Patent application number | Description | Published |
20090001165 | 2-D Barcode Recognition - Systems and methods for 2-D barcode recognition are described. In one aspect, the systems and methods use a charge coupled camera capturing device to capture a digital image of a 3-D scene. The systems and methods evaluate the digital image to localize and segment a 2-D barcode from the digital image of the 3-D scene. The 2-D barcode is rectified to remove non-uniform lighting and correct any perspective distortion. The rectified 2-D barcode is divided into multiple uniform cells to generate a 2-D matrix array of symbols. A barcode processing application evaluates the 2-D matrix array of symbols to present data to the user. | 01-01-2009 |
20090027241 | Fast error-correcting of embedded interaction codes - A fast decoding technique for decoding a position of a bit in a pattern provided on a media surface that can generate large amounts of solution candidates quickly by switching or flipping bits and utilizing a recursion scheme. The fast decoding technique may be employed to simultaneously decode multiple dimensions of a pattern on the media surface. | 01-29-2009 |
20090046952 | SUPER-RESOLUTION IN PERIODIC AND APERIODIC PIXEL IMAGING - A super-resolution algorithm that explicitly and exactly models the detector pixel shape, size, location, and gaps for periodic and aperiodic tilings. The algorithm projects the low-resolution input image into high-resolution space to model the actual shapes and/or gaps of the detector pixels. By using an aperiodic pixel layout such as a Penrose tiling significant improvements in super-resolution results can be obtained. An error back-projection super-resolution algorithm makes use of the exact detector model in its back projection operator for better accuracy. Theoretically, the aperiodic detector can be based on CCD (charge-coupled device) technology, and/or more practically, CMOS (complimentary metal oxide semiconductor) technology, for example. | 02-19-2009 |
20090080774 | Hybrid Graph Model For Unsupervised Object Segmentation - This disclosure describes an integrated framework for class-unsupervised object segmentation. The class-unsupervised object segmentation occurs by integrating top-down constraints and bottom-up constraints on object shapes using an algorithm in an integrated manner. The algorithm describes a relationship among object parts and superpixels. This process forms object shapes with object parts and oversegments pixel images into the superpixels, with the algorithm in conjunction with the constraints. This disclosure describes computing a mask map from a hybrid graph, segmenting the image into a foreground object and a background, and displaying the foreground object from the background. | 03-26-2009 |
20090097772 | Laplacian Principal Components Analysis (LPCA) - Systems and methods perform Laplacian Principal Components Analysis (LPCA). In one implementation, an exemplary system receives multidimensional data and reduces dimensionality of the data by locally optimizing a scatter of each local sample of the data. The optimization includes summing weighted distances between low dimensional representations of the data and a mean. The weights of the distances can be determined by a coding length of each local data sample. The system can globally align the locally optimized weighted scatters of the local samples and provide a global projection matrix. The LPCA improves performance of such applications as face recognition and manifold learning. | 04-16-2009 |
20090119573 | GLOBAL METADATA EMBEDDING AND DECODING - In accordance with embodiments of the invention, global metadata, such as a document identifier, which may be a globally unique identifier, is embedded into an embedded interactive code document by combining a first m-array and a plurality of copies of the first m-array to generate a combined m-array with encoded global metadata such that respective start positions (x | 05-07-2009 |
20090132213 | METHOD FOR MODELING DATA STRUCTURES USING LOCAL CONTEXTS - A method for modeling data affinities and data structures. In one implementation, a contextual distance may be calculated between a selected data point in a data sample and a data point in a contextual set of the selected data point. The contextual set may include the selected data point and one or more data points in the neighborhood of the selected data point. The contextual distance may be the difference between the selected data point's contribution to the integrity of the geometric structure of the contextual set and the data point's contribution to the integrity of the geometric structure of the contextual set. The process may be repeated for each data point in the contextual set of the selected data point. The process may be repeated for each selected data point in the data sample. A digraph may be created using a plurality of contextual distances generated by the process. | 05-21-2009 |
20090219287 | Modeling and rendering of heterogeneous translucent materials using the diffusion equation - An exemplary method includes providing image data for an illuminated physical sample of a heterogeneous translucent material, determining one or more material properties of the material based in part on a diffusion equation where one of the material properties is a diffusion coefficient for diffusion of radiation in the material and where the determining includes a regularization term for the diffusion coefficient, mapping the one or more material properties to a virtual object volume, assigning virtual illumination conditions to the virtual object volume, and rendering the virtual object volume using the virtual illumination conditions as a boundary condition for a system of diffusion equations of the virtual object volume. Other methods, devices and systems are also disclosed. | 09-03-2009 |
20090297046 | Linear Laplacian Discrimination for Feature Extraction - An exemplary method for extracting discriminant feature of samples includes providing data for samples in a multidimensional space; based on the data, computing local similarities for the samples; mapping the local similarities to weights; based on the mapping, formulating an inter-class scatter matrix and an intra-class scatter matrix; and based on the matrices, maximizing the ratio of inter-class scatter to intra-class scatter for the samples to provide discriminate features of the samples. Such a method may be used for classifying samples, recognizing patterns, or other tasks. Various other methods, devices, system, etc., are also disclosed. | 12-03-2009 |
20100067799 | GLOBALLY INVARIANT RADON FEATURE TRANSFORMS FOR TEXTURE CLASSIFICATION - A “globally invariant Radon feature transform,” or “GIRFT,” generates feature descriptors that are both globally affine invariant and illumination invariant. These feature descriptors effectively handle intra-class variations resulting from geometric transformations and illumination changes to provide robust texture classification. In general, GIRFT considers images globally to extract global features that are less sensitive to large variations of material in local regions. Geometric affine transformation invariance and illumination invariance is achieved by converting original pixel represented images into Radon-pixel images by using a Radon Transform. Canonical projection of the Radon-pixel image into a quotient space is then performed using Radon-pixel pairs to produce affine invariant feature descriptors. Illumination invariance of the resulting feature descriptors is then achieved by defining an illumination invariant distance metric on the feature space of each feature descriptor. | 03-18-2010 |
20100067800 | MULTI-CLASS TRANSFORM FOR DISCRIMINANT SUBSPACE ANALYSIS - A multi-class discriminant subspace analysis technique is described that improves the discriminant power of Linear Discriminant Analysis (LDA). In one embodiment of the multi-class discriminant subspace analysis technique, multi-class feature selection occurs as follows. A data set containing multiple classes of features is input. Discriminative information for the data set is determined from the differences of class means and the differences in class scatter matrices by computing an optimal orthogonal matrix that approximately simultaneously diagonalizes autocorrelation matrices for all classes in the data set. The discriminative information is used to extract features for different classes of features from the data set. | 03-18-2010 |
20100074551 | LEARNING-BASED PARTIAL DIFFERENTIAL EQUATIONS FOR COMPUTER VISION - Partial differential equations (PDEs) are used in the invention for various problems in computer the vision space. The present invention provides a framework for learning a system of PDEs from real data to accomplish a specific vision task. In one embodiment, the system consists of two PDEs. One controls the evolution of the output. The other is for an indicator function that helps collect global information. Both PDEs are coupled equations between the output image and the indicator function, up to their second order partial derivatives. The way they are coupled is suggested by the shift and rotational invariance that the PDEs should hold. The coupling coefficients are learnt from real data via an optimal control technique. The invention provides learning-based PDEs that make a unified framework for handling different vision tasks, such as edge detection, denoising, segementation, and object detection. | 03-25-2010 |
20100076723 | TENSOR LINEAR LAPLACIAN DISCRIMINATION FOR FEATURE EXTRACTION - Tensor linear Laplacian discrimination for feature extraction is disclosed. One embodiment comprises generating a contextual distance based sample weight and class weight, calculating a within-class scatter using the at least one sample weight and a between-class scatter for multiple classes of data samples in a sample set using the class weight, performing a mode-k matrix unfolding on scatters and generating at least one orthogonal projection matrix. | 03-25-2010 |
20100080450 | CLASSIFICATION VIA SEMI-RIEMANNIAN SPACES - Described is using semi-Riemannian geometry in supervised learning to learn a discriminant subspace for classification, e.g., labeled samples are used to learn the geometry of a semi-Riemannian submanifold. For a given sample, the K nearest classes of that sample are determined, along with the nearest samples that are in other classes, and the nearest samples in that sample's same class. The distances between these samples are computed, and used in computing a metric matrix. The metric matrix is used to compute a projection matrix that corresponds to the discriminant subspace. In online classification, as a new sample is received, it is projected into a feature space by use of the projection matrix and classified accordingly. | 04-01-2010 |
20100198902 | COMPUTING MINIMAL POLYNOMIALS OF RADICAL EXPRESSIONS - Described is a technology, such as implemented in a computational software program, by which a minimal polynomial is efficiently determined for a radical expression based upon its structure of the radical expression. An annihilation polynomial is found based upon levels of the radical to obtain roots of the radical. A numerical method performs a zero test or multiple zero tests to find the minimal polynomial. In one implementation, the set of roots corresponding to a radical expression is found. The annihilation polynomial is computed by grouping roots of the set according to their conjugation relationship and multiplying factor polynomials level by level. A selection mechanism selects the minimal polynomial based upon the annihilation polynomial's factors. | 08-05-2010 |
20100262643 | COMPUTING MINIMAL POLYNOMIALS - Described is a technology, such as implemented in a computational software program, by which a minimal polynomial is efficiently determined for a radical expression over the ring Z of integer numbers or the ring Q of rational numbers. The levels of the radical are grouped into a level permutation group that is used to find a level permutation set. An annihilation polynomial is found based upon the level permutation set. The annihilation polynomial is factored, and a selection mechanism selects the minimal polynomial based upon the annihilation polynomial's factors. | 10-14-2010 |
20110206276 | HYBRID GRAPH MODEL FOR UNSUPERVISED OBJECT SEGMENTATION - This disclosure describes an integrated framework for class-unsupervised object segmentation. The class-unsupervised object segmentation occurs by integrating top-down constraints and bottom-up constraints on object shapes using an algorithm in an integrated manner. The algorithm describes a relationship among object parts and superpixels. This process forms object shapes with object parts and oversegments pixel images into the superpixels, with the algorithm in conjunction with the constraints. This disclosure describes computing a mask map from a hybrid graph, segmenting the image into a foreground object and a background, and displaying the foreground object from the background. | 08-25-2011 |
20110304745 | LIGHT TRANSPORT RECONSTRUCTION FROM SPARSELY CAPTURED IMAGES - A “Scene Re-Lighter” provides various techniques for using an automatically reconstructed light transport matrix derived from a sparse sampling of images to provide various combinations of complex light transport effects in images, including caustics, complex occlusions, inter-reflections, subsurface scattering, etc. More specifically, the Scene Re-Lighter reconstructs the light transport matrix from a relatively small number of acquired images using a “Kernel Nyström” based technique adapted for low rank matrices constructed from sparsely sampled images. A “light transport kernel” is incorporated into the Nyström method to exploit nonlinear coherence in the light transport matrix. Further, an adaptive process is used to efficiently capture the sparsely sampled images from a scene. The Scene Re-Lighter is capable of achieving good reconstruction of the light transport matrix with only few hundred images to produce high quality relighting results. Further, the Scene Re-Lighter is also effective for modeling scenes with complex lighting effects and occlusions. | 12-15-2011 |
20120134588 | RECTIFICATION OF CHARACTERS AND TEXT AS TRANSFORM INVARIANT LOW-RANK TEXTURES - A “Text Rectifier” provides various techniques for processing selected regions of an image containing text or characters by treating those images as matrices of low-rank textures and using a rank minimization technique that recovers and removes image deformations (e.g., affine and projective transforms as well as general classes of nonlinear transforms) while rectifying the text or characters in the image region. Once distortions have been removed and the text or characters rectified, the resulting text is made available for a variety of uses or further processing such as optical character recognition (OCR). In various embodiments, binarization and/or inversion techniques are applied to the selected image regions during the rank minimization process to both improve text rectification and to present the resulting images of text to an OCR engine in a form that enhances the accuracy of the OCR results. | 05-31-2012 |
20120201459 | Annotation Detection and Anchoring on Ink Notes - Systems and methods for detecting annotation digital ink strokes and further associating annotation digital ink strokes with word digital ink strokes are presented. Ink strokes are captured on a writing surface and then classified as words or annotations. Annotations are then anchored to corresponding words. When words are relocated or edited on the writing surface, the anchored annotations are also relocated and may even be reshaped according to the changes in the anchored words. | 08-09-2012 |
20120306878 | Modeling and Rendering of Heterogeneous Translucent Materals Using The Diffusion Equation - An exemplary method includes providing image data for an illuminated physical sample of a heterogeneous translucent material, determining one or more material properties of the material based in part on a diffusion equation where one of the material properties is a diffusion coefficient for diffusion of radiation in the material and where the determining includes a regularization term for the diffusion coefficient, mapping the one or more material properties to a virtual object volume, assigning virtual illumination conditions to the virtual object volume, and rendering the virtual object volume using the virtual illumination conditions as a boundary condition for a system of diffusion equations of the virtual object volume. Other methods, devices and systems are also disclosed. | 12-06-2012 |