Matsushita Electric Industrial Co., Ltd.

Mie Saitoh

Kunio Yoshida

Motohiko Naka

Takehiko Shida

Takehisa Tanaka

Masaaki Sato

Toshiki Kindo

Katsuhiko Fujiwara

Makoto Shimizu

Akira Yokouchi

Ikuo Akamine

A pattern classification system includes a plurality of classification sections. Each of the classification sections includes a device for storing information of N coefficients W representing a reference pattern, a device for calculating an evaluation value V on the basis of N input signals S and the N coefficients W, the N input signals representing an input pattern, the evaluation value V representing a relation between the input pattern and the reference pattern, a device for storing information of a fixed threshold value R, and a device for comparing the evaluation value V and the threshold value R and for outputting an estimation signal depending on a result of the comparing, the estimation signal including a category signal P which represents a category. A selection section is operative for selecting one of categories in response to the category signals outputted from the classification sections and for outputting a signal Px representing the selected one of the categories. An adjustment section is operative for adjusting parameters in the classification sections in response to the output signal Px of the selection section and a teacher signal T, the parameters including the coefficients W.

Pattern classification system

An automatic adjusting apparatus for adjusting equipment which comprises an adjustment state detecting section and a processing section. The detecting section detects an adjustment state of the equipment so as to output one or a plurality of adjustment state indexes on the basis of the detected adjustment state and the processing section performs the process on the basis of the adjustment state index to adjust the equipment. The processing section is equipped with an adjustment state evaluation section for calculating and outputting, on the basis of the adjustment state index, an adjustment evaluation value and an adjustment amount calculation section for calculating, on the basis of the adjustment state index, adjustment amounts for one or a plurality of portions of the equipment. Also included in the processing section is an adjustment control section for selecting, on the basis of the adjustment evaluation value, an adjustment amount from the outputs of the calculation section. The control section controls the calculation section so as to control the adjustment amount on the basis of the selection result. The calculation section has a function to learn the relation between the adjustment state index and the adjustment amount and a function to calculate the adjustment amount in accordance with the learned relation.

Automatic adjusting apparatus for equipment

A coloring device includes an image sampling device for sampling an input signal block representing a group of n.times.m pixels of a monochromatic image and for outputting first signals representing the sampled pixels of the input signal block of the monochromatic image; and artificial neural network, a connection for providing to the artificial neural network, substantially simultaneously, pattern information on patterns to be contained in the monochromatic image and color information on first data indicating colors given to the patterns indicated by the pattern information prior to generation of a color image signal, the artificial neural network having internal state parameters which are adaptively optimized by using a learning algorithm prior to the generation of a color image, the artificial neural network operating for receiving data representing the first signal, for determining which of colors preliminarily and respectively assigned to patterns to be contained in the group of pixels of the monochromatic image represented by the input signal block is given to a pattern actually contained in the group of pixels represented by the input signal block and for outputting second signals representing second data on three primary colors which are used to represent the determined colors given to the patterns actually contained in the group of pixels represented by the input signal block; and a color image storing device for receiving the second signals outputted from the artificial neural network, for storing the received second signals in locations thereof corresponding to the positions of the pixels represented by the input signal block and for outputting third signals representing the three primary color component images of the pixels represented by the input signal block; wherein the image sampling device further functions for scanning the whole of the monochromatic image by generating successive input signal blocks representing successive groups n.times.m pixels to be sampled, thereby outputting third signals for all pixels of the monochromatic image.

Coloring device for performing adaptive coloring of a monochromatic image

A color correction apparatus for use in an apparatus such as a color copier for operating on data obtained by scanning and color analysis of a source image to obtain color density data for use in printing a copy of the source image, the correction apparatus containing a neural network. Parameter values of the neural network are established by repetitive computations based on amounts of difference between color density data previously used to print a plurality of color samples and color density data produced by the neural network in response to color analysis data obtained by analyzing these color samples.

Color data correction apparatus ultilizing neural network

An apparatus for printing signal correction and printer operation control, for use in applications such as color copiers, utilizes a neural network to convert input image signals, derived for example by scanning and analyzing an original image, into printing density signals which are supplied to a printer. In addition, a detection signal expressing at least one internal environmental condition of the printer, such as temperature, is inputted to the neural network, so that the output printing density signals are automatically compensated for changes in internal environment of the printer.

Printing signal correction and printer operation control apparatus

A data compression and recovery apparatus compresses picture element data of a color image by expressing two primary color values of each picture element as a set of parameter values of a neural network in conjunction with reference color data values of a corresponding block of picture elements. Date recovery is achieved by inputting each block of reference color values to a neural network while establishing the corresponding set of parameter values in the network, to thereby obtain the original pair of encoded primary color values for each of successive picture elements. The third primary color can be used as the reference color.

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Kawasaki, Japan

Mie Saitoh