| Patent application number | Description | Published |
|---|
| 20120233159 | HIERARCHICAL RANKING OF FACIAL ATTRIBUTES - In response to a query of discernible facial attributes, the locations of distinct and different facial regions are estimated from face image data, each relevant to different attributes. Different features are extracted from the estimated facial regions from database facial images, which are ranked in base layer rankings by matching feature vectors in a bipartite graph to a base layer ranking sequence as a function of edge weights parameterized by an associated base layer parameter vector. Second-layer rankings define second-layer attribute vectors as bilinear combinations of the base-layer feature vectors and associated base layer parameter vectors for common attributes, which are matched in a bipartite graph to a second-layer ranking sequence as a function of edge weights parameterized by associated second-layer parameter vectors. The images are thus ranked for relevance to the query as a function of fusing the second-layer rankings. | 09-13-2012 |
| 20120237081 | ANOMALOUS PATTERN DISCOVERY - A trajectory of movement of an object is tracked in a video data image field that is partitioned into a plurality of different grids. Global image features from video data relative to the trajectory are extracted and compared to a learned trajectory model to generate a global anomaly detection confidence decision value as a function of fitting to the learned trajectory model. Local image features are also extracted for each of the image field grids that include object trajectory, which are compared to learned feature models for the grids to generate local anomaly detection confidence decisions for each grid as a function of fitting to the learned feature models for the grids. The global anomaly detection confidence decision value and the local anomaly detection confidence decision values for the grids are into a fused anomaly decision with respect to the tracked object. | 09-20-2012 |
| 20120242832 | VEHICLE HEADLIGHT MANAGEMENT - A method, data processing system, and computer program product for managing a headlight on a vehicle are presented. An image of an area in front of the vehicle is received. A first set of features is identified in the received image. The first set of features in the received image is compared with a number of sets of features from a plurality of previous images. Each image in the plurality of previous images is associated with a headlight setting. A second set of features from a previous image in the plurality of previous images matching the first set of features in the received image is identified. A determination is made whether to change a setting for the headlight on the vehicle based on the headlight setting associated with the previous image. | 09-27-2012 |
| 20120263346 | VIDEO-BASED DETECTION OF MULTIPLE OBJECT TYPES UNDER VARYING POSES - Training data object images are clustered as a function of motion direction attributes and resized from respective original into same aspect ratios. Motionlet detectors are learned for each of the sets from features extracted from the resized object blobs. A deformable sliding window is applied to detect an object blob in input by varying window size, shape or aspect ratio to conform to a shape of the detected input video object blob. A motion direction of an underlying image patch of the detected input video object blob is extracted and motionlet detectors selected and applied that have similar motion directions. An object is thus detected within the detected blob and semantic attributes of an underlying image patch extracted if a motionlet detectors fires, the extracted semantic attributes available for use for searching for the detected object. | 10-18-2012 |
| 20120281873 | INCORPORATING VIDEO META-DATA IN 3D MODELS - A moving object detected and tracked within a field of view environment of a 2D data feed of a calibrated video camera is represented by a 3D model through localizing a centroid of the object and determining an intersection with a ground-plane within the field of view environment. An appropriate 3D mesh-based volumetric model for the object is initialized by using a back-projection of a corresponding 2D image as a function of the centroid and the determined ground-plane intersection. Nonlinear dynamics of a tracked motion path of the object are represented as a collection of different local linear models. A texture of the object is projected onto the 3D model, and 2D tracks of the object are upgraded to 3D motion to drive the 3D model by learning a weighted combination of the different local linear models that minimizes an image re-projection error of model movement. | 11-08-2012 |
| 20120284211 | IDENTIFYING ABNORMALITIES IN RESOURCE USAGE - A method, data processing system, and computer program product for identifying abnormalities in data. A model representing a plurality of modes for an activity generated from training data is retrieved. The training data includes a first plurality of measurements of a first performance of the activity over a period of time. Each of the plurality of modes is identified as one of normal and abnormal. Activity data including a second plurality of measurements of a second performance of the activity is received. A portion of the activity data is compared with the plurality of modes in the model. A notification of an abnormality in the second performance of the activity is generated in response to an identification that the portion of the activity data matches a mode in the plurality of modes identified as abnormal. Confirmation of the abnormality is requested via a user interface. | 11-08-2012 |
| 20120294511 | EFFICIENT RETRIEVAL OF ANOMALOUS EVENTS WITH PRIORITY LEARNING - Local models learned from anomaly detection are used to rank detected anomalies. The local models include image feature values extracted from an image field of video image data with respect to different predefined spatial and temporal local units, wherein anomaly results are determined by failures to fit to applied anomaly detection module local models. Image features values extracted from the image field local units associated with anomaly results are normalized, and image feature values extracted from the image field local units are clustered. Weights for anomaly results are learned as a function of the relations of the normalized extracted image feature values to the clustered image feature values. The normalized values are multiplied by the learned weights to generate ranking values to rank the anomalies. | 11-22-2012 |
| 20120308121 | IMAGE RANKING BASED ON ATTRIBUTE CORRELATION - Images are retrieved and ranked according to relevance to attributes of a multi-attribute query through training image attribute detectors for different attributes annotated in a training dataset. Pair-wise correlations are learned between pairs of the annotated attributes from the training dataset of images. Image datasets may then be searched via the trained attribute detectors for images comprising attributes in a multi-attribute query, wherein images are retrieved from the searching that each comprise one or more of the query attributes and also in response to information from the trained attribute detectors corresponding to attributes that are not a part of the query but are relevant to the query attributes as a function of the learned plurality of pair-wise correlations. The retrieved images are ranked as a function of respective total numbers of attributes within the query subset attributes. | 12-06-2012 |
