Abstract: An image recognition method includes a feature amount extracting step of generating, from an input image, a base feature map group including a plurality of base feature maps; an inferring step of deriving a plurality of inference results using each of a plurality of machine-learned inference devices for a plurality of inference inputs based on the base feature map group; and an integrating step of integrating the plurality of inference results by a specific manner to derive a final inference result, where each of the plurality of inference inputs has some or all base feature maps of the plurality of base feature maps, and each of the plurality of inference inputs has the some or all base feature maps that are different in part or whole from the some or all base feature maps of another inference input in the plurality of inference inputs.

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.

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: 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 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: 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 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 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.

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 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 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: 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: 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: 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.