Kyocera Document Solutions Inc.

Sharp Kabushiki Kaisha Corporation

Sumitomo Electric Industries, Limited

Dmg Mori Seiki Co., Ltd.

Fuji Jukogyo Kabushiki Kaisha

Dmg Mori Company Limited

Subaru Corporation

Mitsubishi Hitachi Tool Engineering, Ltd.

Koji Sato

Takuya Miyamoto

Rui Hamabe

Kazunori Tanaka

Kanako Morimoto

Isao Isshiki

Morihiro Hideta

Shinya Matsuo

Akihiro Ohoka

Tatsuo Nakahata

Hiroki Munetomo

Hisayuki Oode

Toshiya Takahashi

Takeshi Yamamoto

Manabu Saito

Hiroka Itani

Kazuhiro Nakachi

Megumi Itoh

Aki Miake

Katsuyoshi Sunago

Shiro Kaneko

Fukuma Sakamoto

Takeo Abe

Mikio Yamaguchi

Masatoshi Tanaka

Naomichi Higashiyama

Shinichirou Ota

Yasuhiro Inamasu

Hirotake Hayashi

Shoujirou Touma

Kazuo Fujiwaki

Hisayuki Oide

Masahiro Kurakake

Mika Akada

Fumitaka Nishikawa

Masayuki Akatsuka

A sheet identification device includes a light irradiation portion. When forming an identification image, the light irradiation portion projects first pattern light (P) and second pattern light (P) having different dependences on a fiber direction of a surface of a sheet on an identification region (R). The identification image is an image of the identification region (R) of the surface of the sheet on which image formation or image reading is performed, which is used for identifying the fiber direction of the surface of the sheet.

Sheet identification device and image processing apparatus capable of identifying fiber direction of sheet

An object of the present invention is to determine the cause of an image defect that occurs in an image forming device with high accuracy while suppressing the amount of calculation at a processor. A processor () determines the cause of an image defect on the basis of a test image read from an output sheet of an image forming device (). The processor () generates a plurality of feature images by executing a feature extraction process on the test image. Further, the processor () uses each of the plurality of feature images as an input image and determines which of a plurality of cause candidates corresponding to the image defect the input image corresponds to by pattern recognition of the input image.

Image processing method and image processing apparatus

An anomaly detecting unit detects an anomaly object in a target image. A characteristic amount watching unit watches at least two basic characteristic amounts of the anomaly object, determines whether values of the basic characteristic amounts satisfy a predetermined watching determination condition of any one of predetermined plural anomaly types or not, if it is determined that the values of the basic characteristic amounts satisfy the watching determination condition, determines as an auxiliary characteristic amount for the anomaly object a characteristic amount corresponding to the anomaly type of which the values of the basic characteristic amounts satisfy the watching determination condition, and starts watching a value of the auxiliary characteristic amount. An anomaly type determining unit determines an anomaly type of the anomaly object on the basis of the basic characteristic amounts and the auxiliary characteristic amount currently watched by the characteristic amount watching unit.

Image processing apparatus, image forming apparatus, and image processing method

A processor determines a cause of an image defect based on a test image that is obtained through an image reading process performed on an output sheet output from an image forming device. The processor generates an extraction image by extracting, from the test image, a noise point that is a type of image defect. Furthermore, the processor determines a cause of the noise point by using at least one of, in the extraction image: an edge strength of the noise point; a degree of flatness of the noise point; and a pixel value distribution of a transverse pixel sequence that is a pixel sequence traversing the noise point.

Image processing method, image processing apparatus

A processor determines a cause of an image defect based on a test image that is obtained through an image reading process performed on an output sheet output from an image forming device. The processor extracts a vertical stripe part extending along a sub scanning direction in the test image. Furthermore, the processor determines which of predetermined two types of cause candidates is a cause of the vertical stripe part, based on a distribution of a pixel value sequence along a main scanning direction that crosses the sub scanning direction, in a target part including the vertical stripe part in the test image.

A processor determines an image defect in a test image obtained through an image reading process performed on an output sheet output by an image forming apparatus. The processor creates an image to be processed including a horizontal line, extending in a horizontal direction, extracted from the test image. Furthermore, the processor determines the presence or absence of at least one periodicity set in advance in a vertical direction in the image to be processed and determines the cause of the horizontal line according to the determination result on the periodicity.

A processor determines an image defect in a test image obtained through an image reading process performed on an output sheet output by an image forming apparatus. The processor derives a vertical data string that is composed of data about horizontal representative values serving as representative values of a plurality of pixel values in each line in a horizontal direction in the test image. Moreover, the processor determines the presence or absence of at least one periodicity set in advance in the vertical data string by performing frequency analysis on the vertical data string. Furthermore, the processor determines the occurrence or non-occurrence and the cause of periodic density unevenness serving as a type of the image defect according to the determination result on the periodicity.

Method for performing image processing for determining image defect in mixed-color test image through image reading

A processor identifies an original drawing part that is originally drawn in a test image. Furthermore, the processor identifies a noise point in the test image. Furthermore, the processor determines which of predetermined two types of cause candidates is a cause of the noise point by determining a degree of overlapping between the original drawing part and the noise point. The two types of cause candidates are: waste toner dropping in which waste toner that has adhered to a transfer body that transfers a toner image to a sheet, is transferred to the sheet in the image forming device; and carrier developing in which magnetic carrier that has been mixed with toner is transferred to a sheet.

A vector generation unit derives a reference feature vector and a document feature vector. A feature quantity extraction unit performs a dimensionality reduction process on the reference feature vector and the document feature vector so as to set a dimensional value as a first feature quantity and derives a cosine similarity between the reference feature vector and the document feature vector as a second feature quantity. A grid division unit classifies documents into first partial regions obtained by dividing a feature quantity space of the first feature quantity, and classifies the documents into second partial regions obtained by dividing a range of the second feature quantity. A training data extraction unit selects, for each combination of a first partial region and a second partial region, a document classified in both the partial regions and sets documents selected with respect to all combinations as training data.

Training data collection system, similarity score calculation system, document retrieval system, and non-transitory computer readable recording medium storing training data collection program

A processor selects a target sheet from a plurality of predetermined sheet candidates in accordance with selection information that is input via an input device. Furthermore, the processor derives feature information regarding a noise point from a target image that is obtained through an image reading process performed on an output sheet output from an image forming device, the noise point being a dot-like noise image included in the target image. Furthermore, the processor determines whether or not the noise point is a dot-like sheet noise by applying the feature information to a determination algorithm that corresponds to the target sheet, the sheet noise being included in a sheet of the output sheet itself, the determination algorithm being one of a plurality of determination algorithms that respectively correspond to the plurality of sheet candidates.

A processor generates an extraction image by extracting, from a test image, a target specific part that is a horizontal stripe extending in a horizontal direction or a noise point. The processor derives a vertical data sequence from a target area in the extraction image and converts the vertical data sequence to primary conversion data of a frequency domain. The processor converts, to secondary conversion data of a space domain, correction data that is obtained by removing, from the primary conversion data, data of frequency bands other than a particular frequency band corresponding to a target specific part that was determined as having periodicities. The processor classifies the target specific parts into periodic specific part and non-periodic specific part by comparing positions of the target specific parts with a peak position in a waveform represented by the secondary conversion data.

The image processing apparatus includes: an abnormality detection unit configured to detect one or more abnormalities included in a target image and an abnormality exclusion processing unit configured to exclude a specific abnormality from the detected one or more abnormalities. In a case in which a detection area of a certain abnormality among the detected one or more abnormalities and a detection area of another abnormality overlap with each other and a type of the certain abnormality and a type of the another abnormality are different from each other, when brightness information of one of the certain abnormality and the another abnormality satisfies a specific condition, the abnormality exclusion processing unit excludes the one of the certain abnormality and the another abnormality from the detected one or more abnormalities.

Image processing apparatus, method, and computer program product excludes specific anomalies among detected anomalies

A processor identifies a specific part composed of a plurality of significant pixels in a mixed-color test image. Furthermore, the processor identifies a color vector. The color vector represents a vector in a color space from one of a color of the specific part in the mixed-color test image and a color of a reference area including a periphery of the specific part to the other. Furthermore, the processor determines which of a plurality of image creating portions of an image forming device corresponding to a plurality of developing colors, is a cause of an image defect, based on the color vector identified from the mixed-color test image.

Image processing method and apparatus determing cause of image defect based on mixed color test image

An image processing apparatus includes an anomaly detecting unit configured to detect anomalies included in a target image; and an anomaly exclusion processing unit configured to exclude a specific anomaly among the detected anomalies. Further, the anomaly exclusion processing unit excludes one of an anomaly and another anomaly among the detected anomalies, if (a) a detection area of the anomaly, a detection area of the other anomaly, and an overlapping area of the detection areas of the anomaly and the other anomaly satisfy a predetermined condition and (b) a type of the anomaly and a type of the other anomaly are different from each other.

A meeting system includes an identification information acquirer that acquires a card ID, based on a user's operation of touching a reader with an ID cardfor identifying the user, an authentication process operator that determines whether or not to grant an authentication of the user, based on the card ID acquired by the identification information acquirer, and a command process operator that issues a command for commanding an operation corresponding to a desired operation of the user, where the command is associated with the card ID corresponding to the user, if the authentication of the user is granted by the authentication process operator.

Growing community of inventors

Nara, Japan

Koji Sato