18804 results about "Thresholding" patented technology

High dynamic range brightness information is acquired by inputting detection current to a high (adjustable) gain resettable integrator whose output V(t) is compared to a Vth threshold by a comparator whose output is counted by a reset counter as V(t).gtoreq.Vth. When a desired count is attained, data acquisition ends, the counter is read, and the entire circuit is reset. A TOF data acquisition circuit includes first and second sequences of series-coupled delay units, and a like number of latch units coupled between respective delay units. A phase discriminator compares output from each chain and feedback a signal to one of the chains and to a comparator and can equalize delay through each chain. A control voltage is coupled to the remaining chain to affect through-propagation delay time. The latch units can capture the precise time when V(t).gtoreq.Vth. Successive measurement approximation can enhance TOF resolution.

A video signal including illumination flicker component is integrated at each of unit areas (horizontal lines) in a frame (field) of the video signal. The integrated level at each of the unit areas at the frame and the integrated level at the corresponding unit area of an adjacent frame are averaged. Dividing is effected between results of the averaging and integrating every unit area. It is judged whether flicker exists in the video signal by frequency-analyzing results of the dividing result at the unit areas. The unit area may be plural adjacent lines where flickering are negligible. The averaging circuit may be circulation type of or FIR filter. Threshold level for judging the flicker is changed according to a shutter speed control signal. Flicker compensation may be executed by controlling shutter speed or the AGC according to flicker judging result. A still condition at a block in a frame may be detected from the integration result at plural frames. When the block is judged to be still, the flicker is judged. An ac line frequency detection is also disclosed to detect the frequency of the ac line from a video signal generated under illumination including flicker. An imaging circuit may be provided to generate the video signal therein.

A method of gait data collection, the method comprising collecting movement data, determining from the data a movement parameter that includes a third order derivative of position, comparing the movement parameter with a threshold value, and counting at least a near fall if the movement parameter exceeds the threshold value.

Computerized methods and interactive input systems for manipulation of 3D objects are disclosed. An image of 3D object is displayed on a 2D interactive surface, and input is received and is interpreted for manipulating the 3D object. When the 3D object is selected, rotation control handles indicating available rotation directions are displayed. In one embodiment, the method comprises capturing images of a 3D input space, recognizing at least one object in the images, and comparing the recognized objects in the images to determine a difference therebetween based on a difference threshold. Depending on the outcome of the comparison, the recognized objects are emerged and associated with digital content, or only one of the recognized objects is maintained and associated with digital content.

Systems and processes for performing user verification using an imaged-based CAPTCHA are disclosed. The verification process can include receiving a request from a user to access restricted content. In response to the request, a plurality of images may be presented to the user. A challenge question or command that identifies one or more of the displayed plurality of images may also be presented to a user. A selection of one or more of the plurality images may then be received from the user. The user's selection may be reviewed to determine the accuracy of the selection with respect to the challenge question or command. If the user correctly identifies a threshold number of images, then the user may be authenticated and allowed to access the restricted content. However, if the user does not correctly identify the threshold number of images, then the user may be denied access the restricted content.

A method for tracking one or multiple objects from an input video sequence allows a user to select one or more regions that contain the object(s) of interest in the first and the last frame of their choice. An initialization component selects the current and the search frame and divides the selected region into equal sized macroblocks. An edge detection component computes the gradient of the current frame for each macroblock and a threshold component decides then which of the macroblocks contain sufficient information for tracking the desired object. A motion estimation component computes for each macroblock in the current frame its position in the search frame. The motion estimation component utilizes a search component that executes a novel search algorithm to find the best match. The mean absolute difference between two macroblocks is used as the matching criterion. The motion estimation component returns the estimated displacement vector for each block. An output component collects the motion vectors of all the predicted blocks and calculates the new position of the object in the next frame.

Techniques are disclosed for a computer vision engine to update both a background model and thresholds used to classify pixels as depicting scene foreground or background in response to detecting that a sudden illumination changes has occurred in a sequence of video frames. The threshold values may be used to specify how much pixel a given pixel may differ from corresponding values in the background model before being classified as depicting foreground. When a sudden illumination change is detected, the values for pixels affected by sudden illumination change may be used to update the value in the background image to reflect the value for that pixel following the sudden illumination change as well as update the threshold for classifying that pixel as depicting foreground/background in subsequent frames of video.

Methods for displaying results of a spreading activation algorithm and for defining an activation input vector for the spreading activation algorithm are disclosed. A planar disk tree is used to represent the generalized graph structure being modeled in a spreading activation algorithm. Activation bars on some or all nodes of the planar disk tree in the dimension perpendicular to the disk tree encode the final activation level resulting at the end of N iterations of the spreading activation algorithm. The number of nodes for which activation bars are displayed may be a predetermined number, a predetermine fraction of all nodes, or a determined by a predetermined activation level threshold. The final activation levels resulting from activation spread through more than one flow network corresponding to the same generalized graph are displayed as color encoded segments on the activation bars. Content, usage, topology, or recommendation flow networks may be used for spreading activation. The difference between spreading activation through different flow networks corresponding to the same generalized graph may be displayed by subtracting the resulting activation patterns from each network and displaying the difference. The spreading activation input vector is determined by continually measuring the dwell time that the user's cursor spends on a displayed node. Activation vectors at various intermediate steps of the N-step spreading activation algorithm are color encoded onto nodes of disk trees within time tubes. The activation input vector and the activation vectors resulting from all N steps are displayed in a time tube having N+1 planar disk trees. Alternatively, a periodic subset of all N activation vectors are displayed, or a subset showing planar disk trees representing large changes in activation levels or phase shifts are displayed while planar disk trees representing smaller changes in activation levels are not displayed.

A method is provided for automatically classifying text into categories. In operation, a plurality of tokens or features are manually or automatically associated with each category. A weight is then coupled to each feature, wherein the weight indicates a degree of association between the feature and the category. Next, a document is parsed into a plurality of unique tokens with associated counts, wherein the counts are indicative of the number of times the feature appears in the document. A category score representative of a sum of products of each feature count in the document times the corresponding feature weight in the category for each document is then computed. Next, the category scores are sorted by perspective, and a document is classified into a particular category, provided the category score exceeds a predetermined threshold.

The invention discloses a method for recognizing a road traffic sign for an unmanned vehicle, comprising the following steps of: (1) changing the RGB (Red, Green and Blue) pixel value of an image to strengthen a traffic sign feature color region, and cutting the image by using a threshold; (2) carrying out edge detection and connection on a gray level image to reconstruct an interested region; (3) extracting a labeled graph of the interested region as a shape feature of the interested region, classifying the shape of the region by using a nearest neighbor classification method, and removing a non-traffic sign region; and (4) graying and normalizing the image of the interested region of the traffic sign, carrying out dual-tree complex wavelet transform on the image to form a feature vector of the image, reducing the dimension of the feature vector by using a two-dimension independent component analysis method, and sending the feature vector into a support vector machine of a radial basis function to judge the type of the traffic sign of the interested region. By using the method, various types of traffic signs in a running environment of the unmanned vehicle can be stably and efficiently detected and recognized.

A halftone processing section halftone-processes input image data using a plurality of dither threshold planes. An image output section having different output position accuracies between a main scan direction and a sub-scan direction outputs an image corresponding to halftone-processed image data. Each of the dither threshold planes consists of a plurality of the same unit threshold matrixes. In the unit threshold matrix, a relatively medium to high threshold array in a predetermined threshold range corresponding to the entire tone range of the input image data is an aperiodic array and an anisotropic array with neighboring thresholds having close values, in a direction coincident with a scan direction, in which the output position accuracy of the image output means is low. With this configuration, the image output section outputs an image having serial medium and high tone dots in the scan direction.

To reduce degradation of recognition performance due to eye detection errors during face verification and to overcome a problem in that sufficient data to design an optimum feature classifier cannot be obtained during face registration, a method includes shifting the positions of eyes detected during face registration in predetermined directions by a predetermined distance to generate pairs of new coordinate points of the eyes; normalizing a face image on the basis of each pair of new coordinate points of the eyes; using the results of normalization in teaching a feature classifier, thereby coping with eye detection errors. In addition, two threshold values are used to prevent a database from being updated with a face of an unregistered person and to update the database with a normal client's face image that has been used during the latest face verification.

A 3D image may be segmented based on one or more intensity thresholds determined from a subset of the voxels in the 3D image. The subset may contain voxels in a 2D reference slice. A low threshold and a high threshold may be used for segmenting an image, and they may be determined using different thresholding methods, depending on the image type. In one method, two sets of bordering pixels are selected from an image. A statistical measure of intensity of each set of pixels is determined. An intensity threshold value is calculated from the statistical measures for segmenting the image. In another method, the pixels of an image are clustered into clusters of different intensity ranges. An intensity threshold for segmenting the image is calculated as a function of a mean intensity and a standard deviation for pixels in one of the clusters. A further method is a supervised range-constrained thresholding method.

The application presents a question and answer recommendation method based on artificial intelligence, a question and answer recommendation device and a computer device. The question and answer recommendation method based on artificial intelligence includes steps of receiving search terms input by a user, wherein the search terms are questions; matching the search terms with questions having answers in a question and answer knowledge base; if the terms in question and answer knowledge base are not matched with the search terms completely, calculating the semantic similarity between the search terms and the questions having answers in the question and answer knowledge base; judging if the question and answer knowledge base has a question of which semantic similarity with the search terms is larger than a preset threshold value; if it does, using the answer of which semantic similarity with the search terms is more than the preset threshold value as the answer of the search term, and recommending to a user. Answer recommendation is realized according to the semantic similarity, thus the meaning transfer risk is reduced and the interference by meaningless oral expression element is reduced.

Microwave examination of individuals is carried out by transmitting microwave signals from multiple antenna locations into an individual and receiving the backscattered microwave signals at multiple antenna locations to provide received signals from the antennas. The received signals are processed to remove the skin interface reflection component of the signal and the corrected signal data are provided to a hypothesis testing process. In hypothesis testing for detecting tumors, image data are formed from the test statistic used to perform a binary hypothesis test at each voxel. The null hypothesis asserts that no tumor is present at a candidate voxel location. The voxel threshold is determined by specifying a false discovery rate to control the expected proportion of false positives in the image. When the test statistic value associated with a voxel is greater than the threshold, the null hypothesis is rejected and the test statistic is assigned to the voxel. For voxels where the test statistic falls below the threshold, the null hypothesis is accepted and the voxel value is set to zero. The resulting image indicates the locations or other characteristics of detected tumors.

A synchronous quick calibration method of a plurality of video cameras in a three-dimensional scanning system, which includes: (1) setting a regular truncated rectangular pyramid calibration object, setting eight calibration balls at the vertexes of the truncated rectangular pyramid, and respectively setting two reference calibration balls at the upper and lower planes; (2) using the video cameras to pick-up the calibration object, adopting the two-threshold segmentation method to respectively obtain the corresponding circles of the upper and lower planes, extracting centers of the circles, obtaining three groups of corresponding relationships between circle center points in the image and the centres of calibration ball in the space, solving the homography matrix to obtain the internal parameter matrix and external parameter matrix and obtaining the distortion coefficient, taking the solved video camera parameter as the initial values, and then using a non-linear optimization method to obtain the optimum solution of a single video camera parameter; (3) obtaining in sequence the external parameter matrix between a plurality of video cameras and a certain video camera in the space, using the polar curve geometric constraint relationship of the binocular stereo vision to establish an optimizing object function, and then adopting a non-linear optimization method to solve to get the optimum solution of the external parameter matrix between two video cameras.

The invention relates to a detection method for vehicle license plate real-time positioning character segmentation in the technical field of video monitoring, which comprises the following steps of: obtaining a video frame from image acquisition equipment; carrying out image preprocessing; increasing a signal-to-noise ratio; carrying out grey scale transformation; computing a threshold value by adopting an Otsu binarization method and converting a vehicle image into a binary image; firstly carrying out closed operation by using a structural element with the size of length pixels, separating a communicated region at a fine position and smoothing burrs appearing on the edge of the region; carrying out contour detection; and carrying out license plate region accurate positioning: accurately segmenting out each character. The invention has lower requirements for image color, a large amount of system memory resources of a computer can not occupied, the operational speed is quickened after an input image is converted into a grey-scale image, the accurate position of each character can be rapidly found, and each character is segmented out by applying an image shearing method.