| Class / Patent application number | Description | Number of patent applications / Date published |
|---|
| 382226000 | Sequential decision process (e.g., decision tree structure) | 16 |
| 20080199086 | APPARATUS FOR PERFORMING FAST CLOSEST MATCH IN PATTERN RECOGNITION - A method and apparatus for determining a closest match of N input patterns relative to R reference patterns using K processing units. Each of a set of input patterns are loaded into the K processing units. One of the Reference patterns is sequentially loaded into each of the processing units and a distance defining the similarity between the reference pattern and each of the input patterns is calculated. A present calculated distance replaces its corresponding stored present minimum distance if it is has a smaller value. After the R reference patterns have been processed the minimum distance and its corresponding identification for all N input patterns is determined without merging outputs. The minimum distances and the identifications may be read either in parallel or serially. The apparatus is easily scalable by adding processors. The number of reference patterns may be easily increased without altering system configuration. | 08-21-2008 |
| 20080253664 | Object image detection method and object image detection device - An object image detection device is disclosed that is able to rapidly detect an object image from an input image without a great deal of computation. The object image detection device includes an object image classification unit for determining whether the object images are included in an image having a given orientation, an image orientation detection unit for detecting orientation of the input image, an image rotation unit for rotating the object image classification unit according to the detected orientation of the input image, and a detection unit for detecting the object images from the input image by using the rotated object image classification unit. | 10-16-2008 |
| 20080260264 | Method and system for generating aesthetic characters, and business model of the same - The present invention relates to a method and system for generating aesthetic characters, and to a business model for commercially developing the method and system utilizing the Internet or the like. The present invention makes it possible to synthesize a variety of image patterns with arbitrary characters by adding stroke fonts. According to a first aspect, the present invention provides a method and system for generating aesthetic characters, in which an arbitrary image pattern is selected from a pattern database, and data on arbitrary characters constituting single lines are superposed on the selected image pattern, whereby the arbitrary image patterns are processed into various characters and generating aesthetic image patterns. Thus, it is possible to provide character bodies drawn with excellent aesthetic properties that are uniquely designed for attracting public attention, such as in the television broadcasts, Internet sites, headings of journals or newspapers, advertisements, and signboards. | 10-23-2008 |
| 20090016616 | Category Classification Apparatus, Category Classification Method, and Storage Medium Storing a Program - A category classification apparatus includes: an overall classifier that classifies a category to which an image belongs, based on an overall characteristic amount that is obtained from image data, the overall characteristic amount indicating an overall characteristic of the image represented by the image data; and a partial classifier that classifies a category to which the image belongs, based on partial characteristic amounts that are obtained from partial image data included in the image data, the partial characteristic amounts indicating characteristics of portions of the image. | 01-15-2009 |
| 20100209010 | INFORMATION PROCESSING APPARATUS AND INFORMATION PROCESSING METHOD - In an information processing apparatus that processes data using cascade-connected weak classifiers, processing specification information specifying the processing content of each of the weak classifiers is stored. The weak classifiers to be used in processing the data are selected from the weak classifiers by referring to a table in which is specified information for determining the weak classifiers to be used based on a condition for processing the data. The data is then processed by the selected weak classifiers based on the processing specification information that corresponds to those weak classifiers, and an object is extracted from the data using an obtained evaluation value. Through this, a combination of extraction process speed and extraction accuracy can be changed in a flexible manner when extracting a specific object from image data. | 08-19-2010 |
| 20100322525 | Image Labeling Using Multi-Scale Processing - Multi-scale processing may be used to reduce the memory and computational requirements of optimization algorithms for image labeling, for example, for object segmentation, 3D reconstruction, stereo correspondence, optical flow and other applications. For example, in order to label a large image (or 3D volume) a multi-scale process first solves the problem at a low resolution, obtaining a coarse labeling of an original high resolution problem. This labeling is refined by solving another optimization on a subset of the image elements. In examples, an energy function for a coarse level version of an input image is formed directly from an energy function of the input image. In examples, the subset of image elements may be selected using a measure of confidence in the labeling. | 12-23-2010 |
| 20110033122 | Image Processing Using Masked Restricted Boltzmann Machines - Image processing using masked restricted Boltzmann machines is described. In an embodiment restricted Boltzmann machines based on beta distributions are described which are implemented in an image processing system. In an embodiment a plurality of fields of masked RBMs are connected in series. An image is input into a masked appearance RBM and decomposed into superpixel elements. The superpixel elements output from one appearance RBM are used as input to a further appearance RBM. The outputs from each of the series of fields of RBMs are used in an intelligent image processing system. Embodiments describe training a plurality of RBMs. Embodiments describe using the image processing system for applications such as object recognition and image editing. | 02-10-2011 |
| 20130022283 | CADENCE DETECTION FOR INTERLACED VIDEO BASED ON TEMPORAL REGULARITY - A method is proposed for analyzing an interlaced video signal including a first sequence of fields. A temporal regularity estimation process is applied to the first sequence of fields to compute a first metric. Inputs of the temporal regularity estimation process include pixel values from at least two fields having respective ranks differing by more than one in the sequence. The same temporal regularity estimation process is applied to second and third sequences of fields to compute second and third metrics. The second sequence is derived from the first sequence by swapping fields having ranks of the form 2k and 2k+1 for any integer k, while the third sequence is derived from the first sequence by swapping fields having ranks of the form 2k−1 and 2k. The first, second and third metrics are compared in a determination of the time arrangement of the fields in the first sequence. | 01-24-2013 |
| 20140270551 | PERFORMING OBJECT DETECTION OPERATIONS VIA A GRAPHICS PROCESSING UNIT - In one embodiment of the present invention, a graphics processing unit (GPU) is configured to detect an object in an image using a random forest classifier that includes multiple, identically structured decision trees. Notably, the application of each of the decision trees is independent of the application of the other decision trees. In operation, the GPU partitions the image into subsets of pixels, and associates an execution thread with each of the pixels in the subset of pixels. The GPU then causes each of the execution threads to apply the random forest classifier to the associated pixel, thereby determining a likelihood that the pixel corresponds to the object. Advantageously, such a distributed approach to object detection more fully leverages the parallel architecture of the PPU than conventional approaches. In particular, the PPU performs object detection more efficiently using the random forest classifier than using a cascaded classifier. | 09-18-2014 |
| 20150093035 | VIDEO OBJECT CLASSIFICATION WITH OBJECT SIZE CALIBRATION - A camera system comprises an image capturing device and an object classification module connected to the image capturing device. The object classification module is operable to determine whether or not an object in an image is a member of an object class. The object classification module includes multiple decision steps configured in a cascade configuration, wherein at least one of the multiple decision steps is operable to (a) accept an object as a member of the object class, (b) reject an object as a member of the object class, and (c) call on a next step to determine whether or not an object is a member of the object class. | 04-02-2015 |
| 20150332472 | METHOD, APPARATUS AND COMPUTER PROGRAM PRODUCT FOR DISPARITY ESTIMATION IN IMAGES - In an example embodiment, a method, apparatus and computer program product are provided. The method includes facilitating receipt of an image of a scene and determining a graph based on connecting nodes of the image. The nodes are either pixels or superpixels of the image. The graph is determined by determining one or more connections of a node to one or more nodes belonging to a pre-defined image region around the node in the image. The connections are associated with edge weights that are determined based on at least one of similarity parameters and spatial distances between the node and the one or more nodes. The method includes determining disparity values at the nodes of the image based at least on performing tree based aggregation of a cost volume on the graph, where the cost volume is associated with the image and at least one view image of the scene. | 11-19-2015 |
| 20190147225 | IMAGE PROCESSING APPARATUS AND METHOD | 05-16-2019 |
| 382227000 | With a multilevel classifier | 4 |
| 20080253665 | PATTERN IDENTIFICATION APPARATUS AND METHOD THEREOF, ABNORMAL PATTERN DETECTION APPARATUS AND METHOD THEREOF, AND PROGRAM - A pattern identification apparatus for identifying one of a plurality of classes defined in advance, to which data of a pattern identification target belongs, comprises a read unit adapted to read out, from a storage unit in correspondence with each of the plurality of classes, a projection rule to a hyperplane which approximates a manifold corresponding to the class in a feature space an input unit adapted to input identification target data; a calculation unit adapted to calculate, for each class, a projection result obtained by projecting the input identification target data to the hyperplane which approximates the manifold corresponding to each of the plurality of classes, on the basis of the projection rule; and an identification unit adapted to identify, on the basis of the projection result of each classes calculated by said calculation unit, one of the plurality of classes to which the identification target data belongs. | 10-16-2008 |
| 20090129683 | Object Recognition Apparatus,Computer Readable Medium Storing Object Recognition Program, and Image Retrieval Service Providing Method - An object recognition apparatus includes an image input unit capturing image data, an object dictionary unit storing conditions to specify a type of an object, and a processor collating the image data with the conditions and specifying the type of the object imaged in the image data, in which the object dictionary unit classifies the conditions into hierarchies and stores the classified conditions, and the processor performs collation while narrowing down object conditions positioned in lower hierarchies based on a collation result of object conditions positioned in upper hierarchies. | 05-21-2009 |
| 20100135585 | Method and Apparatus of Tile-based Belief Propagation - A method and apparatus of tile-based belief propagation are disclosed. An image is split into a number of tiles. Messages are iteratively generated within each of the tiles based on the messages from neighboring pixels to the tile at a previous iteration, wherein each message represents information of a state of the pixel. The generated messages for sending out of the tiles are stored. Labels are then determined based on the stored messages, wherein each label represents the state of the pixel. | 06-03-2010 |
| 20150036942 | OBJECT RECOGNITION AND TRACKING USING A CLASSIFIER COMPRISING CASCADED STAGES OF MULTIPLE DECISION TREES - An image processor comprises first and second hardware accelerators and is configured to implement a classifier. The classifier in some embodiments comprises a cascaded classifier having a plurality of stages with each such stage implementing a plurality of decision trees. At least one of the first and second hardware accelerators of the image processor is configured to generate an integral image based on a given input image, and the second hardware accelerator is configured to process image patches of the integral image through one or more of a plurality of decision trees of the classifier implemented by the image processor. By way of example, the first and second hardware accelerators illustratively comprise respective front-end and back-end accelerators of the image processor, and an integral image calculator configured to generate the integral image based on the given input image is implemented in one of the front-end accelerator and the back-end accelerator. | 02-05-2015 |
