Abstract: A temporary self-organizing map in which classes are associated with respective vector points is first derived by a first learning section of a data learning apparatus. Thereafter, by use of, for example, an improved method incorporating concept of vicinity learning intp learning vector quantization, a second learning section modifies the temporary self-organizing map and obtains a final self-organizing map. By use of the final self-organizing map thus derived, image meaning determining processing is performed.

Abstract: A fire detection system has a video sensor, a processor, and a database. The database has a statistical model representing the characteristics of a fire. The video sensor captures images, and sends the images to the processor. The processor computes statistics on the characteristics of the captured image, and compares it to the statistical model to determine if a fire is present in the captured image.

Abstract: Designs for cognitive memory systems storing input data, images, or patterns, and retrieving it without knowledge of where stored when cognitive memory is prompted by query pattern that is related to sought stored pattern. Retrieval system of cognitive memory uses autoassociative neural networks and techniques for pre-processing query pattern to establish relationship between query pattern and sought stored pattern, to locate sought pattern, and to retrieve it and ancillary data.

Abstract: In an image enhancement method, a regular enhancement function and an aggressiveness parameter are computed using characteristics of a digital image. A first, relatively slow enhancement algorithm is applied to the digital image using the regular enhancement function, when an aggressiveness parameter is in a predetermined high range. A second, relatively fast enhancement algorithm is applied to the digital image using the regular enhancement function, when an aggressiveness parameter is in a predetermined low range. A modified enhancement function is calculated using characteristics of the digital image and is used with a second, relatively fast enhancement algorithm, when the aggressiveness parameter is in a predetermined medium range.

Abstract: A method and system for container identification are disclosed. The method comprises obtaining a plurality of digital images of a character sequence on the container, extracting the character sequences from the images, combining the character sequences into at least one identification code candidate, and selecting one of the candidates as the identification code. The system comprises at least one camera and a computer system that is electrically coupled to the camera, whereby when the computer system receives a trigger signal, said computer system takes a plurality of digital images from the camera, produces character sequences as partial recognition results for the plurality of digital images, and combines the partial recognition results together to produce the identification code.

Abstract: The present invention discloses methods and systems for discovering printers and shares on a computer network. Each domain on the network is identified, and each computer in the domain is identified. In addition, each printer connected to the computer and each share on the computer is identified. Shortcuts to the identified printers and shares are created on at least one computer on the network. Moreover, drivers are preferably installed on the computer for each printer for which a shortcut was created. In the event that the total number of resources (i.e., shares and/or printers) exceeds a threshold, then the process terminates. Otherwise, the present invention continues until all printers and shares on the network are identified, and the appropriate shortcuts are created. Thus, the present invention provides methods and systems for discovering resources on a network.

Abstract: A three-dimensional object recognizing system comprises a distance image generating portion for generating a distance image by using image pairs picked up by a stereoscopic camera, a grouping processing portion for grouping the distance data indicating the same three-dimensional object on the distance image, an input value setting portion for setting an area containing distance data group of grouped three-dimensional object on the distance image and also setting input values having typical distance data as elements every small area that is obtained by dividing the area by a set number of partition, a computing portion for computing output values having a pattern that responds to a previously set three-dimensional object by using a neural network that has at least the input values Xin as inputs to an input layer, and a discriminating portion for discriminating the type of the three-dimensional object based on the pattern of the output values.

Abstract: A method constructs a strong classifier from weak classifiers by combining the weak classifiers to form a set of combinations of the weak classifiers. Each combination of weak classifiers is boosted to determine a weighted score for each combination of weak classifiers, and combinations of weak classifiers having a weighted score greater than a predetermined threshold are selected to form the strong classifier.

Abstract: The present invention relates to a recognition method for images by probing alimentary canals. First, series first image data is received. Then, according to a plurality of judgments, judge if the first image data exceeds a threshold value. If so, the image data is stored and second image data is inputted for recognition. Thereby, by the plurality of judgments with partially identical characteristics, multiple diseases can be recognized at a time, and repeated operation can be eliminated and the processing time be reduced. In addition, by integrating different recognition methods, the amount of system operation can be reduced, and the operation speed can be thereby improved.

Abstract: A vehicle restraint system has a vision-based occupant classification system for control of airbag deployment during a crash scenario. The classification system utilizes two imaging sensors which together create a stream of paired images received and stored by an occupant classification controller. A computer program product of the controller utilizes the paired images to extract disparity/range features and stereo-vision differential edge density features. Moreover, the controller extracts wavelet features from one of the two paired images. All three features or maps are classified amongst preferably seven classifications by algorithms of the computer program product producing class confidence data fed to a sensor fusion engine of the controller for processing and output of an airbag control signal input into a restraint controller of the vehicle restraint system.

Abstract: A miniature autonomous apparatus for performing scene interpretation, comprising: image acquisition means, image processing means, memory means and communication means, the processing means comprising means for determining an initial parametric representation of the scene; means for updating the parametric representation according to predefined criteria; means for analyzing the image, comprising means for determining, for each pixel of the image, whether it is a hot pixel, according to predefined criteria; means for defining at least one target from the hot pixels; means for measuring predefined parameters for at least one target; and means for determining, for at least one target whether said target is of interest, according to application-specific criteria, and wherein said communication means are adapted to output the results of said analysis.

Abstract: Each video segment in a plurality of video segments is annotated with an indicator of the likelihood that the respective video segment shows a particular feature. The plurality of video segments forms an episode of interest from a given video domain. Initial feature probabilities are calculated for respective ones of the plurality of video segments using a machine learning algorithm. Each initial feature probability indicates the likelihood that its respective video segment shows the particular feature. Refined feature probabilities are determined for respective ones of the plurality of video segments by finding the most probable state sequence in a finite state machine. This is accomplished at least in part using the determined initial feature probabilities. Finally, each of the video segments in the plurality of vides segments is annotated with its respective refined feature probability.

Abstract: A method for detecting and recognizing at least one traffic sign is disclosed. A video sequence having a plurality of image frames is received. One or more filters are used to measure features in at least one image frame indicative of an object of interest. The measured features are combined and aggregated into a score indicating possible presence of an object. The scores are fused over multiple image frames for a robust detection. If a score indicates possible presence of an object in an area of the image frame, the area is aligned with a model. A determination is then made as to whether the area indicates a traffic sign. If the area indicates a traffic sign, the area is classified into a particular type of traffic sign. The present invention is also directed to training a system to detect and recognize traffic signs.

Abstract: A method and an apparatus for modifying image forming data including the steps of establishing parameters of an artificial neuronal net during a teaching process on the basis of uncorrected image data and of providing target data based on the original image to be reproduced. The outputs of the neuronal net are utilized for operating an image reproduction device in accordance with data signals modified by the neuronal net on the basis of deviations between the error-free original image and the image to be reproduced.

Abstract: The present invention is related to a color difference judgment apparatus for judging a color difference between a first color and a second color in RGB color space. The method is comprising: distance calculating means for calculating distance between the first and second colors in RGB color space; threshold-value candidate storage means for storing a plurality of threshold-value candidates; threshold-value selecting means for selecting a threshold value from among the plurality of threshold-value candidates that have been stored in the threshold-value candidate storage means; and comparing decision means for comparing the distance calculated by the distance calculating means and the threshold value selected by the threshold-value selecting means and deciding that the color difference between the first and second colors lies within an allowable range if the distance is less than the threshold value.

Abstract: The present invention discloses an object image detection method, which uses a coarse-to-fine strategy to detect objects. The method of the present invention comprises steps: acquiring an image and pre-processing the image to achieve dimensional reduction and information fusion; using a trained filter to screen features; and sequentially using a coarse-level MLP verifier and a fine-level MLP verifier to perform a neural network image detection to determine whether the features of the image match the features of the image of a target object. The present invention simultaneously uses three mainstream image detection methods, including the statistic method, neural network method and adaboost method, to perform image detection. Therefore, the present invention has the advantages of the rapidity of the adaboost method and the accuracy of the neural network method at the same time.

Abstract: This specification discloses a system of using a neural network to distinguish text and pictures in an image and the method thereof. Using the knowledge of text recognition learned by the neural network in advance, images data of color brightness and gray levels in an image block are processed to generate a greatest text faith value. The system determines the text status of the image block by comparing a text threshold with the greatest text faith value. If the greatest text faith value is larger than the text threshold, then the image block is determined to contain text pixels; otherwise, the image block contains purely picture pixels. This achieves the goal of separating text and pictures in an image.

Abstract: A synaptic connection element for connecting neuron elements inputs a plurality of pulsed signals from different neuron elements N1 through N4, effects a common modulation (time window integration or pulse phase/width modulation) on a plurality of predetermined signals among the plurality of pulse signals, and outputs the modulated pulse signals to different signal lines to a neuron element M1. A neural network for representing and processing pattern information by the pulse modulation is thereby downsized in scale.

Abstract: A feature area extracting section extracts an area having a unique feature in an image input to an image selection support apparatus. A specific area feature collating and determining section determines whether or not the area having a feature and extracted by the feature area extracting section is a specific area. A specific area image reading section decides a rectangular area including the specific area, and reads image information of the rectangular area. The specific area image reading section has at least one of an enlargement displaying section which enlarges and displays the image information read by the specific area image reading section, a thumbnail display section which reduces and displays the input image, and an original image displaying section which enlarges and displays the input image.

Abstract: Methods for identifying and quantifying recurrent and deterministic patterns in digital images are provided. The methods, which are based on Recurrence Quantification Analysis (RQA), generate similarity or dissimilarity distance matrices for digital images that may be used to calculate a variety of quantitative characteristics for the images. Also provided are methods for identifying and imaging spatial distributions of time variable signals generated from dynamic systems. In these methods a time variable signal is recorded for a plurality of area or volume elements into which a dynamic system has been sectioned and RQA is used to calculate one or more RQA variables for each of the area or volume elements, which may then be used to generate a two or three dimensional image displaying the spatial distribution of the RQA variables across the system.

Abstract: A method of comparing an input pattern with a memory pattern includes the steps of loading a representation of said input pattern into cells in an input layer; loading a representation of said memory pattern into cells in a memory layer; loading an initial value into cells in an intermediate layers between said input layer and said memory layer; comparing values of cells in said intermediate layers with values stored in cells of adjacent layers; updating values stored in cells in said intermediate layers based on said step of comparing; and mapping cells in said memory layer to cells in said input layer.

Abstract: A target detection system and method is disclosed that uses a trained detection component and an untrained detection component that enhances image data for candidate target detection. The trained detection component separates candidate targets from clutter within the image data using correlation filters trained from an image library. The image library includes target and clutter images that are used to tune the correlation filters. The untrained detection component separates the candidate targets from the clutter by suppressing clutter using a Fourier frequency transform of the image data. Anomalies are detected in the frequency domain, and retained to highlight candidate targets.

Abstract: Systems, methods, and computer program products implementing techniques for training classifiers. The techniques include receiving a training set that includes positive images and negative images, receiving a restricted set of linear operators, and using a boosting process to train a classifier to discriminate between the positive and negative images. The boosting process is an iterative process. The iterations include a first iteration where a classifier is trained by (1) testing some, but not all linear operators in the restricted set against a weighted version of the training set, (2) selecting for use by the classifier the linear operator with the lowest error rate, and (3) generating a re-weighted version of the training set. The iterations also include subsequent iterations during which another classifier is trained by repeating steps (1), (2), and (3), but using in step (1) the re-weighted version of the training set generated during a previous iteration.

Abstract: A system that facilitates generation of data that can be employed in connection with training a classifier. The system comprises a component that receives a data set that is employed in connection with training the classifier, and an expansion component that applies elastic distortion algorithm(s) to a subset of the data set to generate additional labeled training data.

Abstract: Optical classification method for classifying an occupant in a vehicle by acquiring images of the occupant from a single camera and analyzing the images acquired from the single camera to determine a classification of the occupant. The single camera may be a digital CMOS camera, a high-power near-infrared LED, and the LED control circuit. It is possible to detect brightness of the images and control illumination of an LED in conjunction with the acquisition of images by the single camera. The illumination of the LED may be periodic to enable a comparison of resulting images with the LED on and the LED off so as to determine whether a daytime condition or a nighttime condition is present. The position of the occupant can be monitored when the occupant is classified as a child, an adult or a forward-facing child restraint.

Abstract: A computer-readable medium for performing target object recognition in images and video includes instructions for receiving target image data including a target object, applying non-negative matrix factorization with enforced sparseness to the target image data to generate target extracted image feature data, training a neural network to identify the target object using the target extracted image feature data to obtain a trained neural network, receiving object image data, applying non-negative matrix factorization with enforced sparseness to the object image data to generate object extracted image feature data, analyzing the object extracted image feature data with the trained neural network to obtain a result indicating whether the presence of the target object is identified in the object image data, and storing the result of analyzing the object extracted image feature data.

Abstract: A nondestructive inspection apparatus includes a sensor unit for detecting vibrations transmitted through a test object from a vibration generator and a signal input unit for extracting a target signal from an electric signal outputted from the sensor unit. An amount of characteristics extracting unit is also included for extracting multiple frequency components from the test signal as an amount of characteristics. Further, a decision unit has a competitive learning neural network for determining whether the amount of the characteristics belongs to a category, wherein the competitive learning neural network has been trained by using training samples belong to the category representing an internal state of the test object, wherein distributions of membership degrees of the training samples are set in the decision unit.

Abstract: A system and method responsive to input stimuli is provided by incorporating a computer software program, hardware processing engine, or a specialized ASIC chip processor apparatus to capture concurrent inputs that are responsive to training stimulation, store a model representing a synthesis of the captured inputs, and use the stored model to generate outputs in response to real-world stimulation. Human user forced-choice approval/disapproval generated descriptions and decisions may be dynamically mapped with conventionally presented information and sensor and control data. The model mapping is stored into and out of a conventional mass storage device, such as is used in a relational database for use in generating a response to the stimuli.

Abstract: Th invention relates to a method and system for modifying a digital image (100) consisting of pixels. Said digital image is divided into areas (103). At least one area value is assigned to each area Zi (103). At least one parameter value Vpij (203) is assigned to each of said areas (103). A set of couples (Zi, Vpij) forms a parameter image (201). The inventive method consists (a) in determining the determined parameter values Vpir for each area (103), the parameter image (201) being called determined parameter image, (b) in adjusting the determined parameter image by reducing the variations thereof, (c) in modifying pixel values (102) of the determined pixel (101) of said digital image (100) according to the parameter values (203) of said adjusted parameter image, whereby the digital image is differentially modified for each of said pixels and quasi regularly for contiguous areas.

Abstract: An object positioning solves said problems encountered in machine vision, which employs electro-optic (EO) image sensors enhanced with integrated laser ranger, global positioning system/inertial measurement unit, and integrates these data to get reliable and real time object position. An object positioning and data integrating system comprises EO sensors, a MEMS IMU, a GPS receiver, a laser ranger, a preprocessing module, a segmentation module, a detection module, a recognition module, a 3D positioning module, and a tracking module, in which autonomous, reliable and real time object positioning and tracking can be achieved.

Abstract: Provides quantitative data about a two or more dimensional image. Classifies and counts number of entities an image contains. Each entity comprises a structure, or some other type of identifiable portion having definable characteristics. The entities located within an image may have different shape, color, texture, etc., but still belong to the same classification. Alternatively, entities comprising a similar color/texture may be classified as one type while entities comprising a different color/texture may be classified as another type. May quantify image data according to set of changing criteria and derive one or more classifications for entities in image. I.e., provides a way for a computer to determine what kind of entities (e.g., entities) are in image and counts total number of entities visually identified in image. Information utilized during a training process may be stored and applied across different images.

Abstract: A method and computer program product are disclosed for determining an optimal classifier model for a pattern recognition system and updating the determined model to recognize new output classes. An initial plurality of classifier models are selected from a set of generated classifier models. An optimal representative classifier for each classifier model is selected according to an iterative optimization routine. The representative classifier having the highest associated value for a fitness function is accepted.

Abstract: In a microarray image analysis system, when one of a plurality of statuses is set for a spot of a microarray by the user, the status of a similar spot is automatically determined. In a microarray image, the user determines a status of a spot, the pixel value matrix of an image in a spot region is learned by a neural network, a vertically and horizontally symmetrical image and an image rotated about the center of the region are formed and are learned by the neural network, and the neural network formed by repeating these steps is used for automatically recognizing the status of an undecided spot.

Abstract: An artificial neural network (ANN) based system that is adapted to process an input pattern to generate an output pattern related thereto having a different number of components than the input pattern. The system (26) is comprised of an ANN (27) and a memory (28), such as a DRAM memory, that are serially connected. The input pattern (23) is applied to a processor (22), where it can be processed or not (the most general case), before it is applied to the ANN and stored therein as a prototype (if learned). A category is associated with each stored prototype. The processor computes the coefficients that allow the determination of the estimated values of the output pattern, these coefficients are the components of a so-called intermediate pattern (24). Assuming the ANN has already learned a number of input patterns, when a new input pattern is presented to the ANN in the recognition phase, the category of the closest prototype is output therefrom and is used as a pointer to the memory.

Abstract: A defect inspection apparatus for inspecting an object to be inspected for a defect by processing an image taken from the object, includes: neural networks provided respectively for individual defect types to be classified; a learning unit which makes the neural networks learn based on the corresponding defect types to be classified; and a defect detection unit which classifies and detects defect types using the neural networks that have learned.

Abstract: A video quality measurement (VQM) system for a video stream includes a neural network VQM module having a constructed architecture to calculate a video quality metric based on a hybrid of image metric and packet metric of the video stream. An image metric measuring module receives the video stream and calculates the image metric of the video stream. A packet metric measuring module obtains information about packet-level characteristics of the video stream to calculate the packet metric. The image metric and the packet metric are inputted to the neural network VQM module to calculate the video quality metric. The VQM system further includes a VQM test-bed that determines and validates the architecture of the neural network VQM module. Furthermore, a video quality measurement (VQM) method based on a hybrid of image metric and packet metric is also described.

Abstract: A method and computer product is disclosed for analyzing video surveillance data from a plurality of video inputs recording entrants to a secured location. A plurality of entrant profiles are constructed, detailing typical attributes of the entrants, via data mining algorithms. Video inputs are analyzed, with a plurality of independent automated decision-making systems each determining if an abnormal condition exists based upon the constructed profiles. The determinations of the plurality of decision-making systems are then processed to determine if an alarm condition exists.

Abstract: A recognition device which judges whether a target is identical with a predetermined reference, comprising: a holding unit which holds the target; multiple deformation units which deform the target held by the holding unit with at least one degree of flexibility in deformation; multiple deformed amount estimation units which correspond to the deformation units in a one-to-one relationship and estimate a deformed amount of the target from the reference with respect to the flexibility in deformation according to the corresponding deformation unit; an estimated error evaluation unit which evaluates an estimated error of the deformed amount estimated by the deformed amount estimation unit; an adjustment unit which operates any of the deformation units with precedence according to the estimated error evaluated by the estimated error evaluation unit; a similarity calculation unit which calculates a similarity between the reference and the target which is deformed by the deformation unit operated with precedence by

Abstract: Cells i corresponding to pixels are initialized into a non-excitation state, to calculate coupling weights Wik between the eight cells k adjacent to the cells i, thereby determining leader cells pi=1 based on calculation results. Next, one leader cell yet to be excited is selected as a self-excitable cell. The selected cell is put into the excitation state, the excitable cells are selected based on the coupling weights between the adjacent cells, and the selected cells are put into the excitation state. These operations are repeated until no excitable cell is detected any more and, if there no excitable cell is detected any more, inhibition processing is performed, thereby completing image segmentation of one region. These operations are repeated until there is no non-excited and non-inhibited leader cell any more, thereby pinpointing regions belonging to the same category from an input image and identifying them as an image segmentation regions.

Abstract: A face recognition method using artificial neural network and an apparatus thereof are provided. The apparatus comprises an eigenpaxel selection unit which generates eigenpaxels indicating characteristic patterns of a face and selects a predetermined number of eigenpaxels among the generated eigenpaxels; an eigenfiltering unit which filters an input image with the selected eigenpaxels; a predetermined number of neural networks, each of which corresponds to one of the selected eigenpaxels, receives an image signal which is filtered by the corresponding eigenpaxel, and output a face recognition result; and a determination unit which receives the recognition result from each of the neural networks and outputs a final face recognition result of the input image.

Abstract: A system and method facilitating pattern recognition is provided. The invention includes a pattern recognition system having a convolutional neural network employing feature extraction layer(s) and classifier layer(s). The feature extraction layer(s) comprises convolutional layers and the classifier layer(s) comprises fully connected layers. The pattern recognition system can be trained utilizing a calculated cross entropy error. The calculated cross entropy error is utilized to update trainable parameters of the pattern recognition system.

Abstract: Provides quantitative data about a two or more dimensional image. Classifies and counts number of entities an image contains. Each entity comprises a structure, or some other type of identifiable portion having definable characteristics. The entities located within an image may have different shape, color, texture, etc., but still belong to the same classification. Alternatively, entities comprising a similar color/texture may be classified as one type while entities comprising a different color/texture may be classified as another type. May quantify image data according to set of changing criteria and derive one or more classifications for entities in image. I.e., provides a way for a computer to determine what kind of entities (e.g., entities) are in image and counts total number of entities visually identified in image. Information utilized during a training process may be stored and applied across different images.

Abstract: A device, a method and a computer program to model and report an anatomic structure, for example for coronary angiography is provided. The graphical editor models and reports the morphology/topology of the coronary anatomy with its clinical relevant items, i.e. grafts, stents, stenosis/occlusion and turtuosity. From the model different presentations (views) with adjustable display attribute settings, like scale and shape of the arteries can be constructed.

Abstract: The invention provides a method for identifying or quantifying characteristics of interest of unknown objects, comprising training a single neural network model with training sets of known objects having known values for the characteristics; validating the optimal neural network model; and analyzing unknown objects having unknown values of the characteristics by imaging them to obtain a digital image comprising pixels representing the unknown objects, background and any debris; processing the image to identify, separate, and retain pixels representing the unknown objects from pixels and to eliminate background and debris; analyzing the pixels representing each of the unknown objects to generate data representative of image parameters; providing the data to the flash code deployed from the candidate neural network model; analyzing the data through the flash code; and receiving output data (the unknown values of the characteristics of interest of the unknown objects) from the flash code in a predetermined forma

Abstract: In accordance with a first aspect of the present invention, there is provided a method for detecting a defect on a portion of an element comprising the steps of: acquiring an image of said portion; analyzing said image to highlight problematic regions of said portion; calculating a probability that said problematic region is a defect; if said probability is higher than a threshold value, determining a position of said defect on said element. Another method for classifying a defect on an element is provided. The method comprises: acquiring an image of said defect; calculating a probability that said defect corresponds to one of a series of types of defects; if said probability is higher than a threshold value, determining that said defect is a defect of that particular type. Another method for recommending a most suitable rehabilitation technique for a defect is provided.

Abstract: A template creating unit (32) creates a waveform template formed of expectations of a signal waveform of the value of each parameter and a probability template formed of information on occurrence probability of each expectation of the parameters on the basis of the signal waveform measured. A matching judging unit (33) performs template matching of another signal waveform measured while using the expectation occurrence probability information included in the probability template as weight information for each parameter value. Further the template creating unit (32) creates a waveform template and a probability template considering the measured another signal waveform and prepares for the next pattern matching. Thus template matching is performed with improved matching accuracy for waveform of a signal varying with the value of a parameter.

Abstract: Methods and apparatus for color correction of color image data obtained by an electronic camera determine a correction to data representative of the color image based upon an estimated illuminant using a neural network, multilayer perceptron models and/or coactive neuro-fuzzy inference system models, and apply the correction to the data representative of the color image. Data representative of the color corrected data may be recorded or transmitted. A method of recording image data obtained by an electronic camera captures a color image, generates data representative of the image, estimates an illuminant for the captured color image, generates data representative of the estimated illuminant and records the data representative of the image with the data representative of the estimated illuminant.

Abstract: Databases and a system for operating on such databases are described in which data representative of first head shapes and corresponding modified head shapes and corresponding cranial remodeling devices are provided.

Abstract: Methods and systems of the present invention may be used to recognize digital image data arranged in rows and columns. Exemplary embodiments may include a feature extractor for extracting feature information from data representing the rows and columns of the digital image data, a feature compressor for compressing the extracted feature information, and a neural network for classifying the digital image data from the compressed, extracted feature information.

Abstract: The invention relates to a method of detecting flaws in the surface of a test object relative to the surface of a flawless master part by constructing in an artificial neuronal net a virtual master part for comparison with characteristic numbers derived from the grey values of sequential images of the test object recorded by a digital camera.