Augmenting layer-based object detection with deep convolutional neural networks

A computer-implemented method for performing object recognition comprising:

• receiving image data;
• creating a mask image by segmenting the image data into a plurality of components;
• determining a first likelihood score from the image data and the mask image using a layered classifier;
• determining a second likelihood score from the image data and the mask image using a deep convolutional neural network (CNN);
• and
• determining a class for at least a portion of the image data based on the first likelihood score and the second likelihood score.

A system for performing object recognition comprising:

• a processor;
• and
• a memory storing instructions that, when executed, cause the system to:
• create a mask image by segmenting image data into a plurality of components;
• determine a first likelihood score from the image data and the mask image using a layered classifier;
• determine a second likelihood score from the image data and the mask image using a deep convolutional neural network (CNN);
• and
• determine a class for at least a portion of the image data based on the first likelihood score and the second likelihood score.