A system, comprising: a display; a camera configured to capture a real scene image; at least one convolutional neural network processor communicatively coupled to the camera, the at least one CNN processor configured to: receive real scene image data corresponding to the captured real scene image from the camera; identify objects in the real scene image; classify each object of the identified objects as a foreground object or a background object; generate object classification and position data indicative of classifications and positions of the identified objects; and output the object classification and position data; and at least one processor communicatively coupled to the camera, the at least one CNN processor, and the display, the at least one processor configured to: receive the real scene image data corresponding to the captured real scene image from the camera; receive the object classification and position data from the at least one CNN processor; perform at least one image segmentation operation on the real scene image to fill in the objects; generate filled-in object data indicative of the filled-in objects; during an object fusion step, utilize the object classification and position data and the filled-in object data to generate a blending value pixel mask corresponding to foreground objects; receive virtual scene image data corresponding to a virtual scene image; based at least on the blending value pixel mask, blend the virtual scene image and the real scene image to create a mixed reality scene image as mixed reality scene image data; and output the mixed reality scene image data to the display for presentation to a user, wherein the at least one CNN processor and the at least one processor are configured to process the real scene image data concurrently. a display; a camera configured to capture a real scene image; at least one convolutional neural network processor communicatively coupled to the camera, the at least one CNN processor configured to: receive real scene image data corresponding to the captured real scene image from the camera; identify objects in the real scene image; classify each object of the identified objects as a foreground object or a background object; generate object classification and position data indicative of classifications and positions of the identified objects; and output the object classification and position data; and receive real scene image data corresponding to the captured real scene image from the camera; identify objects in the real scene image; classify each object of the identified objects as a foreground object or a background object; generate object classification and position data indicative of classifications and positions of the identified objects; and output the object classification and position data; and at least one processor communicatively coupled to the camera, the at least one CNN processor, and the display, the at least one processor configured to: receive the real scene image data corresponding to the captured real scene image from the camera; receive the object classification and position data from the at least one CNN processor; perform at least one image segmentation operation on the real scene image to fill in the objects; generate filled-in object data indicative of the filled-in objects; during an object fusion step, utilize the object classification and position data and the filled-in object data to generate a blending value pixel mask corresponding to foreground objects; receive virtual scene image data corresponding to a virtual scene image; based at least on the blending value pixel mask, blend the virtual scene image and the real scene image to create a mixed reality scene image as mixed reality scene image data; and output the mixed reality scene image data to the display for presentation to a user, wherein the at least one CNN processor and the at least one processor are configured to process the real scene image data concurrently. receive the real scene image data corresponding to the captured real scene image from the camera; receive the object classification and position data from the at least one CNN processor; perform at least one image segmentation operation on the real scene image to fill in the objects; generate filled-in object data indicative of the filled-in objects; during an object fusion step, utilize the object classification and position data and the filled-in object data to generate a blending value pixel mask corresponding to foreground objects; receive virtual scene image data corresponding to a virtual scene image; based at least on the blending value pixel mask, blend the virtual scene image and the real scene image to create a mixed reality scene image as mixed reality scene image data; and output the mixed reality scene image data to the display for presentation to a user, wherein the at least one CNN processor and the at least one processor are configured to process the real scene image data concurrently.