19362 results about "Thresholding" patented technology

A decodable indicia reading system can be provided for use in locating and decoding a bar code symbol represented within a frame of image data. The system can comprise a central processing unit (CPU), a memory communicatively coupled to the CPU, and two or more image sensors communicatively coupled to the CPU or to the memory. The system can be configured to select an image sensor for indicia reading by cycling through available image sensors to detect an image sensor suitable for an attempted indicia reading operation by comparing a measured parameter value to a pre-defined sensor-specific threshold value. The system can be further configured to select the first suitable or the best suitable image sensor for the attempted decodable indicia reading operation based upon the comparison result. The system can be further configured to notify the system operator which image sensor has been selected. The system can be further configured to obtain a decodable indicia image by the selected image sensor.

A system and methods are provided for interactive construction of data queries. One method comprises: generating a query based upon a plurality of user-identified data items, wherein the user-identified data items are data items representing desired results from a query, and wherein information related to the user-identified data items is included in a “given” clause of the query, assigning received input data to a hierarchical set of categories, presenting to a user a plurality of new query results, wherein the plurality of new query results are determined by scanning the received input data to find data elements in the same hierarchical categories as those in the “given” query clause and not in the same hierarchical categories as those of an “unlike” clause of the query, receiving from the user an indication as to whether each query result of the presented plurality of new query results is a desirable query result, adding query results indicated by the user as desirable to the “given” clause of the query, adding query results indicated by the user as undesirable to the “unlike” clause of the query, evaluating a metric indicative of the accuracy of the query, and responsive to a determination that the query achieves a predetermined threshold level of accuracy, storing the query.

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.

Fast approximate focus operations providing an approximately focused image that is sufficiently focused to support certain subsequent inspection operations. The operations are particularly advantageous when used to provide images for successive inspection operations that predominate when inspecting planar workpieces. Improved inspection throughput is provided because, in contrast to conventional autofocus operations, the fast approximate focus operations do not acquire an image stack during a run mode as a basis for determining a best focused image. Rather, during learn mode, a representative feature-specific focus curve and a focus threshold value are determined and used during run mode to provide an approximately focused image that reliably supports certain inspection operations. In one embodiment, an acceptable approximately focused inspection image is provided within a limit of two focus adjustment moves that provide two corresponding images. The adjustment moves are based on the representative feature-specific focus curve provided in learn mode.

A method of adjusting a speed of a mobile machine is provided. Image data of a location is collected where currently generated sensor data and previously generated sensor data indicate a discontinuity in sensor data. The image data is analyzed to determine if a non-motion blur score for the image data is above a threshold value. Then, a speed of the mobile machine is adjusted based on a determination that the non-motion blur score is above the threshold value.

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 device or method for video compression uses a technique in which changes in the image are encoded by motions of block of the image and signals indicating evolutions in the block. To determine the motions of the blocks of a each frame, a search is performed for a similar block of a previous frame based on points of the previous frame which are arranged in successive diamond shaped zones. The diamond shaped zones may be centred on the position of the block in previous frame, or one or more predicted motions of the block. The method terminates according to criteria defined using thresholds.

Motion estimation is the science of predicting the current frame in a video sequence from the past frame (or frames), by slicing it into rectangular blocks of pixels, and matching these to past such blocks. The displacement in the spatial position of the block in the current frame with respect to the past frame is called the motion vector. This method of temporally decorrelating the video sequence by finding the best matching blocks from past reference frames—motion estimation—makes up about 80% or more of the computation in a video encoder. That is, it is enormously expensive, and methods do so that are efficient are in high demand. Thus the field of motion estimation within video coding is rich in the breadth and diversity of approaches that have been put forward. Yet it is often the simplest methods that are the most effective. So it is in this case. While it is well-known that a full search over all possible positions within a fixed window is an optimal method in terms of performance, it is generally prohibitive in computation. In this patent disclosure, we define an efficient, new method of searching only a very sparse subset of possible displacement positions (or motion vectors) among all possible ones, to see if we can get a good enough match, and terminate early. This set of sparse subset of motion vectors is preselected, using a priori knowledge and extensive testing on video sequences, so that these “predictors” for the motion vector are essentially magic. The art of this method is the preselection of excellent sparse subsets of vectors, the smart thresholds for acceptance or rejection, and even in the order of the testing prior to decision.

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.

A method of operation of a navigation system includes: extracting navigation-related web documents having a point of interest; generating formatting sequences of the navigation-related web documents; selecting a user-defined percentile representing reciprocal fraction of an expected number of clusters; calculating a threshold value for a first cluster with the threshold value to be equal to the user-defined percentile of a first normalized distribution of sample comparison values between the first cluster and formatting sequence samples from the formatting sequences, the first cluster is from the clusters; computing an associated comparison value between a first formatting sequence from the formatting sequences and the first cluster; grouping the first formatting sequence with the first cluster when the associated comparison value exceeds the threshold value for the first cluster; and generating a travel route for the point of interest related to the first cluster for displaying on a device.

A machine vision process monitors and controls safe working practice in a production area by capturing and processing image data relative to personal protective equipment (PPE) worn by individuals, movement of various articles, and movement-related conformations of individuals and other objects in the production area. The data is analyzed to determine whether there is a violation of a predetermined minimum threshold image, movement, or conformation value for a predetermined threshold period of time. The determination of a safety violation triggers computer activation of a safety control device. The process is carried out using a system including an image data capturing device, a computer and computer-readable program code, and a safety control device.

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.

In some embodiments, the invention includes a method for locating text in digital images. The method includes scaling a digital image into images of multiple resolutions and classifying whether pixels in the multiple resolutions are part of a text region. The method also includes integrating scales to create a scale integration saliency map and using the saliency map to create initial text bounding boxes through expanding the boxes from rectangles of pixels including at least one pixel to include groups of at least one pixel adjacent to the rectangles, wherein the groups have a particular relationship to a first threshold. The initial text bounding boxes are consolidated. In other embodiments, a method includes classifying whether pixels are part of a text region, creating initial text bounding boxes, and consolidating the initial text bounding boxes, wherein the consolidating includes creating horizontal projection profiles having adaptive thresholds and vertical projection profiles having adaptive thresholds.

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.

A system and process for broadcast and communication with bi-level or sketch video at extremely low bandwidths is described. Essentially, bi-level and sketch video presents the outlines of the objects in a scene being depicted. Bi-level and sketch video provides a clearer shape, smoother motion, shorter initial latency and cheaper computational cost than do conventional DCT-based video compression methods. This is accomplished by converting each color or gray-scale image frame to bi-level or sketch image frame using adaptive thresholding method, compressing bi-level or sketch image frames into bi-level or sketch video using adaptive context-based arithmetic coding method. Bi-level or sketch video is particularly suitable to such small devices as Pocket PCs and mobile phones that possess small display screen, low bandwidth connection, and light computational power.

A system for selecting and applying an appropriate print quality defect correction technique to compensate for specified print quality defects includes a printhead deployed to perform an operation that forms an image on a print medium sheet composed of multiple adjacently-positioned swaths of print, a sensor mechanism deployed to perform an operation that scans the image, detects the presence of specified print quality defects in the multiple adjacently-positioned swaths of print, and generates an output corresponding to the detected defect, and a control mechanism communicating with and controlling operations of the printhead and sensor mechanism and storing an algorithm that responds to the sensor mechanism output by analyzing and comparing the output with a stored threshold value and when the output exceeds the stored threshold value selecting and applying an appropriate print quality defect correction technique to the printhead that compensates for the detected print quality defect in the multiple adjacently-positioned swaths of print in subsequent images that are formed by the printhead.

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.

The face detection system and method attempts classification of a test image before performing all of the kernel evaluations. Many subimages are not faces and should be relatively easy to identify as such. Thus, the SVM classifier try to discard non-face images using as few kernel evaluations as possible using a cascade SVM classification. In the first stage, a score is computed for the first two support vectors, and the score is compared to a threshold. If the score is below the threshold value, the subimage is classified as not a face. If the score is above the threshold value, the cascade SVM classification function continues to apply more complicated decision rules, each time doubling the number of kernel evaluations, classifying the image as a non-face (and thus terminating the process) as soon as the test image fails to satisfy one of the decision rules. Finally, if the subimage has satisfied all intermediary decision rules, and has now reached the point at which all support vectors must be considered, the original decision function is applied. Satisfying this final rule, and all intermediary rules, is the only way for a test image to garner a positive (face) classification.

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.

An imaging apparatus includes a first image capturing unit that captures first image data through a first optical system, a second image capturing unit that captures second image data through a second optical system, and a mode switching unit that switches between a normal mode and a power saving and speedy capturing mode that has a lower consumed power than the normal mode. In a case of operating in the power saving and speedy capturing mode, an operation stopping unit stops the first image capturing unit and the control unit captures an image by use of the second image capturing unit. A battery remaining amount can also be detected. In a case the battery remaining amount is less than a threshold value, a display mode switching unit switches from first display mode displaying a monitored image of first image data to a second display mode displaying a monitored image of second image data.

A method of filtering one or more events associated with one or more computer environments for display in a performance monitoring system, wherein each of the one or more events is generated when a threshold associated with a first parameter is met, the method comprising the steps of: receiving a filter representing at least one of: a set of the one or more computer environments or at least one second parameter; and in response to the receiving step, filtering the one or more events.

The invention discloses a workpiece apparent defect detection method based on machine vision. Firstly, a visual system is used for guiding a robot, the pose of a target workpiece is precisely positioned according to a template matching algorithm based on a gray value, and then workpiece apparent defect detection is carried out and comprises the steps that firstly, a workpiece image is acquired and pretreated through median filtering; secondly, the target workpiece is divided through a global threshold value, and workpiece pose correction is carried out; thirdly, burr interference of the edge of the workpiece is removed through mathematical morphology open operation; fourthly, notches, material sticking, cracking, indentation, needle eyes, scratches and foaming apparent defects are detected. The method solves the problems that the artificial detection speed is slow, the efficiency is low and precision is poor; the problems that according to current vision detection, defect types are singular, the imaging quality is poor, and the false drop rate is high are solved, and the automation degree of precise workpiece production and product quality are improved.

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.

A computerized method and system for detection and quantification of changes in color on skin or internal tissue or organs within the human body. Digital images of the skin or tissue containing an area of interest are analyzed using the software of the invention, based on at least one channel of the LAB color system. The invention calculates the intensity and distribution of color present within the predetermined area of interest, and results in display of at least one color information attribute. The invention allows color calibration of the image to be performed in relation to a reference label having predetermined colors which surrounds the area of interest, prior to the analysis. The invention allows a threshold analysis to be performed, which graphically depicts the size and location of areas exhibiting significant change in color, which is a determinant factor in medical diagnosis.

An image without use of a flash is taken, along with an image using a flash. A difference image is generated by subtracting the flash-less image from the flash image. A threshold is applied to the difference image such that only large differences in intensity remain in the difference image. This artifact image is then subtracted from the flash image, thereby removing flash artifacts such as specular reflections and red-eye. The threshold used may be automatically calculated or may be set by the user. For some applications it may be desirable to set separate thresholds for each dimension of the color space (such as red, green, and blue) used. Once again these separate thresholds may be automatically calculated or may be set by the user.

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 and system of matching features in a pair of images using line signatures. The method includes determining a first similarity measure between a first line signature in a first image in the pair of images and a second line signature in a second image in the pair of images; determining a second similarity measure between the first line signature in the first image and a third line signature in the second image; comparing the first similarity measure with a first threshold value; comparing a difference between the first similarity and the second similarity with a second threshold value; and if the first similarity measure is greater than the first threshold value and the difference between the first similarity and the second similarity is greater than the second threshold value, the first line signature and the second line signature produce a match.

The present disclosure relates to an image capture device and a technique for capturing an image. The technique includes selecting at least one subject displayed on an image capture device as an image trigger condition and entering a threshold level corresponding to the at least one image trigger condition. The technique further provides monitoring a signal from at least one sensor detecting the at least one image trigger condition and obtaining at least one digital image upon detecting the sensed image trigger condition meeting the threshold level.

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.