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, and an anomaly unification processing unit. The anomaly detecting unit is configured to detect anomalies included in a target image. The anomaly unification processing unit is configured to unify specific anomalies among the detected anomalies. Further, if a type of an anomaly among the detected anomalies is the same as a type of another anomaly among the detected anomalies, the anomaly unification processing unit (a) determines whether the anomaly and the other anomaly should be unified or not on the basis of a relationship between a position of the anomaly and a position of the other anomaly, and (b) unifies the anomaly and the other anomaly if it is determined that the anomaly and the other anomaly should be unified.

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 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 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.