Patent application number | Description | Published |
20090040054 | REAL-TIME DRIVING DANGER LEVEL PREDICTION - Systems and methods are disclosed to predict driving danger by capturing vehicle dynamic parameter, driver physiological data and driver behavior feature; applying a learning algorithm to the features; and predicting driving danger. | 02-12-2009 |
20090191513 | MONITORING DRIVING SAFETY USING SEMI-SUPERVISED SEQUENTIAL LEARNING - A computer-implemented method and system for predicting operation risks of a vehicle. The method and system obtains a training data stream of vehicular dynamic parameters and logging crash time instances; partitions the data stream into units representing dimension vectors, labels the units that overlap the crash time instances as most dangerous; labels the units, which are furthest from the units that are labeled as most dangerous, as most safe; propagates the most dangerous and the most safe labeling information of the labeled units to units which are not labeled; estimates parameters of a danger-level function using the labeled and unlabeled units; and applies the danger-level function to an actual data stream of vehicular dynamic parameters to predict the operation risks of the vehicle. | 07-30-2009 |
20110116708 | FAST IMAGE PARSING BY GRAPH ADAPTIVE DYNAMIC PROGRAMMING - Systems and methods are disclosed to perform image parsing on one or more images by identifying a set of similar regions from each image; assigning one or more region labels to each region and generating multiple hypotheses for region label assignment; and detecting class, location and boundary of each object in the image, wherein object classification, detection and segmentation are performed jointly during image parsing. | 05-19-2011 |
20110116711 | LOCALITY-CONSTRAINED LINEAR CODING SYSTEMS AND METHODS FOR IMAGE CLASSIFICATION - Systems and methods are disclosed for classifying an input image by detecting one or more feature points on the input image; extracting one or more descriptors from each feature point; applying a codebook to quantize each descriptor and generate code from each descriptor; applying spatial pyramid matching to generate histograms; and concatenating histograms from all sub-regions to generate a final representation of the image for classification. | 05-19-2011 |
20120219186 | Continuous Linear Dynamic Systems - Aspects of the present invention include systems and methods for segmentation and recognition of action primitives. In embodiments, a framework, referred to as the Continuous Linear Dynamic System (CLDS), comprises two sets of Linear Dynamic System (LDS) models, one to model the dynamics of individual primitive actions and the other to model the transitions between actions. In embodiments, the inference process estimates the best decomposition of the whole sequence into continuous alternating between the two set of models, using an approximate Viterbi algorithm. In this way, both action type and action boundary may be accurately recognized. | 08-30-2012 |
20120219213 | Embedded Optical Flow Features - Aspects of the present invention include systems and methods for generating an optical flow-based feature. In embodiments, to extract an optical flow feature, the optical flow at sparse interest points is obtained, and Locality-constrained Linear Coding (LLC) is applied to the sparse interest points to embed each flow into a higher-dimensional code. In embodiments, for an image frame, the multiple codes are combined together using a weighted pooling that is related to the distribution of the optical flows in the image frame. In embodiments, the feature may be used in training models to detect actions, in trained models for action detection, or both. | 08-30-2012 |
20130132316 | Substructure and Boundary Modeling for Continuous Action Recognition - Embodiments of the present invention include systems and methods for improved state space modeling (SSM) comprising two added layers to model the substructure transition dynamics and action duration distribution. In embodiments, the first layer represents a substructure transition model that encodes the sparse and global temporal transition probability. In embodiments, the second layer models the action boundary characteristics by injecting discriminative information into a logistic duration model such that transition boundaries between successive actions can be located more accurately; thus, the second layer exploits discriminative information to discover action boundaries adaptively. | 05-23-2013 |
20140016830 | Small Vein Image Recognition and Authorization Using Constrained Geometrical Matching and Weighted Voting Under Generic Tree Model - An automated registration and authentication system combines a generative and discriminative approach to improve the matching of a query object to a database of registered objects. The discriminative approach uses a voting mechanism to identify a most likely match, and the generative approach uses ASIFT transforms to determine a best geometric match. The two results are combined using a technique base on Bayesian inference theory. | 01-16-2014 |
20140019489 | Constructing Incremental Tree Model for Vein Image Recognition and Authentication - An indexed hierarchical tree search structure implements incremental tree modification to add new registrants to the tree without requiring reconstruction of the tree. Only data within a leaf node that receives new data is used in the incremental tree modification. Consequently, the original data set used in the creation of the hierarchical tree may be deleted after construction of the tree. | 01-16-2014 |
20140025607 | Confidence Based Vein Image Recognition and Authentication - An indexed hierarchical tree search structure converts each registration sample into an equivalent registration model based on the clustering of its registration item descriptors in the leaf nodes of the hierarchical tree. Query item descriptors from a query sample from someone wanting to be recognized are distributed into the hierarchical tree. A query model is defined based on the clustering of query item descriptors at the leaf nodes, and registration and verification are made based on comparison of the query model and the registration models. | 01-23-2014 |
20140126810 | Computer Vision Methods And Systems To Recognize And Locate An Object Or Objects In One Or More Images - Embodiments of the present invention include systems and methods for identifying and locating an object in an image. In embodiments, an object in an image may be identified by segmenting a first image of an object into one or more superpixels; extracting local descriptors from the first image, each of the descriptors having an interest point with a location; correlating the local descriptors to the superpixels based on locations of the local descriptors and superpixels; determining a probability for an object label for each of a set of the superpixels; and assigning an object label to each of the set of the superpixels based on the probability and a smoothness factor that includes weighting in terms of one or more of spatial, colors, angular distances between superpixels. The superpixels of an image may be concatenated to predict an object label for the image and to determine the location of the image. | 05-08-2014 |
20140133762 | Point Set Matching with Outlier Detection - Aspects of the present invention include point set matching systems and methods. In embodiments, a tree model is used to find candidate matching locations for a set of query points. In embodiments, a similitude transform is assumed, and the parameters are separately solved to reduce computation complexity. In embodiments, the dominant scaling (α) and rotation (R) parameters are obtained by identifying a maximum in an accumulator space. A translation (t) matrix is calculated in another 1D accumulator space. With the obtained similitude transform, outliers can be reliably detected. This two-stage approach reduces the complexity and calculation time of determining a similitude transform and increases the accuracy and ability to detect outliers. | 05-15-2014 |
20140161355 | Sparse Coding Based Superpixel Representation Using Hierarchical Codebook Constructing And Indexing - Embodiments of the present invention include systems and methods for identifying an object in an image. In embodiments, object identification includes using smooth encoding from a tree structure to generate a feature from a descriptor. In embodiments, the smooth encoding may be performed by, having identifying a leaf node for a descriptor, moving up the tree voting structure a number of levels from the identified leaf node to a branch node to identify leaf nodes dependent from the branch node; and then, by determining a sparse code under a condition that a distance between the descriptor and centroids of the leaf nodes dependent from the branch node weighted by the sparse code is minimized, wherein each element of the sparse code representing a weight corresponding to leaf nodes. | 06-12-2014 |