System and method for object recognition and classification using a three-dimensional system with adaptive feature detectors
First Claim
Patent Images
1. A method, comprising:
- imaging an object in three-dimensions by collecting three-dimensional data points of the object;
binning the data points of the imaged object into three-dimensional bins having a predetermined three-dimensional size to create a binned scanned object in a three-dimensional bin space;
determining a density value of the data in each of the bins by calculating a number of the data points in each of the bins;
inputting the density value of each of the bins into a first layer of a computational system including a corresponding processing element for each of the bins;
calculating outputs of the processing elements, wherein all of the processing elements have weights of a same value connecting the processing elements to corresponding density values; and
communicating an estimated class of the scanned object based on the calculated outputs, wherein the calculating includes;
performing a nonlinear transformation on a product of a connected weight and the density value for each bin;
sub-sampling the outputs by averaging the outputs over two adjacent processing elements in three dimensions; and
repeating the calculating process using additional layers of the computational system, the output of the computational system connected to all processing elements of a last layer of the computational system.
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Abstract
A method including imaging an object in three-dimensions; binning data of the imaged object into three-dimensional regions having a predetermined size; determining a density value p of the data in each bin; inputting the p density values of the bins into a first layer of a computational system including a corresponding processing element for each of the bins; calculating an output O of the processing elements of the computational system while restricting the processing elements to have weights Wc1 connecting the processing elements to the corresponding p density values; and communicating an estimated class of the scanned object based on the calculated system outputs.
27 Citations
20 Claims
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1. A method, comprising:
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imaging an object in three-dimensions by collecting three-dimensional data points of the object; binning the data points of the imaged object into three-dimensional bins having a predetermined three-dimensional size to create a binned scanned object in a three-dimensional bin space; determining a density value of the data in each of the bins by calculating a number of the data points in each of the bins; inputting the density value of each of the bins into a first layer of a computational system including a corresponding processing element for each of the bins; calculating outputs of the processing elements, wherein all of the processing elements have weights of a same value connecting the processing elements to corresponding density values; and communicating an estimated class of the scanned object based on the calculated outputs, wherein the calculating includes; performing a nonlinear transformation on a product of a connected weight and the density value for each bin; sub-sampling the outputs by averaging the outputs over two adjacent processing elements in three dimensions; and repeating the calculating process using additional layers of the computational system, the output of the computational system connected to all processing elements of a last layer of the computational system. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method, comprising:
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imaging an object in three-dimensions by collecting three-dimensional data points of the object; binning the data points of the imaged object into three-dimensional bins having a predetermined three-dimensional size to create a binned scanned object in a three-dimensional bin space; determining a density value of the data in each of the bins by calculating a number of the data points in each of the bins; inputting the density value of each of the bins into a first layer of a computational system including a corresponding processing element for each of the bins; and calculating outputs of the processing elements, wherein all of the processing elements have weights of a same value connecting the processing elements to corresponding density values, wherein the weights are obtained through unsupervised training of the computational system, and the unsupervised training includes; initializing a plurality of weights Wc1(num) for num =1, 2,,... Nu, wherein Nu is a predetermined integer; for each num, computing an output O for a randomly selected bin of data; determining a num_max corresponding to a maximum computed output O; adjusting the weight Wc1(num_max) to implement an unsupervised training algorithm; and repeating the computing, determining, and adjusting steps until changes in the weights Wc1 are smaller than a predetermined value. - View Dependent Claims (13, 14, 15, 16)
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17. A method, comprising:
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imaging an object in three-dimensions by collecting three-dimensional data points of the object; binning the data points of the imaged object into three-dimensional bins having a predetermined three-dimensional size to create a binned scanned object in a three-dimensional bin space; determining a density value of the data in each of the bins by calculating a number of the data points in each of the bins; inputting the density value of each of the bins into a first layer of a computational system including a corresponding processing element for each of the bins; and calculating outputs of the processing elements, wherein all of the processing elements have weights of a same value connecting the processing elements to corresponding density values, wherein the weights are obtained through unsupervised training of the computational system, and the supervised training includes; obtaining a representative set of examples of objects with the classes; training the system using a supervised training algorithm until a predetermined error value is reached; testing the trained system on a set of unknown objects to obtain error value; determining whether the error value is within tolerance of a training error; and repeating the supervised training process until the error value is within the tolerance of the training error. - View Dependent Claims (18, 19, 20)
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Specification