245 results about "Discriminant" patented technology

A system that incorporates an interactive graphical user interface for visualizing clusters (categories) and segments (summarized clusters) of data. Specifically, the system automatically categorizes incoming case data into clusters, summarizes those clusters into segments, determines similarity measures for the segments, scores the selected segments through the similarity measures, and then forms and visually depicts hierarchical organizations of those selected clusters. The system also automatically and dynamically reduces, as necessary, a depth of the hierarchical organization, through elimination of unnecessary hierarchical levels and inter-nodal links, based on similarity measures of segments or segment groups. Attribute / value data that tends to meaningfully characterize each segment is also scored, rank ordered based on normalized scores, and then graphically displayed. The system permits a user to browse through the hierarchy, and, to readily comprehend segment inter-relationships, selectively expand and contract the displayed hierarchy, as desired, as well as to compare two selected segments or segment groups together and graphically display the results of that comparison. An alternative discriminant-based cluster scoring technique is also presented.

A discriminative grammar framework utilizing a machine learning algorithm is employed to facilitate in learning scoring functions for parsing of unstructured information. The framework includes a discriminative context free grammar that is trained based on features of an example input. The flexibility of the framework allows information features and / or features output by arbitrary processes to be utilized as the example input as well. Myopic inside scoring is circumvented in the parsing process because contextual information is utilized to facilitate scoring function training.

In a vehicle speed control system, a preset offset distance is taken as a zero value of an inter-vehicle distance in a brake discriminant. When the current value of a corrected distance condition evaluation index is higher than the brake discriminant, a value on the brake discriminant is set as a target value of the corrected distance condition evaluation index. Deceleration control is started to decelerate the subject vehicle so that the deceleration of the subject vehicle becomes equal to the target deceleration of the subject vehicle computed based on the target relative speed corresponding to the set target value and the actual relative speed.

A wavelet-based band difference-sum ratio method reduces the computation cost of classification and feature extraction (identification) tasks. A Generalized Difference Feature Index (GDFI), computed using wavelets such as Daubechies wavelets, is employed in a method to automatically generate a large sequence of generalized band ratio images. In select embodiments of the present invention, judicious data mining of the large set of GDFI bands produces a small subset of GDFI bands suitable to identify specific Terrain Category / Classification (TERCAT) features. Other wavelets, such as Vaidyanathan, Coiflet, Beylkin, and Symmlet and the like may be employed in select embodiments. The classification and feature extraction (identification) performance of the band ratio method of the present invention is comparable to that obtained with the same or similar data sets using much more sophisticated methods such as discriminants, neural net classification, endmember Gibbs-based partitioning, and genetic algorithms.

A method and system determines mobile station position information based upon base station identification and a repeater discriminant. A position location database includes base station identifications and repeater discriminant combinations, and unique position information associated with each combination. A repeater implements a discriminant to a transmitted signal in either the forward link, reverse link, or both. The position location database is accessed to determine mobile station position information based upon a base station identification and repeater discriminant.

Embodiments of a dialog system employing a discriminative action selection solution based on a trainable machine action model. The discriminative machine action selection solution includes a training stage that builds the discriminative model-based policy and a decoding stage that uses the discriminative model-based policy to predict the machine action that best matches the dialog state. Data from an existing dialog session is annotated with a dialog state and an action assigned to the dialog state. The labeled data is used to train the discriminative model-based policy. The discriminative model-based policy becomes the policy for the dialog system used to select the machine action for a given dialog state.

A method for operating an exchange where financial instruments are traded that includes applying a discriminant to trade transactions, dividing the trade transactions into groups and then routing trade transactions to selected exchanges for handling. The method also allows transactions to be matched within each exchange and allows qualified traders to make manual trades within the various exchanges.

An apparatus and method for identifying remote communications transmitted via a repeater from remote communications not transmitted via the repeater. The method comprises the steps of receiving a plurality of signal transmissions originating from a plurality of remote stations, wherein each of the signal transmissions is associated with a call originating from one of the plurality of remote stations; processing the plurality of received signal transmissions to identify received transmissions that include a discriminant applied by a repeater; and designating each of the plurality of received signal transmissions as being transmitted via the repeater if the received signal transmission includes the discriminant.

The invention discloses a multi-target tracking method based on track association. The method comprises the following steps: to begin with, generating a local track based on a scene self-adaption method and realizing association of detection responses and original tracks; then, realizing global track association based on an appearance model of increment linear discriminant; and finally, realizing vacancy filling between track fragments to obtain a complete and smooth tracking track based on a nonlinear motion model. The method can realize correct association of multiple targets under complex conditions of target occlusion, similar appearance characteristics of different targets and abrupt change of direction of a moving target and the like; and finally, a stable and continuous tracking track can be obtained.

Stand-alone or assistive pattern recognition system and process enabling error free classification of all objects in a training set and application to unclassified objects. Parameters and / or features of the data objects in a training set are selected and measured, from which discriminants are computed. The measured data is plotted in discriminant space and decision boundaries or thresholds determined, preferably such that at least one object from one class is isolated from the remaining objects, removed from the training set, and the process repeated until an acceptable number of unclassified objects remain. The system can be applied sequentially to classify all the members of the training set belonging to one class and then applied to objects in other classes. Fuzzy quantifiable determinations of an object's likelihood of class membership can be made. Objects' positions and classifications are obtainable in an optical system using Fourier techniques without limitation to linearly discriminable problems.

A system and method for populating a database with a set of image sequences of an object is disclosed. The database is used to detect localization of a guidewire in the object. A set of images of anatomical structures is received in which each image is annotated to show a guidewire, catheter, wire tip and stent. For each given image a Probabilistic Boosting Tree (PBT) is used to detect short line segments of constant length in the image. Two segment curves are constructed from the short line segments. A discriminative joint shape and appearance model is used to classify each two segment curve. A shape of an n-segment curve is constructed by concatenating all the two segment curves. A guidewire curve model is identified that includes a start point, end point and the n-segment curve. The guidewire curve model is stored in the database.

The invention relates to a high reliable inrush current brake method for a transformer, which comprises the steps of sampling, calculating the three-phase differential current fundamental wave content and the second harmonic content of a current sampling point, setting and calculating a floating threshold, and carrying out differential starting judgment, the judgment of the change characteristics of the fundamental wave content and the judgment of the change characteristics of the ratio between second harmonic content and the fundamental wave content by a discriminant so as to determine reliable locking or immediate differential opening. On the basis of a traditional second harmonic braking principle, the method sets the floating threshold, tracks and masters the change characteristics of the fundamental wave and the second harmonic in a real-time manner, analyzes and judges the operation state of the transformer in time by utilizing the change characteristics of the fundamental wave and the second harmonic, solves the contradiction of the right and fast motion between the inrush current brake and differential protection in the current method and ensures the reliable brake of the transformer under the condition of the only existence of the inrush current and the right and fast motion of the transformer under the situation of any fault in the sample space. Meanwhile, the method reduces the value scope of the brake coefficient of the second harmonic and improves the sensitivity of inrush current brake.

The invention discloses an image retrieval system integrating classification with hash partitioning, which comprises a downloading module, a classification module training module, an image classification module, a characteristic extraction module, a recording table building module, a partitioning module, a request processing module, a retrieval module, a similarity acquiring module and a result returning module. The downloading module is used for downloading images so as to build an image library, the classification model training module classifies the images in the image library according tothe shapes and selects representative sample images from the image library to form a sample library firstly, then extracts characteristic descriptors of the classification bottom layer of all images in the sample library and performs trainings on the characteristic descriptors of the classification bottom layer by a support vector machine so as to obtain discriminant of each classification. Classification models are formed according to the discriminants of all the classifications. Precision ratio of the image retrieval system is increased, the problem of low recall ratio during classificationmistakes is overcome, and the retrieval speed of the image retrieval system is increased integrally.

A system is described for interactively analyzing plays of a sporting event based on real-world positional tracking data. Using positional information regarding the players and / or ball and / or other objects obtained from a tracking system, along with identified event data and contextual information, the system processes a library of plays (e.g., one or more seasons' worth of a league's contests) into a searchable database of plays using multiple alignment templates and discriminative clustering techniques. A user interface is described for interacting with the database in a graphical manner, whereby users can query a graphical depiction of a play and receive the most similar plays from the library, along with statistical information relating to the plays. The user interface further permits the user to modify the query graphically (e.g., moving or exchanging players, ball trajectories, etc.) and obtain updated statistical information for comparison.

The invention discloses a knowledge network-based text indexing system and method. The text indexing system comprises a single text feature extraction unit, a multi-text word relation extraction unit, a knowledge tree generating unit, a knowledge tree application unit and a knowledge base storage unit. The text indexing method comprises the following steps of: partitioning words in a text input to the text indexing system, and acquiring text feature words in the text; deducing a class word TAG corresponding to the text according to node positions of a knowledge tree corresponding to the text feature words; and judging the validity of the TAG through a judgment type model based on the TAG, then extracting a reliable TAG word set, and repositioning a text feature word set through the reliable TAG word set to form a reliable text feature word set. According to the system and the method, content word extraction, class labeling and phrase extraction are integrated, so that the extraction effects can be mutually promoted; and the semantics of the words are expressed through the nodes of the knowledge network, so that different meanings are reduced.

The invention discloses a target prospect collaborative segmentation method combing significant detection and discriminant study. The method comprises the steps as follows: step one, each image in an image set is divided into a plurality of superpixel blocks, and characteristics of each superpixel block are extracted; step two, an image concentrated and shared significant area in the image set is extracted to serve as a target prospect, a non-significant area and an area which has significance but is not the image concentrated and shared area are taken as a background area, low-rank matrix decomposition is adopted to perform significant detection on the images, and logistic regression is adopted to select the shared significant area as a final target. According to the target prospect collaborative segmentation method combing significant detection and discriminant study, the significant area can be effectively detected by means of the low-rank matrix decomposition, the influence of background consistency is removed, and by means of the discriminant study, the shared and significant area can be extracted; the low-rank matrix decomposition and the discriminant study process are combined and optimized under the unified framework, are mutually influenced and are commonly promoted; and finally, the shared and significant area can be obtained to serve as the target prospect area.

The invention discloses an automatic complicated target identification method based on a hierarchical object semantic graph, and relates to a target identification technology. The automatic complicated target identification method comprises the following steps of: establishing a multi-class complicated target image representative set; performing multi-scale partitioning on an image of a training set, gradually calculating characteristic information of each part object, and constructing a hierarchical semantic graph; counting partial characteristic attributes of objects by using a judgment type classifier by adopting a spiral mixed learning mode, calculating interactive influence among the objects by combining a generation type message transmission mechanism, and deducing and calculating the hierarchical semantic graph; and resolving a target of interest in the image by using the hierarchical object semantic graph obtained by learning, and realizing positioning, extraction and type identification of a plurality of classes of complicated targets. The method is relatively high in intelligentization degree; and requirements for identifying a plurality of classes of complicated targetsin natural and remotely sensed scene images and explaining the images can be met.

Methods and apparatus are provided for generating a decision trees using linear discriminant analysis and implementing such a decision tree in the classification (also referred to as categorization) of data. The data is preferably in the form of multidimensional objects, e.g., data records including feature variables and class variables in a decision tree generation mode, and data records including only feature variables in a decision tree traversal mode. Such an inventive approach, for example, creates more effective supervised classification systems. In general, the present invention comprises splitting a decision tree, recursively, such that the greatest amount of separation among the class values of the training data is achieved. This is accomplished by finding effective combinations of variables in order to recursively split the training data and create the decision tree. The decision tree is then used to classify input testing data.

A method for representing images for pattern classification extends the conventional Isomap method with Fisher Linear Discriminant (FLD) or Kernel Fisher Linear Discriminant (KFLD) for classification. The extended Isomap method estimates the geodesic distance of data points corresponding to images for pattern classification, and uses pairwise geodesic distances as feature vectors. The method applies FLD to the feature vectors to find an optimal projection direction to maximize the distances between cluster centers of the feature vectors. The method may apply KFLD to the feature vectors instead of FLD.

The invention discloses a target tracking method based on supervised significance detection. The target tracking method comprises steps that a searching area of a current frame is divided into super pixels, and super pixel characteristics of a target and a background are extracted, and a support vector machine SVM is used to learn the discriminant appearance model; each time when a new frame of image occurs, the super pixel segmentation of the searching area is carried out, and first-stage significance detection is carried out by using manifold sequencing based on a graph model; the probability of every super pixel of the new frame of image belonging to the target is calculated according to the discriminant appearance model, and classification results are adjusted, and by combining with the first-stage significance detection, a classification result is adjusted, and random walk seed points are selected by combining with the first-stage significance detection, and a second-stage saliency map is acquired by adopting random walk; by adopting the weighting of the saliency map and the classification result, a confidence graph is acquired, and by processing the confidence graph, an integral image is used to estimate the new position and the new dimension of the target. Problems such as rapid motion and deformation are effectively processed, and therefore robustness tracking is realized.

A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective deep neural network is trained for each of the marginal search spaces, resulting in a series of trained deep neural networks. Each of the trained deep neural networks can evaluate hypotheses in a current parameter space using discriminative classification or a regression function. An anatomical object is detected in a medical image by sequentially applying the series of trained deep neural networks to the medical image.

The invention discloses an image scene segmentation and layering joint solution method based on component set sampling. The method comprises the following steps of: performing over-segmentation treatment on an input image to obtain a super-pixel set of the image; training on a training dataset to obtain a discriminant model of semantic category and a discriminant model of layer category, and obtain a probability value (of each super-pixel in the input image) belonging to each semantic category and a probability vale belonging to each layer category according to the two models; structuring a candidate graph structure of the input image and calculating a node weighted value, a positive side weighted value and a negative side weighted value; and based on the a candidate graph structure, obtaining an optimal solution by reasoning via a component set sampling algorithm, wherein the optimal solution includes the exact semantic category and the exact layer category of each super-pixel of the input image. The method disclosed by the invention can be widely used for semantic information and layer information labeling of computer visual systems of military, aviation, aerospace, monitoring and manufacturing, and the like.

The present invention, in its various aspects and embodiments comprises a optical multi-discriminant (“OMD”) LADAR technique. The technique includes a method, comprising: receiving a dichroic, polarimetric optical signal simultaneously with radiation of a third wavelength from a field of view through a common aperture; and capturing three-dimensional, polarimetric data from each wavelength of the optical signal and two-dimensional data from the radiation. The technique also includes an apparatus, comprising: means for receiving a reflection of a polarimetric, dichroic optical signal and a second signal of a third wavelength, separating the optical signal from the second signal by wavelength, separating the dichroic constituents of the optical signal by wavelength, and separating the polarized elements of the dichroic constituents; and a common aperture through which the optical signal is transmitted and the reflection and the second signal are received.

The invention discloses an active contour tracing method based on superpixel. The method comprises the following steps of carrying out the superpixel segmentation on a training image to obtain a training sample pool of a target and a background, adopting a measuring learning method to obtain a distance measuring projection matrix according to a training sample, establishing a judgment-type appearance model, carrying out the superpiexel segmentation on each frame of test image of a sequence image, obtaining a confidence diagram corresponding to the test image according to the established judgment-type appearance model, obtaining a speed field of the test image, substituting the speed field into an evolution equation of a level set method, and obtaining a contour trace result of the test image. Compared with the prior art, the contour evolution efficiency of each frame of test image can be improved, and the tracing accuracy and tracing efficiency of the sequence image can be improved.

The invention discloses a multi-modal data fusion method based on a discriminant multi-modal deep confidence network. The multi-modal data fusion method based on the discriminant multi-modal deep confidence network comprises the steps that the discriminant multi-modal deep confidence network is established; for the deep confidence network corresponding to multi-modal data, the weight of the network after the deep confidence network is optimized is obtained by means of limited Boltzmann machines; objective functions of the multi-modal Boltzmann machines are minimized by means of the alternative optimization strategy, the weights of the optimized Boltzmann machines are obtained, and a final discriminant multi-modal deep confidence network model is obtained; the multi-modal data to be fused are input into the deep confidence network model, and then a fusion result is obtained. The invention further discloses a multi-modal data fusion system based on the discriminant multi-modal deep confidence network. According to the multi-modal data fusion method and system based on the discriminant multi-modal deep confidence network, monitored label information is introduced into a traditional multi-modal deep confidence network, the relations between the data with different modals are mined in a discriminant mode, and thus the high accuracy rate can be guaranteed during a large-scale multi-modal data classifying and searching task.

The invention discloses a multi-scale superpixel-fused target tracking method. Through the method, a more accurate target confidence map can be obtained by constructing a superpixel-based discriminant appearance model, and consequently the target tracking accuracy degree and robustness are effectively improved. Specifically, the method includes the first step of adopting superpixel classification results with different scales to vote for properties of one pixel point and then obtaining the more accurate target confidence map, and the second step of updating the appearance model by continuously updating a classifier so that the appearance model can continuously adapt to scenes such as illumination changes and complicated backgrounds. Consequently, more accurate and robust tracking can be achieved.

The invention discloses an esophageal cancer pathological image automatic discrimination device based on a convolution neural network and a discrimination method thereof. The device comprises an imageacquisition module, an image processing module, a data storage module, a migration learning module, a network training module and a discrimination module. The screening method of the invention comprises the following steps: 1, an image acquisition module collects pathological images and constructs an image database of pathological slices of esophageal cancer; 2, each pathological image database is expanded through an image processing module; 3, the expanded pre-training network pathological image data set is used to complete the migration learning; 4. on the basis of the acquired convolutional neural network structure, the network is trained with the expanded pathological image data set of esophageal cancer and the weights are fine-tuned to get the discriminant network model, and the intelligent discriminant is realized with the discriminant module. The invention overcomes the over-fitting problem in the depth learning process caused by the labeled esophageal cancer pathological imagedata set as a training sample due to the lack of large-scale disclosure, and improves the recognition rate.

The invention discloses a control method for preventing a tension measuring roller from slipping relative to a continuous rolling strip. The method comprises that correlation coefficients are measured in advance; after a rolling mill unit begins to thread the strip, a system determines whether slipping exists between the strip and the tension measuring roller in real time through a critical condition discriminant; when the critical condition discriminant is not met, an electromagnetic clutch keeps a closed state, and the tension measuring roller is transmitted through a motor directly; when the critical condition discriminant is met, the electromagnetic clutch is separated, and the tension measuring roller is transmitted through the strip; when the action tension value reaches or exceeds the preset tension value, the system continues to increase the tension value continuously if determining that slipping still exists, till the system determines slipping is absent to separate the electromagnetic clutch, and the tension measuring roller is transmitted by the strip. By means of the control method, the tension measuring roller can be transformed into an inertia roller at the appropriate time and overcome slip relative to the strip, the lower surface of the strip can be effectively protected, and the tension measuring roller service life can be prolonged.

The invention discloses a human movement tracking method based on combination of production and discriminant. The human movement tracking method is mainly used for solving the problem of inaccurate tracking result of the human movement in the prior art. The implementation steps are as follows: building a human skeleton model; pre-treating a video picture to obtain a detection joint; extracting bandlet 2 characteristic of the video picture; inputting the extracted bandlet 2 characteristic and predicting the human gesture by using double-Gauss; initializing the human skeleton model according to the detected joint; structuring 2D and 3D similarity functions between the joint predicted by the double-Gauss and the detected joint; minimizing the similarity function under the restriction of the skeleton length to obtain a group of human gesture; and selecting the state with the minimum Euclidean distance to the former frame skeleton from the obtained human gesture as the best movement gesture of the current frame. Compared with the existing human tracking method, the human movement tracking method based on combination of production and discriminant has the advantages of high accuracy of tracking result and high stability, and can be applied to the medical treatment, the physical training, the animation production and an intelligent monitoring system.