Patent application number | Description | Published |
20100327064 | Method and System for Coding Information Subject to Motion Blur - Symbols in information are encoded as a codeword using a differential orthogonal code. The codeword is stored in a substrate. A moving sensor acquires an image of the codeword in the substrate and decodes the codeword using a balanced differential decoder. The codeword can be painted as lane markings on a road surface. | 12-30-2010 |
20100332425 | Method for Clustering Samples with Weakly Supervised Kernel Mean Shift Matrices - A method clusters samples using a mean shift procedure. A kernel matrix is determined from the samples in a first dimension. A constraint matrix and a scaling matrix are determined from a constraint set. The kernel matrix is projected to a feature space having a second dimension using the constraint matrix, wherein the second dimension is higher than the first dimension. Then, the samples are clustered according to the kernel matrix. | 12-30-2010 |
20110235900 | Method for Training Multi-Class Classifiers with Active Selection and Binary Feedback - A multi-class classifier is trained by selecting a query image from a set of active images based on a membership probability determined by the classifier, wherein the active images are unlabeled. A sample image is selected from a set of training image based on the membership probability of the query image, wherein the training images are labeled. The query image and the sample images are displayed to a user on an output device. A response from the user is obtained with an input device, wherein the response is a yes-match or a no-match. The query image with the label of the sample image is added to the training set if the yes-match is obtained, and otherwise repeating the selecting, displaying, and obtaining steps until a predetermined number of no-match is reached to obtain the multi-class classifier. | 09-29-2011 |
20120206438 | Method for Representing Objects with Concentric Ring Signature Descriptors for Detecting 3D Objects in Range Images - A 3D object is represented by a descriptor, wherein a model of the 3D object is a 3D point cloud. A local support for each point p in the 3D point cloud is located, and reference x, y, and z axes are generated for the local support. A polar grid is applied according to the references x, y, and z axes a along an azimuth and a radial directions on an xy plane centered on the point p such that each patch on the grid is a bin for a 2D histogram, wherein the 2D histogram is a 2D matrix F on the grid and each coefficient of the 2D matrix F corresponds to the patch on the grid. For each grid location (k, l), an elevation value F(k, l) is estimated by interpolating the elevation values of the 3D points within the patches to produce the descriptor for the point p. | 08-16-2012 |
20120207384 | Representing Object Shapes Using Radial Basis Function Support Vector Machine Classification - A shape of an object is represented by a set of points inside and outside the shape. A decision function is learned from the set of points an object. Feature points in the set of points are selected using the decision function, or a gradient of the decision function, and then a local descriptor is determined for each feature point. | 08-16-2012 |
20120250933 | Method for Tracking Tumors in Bi-Plane Images - A tumor is tracked in sequences of biplane images by generating a set of segmentation hypotheses using a 3D model of the tumor, a biplane geometry, and a previous location of the tumor as determined from the pairs of biplane images. Volume prior probabilities are constructed based on the set of hypotheses. Seed pixels are selected using the volume prior probabilities, and a bi-plane dual image graph is constructed using intensity gradients and the seed pixels to obtaining segmentation masks corresponding to tumor boundaries using the image intensities to determine a current location of the tumor. | 10-04-2012 |
20120251013 | Method for Compressing Textured Images - A method compresses an image partitioned into blocks of pixels, for each block the method converts the block to a 2D matrix. The matrix is decomposing into a column matrix and a row matrix, wherein a width of the column matrix is substantially smaller than a height of the column matrix and the height of the row matrix is substantially smaller than the width of the row matrix. The column matrix and the row matrix are compressed, and the compressed matrices are then combined to form a compressed image. | 10-04-2012 |
20120254077 | Data Driven Frequency Mapping for Kernels Used in Support Vector Machines - Frequency features to be used for binary classification of data using a linear classifier are selected by determining a set of hypotheses in a d-dimensional space using d-dimensional labeled training data. A mapping function is constructed for each hypothesis. The mapping functions are applied to the training data to generate frequency features, and a subset of the frequency are selecting iteratively. The linear function is then trained using the subset of frequency features and labels of the training data. | 10-04-2012 |
20130156300 | Multi-Class Classification Method - A test sample is classified by determining a nearest subspace residual from subspaces learned from multiple different classes of training samples, and a collaborative residual from a collaborative representation of a dictionary constructed from all of the test samples. The residuals are used to determine a regularized residual. The subspaces, the dictionary and the regularized residual are inputted into a classifier, wherein the classifier includes a collaborative representation classifier and a nearest subspace classifier, and a label is assigned to the test sample using the classifier, and wherein the regularization parameter balances a trade-off between the collaborative representation classifier the nearest subspace classifier. | 06-20-2013 |
20130156340 | Image Filtering by Sparse Reconstruction on Affinity Net - A method reduces multiplicative and additive noise in image pixels by clustering similar patches of the pixels into clusters. The clusters form nodes in an affinity net of nodes and vertices. From each cluster, a dictionary is learned by a sparse combination of corresponding atoms in the dictionaries. The patches are aggregated collaboratively using the dictionaries to construct a denoised image. | 06-20-2013 |
20130191425 | Method for Recovering Low-Rank Matrices and Subspaces from Data in High-Dimensional Matrices - A method recovers an uncorrupted low-rank matrix, noise in corrupted data and a subspace from the data in a form of a high-dimensional matrix. An objective function minimizes the noise to solve for the low-rank matrix and the subspace without estimating the rank of the low-rank matrix. The method uses group sparsity and the subspace is orthogonal. Random subsampling of the data can recover subspace bases and their coefficients from a much smaller matrix to improve performance. Convergence efficiency can also be improved by applying an augmented Lagrange multiplier, and an alternating stepwise coordinate descent. The Lagrange function is solved by an alternating direction method. | 07-25-2013 |
20130223734 | Upscaling Natural Images - A natural input image is upscaled, first by interpolation. Second, edges in the interpolated image are sharpened by a lion-parametric patch transform. The result is decomposed into an edge layer and a detail layer. Only pixels in the detail layer enhanced, and the enhanced detail layer is merged with the edge layer to produce a high resolution version of the input image. | 08-29-2013 |
20130236090 | Learning Dictionaries with Clustered Atoms - A dictionary of atoms for coding data is learned by first selecting samples from a set of samples. Similar atoms in the dictionary are clustered, and if a cluster has multiple atoms, the atoms in that cluster are merged into a single atom. The samples can be acquired online. | 09-12-2013 |
20130251245 | Method for Reducing Blocking Artifacts in Images - Blocking artifacts are reduced by projecting each patch obtained from an input image onto a set of bases vectors to determine multiple representations for each patch. The set of bases vectors are learned from a training image, and the bases vectors include a full basis vector, and one or two subspace bases vectors. An optimal basis vector is determined in the set of bases vectors for each patch according to the projection. A threshold is applied to coefficients of the optimal basis vector to determine a filtered representation for each patch, and a reconstructed patch is generated using the filtered representation. Then, the aggregating the reconstructed patches are aggregated to produce an output image. | 09-26-2013 |
20140093160 | 3D Object Tracking in Multiple 2D Sequences - A tumor is tracked in multiple sequences of images acquired concurrently from different viewpoints. Features are extracted in each set of current images using a window. A regression function, subject to motion constraints, is applied to the features to obtain 3D motion parameters, which are applied to the tumor as observed in the images to obtain a 3D location of the object. Then, the shape of the 3D object at the 3D location is projected onto each image to update the location of the window for the next set of images to be processed. | 04-03-2014 |
20140219552 | Denoising of Images with Nonstationary Noise - An input image is denoised by first constructing a pixel-wise noise variance map from the input image. The noise has spatially varying variances. The input image is partitioned into patches using the noise variance map. An intermediate image is determined from the patches. Collaborative filtering is applied to each patch in the intermediate image using the noise variance map to produce filtered patches. Then, the filtered patches are projected to an output image. | 08-07-2014 |
20140300599 | Method for Factorizing Images of a Scene into Basis Images - A set of nonnegative lighting basis images representing a scene illuminated by a set of stationary light sources is recovered from a set of input images of the scene that were acquired by a stationary camera. Each image is illuminated by a combination of the light sources, and at least two images in the set are illuminated by different combinations. The set of input images is decomposed into the nonnegative lighting basis images and a set of indicator coefficients, wherein each lighting basis image corresponds to an appearance of the scene illuminated by one of the light sources, and wherein each indicator coefficient indicates a contribution of one of the light sources to one of the input images. | 10-09-2014 |
20140300600 | Method for Detecting 3D Geometric Boundaries in Images of Scenes Subject to Varying Lighting - Three-dimensional (3D) geometric boundaries are detected in images of a scene that undergoes varying lighting conditions caused by light sources in different positions, from a set of input images of the scene illuminated by at least two different lighting conditions. The images are aligned, e.g., acquired by a stationary camera, so that pixels at the same location in all of the input images correspond to the same point in the scene. For each location, a patch of corresponding pixels centered at the location is extracted from each input image. For each location, a confidence value that there is a 3D geometric boundary at the location is determined. | 10-09-2014 |
20140341421 | Method for Detecting Persons Using 1D Depths and 2D Texture - A method detects an object in a scene by first determining an active set of window positions from depth data. Specifically, the object can be a person. The depth data are acquired by a depth sensor. For each window position perform the following steps. Assign a window size based on the depth data. Select, a current window from the active set of window positions. Extract a joint feature from the depth data and texture data for the current window, wherein the texture data are acquired by a camera. Classify the joint feature to detect the object. The classifier is trained with joint training features extracted from training data including training depth data and training texture data acquired by the sensor and camera respectively. Finally, the active set of windows position is updated before processing the next current window. | 11-20-2014 |
20150030231 | Method for Data Segmentation using Laplacian Graphs - A method segments n-dimensional by first determining prior information from the data. A fidelity term is determined from the prior information, and the data are represented as a graph. A graph Laplacian is determined from the graph from the graph, and a Laplacian spectrum constraint is determined from the graph Laplacian. Then, an objective function is minimized according to the fidelity term and the Laplacian spectrum constraint to identify a segment of target points in the data. | 01-29-2015 |