55 results about "Fuzzy filter" patented technology

We present an iterative method for reducing artifacts in computed tomography (CT) images. First, a filter is applied to the experimental projection data that adaptively expands the detector element size in regions with low photon counts, until the desired number of photons are detected. The initial image is then calculated using an existing reconstruction technique. In each iteration, artifacts and noise in the current image are reduced by using an edge-preserving blur filter. Metal pixels (determined from the initial image) are replaced with smaller values. The resulting image is forward projected. Rays that go through metal are replaced with the forward projected values. Rays that do not pass near metal are kept at the experimental values. Filtered backprojection is then performed on the new projection data to determine the updated image. Finally, after the last iteration, metal pixels are copied from the initial image.

A method reduces artifacts in an input image. A variance image is generated from the input image. The input image is partitioned into a plurality of blocks of pixels. A set of classifications is defined. The set of classifications includes smooth, texture, and edge. A particular classification is assigned to each block of pixels of the input image according to the variance image, to generate smooth blocks, texture blocks, and edge blocks. A fuzzy filter is applied to each pixel of only each edge block.

A set of volatile markers are determined which are characteristic of a particular condition or disease, and which will be found in the exhaled breath of a person or odor from other parts of a body or from an entity. These markers are detected in the breath odor or gaseous emanations from the body or entity noninvasively using a volatile substance detector of sufficient sensitivity, such as an artificial olfactory system. The detected marker data is processed in an artificial neural network / fuzzy filter system with an algorithm that intelligently adapts to the individual body or entity and also optionally (if necessary) with a correction algorithm to eliminate environmental and other erroneous contributions to the markers. Any number of markers may be used, depending on how well they correlate with the condition and how accurate a result is desired, i.e. general screening or accurate diagnosis and monitoring.

An edge-preserving diffusion filter maintains the sharp edges in images while smoothing out image noise. An edge-preserving diffusion filter applies an edge-preserving smoothing filter to an image to form a filtered image. The modified image is then blurred by a blurring filter to form a blurred image. The modified image and the blurred image are blended together to form an output image based on an error metric associated with each pixel. The edge-preserving diffusion filter may be utilized to perform a multilevel decomposition of the image. The edge-preserving diffusion filter may be applied to an unfiltered image to produce a base image. The difference between the unfiltered image and the base image defines a detail image. The detail image may be used as the input for recursively generating additional levels of detail. The multilevel decomposition may utilize filter kernels associated with different contrast levels for each iteration.

The invention comprises processes for determining and applying a deblurring filter that reduces inspection system distortion, of mask inspection images, by compensating for the non-uniform frequency response of the inspection system. In particular, an adaptive filter is determined empirically for an inspection system: one or more training images are obtained by the inspection system and the filter is determined from such images. In this way, the filter can adapt to the characteristics of each individual inspection system. An example adaptive filter, known as a Weiner filter, is determined and applied.

An invention provides a system and method for filtering pixels in an image using only fixed-point and summation operations. First, a filtering window is centered on an input pixel. Based on a difference between the intensity of the input pixel and its neighboring pixels, fuzzy filter weights are obtained. A sum of the fuzzy filter weights is used to determine a normalization factor. Then, the pixel intensities, fuzzy filter weights and the normalization factor are used to obtain an output pixel corresponding to the input pixel.

An optical low pass filter or blur filter, and method of making the filters, using an article having a birefringent surface for refracting incoming light when used with an image sensor. The birefringent surface of the article, such as a film, is structured or tilted such that, when the blur filter is placed within an optical path between a lens and the image sensor, the birefringent surface causes refraction of a light signal in the optical path into multiple light signals each being incident upon different sub-pixels within the pixels in the image sensor to prevent or reduce artifacts, such as undesirable color moiré effects, in the resulting digital image. The structures on the surface have a variable pitch or angles. The variable pitch can include a periodic, aperiodic, or quasi-aperiodic pitch, to reduce diffractive artifacts in the resulting image.

A system consisting of a base GPS receiver with a clear view of the sky, one or more remote GPS receivers with restricted views of the sky and a processing center that batch processes range information provided by the GPS receivers determines the positions of the remote GPS receivers to within tight tolerances. The base GPS receiver and the remote GPS receivers produce range information based on the satellite signals that the respective receivers can track at a given time, and provide the range information to the processing center. The range information includes both code and carrier measurements for each of the signals that are being tracked by the respective GPS receivers. The center collects the range information over an extended period of time, for example, hours, days or weeks, and then batch processes the collected information in multiple passes through the data, to calculate the precise latitude, longitude and height of the receiver. The center batch processes the data using a floating ambiguity filter that, after a first pass through the data, is initialized with the position calculated in an earlier pass. The center also calculates the quality of the collected range information by fixing the position and position covariance of the floating ambiguity filter and using double differences with the base GPS receiver measurements, to ensure that the information used in subsequent position calculations is sufficiently reliable.

A method reduces artifacts in an input image. A variance image is generated from the input image. The input image is partitioned into a plurality of blocks of pixels. A set of classifications is defined. The set of classifications includes smooth, texture, and edge. A particular classification is assigned to each block of pixels of the input image according to the variance image, to generate smooth blocks, texture blocks, and edge blocks. A fuzzy filter is applied to each pixel of only each edge block.

An imaging apparatus (50) for generating an image signal from incident light with higher spatial frequencies of the incident light limited to reduce undersampling artifacts. Apparatus (50) includes a detector plane (62) for generating the image signal from an array of photosites, (63) and an optical section having a relatively thin lithium niobate rhomboidal filter (51) interposed in the path of the incident light so as to produce a blurred image on the photosites. A filter configuration is disclosed which produces four spots (58) at the imaging sensors (62) positioned at the corners of a rhombus.

Call initiation on a device with potential for a large number of stored telephone numbers is simplified through the use of an integrated field for entry of an ambiguous filter string. As it is not clear, a priori, whether the user is manually specifying, in the integrated field, a telephone number or providing a filter string for filtering contacts, filtering is performed based on at least one filter string. Among the contacts that are part of a filtering result list, a default contact is selected for quick access.

What is disclosed is a system and method for image quality (IQ) defect detection via classification of halftone dots present on customer prints. In one embodiment, spatially-varying thresholds are applied on the sensed image in order to classify the halftone dots for IQ defect detection. The resolving power of the sensor is characterized and the sensor responses to patches printed with the print device are characterized. A de-blurring filter is designed which is appropriate for compensating the characterized resolving power of the sensor. Image enhancement is applied to the image using the de-blurring filter. Halftone dots present on the prints are classified by analyzing the enhanced image with the results of the sensor response characterization. Once classified, single separation halftone dot images can be more readily analyzed for defects. The present method allows sensor resolution as low as the size of the halftone dots of the printer under test.

A method filters pixels in a sequence of images. Each image in the sequence is partitioned into blocks of pixels, and the images are processed sequentially. The energy is determined for each block of pixels in each image. The energy of each block is based on variances of intensities of the pixels in the sequence of images. A 3D fuzzy filter is applied to each current pixel in each current block during the sequential processing. The 3D fuzzy filter considers the energy of the block, and the intensities of pixels spatially adjacent and temporally adjacent to the current pixel to remove blocking and ringing artifacts.

Disclosed is a method for generating a motion search area of a video CODEC for implementing motion compensation through motion estimation as a unit of block. Compression efficiency can be further improved by additionally creating various reference frames by an intentional motion blur and a deblur filter, filtering a motion search area using a motion blur filter and a motion deblur filter as a unit of block which is a unit of video coding, and selecting reference frame having small residual frame data during execution of the motion estimation as final reference frames among original reference frames and various reference frames (motion search areas) additionally created.

An image capturing device and a method for generating a bokeh effect are provided. The method includes the following steps. An image including a current input pixel is captured. Next, blurring processes are performed on the image by using a first image blur filter and a second image blur filter so as to generate a plurality of first blur images and second blur images corresponding to different blur levels. A distance between the current input pixel and a focal plane is calculated to obtain a current distance. A first current blur image and a second current blur image are respectively selected from the first blur images and the second blur images according to the current distance. Next, a first current blur pixel of the first current blur image and a second current blur pixel of the second current blur image are combined to generate a current output pixel.

The invention relates to an algorithm of a progress bar of a breathing lamp effect. The algorithm includes the steps that firstly, a new control class BurnProgressView is user-defined on the basis of layout control Linearlayout, and an initialization method is added to a construction function; secondly, in onSizeChanged callback, the size of the circular progress bar is initialized according to the control size, and an inner ring area and an outer ring area are determined according to the width of a drawing pen so that gradient radian lines can be conveniently drawn on the two sides of the progress bar; thirdly, a thread of a breathing lamp change is started, the radiuses of a drawing pen fuzzy filter are circularly switched, and a height mark of corrugated lines is changed to be consistent with the rhythm of a breathing lamp; fourthly, in onDraw callback, progresses and effects are sequentially and dynamically drawn. According to the algorithm, the ring-shaped progress bar and the surrounding of the breathing lamp effect are adopted, attractive, personalized, visually and easily understood progress bar display is achieved through the circular progress bar and the breathing lamp effect, the algorithm efficiency is high, few resources are occupied, the universality is good, and the algorithm is suitable for being used in the Android system.

An edge-preserving diffusion filter maintains the sharp edges in images while smoothing out image noise. An edge-preserving diffusion filter applies an edge-preserving smoothing filter to an image to form a filtered image. The modified image is then blurred by a blurring filter to form a blurred image. The modified image and the blurred image are blended together to form an output image based on an error metric associated with each pixel. The edge-preserving diffusion filter may be utilized to perform a multilevel decomposition of the image. The edge-preserving diffusion filter may be applied to an unfiltered image to produce a base image. The difference between the unfiltered image and the base image defines a detail image. The detail image may be used as the input for recursively generating additional levels of detail. The multilevel decomposition may utilize filter kernels associated with different contrast levels for each iteration.

An optical low pass filter or blur filter, and method of making the filters, using an article having a birefringent surface for refracting incoming light when used with an image sensor. The birefringent surface of the article, such as a film, is structured or tilted such that, when the blur filter is placed within an optical path between a lens and the image sensor, the birefringent surface causes refraction of a light signal in the optical path into multiple light signals each being incident upon different sub-pixels within the pixels in the image sensor to prevent or reduce artifacts, such as undesirable color moire effects, in the resulting digital image.

The concatenative speech synthesizer employs demi-syllable subword units to generate speech. The synthesizer is based on a source-filter model that uses source signals that correspond closely to the human glottal source and that uses filter parameters that correspond closely to the human vocal tract. Concatenation of the demi-syllable units is facilitated by two separate cross face techniques, one applied in the time domain in the demi-syllable source signal waveforms, and one applied in the frequency domain by interpolating the corresponding filter parameters of the concatenated demi-syllables. The dual cross fade technique results in natural sounding synthesis that avoids time-domain glitches without degrading or smearing characteristic resonances in the filter domain.

The invention discloses an image denoising method and system based on threshold value block matching. The image denoising method includes acquiring several images to be processed in time succession, calculating threshold values matched with blocks according to brightness of the images to be processed and processing by fuzzy filter to obtain fuzzy images, layering according to calculation distance of SAD (sum of absolute difference), and matching the blocks of the fuzzy images according to layering search; using the first image of the continuous images to be processed as a reference image, accumulatively processing the images to be processed one by one, and accumulating the image blocks with SAD smaller than the threshold values to the corresponding positions of the reference image so as to obtain cumulative sum and the images; dividing color values of various pixel points in the cumulative sum and the images from the cumulative frequency of the pixel points correspondingly to obtain denoised images. Accordingly, block matching efficiency is greatly improved, accuracy in matching is ensured, denoising effect is improved, and the image denoising method can be conveniently introduced into the industrial production.

The invention provides a filtering method, a device and electronic equipment, and relates to the technical field of filtering. The filtering method comprises the following steps: establishing an overall interval II-type T-S fuzzy time delay system model; establishing an overall interval II-type fuzzy filter model; establishing a filtering error model according to the interval II-type T-S fuzzy time delay system model and the interval II-type fuzzy filter model, and filtering according to the filtering error model. The filtering method, device and electronic equipment provided by the invention can better process a nonlinear system with parameter uncertainty, and accomplish the design of the filter parameter, so that the filtering is more stable.

An image recognition apparatus according to one aspect of the present invention has a measurement unit measuring a blur level of an image, a comparison unit comparing the blur level measured in the measurement unit with a threshold, an image processing unit applying to the image a blurring filter which increases the blur level when the blur level measured in the measurement unit is smaller than the threshold, and applying to the image a deblurring filter which decreases the blur level when the blur level measured in the measurement unit is larger than the threshold, and a recognition unit recognizing the image from features of the image processed in the image processing unit.

What is disclosed is a system and method for image quality (IQ) defect detection via classification of halftone dots present on customer prints. In one embodiment, spatially-varying thresholds are applied on the sensed image in order to classify the halftone dots for IQ defect detection. The resolving power of the sensor is characterized and the sensor responses to patches printed with the print device are characterized. A de-blurring filter is designed which is appropriate for compensating the characterized resolving power of the sensor. Image enhancement is applied to the image using the de-blurring filter. Halftone dots present on the prints are classified by analyzing the enhanced image with the results of the sensor response characterization. Once classified, single separation halftone dot images can be more readily analyzed for defects. The present method allows sensor resolution as low as the size of the halftone dots of the printer under test.

The invention provides a two-joint robot tracking fuzzy control design method, and relates to the technical field of robot control. The method includes that a two-joint robot system is built and has two permanent magnet direct current motors to control a first joint and a second joint to rotate respectively, wherein the first joint is hinged to the second joint. According to the dynamics principle, a mathematical model of the two-joint robot system is built, and the mathematical model belongs to a nonlinear system with strong coupling. The nonlinear system is converted into a fuzzy system based on the mathematical model and the expression method of the fuzzy model. A tracking fuzzy output feedback controller is designed based on measurement signals of the two-joint robot system. Finally, adelta operator fuzzy filter is designed for filtering measurement noise. Measurement noise existing in the plane position measurement at the tail end of the robot joint can be effectively eliminated,and the control precision of the tail end position of the two-joint robot is guaranteed.

The present invention provides a generation method of an image bokeh effect and an image acquisition device. The generation method of the image bokeh effect comprises the following steps: obtaining image frames including a current input pixel, and performing fuzzification processing of the image frames by respectively employing a first image fuzzy filter and a second image fuzzy filter to generate a plurality of first blurred images and second blurred images with respect to different fuzzy degrees; calculating the distance between the current input pixel and a focusing plane to obtain a current distance; respectively selecting a first current blurred image and a second current blurred image from the first blurred images and the second blurred images according to the current distance, and combining the first current blurred pixel of the first current blurred image and the second current blurred pixel of the second current blurred image according to the current distance to generate a current output pixel.

The image processing method and device is used to convert progressive scanned and displayed image data into interlaced scanned and displayed image data. The device includes a fuzzy filter lens unit, a scanning size regulator and a converter. The method includes the following steps: receiving progressive scanned and displayed image data; substituting the color value of each pixel with a combined value of the color values of the said pixel and adjacent pixels so as to obtain fuzzy filter lens processed progressive scanned and displayed image data; regulating the picture size to obtain new progressive scanned and displayed image data; and converting the new progressive scanned and displayed image data into interlaced scanned and displayed image data for output.

The invention discloses a target detection and identification method under rain and snow weather conditions, which comprises the following steps: firstly, acquiring to-be-detected rain and snow weather background image data containing a target, and constructing a training data set under the corresponding rain and snow weather conditions through a fuzzy filter; optimizing a separation model of a rain and snow layer and a background layer, and respectively processing a rain area and a rain-free area so as to weaken detail loss of the rain and snow-free area; building a context expansion rain removal network based on the scene information so as to restore background images under different rain and snow degrees; building a convolutional neural network based on local feature learning, and improving the target recognition rate; taking the rain and snow removed image as the input of an optimized target detection network model, extracting an interested area in the image data by the target detection network model, and outputting a target category; the rain and snow removal target detection model provided by the invention has good generalization and universality, and can be widely applied to high-precision automatic identification and detection of various targets under different rain and snow degrees and other actual scenes.

An image recognition apparatus according to one aspect of the present invention has a measurement unit measuring a blur level of an image, a comparison unit comparing the blur level measured in the measurement unit with a threshold, an image processing unit applying to the image a blurring filter which increases the blur level when the blur level measured in the measurement unit is smaller than the threshold, and applying to the image a deblurring filter which decreases the blur level when the blur level measured in the measurement unit is larger than the threshold, and a recognition unit recognizing the image from features of the image processed in the image processing unit.