Object learning and recognition method and system

1. A computer-implemented object recognition system, comprising:

an input unit configured to receive a depth image representing an object;

a processor configured to use a classification tree to recognize, from the depth image of the object, a visible object part and a hidden object part; and

a volume constructing unit configured to construct a volume of the object in a single data space, based on the recognized visible object part and the recognized hidden object part;

wherein the volume constructing unit is configured to construct the volume, based on a relative depth value stored in a leaf node of the classification tree.

2. The computer-implemented object recognition system of claim 1 , wherein the processor is configured to extract additional information regarding the object, based on the volume.

3. The computer-implemented object recognition system of claim 2 , wherein the additional information comprises information regarding at least one of: a shape, a pose, a key joint, and a structure associated with the object.

4. The computer-implemented object recognition system of claim 1 , wherein the relative depth value indicates a difference value between a depth value of the recognized visible object part and a depth value of the recognized hidden object part.

5. The computer-implemented object recognition system of claim 1 , further comprising:

a size adjusting unit configured to adjust at least one size of a width and a height of an object model of the object.

6. The computer-implemented object recognition system of claim 1 , wherein the classification tree comprises a probability value of the visible object part, and a probability value of the hidden object part.

7. The computer-implemented object recognition system of claim 1 , wherein the classification tree comprises a relative depth value associated with the visible object part and the hidden object part.

8. The computer-implemented object recognition system of claim 1 , wherein the classification tree is represented by using at least a portion of the hidden object part as a plurality of layers.

9. The computer-implemented object recognition system of claim 1 , wherein the processor is configured to recognize the visible object part and the hidden object part based upon a single depth image.

10. A computer-implemented object recognition system, comprising:

an input unit configured to receive a depth image representing an object; and

a processor configured to use a classification tree to recognize, from the depth image of the object, a visible object part and a hidden object part;

wherein the processor is configured to apply the depth image to the classification tree,

wherein, when a current node of the classification tree is a split node, the processor is configured to read a value of a feature and a threshold from the split node, apply the value of the feature and the threshold to a split function, and search for one of a left child node and a right child node of the current node based on a result of the split function, and

wherein, when the current node is a leaf node, the processor is configured to read, from the leaf node, a first histogram of the visible object part and a second histogram of the hidden object part, recognize the visible object part from the depth image, based on the read first histogram, and recognize the hidden object part from the depth image, based on the read second histogram.

11. The computer-implemented object recognition system of claim 10 , wherein, when a result value of the split function does not meet the threshold, the processor is configured to search for the left child node, and

wherein, when the result value meets the threshold, the processor is configured to search for the right child node.

12. The computer-implemented object recognition system of claim 11 , wherein the classification tree is represented using at least a portion of the hidden object part as a plurality of layers, with the processor configured to read a plurality of hidden object histograms from the leaf node, and recognize at least one hidden object part from the depth image based on the read plurality of hidden object histograms.

13. The computer-implemented object recognition system of claim 12 , wherein the plurality of hidden object histograms include at least one of: a hidden object histogram representing muscles of a human body, a hidden object histogram representing a skeletal structure of the human body, a hidden object histogram representing internal organs of the human body, a hidden object histogram representing a cardiovascular system of the human body, and a hidden object histogram representing a nervous system for the human body.

14. A computer implemented object recognition method, comprising:

receiving, as an input, a depth image representing an object to be analyzed;

recognizing a visible object part and a hidden object part of the object, from the depth image of the object, by using a processor and a classification tree; and

constructing a volume of the object in a single data space, based on the recognized visible object part and the recognized hidden object part, and based on a relative depth value stored in a leaf node of the classification tree, by using the processor.

15. A non-transitory computer readable recording medium comprising computer readable code to control at least the processor to implement the method of claim 14 .

16. A computer-implemented object recognition system, comprising:

an input unit configured to receive a depth image representing an object; and

a processor configured to use a classification tree to recognize, from the depth image of the object, a visible object part and a hidden object part;

a classification tree obtaining unit configured to obtain the classification tree, wherein the classification tree is learned based upon consideration of modeled visible and hidden object parts for each of plurality of nodes of the classification tree.

17. The computer-implemented object recognition system of claim 16 , wherein the classification tree obtaining unit configured to generate the classification tree through learning based upon consideration of visible and hidden object parts, for each of a plurality of nodes, and obtain the generated classification tree to be available to the processor for the recognizing of the visible object part and the hidden object part.

18. The computer-implemented object recognition system of claim 17 , further comprising:

a casting unit configured to cast rays towards a plurality of voxels of a three-dimensional (3D) object model of the object, by using a virtual camera;

an image layer generating unit configured to generate a plurality of image layers sequentially every time the rays penetrate a surface of the 3D object model;

a capturing unit configured to capture a depth value and a voxel identifier (ID) of the surface, for each of the plurality of image layers, and to store the captured depth value and the captured voxel ID in each of the plurality of image layers; and

a training data generating unit configured to set an image layer with a minimum distance from the virtual camera to be visible object part data associated with the visible object part, to set the other image layers to be hidden object part data associated with the hidden object part, and to generate the training data.

19. A computer-implemented object recognition method, comprising:

obtaining a classification tree by using a processor, wherein the classification tree is learned based upon consideration of modeled visible object parts and hidden object parts, for each of the plurality of nodes of the classification tree; and

recognizing, by using a processor and the classification tree, a visible object part and a hidden object part of an object, based on a depth image of the object.

20. The computer-implemented object recognition method of claim 19 , further comprising:

generating the classification tree, through learning based upon consideration of visible object parts and hidden object parts for each of the plurality of nodes of the classification tree.