Systems and methods for real-time object recognition

• US 20070041638A1
• Filed: 04/28/2006
• Published: 02/22/2007
• Est. Priority Date: 04/28/2005
• Status: Abandoned Application

• Current Assignee: Florida State University (State University System of Florida)
• Original Assignee: Florida State University (State University System of Florida)
• Inventors: Mio, Washington, Liu, Xiuwen
• Application Number:
  US11/413,696
• Publication Number:
  US 20070041638A1
• Time in Patent Office: Days
• US Class Current: 382/170
• CPC Class Codes: G06F 18/24323 Tree-organised classifiers
• CPC Class Codes: G06V 10/50 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis

Abstract

Systems and methods are provided for the real-time object recognition of target objects, which includes the identification of target objects within images. In particular, images are received from an imaging device and analyzed by a workstation. The workstation applies one or more filters to the received images to generate one or more filtered images. One or more windows (e.g., sub-regions, sub-rectangles, etc.) of the filtered images are then analyzed in order to obtain histogram features. The workstation obtains a representation of these histogram features, which may be a simplified version or reduced dimension of the histogram features. The workstation then applies classifiers to the representation of the histogram features to recognize any objects in the received images.

Claims

1. A method for real-time object recognition, comprising:

receiving at least one image from at least one imaging device;

obtaining a plurality of histogram features from the at least one image, wherein obtaining the plurality of histogram features includes;

applying one or more filters to the received images to generate one or more filtered images; and

analyzing one or more windows of the filtered images for obtaining the histogram features;

obtaining at least one representation of the histogram features;

recognizing an object in the at least one received image by applying one or more classifiers to the representation of the histogram features.

2. The method of claim 1, wherein analyzing one or more windows of the filtered images includes a summation of a plurality of pixels of the one or more windows.

3. The method of claim 1, wherein recognizing the object includes recognizing the object by traversing one or more nodes of a decision tree until a terminal node is reached, wherein each node of the decision tree specifies the filters to be applied, the windows to be analyzed, and the one or more classifiers to be applied to the representation of the histogram features.

4. The method of claim 3, wherein the classifiers of the decision tree are determined by comparing training set images to cross-validation set images.

5. The method of claim 1, wherein obtaining at least one representation of the filtered images includes projecting at least a portion of the histogram features onto a subspace of the histogram features space.

6. The method of claim 5, wherein at least one of the classifiers operates in the subspace.

7. The method of claim 1, wherein recognizing the object includes recognizing the object in the at least one received image by applying one or more classifiers to the representation of the histogram features in accordance with one of optimal component analysis and splitting factor analysis.

8. A method for training a vision system for real-time object recognition, comprising:

receiving a plurality of training data having a plurality of classes of target objects and backgrounds, wherein the training data includes training set images and cross-validation set images for each class;

retrieving histogram features from the training data, wherein each histogram feature is associated with a filter and a window;

determining optimal histogram features for one or more classes; and

storing classifiers for the optimal histogram features in one or more nodes of a decision tree, wherein each node of the decision tree provides for discrimination between classes based upon representations of histogram features retrieved from input images.

9. The method of claim 8, wherein determining the optimal histogram features includes determining the recognition performance of the histogram features of the training set images when applied to the cross-validation set images.

10. The method of claim 8, further comprising clustering at least a portion of the plurality of classes in order to obtain a smaller number of classes of target objects and backgrounds.

11. The method of claim 8, further comprising storing filters and windows associated with the optimal histogram features in one or more nodes of the decision tree, wherein the nodes determine at least in part which histogram features of the input images are retrieved.

12. The method of claim 8, wherein receiving a plurality of training data includes receiving, for each class of target objects, images of target objects at varying scales.

13. The method of claim 8, wherein retrieving histogram features from the training data includes applying one or more filters to the training data, obtaining a window of the filtered training data, and performing a summation of a plurality of pixels within the window.

14. A system for real-time object recognition, comprising:

an imaging device for providing input images;

a workstation in communication with the imaging device for receiving the at least one input image, wherein the workstation is operative to;

apply one or more filters to the at least one input image to generate one or more filtered images;

analyze one or more windows of the filtered images to obtain the histogram features;

obtain at least one representation of the histogram features; and

recognize an object in the at least one received image by applying one or more classifiers to the representation of the histogram features.

15. The system of claim 14, wherein the histogram features are associated with a summation of a plurality of pixels of the one or more windows.

16. The method of claim 14, wherein the workstation further includes a decision tree having a plurality of nodes, wherein each node of the decision tree specifies the filters to be applied, the windows to be analyzed, and the one or more classifiers to be applied to the representation of the histogram features.

17. The method of claim 16, wherein the object is recognized by traversing one or more nodes of a decision tree until a terminal node is reached.

18. The method of claim 16, wherein the classifiers of the decision tree are determined by comparing training set images to cross-validation set images.

19. The method of claim 14, wherein the at least one representation of the histogram features are associated with projections of at least a portion of the histogram features onto a subspace of the histogram features space.

20. The method of claim 19, wherein at least one of the classifiers operates in the subspace.