Class / Patent application number | Description | Number of patent applications / Date published |
382228000 | Statistical decision process | 20 |
20080285862 | Probabilistic Boosting Tree Framework For Learning Discriminative Models - A probabilistic boosting tree framework for computing two-class and multi-class discriminative models is disclosed. In the learning stage, the probabilistic boosting tree (PBT) automatically constructs a tree in which each node combines a number of weak classifiers (e.g., evidence, knowledge) into a strong classifier or conditional posterior probability. The PBT approaches the target posterior distribution by data augmentation (e.g., tree expansion) through a divide-and-conquer strategy. In the testing stage, the conditional probability is computed at each tree node based on the learned classifier which guides the probability propagation in its sub-trees. The top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. In the training stage, a tree is recursively constructed in which each tree node is a strong classifier. The input training set is divided into two new sets, left and right ones, according to the learned classifier. Each set is then used to train the left and right sub-trees recursively. | 11-20-2008 |
20080298691 | FLEXIBLE MQDF CLASSIFIER MODEL COMPRESSION - A memory footprint of an Modified Quadratic Discriminant Function (MQDF) pattern recognition classifier is reduced without resulting in unacceptable classification accuracy degradation. Covariance matrices for multiple classes are clustered into a smaller number of matrices where different classes share the same set of eigenvectors. According to another approach, different numbers of principal components are stored for different classes based on criteria such as class usage frequency, larger variation in writing, and the like, resulting in fewer principal components to be stored in memory. | 12-04-2008 |
20090010551 | IMAGE PROCESING APPARATUS AND IMAGE PROCESSING METHOD - In an image processing apparatus which processes time-series images picked up by an imaging device in time series, a motion-vector calculating unit calculates a motion vector between plural images constituting the time-series images with respect to plural pixel regions set in the image. A newly-appearing-rate estimating unit estimates a newly-appearing rate which is a rate of a region newly appears in an image between plural images based on the motion vector. A display-time determination coefficient calculating unit calculates a display time of an image according to the newly-appearing rate. | 01-08-2009 |
20090208118 | CONTEXT DEPENDENT INTELLIGENT THUMBNAIL IMAGES - An apparatus and method are disclosed for context dependent cropping of a source image. The method includes identifying a context for the source image, identifying a visual class corresponding to the identified context from a set of visual classes, applying a class model to the source image to identify a candidate region of the image based on its relevance to the visual class, and identifying a subpart of the source image for cropping, based on the location of the candidate region. | 08-20-2009 |
20090238473 | CONSTRUCTION OF EVIDENCE GRID FROM MULTIPLE SENSOR MEASUREMENTS - A system includes at least one sensor device configured to transmit a first detection signal over a first spatial region and a second detection signal over a second spatial region. The second region has a first sub-region in common with the first region. The system further includes a processing device configured to assign a first occupancy value to a first cell in an evidence grid. The first cell represents the first sub-region, and the first occupancy value characterizes whether an object has been detected by the first detection signal as being present in the first sub-region. The processing device is further configured to calculate, based on the first and second detection signals, the probability that the first occupancy value accurately characterizes the presence of the object in the first sub-region, and generate a data representation of the first sub-region based on the probability calculation. | 09-24-2009 |
20100104203 | Method and Apparatus for Acquisition, Compression, and Characterization of Spatiotemporal Signals - The present invention provides methods and apparatus for acquisition, compression, and characterization of spatiotemporal signals. In one aspect, the invention assesses self-similarity over the entire length of a spatiotemporal signal, as well as on a moving attention window, to provide cost effective measurement and quantification of dynamic processes. The invention also provides methods and apparatus for measuring self-similarity in spatiotemporal signals to characterize, adaptively control acquisition and/or storage, and assign meta-data for further detail processing. In some embodiments, the invention provides for an apparatus adapted for the characterization of biological units, and methods by which attributes of the biological units can be monitored in response to the addition or removal of manipulations, e.g., treatments. The attributes of biological units can be used to characterize the effects of the abovementioned manipulations or treatments as well as to identify genes or proteins responsible for, or contributing to, these effects. | 04-29-2010 |
20100246980 | SYSTEM AND METHOD FOR AUTOMATIC LANDMARK LABELING WITH MINIMAL SUPERVISION - A system and method for estimating a set of landmarks for a large image ensemble employs only a small number of manually labeled images from the ensemble and avoids labor-intensive and error-prone object detection, tracking and alignment learning task limitations associated with manual image labeling techniques. A semi-supervised least squares congealing approach is employed to minimize an objective function defined on both labeled and unlabeled images. A shape model is learned on-line to constrain the landmark configuration. A partitioning strategy allows coarse-to-fine landmark estimation. | 09-30-2010 |
20120002888 | Method and Apparatus for Automatic Pattern Analysis - A method and apparatus is disclosed for pattern analysis by arranging given data so that high-dimensional data can be more effectively analyzed. The method allows arrangements of given data so that patterns can be discovered within the data. By utilizing maps that characterizes the data and the type or the set it belongs to, the method produces many data items from relatively few input data items, thereby making it possible to apply statistical and other conventional data analysis methods. In the method, a set of maps from the data or part of the data is determined. Then, new maps are generated by combining existing maps or applying certain transformations on the maps. Next, the results of applying the maps to the data are examined for patterns. Optionally, certain strong patterns are chosen, idealized, and propagated backwards to find a data reflecting that pattern. | 01-05-2012 |
20130129233 | System and Method for Classifying the Blur State of Digital Image Pixels - A blur classification module may compute the probability that a given pixel in a digital image was blurred using a given two-dimensional blur kernel, and may store the computed probability in a blur classification probability matrix that stores probability values for all combinations of image pixels and the blur kernels in a set of likely blur kernels. Computing these probabilities may include computing a frequency power spectrum for windows into the digital image and/or for the likely blur kernels. The blur classification module may generate a coherent mapping between pixels of the digital image and respective blur states, or may perform a segmentation of the image into blurry and sharp regions, dependent on values stored in the matrix. Input image data may be pre-processed. Blur classification results may be employed in image editing operations to automatically target image subjects or background regions, or to estimate the depth of image elements. | 05-23-2013 |
20130129234 | Perceptual Rating Of Digital Image Retouching - A method is provided for automatically providing a digital image rating of photo retouching. The method includes the step of receiving at a computer, including a processor, a first set of pixel data of an original image and a second set of pixel data of a retouched image. The method also includes using the processor to determine a plurality of geometric statistics and a plurality of photometric statistics from the first and second sets of pixel data. The method further includes the step of using the processor to quantify a rating of the retouched image based upon the geometric statistics and photometric statistics to indicate deviation of the retouched image from the original image. A system is also provided to perform the steps. | 05-23-2013 |
20130182963 | SELECTING IMAGES USING RELATIONSHIP WEIGHTS - A method of making a selected-image collection including providing a set of relationships between an individual and a plurality of different persons, each relationship in the relationship set having a pre-determined relationship weight value; providing a collection of images, the image collection including images having at least two persons of the plurality of different persons present in the images; using a processor to select from the relationship set a relationship weight value corresponding to each person present in each image in the image collection; assigning an image weight value to each image in the image collection, the image weight value corresponding to a combination of the selected relationship weight value(s); and selecting images from the image collection based on the image weight values to make the selected-image collection. | 07-18-2013 |
20130223751 | METHOD, APPARATUS AND COMPUTER PROGRAM PRODUCT FOR PROVIDING PATTERN DETECTION WITH UNKNOWN NOISE LEVELS - An apparatus for providing pattern detection may include a processor. The processor may be configured to iteratively test different models and corresponding scales for each of the models. The models may be employed for modeling parameters corresponding to a visually detected data. The processor may be further configured to evaluate each of the models over a plurality of iterations based on a function evaluation of each of the models, select one of the models based on the function evaluation of the selected one of the models, and utilize the selected one of the models for fitting the data. | 08-29-2013 |
20140161364 | OBJECT DETECTION APPARATUS AND OBJECT DETECTION METHOD - An object detection apparatus that detects an object to be detected captured in a determination image according to a feature amount of the object to be detected preliminarily learned by the use of a learning image, the object detection apparatus including a detector | 06-12-2014 |
20140193088 | Rapid Auto-Focus Using Classifier Chains, Mems And Multiple Object Focusing - A smart-focusing technique includes identifying an object of interest, such as a face, in a digital image. A focus-generic classifier chain is applied that is trained to match both focused and unfocused faces and/or data from a face tracking module is accepted. Multiple focus-specific classifier chains are applied, including a first chain trained to match substantially out of focus faces, and a second chain trained to match slightly out of focus faces. Focus position is rapidly adjusted using a MEMS component. | 07-10-2014 |
20140348434 | ACCELERATED OBJECT DETECTION FILTER USING A VIDEO MOTION ESTIMATION MODULE - Systems, apparatus and methods are described related to accelerated object detection filter using a video estimation module. | 11-27-2014 |
20140355895 | ADAPTIVE MOTION INSTABILITY DETECTION IN VIDEO - One or more apparatus and method for adaptively detecting motion instability in video. In embodiments, video stabilization is predicated on adaptive detection of motion instability. Adaptive motion instability detection may entail determining an initial motion instability state associated with a plurality of video frames. Subsequent transitions of the instability state may be detected by comparing a first level of instability associated with a first plurality of the frames to a second level of instability associated with a second plurality of the frames. Image stabilization of received video frames may be toggled first based on the initial instability state, and thereafter based on detected changes in the instability state. | 12-04-2014 |
20140376824 | CLASSIFYING IMAGE FEATURES - Methods are disclosed for classifying different parts of a sample into respective classes based on an image stack that includes one or more images. | 12-25-2014 |
20150043831 | SYSTEMS AND METHODS FOR INFERENTIAL SHARING OF PHOTOS - Techniques for separating shareable images from non-shareable images. In various implementations, image metadata and feature analysis may be used to evaluate the “shareability” of a photograph associated with a particular user. In some implementations, single photos may be determined to be shareable. In another implementation, an event associated with multiple photos may be determined to be shareable. In some implementations, a photo may be determined to be shareable with a single recipient. In another implementation, a photo may be determined to be shareable with multiple recipients. In yet another implementation, these techniques may be assisted by supervised machine learning. In still yet another implementation, photos determined to be shareable may be suggested to a user for sharing, or automatically shared, per an opt-in feature. | 02-12-2015 |
20160125272 | OBJECT RECOGNIZER AND DETECTOR FOR TWO-DIMENSIONAL IMAGES USING BAYESIAN NETWORK BASED CLASSIFIER - System and method for determining a classifier to discriminate between two classes—object or non-object. The classifier may be used by an object detection program to detect presence of a 3D object in a 2D image. The overall classifier is constructed of a sequence of classifiers, where each such classifier is based on a ratio of two graphical probability models. A discreet-valued variable representation at each node in a Bayesian network by a two-stage process of tree-structured vector quantization is discussed. The overall classifier may be part of an object detector program that is trained to automatically detect different types of 3D objects. Computationally efficient statistical methods to evaluate overall classifiers are disclosed. The Bayesian network-based classifier may also be used to determine if two observations belong to the same category. | 05-05-2016 |
20170236008 | Systems and Methods for Detecting Free-Standing Groups of Individuals | 08-17-2017 |