883 results about "Smoothing filter" patented technology

A background noise estimate based upon a modified Doblinger noise estimate is used for modulating the output of a pseudo-random phase spectrum generator to produce the comfort noise. The circuit for estimating noise includes a smoothing filter having a slower time constant for updating the noise estimate during noise than during speech. Comfort noise is smoothly inserted by basing the amount of comfort noise on the amount of noise suppression. A discrete inverse Fourier transform converts the comfort noise back to the time domain and overlapping windows eliminate artifacts that may have been produced during processing.

An interlaced to progressive scan video converter which identifies object edges and directions, and calculates new pixel values based on the edge information. Source image data from a single video field is analyzed to detect object edges and the orientation of those edges. A 2-dimensional array of image elements surrounding each pixel location in the field is high-pass filtered along a number of different rotational vectors, and a null or minimum in the set of filtered data indicates a candidate object edge as well as the direction of that edge. A 2-dimensional array of edge candidates surrounding each pixel location is characterized to invalidate false edges by determining the number of similar and dissimilar edge orientations in the array, and then disqualifying locations which have too many dissimilar or too few similar surrounding edge candidates. The surviving edge candidates are then passed through multiple low-pass and smoothing filters to remove edge detection irregularities and spurious detections, yielding a final edge detection value for each source image pixel location. For pixel locations with a valid edge detection, new pixel data for the progressive output image is calculated by interpolating from source image pixels which are located along the detected edge orientation.

In a mobile telecommunication system the transmission quality is dependent on a wide range of radio conditions. A method for extracting a useful estimation of the radio environment of a mobile station in communication in order to predict how the radio conditions will change in the near future for a considered mobile station in the near future. A measurement report is sent from the mobile station to the base station. The quality measure in the measurement report is filtered over the area by a smoothing filter (220), and then processed through an updating filter (230) resulting in a time varying map (240). Several maps are classified (260). Classified data are grouped (270) resulting in the final map, which is stored in a database (280). The final map may be used to enhance the performance of radio network algorithms.

A ringing area detector classifies the input image into two regions: a mosquito noise region (i.e. filtering region) and a non-mosquito noise region (i.e. non-filtering region), and uses this classification information to adaptively remove the mosquito noise in a mosquito noise reduction system. The mosquito noise reduction system includes a ringing area detector, a local noise power estimator, a smoothing filter, and a mixer. The ringing area detector includes an edge detector, a near edge detector, a texture detector, and a filtering region decision block. The ringing detection block detects the ringing area where the smoothing filter is to be applied. The local noise power estimator controls the filter strength of the smoothing filter. The smoothing filter smoothes the input image. The mixer mixes the smoothed image and the original image properly based on the region information from the ringing area detection block.

To efficiently execute correction of pixel values of a motion-presence area of captured image data. In correction processing for pixel values of a particular area such as a motion-presence area, initial values of the differences or ratios between white balance adjusted image data and flash image data are set, and conversion of the initial values is performed in accordance with a smoothing filter or a conversion expression whose weight is set on the basis of flash image data, and estimated values of image differences or image ratios in the motion-presence area are calculated to apply the estimated values to execute pixel value correction of the motion-presence area. In addition, after white balance adjustment using a low resolution image and pixel value correction of the motion-presence section, a high resolution corrected image is generated on the basis of the correspondence of a corrected image to low resolution image data.

The invention discloses a behavior identification method based on a recurrent neural network and human skeleton movement sequences. The method comprises the following steps of normalizing node coordinates of extracted human skeleton posture sequences to eliminate influence of absolute space positions, where a human body is located, on an identification process; filtering the skeleton node coordinates through a simple smoothing filter to improve the signal to noise ratio; sending the smoothed data into the hierarchic bidirectional recurrent neural network for deep characteristic extraction and identification. Meanwhile, the invention provides a hierarchic unidirectional recurrent neural network model for coping with practical real-time online analysis requirements. The behavior identification method based on the recurrent neural network and the human skeleton movement sequences has the advantages of designing an end-to-end analyzing mode according to the structural characteristics and the motion relativity of human body, achieving high-precision identification and meanwhile avoiding complex computation, thereby being applicable to practical application. The behavior identification method based on the recurrent neural network and the human skeleton movement sequence is significant to the fields of intelligent video monitoring based on the depth camera technology, intelligent traffic management, smart city and the like.

A method for treating noise in low-dose computed tomography projections and reconstructed images comprises acquiring raw data at a low mA value, applying a domain specific filter in a sinogram domain of the raw data, and applying an edge preserving smoothing filter in an image domain of the raw data after filtering in the sinogram domain.

Methods and systems for hierarchically layered adaptive median motion vector smoothing are disclosed. Aspects of one method may include generating motion vectors (MVs) for video pictures at each level of a hierarchical motion estimation process, where each level may use different resolution video pictures. The MVs may be smooth filtered at each level to reduce or remove spurious MVs. The smooth filtering may comprise scalar median filtering and / or vector median filtering. The smooth filtering may receive as inputs a plurality of MVs, for example, the MV being filtered and eight MVs from the surrounding video blocks. The vector costs of the nine MVs may be compared to a threshold vector cost, and those MVs with costs above the threshold value may be discarded. The threshold value may be dynamically changed based on, for example, content of the video pictures and / or to adjust a window size of the smoothing filter.

A method for high dynamic range compression uses a modified cumulative histogram as a compression curve. This curve is computed from the cumulative histogram of the image with constraints that the local derivative on the curve does not exceed a certain limit. The limit is fixed along the curve or the limit is variable, taking into account noise characteristics at various pixel values. To provide appropriate detail preservation, a smoothing filter is used to separate the image into an illumination image, referred to as a base image, and a detail image, and the compression curve is applied to the base image only. The compression method provides high dynamic range compression of the image while preserving the global contrast perception. Conventional global algorithms for high dynamic range compression are not capable of achieving this result. The proposed high dynamic range compression method also minimizes noise amplification while lightening the dark areas during image compression.

In an apparatus for suppressing noise in an input image signal representing a radiographic image: at least one first characteristic of the input image signal is obtained by calculation based on information indicating an exposure dose with which the radiographic image has been produced; at least one second characteristic of a smoothing filter is adapted to the input image signal based on the at least one first characteristic; and the input image signal is processed by using the smoothing filter so as to smooth the radiographic image. In addition, a plurality of band-limited image signals respectively representing a plurality of band-limited images belonging to a plurality of different frequency bands are generated based on the input image signal, each of the plurality of band-limited image signals is processed by using the smoothing filter so as to smooth each of the plurality of band-limited images.

A method for detecting and enhancing edges in an ultrasound image includes the steps of applying an edge detection filter to at least one of a plurality of pixels of an ultrasound image to compute an edge detection value having a magnitude and a direction; and applying either a directional edge enhancement filter or a non-directional smoothing filter to at least one of the plurality of pixels of the ultrasound image.

A system encoding and decoding video using intra prediction that uses an edge based determination technique together with smoothing filters.

A combination of noise suppression using a Bark band modified Weiner filter and linear noise reduction improves elimination of noise in a telephone. A detector for detecting long, non-speech intervals is coupled to the output of the noise suppresser and controls selection of noise suppression or noise reduction. A gain smoothing filter has a long time constant when noise reduction is used and provides a gradual transition from one level of gain to another. Comfort noise is smoothly inserted by updating the data for generating comfort noise only during detected long, non-speech intervals.

An input image signal 101 is divided into MC block units and, when coding processing is performed in these divided units, a motion compensation section 107 generates a motion-compensated prediction image 106a by detecting movement amounts in predetermined MC block units, a smoothing filter section 124 performs, with respect to the prediction image 106a, smoothing of pixels located at the boundaries of adjoining MC blocks on the basis of predetermined evaluation criteria, and a prediction residual signal 108, which is obtained from the difference between the prediction image 106b obtained by the smoothing, and the input image (input image signal 101), is encoded. Accordingly, it is possible to use relatively straightforward computation to perform processing, with respect to a prediction frame image generated by block-unit motion-compensated interframe prediction (MC), to adaptively smooth a discontinuous waveform generated between MC blocks of the prediction frame image, whereby the efficiency of low bit rate coding that employs interframe MC is improved.

Conventionally, in image processing using a smoothing filter, the large amount of calculation for 25× the number of pixels takes much processing time. A computer 21 as the nucleus of image processing determines whether or not a pixel of interest is a color blur pixel, based on the intensity of a low-density element color, at steps S130 and S140. If the pixel is a color blur pixel, the computer 21 determines whether or not the color blur pixel is an edge pixel at step S150. If the pixel is not an edge pixel, the computer 21 uses a smoothing filter at step S152, while if the pixel is an edge pixel, the computer 21 uses a median filter at step S154, in image processing to reduce the color blur. This reduces the amount of calculation and realizes high-speed image processing.

The invention discloses a support vector machine based surface electromyogram signal multi-hand action identification method. The method comprises the following main steps: 1) obtaining electromyogram data, performing smooth filtering on a signal, and generating data samples through sampling windows of different scales; 2) by taking the data samples as units, extracting a novel multi-feature set containing 19 time domain, frequency domain and time-frequency domain features from each data sample, and performing normalization and minimum redundancy maximum correlation criterion based feature selection on eigenvectors; 3) designing a Pearson VII generalized kernel based support vector machine classifier and optimizing parameters of a support vector machine by using a cross validation based coarse grid search optimization algorithm; and 4) training a classification model by using data samples in a training set and optical classifier parameters obtained in the parameter optimization process of the step 3) and inputting data samples in a test set into the classification model to perform classification testing.

A step-down type DC-DC power-supply device implements both the stabilization of the control loop and the responsibility at the same time. In the power-supply device, an output power signal is fed back to an error amplifier after having passed through a CR smoothing filter provided independently of a power LC smoothing filter. Also, independently of the duty controls over Power MOSFETs, i.e., upper-side / lower-side semiconductor switching components in the steady state, an output from the power LC smoothing filter is added to an upper and lower limit-mode-equipped control circuit, thereby, at the transient state, forcefully setting the duty alpha at either 0% or 100%.

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 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.

The invention discloses an unmanned aerial vehicle man-machine interaction method based on three-dimensional continuous gesture recognition. The method comprises the following steps: acquiring human skeleton tracking data through a Kinect sensor, wherein the human skeleton tracking data at least include skeleton point tracking data of a right hand, a left hand, a right elbow, a left elbow, a right shoulder and a left shoulder; extracting a gesture track feature and preprocessing the gesture track feature, wherein preprocessing includes gesture start-stop detection, smooth filtering processing, resampling and position normalization; performing gesture recognition by using a neural network algorithm; and converting a gesture recognition result into a corresponding control instruction for controlling flight of an unmanned aerial vehicle. Through adoption of the method, gestures can be recognized accurately, so that a user can control flight of the unmanned aerial vehicle more freely and conveniently.

This invention provides a liquid jet apparatus including: a plurality of nozzles provided at a liquid jet head; an actuator provided corresponding to the nozzles; a driving part that applies a drive signal to the actuator, wherein the driving part includes: a drive waveform signal generating circuit that generates a drive waveform signal to be a reference of a signal controlling the driving of actuator; a modulation circuit that performs a pulse modulation of the drive waveform signal generated in the drive waveform signal generating circuit; a digital power amplifier that performs a power amplification of the modulation signal having been subject to the pulse modulation in the modulation circuit; a smoothing filter that smoothes a power amplification modulation signal having been subject to the power amplification in the digital power amplifier and supplies to the actuator as the drive signal; and a modulation signal correcting part that corrects the modulation signal in accordance with power supply voltage to the digital power amplifier.

The invention discloses an image defogging method based on dark channel information, belonging to the technical field of image treatment and computer vision. The method disclosed by the invention comprises the following steps of: calculating the minimum value of each pixel of each color channel of an input image via a minimum value filter; then calculating dark channel counting values of the image under different scale parameters and carrying out Gaussian smoothing filter on the dark channel counting value corresponding to each scale parameter; distributing different weight values to the filtered dark channel counting values according to different scale parameters; and carrying out weighted optimization on the dark channel counting values and calculating a transmission coefficient of the scene so that the defogging of the image is realized. The application of the invention can avoid complex soft matting optimization steps and computing complexity of the defogging; the image with high quality can be obtained after the defogging; and the requirement on real-time treatment application can be met.

A bidirectional DC-AC inverter is disclosed. The DC-AC inverter includes an H bridge circuit, a connecting portion connected to the H bridge circuit, a smoothing filter that connects the connecting portion to the H bridge circuit, and a control section. The H bridge circuit has four switching elements. The four switching elements include two first switching elements first ends of which are connected to each other and two second switching elements second ends of which are connected to each other. The smoothing filter has a capacitor and two coils connected to the capacitor. The control section controls the first switching elements in such a manner that the two coils function as booster coils when an alternating voltage input from a system power supply is converted to a direct current voltage.

The invention provides a method for extracting characteristic parameters in human body waveform, comprising the following steps: searching a waveform peak value in a waveform period on original waveform by an n / m sliding window logical approach; searching the lowest point of the waveform around the obtained peak value points, thereby obtaining the initial point of the waveform; carrying out first-order difference and second-order difference operations on the original waveform; utilizing the second-order difference result waveform to find out a dicrotic notch peak value from an original oscillogram, and utilizing the result waveforms of the first-order difference and second-order difference to find out the peak value before dicrotic pulse from the original oscillogram. The invention also comprises utilizing a two-point smoothing filter method for N times to smoothly filter the original waveform. The characteristic parameter extraction in waveform can be realized on a computer by the characteristic parameter extraction method of the invention, and compared with the common artificial method in the existing operation, the method of the invention has the advantages of high accuracy and reliable marking result.

A ringing area detector classifies the input image into two regions: a mosquito noise region (i.e. filtering region) and a non-mosquito noise region (i.e. non-filtering region), and uses this classification information to adaptively remove the mosquito noise in a mosquito noise reduction system. The mosquito noise reduction system includes a ringing area detector, a local noise power estimator, a smoothing filter, and a mixer. The ringing area detector includes an edge detector, a near edge detector, a texture detector, and a filtering region decision block. The ringing detection block detects the ringing area where the smoothing filter is to be applied. The local noise power estimator controls the filter strength of the smoothing filter. The smoothing filter smoothes the input image. The mixer mixes the smoothed image and the original image properly based on the region information from the ringing area detection block.

The present invention provides an excellent electric power steering apparatus in which torque ripple caused by a quantization error of a current control system generated in case of slow steering state and steering holding state, namely, vibration and noise on a steering wheel can be suppressed. A current controller in which a steady state gain of feedback control is a finite value is used so as to be capable of suppressing the vibration and noise. Further, according to the present invention, a smoothing filter is included in a current control system so that the quantization error is smoothed so that the torque ripple is suppressed.

There is provided a method of reducing block noise, mosquito noise and other image noises in an image by a filtering process using a smoothing filter, which block noise, mosquito noise and other image noises being caused at the time of decoding encoded, compressed image data on a block-by-block basis. The method includes changing the extent or intensity of the image noise reduction in the filtering process in a continuous or stepwise manner according to an output size or expansion rate of an image to be outputted to printer paper, photographic paper or other output media, thereby allowing the extent or intensity of the image noise reduction to be increased as the output size or expansion rate of the image increases. This method is capable of allowing the extent of the image noise reduction applied to image data to be perceived in a similar fashion, irrespective of the output size.

A method for neutral pixel detection using color space feature vectors wherein one color space coordinate represents lightness is provided. The method includes the following steps: a) receiving an input image represented in a first color space; b) converting the input image to a second color space wherein one coordinate represents lightness; c) selecting a pixel in the second color space representation to be classified; d) computing second color space feature vectors associated with the selected pixel; and e) classifying the selected pixel between neutral and color classes based on the values computed for the second color space feature vectors. Typically, the input image is processed using a smoothing filter to create a smoothed input image prior to the conversion to the second color space. The method can be adapted to page processing or strip processing schemes with respect to the input image.