Abstract: An image forming apparatus includes: an image forming device configured to form an image on a sheet; a fan configured to be driven to perform ventilation; and at least one controller configured to function as: a unit configured to store flag information indicating a state where a dew condensation countermeasure is required in a non-volatile memory; a unit configured to restrict image formation by the image forming device and execute dew condensation countermeasure processing to cause the fan to perform predetermined driving; and a unit configured to delete, from the non-volatile memory, the flag information upon completion of the dew condensation countermeasure processing, wherein the controller executes the dew condensation countermeasure processing upon activation of the image forming apparatus based on at least storage of the flag information in the non-volatile memory.

Abstract: A learning classification device includes a data memory, a learning unit, and a classification unit. The data memory is configured to store training data for learning a decision tree. The learning unit is configured to read a plurality of feature quantities included in the training data from the data memory by single access and derive data of a node based on the plurality of feature quantities, to learn the decision tree. The classification unit is configured to determine where the training data read from the data memory is to be split to from the node, based on the data of the node derived by the learning unit.

Abstract: An information processing system, having one or more information processing apparatuses, includes a data input unit configured to take as input first data being multidimensional; a dimension reduction unit configured to generate, based on the first data, second data representing a characteristic of the first data, the second data having a prescribed number of dimensions fewer than a number of dimensions of the first data; and a distinguishing unit configured to distinguish whether the first data is normal data or abnormal data by a semi-supervised anomaly detection, based on the first data and the second data.

Abstract: An image capturing apparatus includes: an optical system that gives aberration to incident light; an image capturing unit that converts the light that has passed through the optical system into pixels, and captures an image; and an inverse transform unit that obtains a first inverse transform filter for restoring the aberration for each predetermined part of the captured image captured by the image capturing unit, and performs a first inverse transformation process on the captured image by the first inverse transform filter.

Abstract: An image processing apparatus is configured to process an image. The image processing apparatus includes: a calculator configured to calculate a parameter for each of a plurality of areas and for each of a plurality of models describing a plurality of body shapes, each of the plurality of models having the plurality of areas, and probabilities indicating to which of the plurality of models input images input for learning belong; and a comparator configured to compare an input inspection image with the plurality of models whose parameters are optimized based on the probabilities.

Abstract: An information processing device inspects a target image that contains an image of an inspection target. The information processing device includes a pre-processor, a first calculator, a second calculator, and a determiner. The pre-processor is configured to perform pre-processing for comparing the target image with a reference image or a plurality of reference images. The first calculator is configured to define, in the target image, a region of interest (ROI) and surrounding regions that are adjacent to the ROI, and calculate a feature value of the ROI. The second calculator is configured to calculate an outlier from comparison with feature values of images corresponding to the ROI and the surrounding regions in the reference images. The outlier numerically indicates singularity of an image at the ROI. The determiner is configured to provide, based on the outlier, an indicator to be used for the inspection.

Abstract: An information processing apparatus and method are disclosed, each of which: using a set of normal data, learns a first model for determining the normal data; sets, out of a plurality of abnormality candidate areas, the abnormality candidate areas selected by a user as correct data and the abnormality candidate areas not selected by the user as incorrect data, to learn a second model for identifying the correct data and the incorrect data, each abnormality candidate area indicating a candidate area of an abnormality and detected based on the first model from each of a plurality of captured images; obtains the captured images; detects the abnormality candidate areas from the respective captured images, using the first model; determines whether the abnormality candidate areas detected belong to the correct data or the incorrect data, using the second model; and controls to output a determination.

Abstract: An apparatus and a method are disclosed, each of which applies a plurality of different spatial filters to one input image to generate a plurality of filtered images; calculates, for each of a plurality of pixels included in each of the plurality of filtered image, a score indicating a value determined by a difference from a corresponding one of a plurality of model groups, using the plurality of model groups that respectively correspond to the plurality of filtered images and each including one or more models having a parameter representing a target shape; calculates an integrated score indicating a result of integrating the scores of the respective plurality of pixels corresponding to each other over the plurality of filtered images; and determines an abnormality based on the integrated score.

Abstract: A detection device includes a camera, a recognition unit, and a detection unit. The camera acquires image data obtained by capturing surroundings of a vehicle. The recognition unit recognizes switching of signal indication of a traffic signal from the image data. The detection unit detects a dangerous driving degree indicating a degree of dangerous driving done by a driver of the vehicle based on acceleration data of the vehicle acquired after the signal indication is switched.

Abstract: An object detection apparatus includes a light illumination unit that emits illuminating light; an imaging unit that captures a first image of distant photographic subjects by detecting lights from the photographic subjects, captures a second image of foreign matter attached to a monitoring area by detecting the illuminating light reflected from the foreign matter, and generates an image frame; a target detection unit that detects a detection target among the distant photographic subjects based on the first image; a foreign matter detection unit that detects the foreign matter based on the second image; and an exposure amount change unit that changes an exposure amount of the imaging unit. The target detection unit detects the detection target using plural imaging frames with different exposure amounts. The foreign matter detection unit detects the foreign matter using the imaging frame other than the imaging frame with the greatest exposure amount.

Abstract: A driving determination device includes an acquirer configured to acquire at least a captured image of a driving body in a driving direction and information that changes with movement of the driving body; a driving level calculator configured to calculate a driving level for evaluating a driving method for the driving body for each predetermined determination item, using at least one of the acquired captured image and the acquired information that changes with the movement of the driving body; an itemized calculator configured to calculate values based on a plurality of the calculated driving levels for each determination item; and an evaluation result calculator configured to calculate a value for comprehensively evaluating the driving method for the driving body, using the values based on the driving levels for each determination item.

Abstract: An image processing apparatus is configured to process an image. The image processing apparatus includes: a calculator configured to calculate a parameter for each of a plurality of areas and for each of a plurality of models describing a plurality of body shapes, each of the plurality of models having the plurality of areas, and probabilities indicating to which of the plurality of models input images input for learning belong; and a comparator configured to compare an input inspection image with the plurality of models whose parameters are optimized based on the probabilities.

Abstract: According to an embodiment, provided is an image processing system that includes: a light source that emits light; an imaging unit including an imaging element that captures light emitted and reflected from a foreign matter attached to the other surface of the transparent member and that captures light transmitted through the transparent member from the other surface side; and an image analyzing unit that analyzes captured image data. The captured image data is formed of a first image area frame used for detecting a foreign matter, and is formed of a second image area frame that corresponds to an effective imaging area excluding a predetermined area. Different rules are applied to read pixel data of between the first image area frame and the second image area frame.

Abstract: An image processing system includes a brightness polarization superimposing unit. The brightness polarization superimposing unit superimposes, on a brightness image, polarization information of an image IMP that includes the polarization information, as a change in brightness of each pixel. The image processing system has a function of outputting an image obtained by superimposition by the brightness polarization superimposing unit as an output image IMO.

Abstract: An information processing device inspects a target image that contains an image of an inspection target. The information processing device includes a pre-processor, a first calculator, a second calculator, and a determiner. The pre-processor is configured to perform pre-processing for comparing the target image with a reference image or a plurality of reference images. The first calculator is configured to define, in the target image, a region of interest (ROI) and surrounding regions that are adjacent to the ROI, and calculate a feature value of the ROI. The second calculator is configured to calculate an outlier from comparison with feature values of images corresponding to the ROI and the surrounding regions in the reference images. The outlier numerically indicates singularity of an image at the ROI. The determiner is configured to provide, based on the outlier, an indicator to be used for the inspection.

Abstract: A learning apparatus includes: a learning performing unit configured to learn parameters of a multilayer neural network with regularization; a determining unit configured to determine whether learning has progressed; and a changing unit configured to reduce effect of the regularization in response to the determining unit determining that the learning has progressed.

Abstract: An image processing apparatus includes a light source configured to emit light onto a glass; an image-capturing unit configured to capture light from an image-capturing region including reflection light of the emitted light from the glass; an object detection filter used to detect an object attached to the glass, light from a portion of the image-capturing region entering the object detection filter; an exposure control unit configured to determine a first exposure amount used in image capturing for a first region where the object detection filter does not exist and a second exposure amount used in image capturing for a second region where the object detection filter exists; and an image analysis unit configured to analyze a captured image obtained by the image-capturing unit. The image-capturing unit switches an exposure amount used in an image-capturing process for the image-capturing region between the first exposure amount and the second exposure amount.

Abstract: A learning method for a multilayer neural network, implemented by a computer, includes starting first learning with an initial value of a learning rate, and maintaining the learning rate at the initial value or reducing the learning rate from the initial value as the first learning progresses. The learning rate is increased after the first learning. Second learning is started with the increased learning rate, and the increased learning rate is reduced as the second learning progresses.

Abstract: An attached matter detector includes a light source configured to irradiate light toward a plate-like transparent member from one surface thereof, an imaging device configured to image reflection light by attached matter on a surface of the plate-like transparent member, a memory configured to previously store flare information obtained by the image device by turning on the light source in a state without having disturbance light and the attached matter on the surface of the plate-like transparent member, and a difference information-obtaining device configured to obtain difference information between a light-up image as an image obtained by the imaging device with the turned-on light source and a light-out image as an image obtained by the imaging device with the turned-off light source, wherein the attached matter on the surface of the plate-like transparent member is detected based on the difference information and the flare information stored in the memory.

Abstract: An object detection apparatus includes a light illumination unit that emits illuminating light; an imaging unit that captures a first image of distant photographic subjects by detecting lights from the photographic subjects, captures a second image of foreign matter attached to a monitoring area by detecting the illuminating light reflected from the foreign matter, and generates an image frame; a target detection unit that detects a detection target among the distant photographic subjects based on the first image; a foreign matter detection unit that detects the foreign matter based on the second image; and an exposure amount change unit that changes an exposure amount of the imaging unit. The target detection unit detects the detection target using plural imaging frames with different exposure amounts. The foreign matter detection unit detects the foreign matter using the imaging frame other than the imaging frame with the greatest exposure amount.