Abstract: A sheet identification device includes a light irradiation portion. When forming an identification image, the light irradiation portion projects first pattern light (P11) and second pattern light (P12) having different dependences on a fiber direction of a surface of a sheet on an identification region (R1). The identification image is an image of the identification region (R1) 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.

Abstract: 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 (80) determines the cause of an image defect on the basis of a test image read from an output sheet of an image forming device (2). The processor (80) generates a plurality of feature images by executing a feature extraction process on the test image. Further, the processor (80) 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.

Abstract: 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.

Abstract: An object of the present invention is to extract image defects from a test image for each type by a simple process. A processor (80) generates a first preprocessed image and a second preprocessed image by executing a main filter process with each of a horizontal direction and a vertical direction of the test image used as a processing direction. The main filter process is a process of converting the pixel value of each pixel of interest sequentially selected from the test image into a converted value obtained by a process of emphasizing the difference between the pixel values of an area of interest and the pixel values of two adjacent areas. The processor (80) extracts, as the image defects, a first singular part that is present in the first preprocessed image and is not common to both images, a second singular part that is present in the second preprocessed image and is not common to both images, and a third singular part that is common to both images.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A vector generation unit derives a feature vector of a reference document and a feature vector of a population document. A feature quantity extraction unit performs a dimensionality reduction process to reduce dimensionality of the above feature vectors and sets a dimensional value obtained by the dimensionality reduction process as a first feature quantity, and derives a cosine similarity between the feature vector of the reference document and the feature vector of the population document as a second feature quantity. A retrieval range control unit extracts a specific number of population documents, starting from the population document with the shortest distance to the reference document in a feature quantity space of the first feature quantity, so as to limit a retrieval range. A training data extraction unit extracts, as training data, a specific number of documents from the extracted documents, starting from the document with the lowest cosine similarity.

Abstract: A feature-amount extraction unit generates a base feature-map group constituted by a plurality of base feature maps from an input image, applies a plurality of statistic calculations to the base feature maps in the base feature-map group, and generates a plurality of types of statistic maps. The inference unit derives inference results of segmentation for inference inputs based on the plurality of statistic maps. Each of the plurality of types of statistic calculations described above is processing of calculating a statistic with a specific window size and a specific calculation formula, and the plurality of types of statistic calculations are different from each other in at least either one of the window size and the calculation formula.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.