SYSTEMS, CIRCUITS, AND METHODS FOR EFFICIENT HIERARCHICAL OBJECT RECOGNITION BASED ON CLUSTERED INVARIANT FEATURES

• US 20130216143A1
• Filed: 02/07/2013
• Published: 08/22/2013
• Est. Priority Date: 02/07/2012
• Status: Active Grant

• Current Assignee: STMicroelectronics SRL (STMicroelectronics NV)
• Original Assignee: STMicroelectronics SRL (STMicroelectronics NV)
• Inventors: Pau, Danilo Pietro, Pasteris, Paolo, Condello, Giovanni
• Granted Patent: US 9,204,112 B2
• Time in Patent Office: Days
• US Class Current: 382/225
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 2218/08 Feature extraction
• CPC Class Codes: G06V 10/40 Extraction of image or video features
• CPC Class Codes: G06V 10/462 Salient features, e.g. scale invariant feature transforms [SIFT]
• CPC Class Codes: G06V 20/20 in augmented reality scenes
• CPC Class Codes: H04N 19/124 Quantisation
• CPC Class Codes: H04N 19/13 Adaptive entropy coding, e.g. adaptive variable length coding [AVLC] or context adaptive binary arithmetic coding [CABAC]
• CPC Class Codes: H04N 19/91 Entropy coding, e.g. variable length coding [VLC] or arithmetic coding

Abstract

One embodiment is a method for selecting and grouping key points extracted by applying a feature detector on a scene being analyzed. The method includes grouping the extracted key points into clusters that enforce a geometric relation between members of a cluster, scoring and sorting the clusters, identifying and discarding clusters that are comprised of points which represent the background noise of the image, and sub-sampling the remaining clusters to provide a smaller number of key points for the scene.

Claims

1. A method for selecting and grouping key points extracted by applying a feature detector on a scene being analysed, the method comprising:

grouping the extracted key points into clusters that enforce a geometric relation between members of a cluster;

scoring and sorting the clusters;

identifying and discarding clusters that are comprised of points which represent the background noise of the image; and

sub-sampling the remaining clusters to provide a smaller number of key points for the scene.

2. The method of claim 1 comprising iteratively applying the operations of grouping through sub-sampling to a set of reference images including sets comprised of a single reference image.

3. The method of claim 2 further comprising using clusters information to match clusters in the two images by using a metric which is defined by selected invariant features descriptors and by exploiting the sub-sampled cluster data to reduce the complexity of the matching phase including an early discard of clusters that cannot be probably found between extracted clusters.

4. The method of claim 3 further comprising tracking multiple independently moving objects in the scene being analyzed.

5. The method of claim 3 further comprising detecting multiple objects found in the scene being analyzed.