|New Mexico Supercomputing Challenge|
Challenge Team Abstract
I hope to create a program that will be able to recognize objects from the real world in actual photographs when given the photograph and the coordinates of a point within the region of the object in the photograph to be recognized. The final program should be efficient enough to be used in robotics, although, due to the planned solution method involving neural networks, the generation of the synaptic weights of the neural network may require much more processing than the final program, which just executes the neural network. During the training phase, photographs containing the objects to be distinguished and the coordinates of several points within this object will be entered into the training program for calculation of the appropriate synaptic weights. During the execution phase, several pictures containing examples of the objects which are to be recognized, at least one for every object present in training for which a point was specified, will be entered with the coordinates of a point located somewhere within the object, and in this way the success of the training will be determined. This program should be able to discern at least 500 different objects, some of which are similar and some of which are completely different.
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