Abstract:
An information processing apparatus operable to perform computation processing in a neural network comprises a coefficient storage unit configured to store filter coefficients of the neural network, a feature storage unit configured to store feature data, a storage control unit configured to store in the coefficient storage unit a part of previously obtained feature data as template feature data, a convolution operation unit configured to compute new feature data by a convolution operation between feature data stored in the feature storage unit and filter coefficients stored in the coefficient storage unit, and compute, by a convolution operation between feature data stored in the feature storage unit and the template feature data stored in the coefficient storage unit, correlation data between the feature data stored in the feature storage unit and the template feature data.
Abstract:
A transmission device divides image data into a plurality of regions on the basis of a similarity of pixels and transmits the image data and region representative points of the plurality of regions. A reception device receives the image data and the region representative points transmitted from the transmission device. The reception device generates region labels used for identifying the plurality of regions using the image data and the region representative points.
Abstract:
There is provided an information processing apparatus. A multidimensional input vector is input. For each dimension of the input vector, a function value of a single-variable function with an element of the dimension as a variable is derived, by referring to a lookup table indicating a correspondence between a variable and a function value of the single-variable function. A product of the single-variable functions approximates a function value of a multiple-variable function. For each dimension of the input vector, a product of the function value derived by the derivation unit and a predetermined coefficient corresponding to the dimension is calculated. A value calculated using the total of the products calculated by the product calculation unit for each dimension of the input vector is output as a classification index indicating a class of the input vector.
Abstract:
There is provided an information processing apparatus. A multidimensional input vector is input. For each dimension of the input vector, a function value of a single-variable function with an element of the dimension as a variable is derived, by referring to a lookup table indicating a correspondence between a variable and a function value of the single-variable function. A product of the single-variable functions approximates a function value of a multiple-variable function. For each dimension of the input vector, a product of the function value derived by the derivation unit and a predetermined coefficient corresponding to the dimension is calculated. A value calculated using the total of the products calculated by the product calculation unit for each dimension of the input vector is output as a classification index indicating a class of the input vector.
Abstract:
A transmission device divides image data into a plurality of regions on the basis of a similarity of pixels and transmits the image data and region representative points of the plurality of regions. A reception device receives the image data and the region representative points transmitted from the transmission device. The reception device generates region labels used for identifying the plurality of regions using the image data and the region representative points.