Target object classification using three-dimensional geometric filtering

1. An apparatus for classifying target objects using three-dimensional geometric filtering, comprising:

a patch receiver to receive patches with objects to be classified;

a geometric filter to filter out patches corresponding to objects with sizes outside a target range of object sizes using three dimensional geometry to generate filtered patches;

a background remover to remove background pixels from the filtered patches to generate preprocessed patches without the removed background pixels;

a classification score calculator to calculate a classification score for each of the preprocessed patches;

a target object detector to detect a target object in response to detecting a classification score exceeding a threshold score; and

a virtual target object displayer to display the detected target object as a virtual target object in a virtual reality display in real time.

2. The apparatus of claim 1 , comprising a score averager to average classification scores received over a predetermined time for each of the preprocessed patches.

3. The apparatus of claim 1 , wherein the target object comprises a human hand.

4. The apparatus of claim 1 , wherein the background remover is to remove background pixels based on an image segmentation.

5. The apparatus of claim 1 , wherein the target object range comprises lower target object bounds and upper target object bounds for a volume and a surface area of a bounding to be calculated for an object in each patch.

6. The apparatus of claim 1 , wherein the background remover comprises a convolutional neural network trained to remove background noise based on a depth image segmentation.

7. The apparatus of claim 1 , wherein the classification score calculator comprises a convolutional neural network trained to classify patches using a set of training images of target objects.

8. The apparatus of claim 1 , wherein the apparatus comprises a pipelined image processor.

9. A method for classifying target objects in images, comprising:

receiving, via a processor, patches with objects to be classified;

filtering out, via the processor, patches corresponding to objects with sizes outside a target range using three dimensional geometry to generate filtered patches;

removing, via the processor, background pixels from the filtered patches to generate preprocessed patches without the removed background pixels;

calculating, via the processor, a classification score for each of the preprocessed patches;

detecting, via the processor, a target object in response to detecting a classification score exceeding a threshold score; and

displaying, via the processor, the detected target object as a virtual target object in a virtual reality display in real time.

10. The method of claim 9 , comprising averaging, via the processor, classification scores received over a predetermined time for each of the preprocessed patches.

11. The method of claim 9 , wherein using three dimensional geometry to generate filtered patches comprises calculating, via the processor, a bounding box for each patch using a principal component analysis and comparing, via the processor, a volume and a surface area of the bounding box for each patch to a lower bounding box threshold and a higher bounding box threshold.

12. The method of claim 9 , wherein removing the background pixels comprises segmenting the image.

13. At least one computer readable medium for classifying target objects using three-dimensional geometric filtering having instructions stored therein that, in response to being executed on a computing device, cause the computing device to:

receive patches with objects to be classified;

filter out patches corresponding to objects with sizes outside a target range of object sizes using three dimensional geometry to generate filtered patches;

remove background pixels from the filtered patches using image segmentation to generate preprocessed patches without the removed background pixels;

calculate a classification score indicating a probability that a patch is a target object for each of the preprocessed patches;

detect a target object in response to detecting a classification score exceeding a threshold score; and

display the detected target object as a virtual target object in a virtual reality display in real time.

14. The at least one computer readable medium of claim 13 , comprising instructions to average classification scores received over a predetermined time for each of the preprocessed patches.

15. The at least one computer readable medium of claim 13 , comprising instructions to detect a target object in response to detecting an average classification score of a preprocessed patch exceeds a threshold classification score.

16. The at least one computer readable medium of claim 13 , wherein the target object comprises a human hand.