

The invention provides an image search method based on a structure semantic histogram. The method comprises the steps that the advantages based on a visual perception mechanism and a vision vocabulary model are considered for image searching, a novel image feature expressing method of the structure semantic histogram is provided, the structure semantic histogram can be regarded as the distillation of a mainstream vision vocabulary model, is used for analyzing natural images, and has more abundant information than a mainstream vision vocabulary method. The image search method integrates the advantages of vision vocabularies, semantic features and the histogram, simulates a human visual perception mechanism in a certain degree, and can express structure space information, vision significance information and uniform color information of semantic information and the vision vocabularies.

A 3D-HEVC inter-frame information hiding method based on visual perception includes steps of information embedding and information extraction. In the step of information embedding, the human visual perception characteristic is considered, stereo salient images are obtained by a stereo image salient model, and the stereo salient images are divided into salient blocks and non-salient blocks with an otsu threshold. The coding quantization parameters are modified according to different modulation rules for different regions. Then, based on the modified quantization parameters, the coding-tree-units are coded to complete the information embedding. In the step of information extraction, no original video is needed, no any side information needs to be transmitted, and the secret information can be blindly extracted. The present invention combines with the human visual perception characteristic, and selects P frames and B frames as embedded frames for effectively reducing the decrease of the stereo video subjective quality.

The invention relates to a method for extracting the edge information of digit image, which is characterized in that: fixing the gradient image of digit image; shielding the gradient image based on the human visual sensitive character; based on the shield gradient image, extracting the edge information of digit image. The invention can consist the edge extracted result and the analyzed edge condition, to make the edge extracted effect meet the human visual sensitive character, to realize better following application and improve the digit image quality in video communication.

The invention discloses a moving target detection method on the basis of a pulse coupled neural network, which comprises the following steps of a, sensing a video image sequence by utilizing the pulse coupled neural network and extracting global features of a video image; b, establishing a global feature histogram of each pixel; c, for each pixel, utilizing the global feature histograms corresponding to first K frames to establish an initial background model of the pixel; d, for each pixel, calculating similarity of a global feature histogram of a current frame image and the corresponding global feature histogram in the background model and detecting whether the pixel is a moving target; e, for each pixel, utilizing the global feature histogram of the current frame image to update the background model of the pixel. The moving target detection method uses the integral characteristics of human visual perception for reference, utilizes the pulse coupled neural network to extract the global features of the image and is beneficial for inhibiting the negative influence of disturbance of a dynamic background on detection of the moving target so as to improve accuracy of detecting the moving target.

The invention provides a significance detection method based on selection of a regional candidate sample, and belongs to the technical field of artificial intelligence. On the basis of existing prior knowledge, depth characteristics and a classifier are introduced, and a selecting mechanism from rough to fine is used, the significance and targeted performance of the regional candidate sample are evaluated, a detection result is further optimized by utilizing super pixels, and a significant object in an image can be detected effectively. Compared with a traditional method, the detection result is more accurate. Especially for a multi-object image or an image in which the object is very similar to background, the detection result satisfies visual perception of humans more, and an obtained significance map is more accurate.

The invention relates to a segmentation method of fabric printing cycle pattern primitives based on adaptive template matching, comprising the following steps: 1. the adaptive template selection: automatically determining an adaptive size template image in an image containing the printing cycle pattern; 2. template matching: template pattern in the step 1 is the most template, template matching iscarried out with the original image, and regions similar to the template pattern in the original image are found; 3, determining the size of the cycle: determining the size of the pattern cycle according to the positional relationship between the regions similar to the template pattern found in the step 2; 4. cyclic pattern primitive segmentation: according to the cyclic size determined in step (3), the original image is traversed and a complete pattern primitive conforming to human visual perception is segmented. The invention can automatically segment a complete pattern primitive from an image with a circulating pattern, avoids the shortcomings of slow manual segmentation speed, low efficiency and poor segmentation precision, and accelerates the flow of printing pattern development anddesign.

The invention discloses a performance analysis method for the automatic segmentation result of an image. The method comprises the following steps: enduing each pixel with different weights by calculating the perception uniformity degree among multiple manually-marked segmentation results on the pixels, wherein the higher the perception uniformity degree is, the higher the weight corresponding to the pixel is; and finally calculating a weighted evaluation number as the final evaluation index for the segmentation results to be evaluated. The performance analysis index obtained by the method can quantitatively reflect the uniformity degree between the automatic segmentation result of the image processed by some computer algorithm and the manually-marked segmentation result, thereby objectively reflecting the closeness degree between some computer image automatic segmentation algorithm and the human visual perception in the aspect of image automatic segmentation.