Abstract: A method includes receiving, with a computing system, a video item. The method further includes identifying a first set of features within a first frame of the video item. The method further includes identifying, with the computing system, a second set of features within a second frame of the video item, the second frame being subsequent to the first frame. The method further includes determining, with the computing system, differences between the first set of features and the second set of features. The method further includes assigning a clip category to a clip extending between the first frame and the second frame based on the differences.

Abstract: Provided are an image processing apparatus and method. The image processing apparatus includes a decoder configured to decode image frames of an image; an image quality controller configured to obtain a genre recognition confidence of a previous image frame and a genre recognition confidence of a current image frame, and identify image quality control factor value, based on the genre recognition confidence of the previous image frame and the genre recognition confidence of the current image frame; and an image quality processor configured to image-quality process at least one of the decoded image frames by using the image quality control factor value, and output the processed at least one of the decoded image frames.

Abstract: An aerial imaging system is disclosed. The aerial imaging system includes an aerial vehicle; a camera system mounted on the aerial vehicle; and an image storage medium. The camera system includes a lens and a plurality of image sensors. The plurality of image sensors includes a reference image sensor and a plurality of fixed wavelength sensors. The reference image sensor is configured to capture images including light at a plurality of wavelength ranges and to output a multilayer image. The plurality of fixed wavelength sensors are each configured to capture images including light at a selected wavelength range and to each output a single layer image. The image storage medium is configured to store the multilayer image and the plurality of single layer images.

Abstract: A segmentation platform is interposed between an intake server and remote user-accessible computers. The segmentation platform includes a model training computer processor configured to generate a predictive model for determining a segment corresponding to received data sets based upon claim segment rules and historical data sets stored in data storage devices. The segmentation platform may be configured to dynamically update workload data corresponding to a plurality of users, and responsive to that updating and based upon application of the predictive model, segment the received current data sets and transmit electronic claim files to respective remote user-accessible computing devices. The predictive model is continually adapted through activation of the model training component to update the predictive model with new current claim data sets.

Abstract: System and method for controlling a machine, including: receiving a first image processing model trained to classify an input image into a first class for images containing at least one object of a first type or a second class for images not containing an object of the first type; identifying a subset of inference results that are false positive results; generating a set of new training data from the first set of images, including augmenting an image in the first set of images to obtain a respective plurality of images and labeling the respective plurality of images as containing at least one object of a pseudo first class; training a second image processing model to classify an input image into the first class, the second class, and the first pseudo class; and modifying a device setting of a machine based on an inference result of the second image processing model.

Abstract: Embodiments provide computer systems, computer-executable methods and one or more non-transitory computer-readable media for offering one or more promotions to consumers using a promotion and marketing service. User input may be received from a first consumer interface associated with a first consumer, the user input including an interest indication relating to a first promotion. An association between the first consumer and a second consumer for sharing of promotions may be programmatically retrieved or generated. Based on the association, it may be determined whether to offer the first promotion to the second consumer based on one or more characteristics associated with the second consumer. Based on a determination that the first promotion should be offered to the second consumer, an indication may be outputted, the indication configured to cause an impression of the first promotion to be generated on a second consumer interface associated with the second consumer.

Abstract: A method of storing blockchain state data, including: after execution of a transaction in a target block is completed, based on a latest account state of a target account related to the transaction in the target block after update, generating updated data nodes corresponding to the current Merkle state tree of the target block and, based on generated historical data nodes and data nodes other than the reused data nodes corresponding to the target account on the historical Merkle state tree of the previous block of the target block, creating the historical Merkle state tree for the target block.

Abstract: An apparatus and method for training a classification model and an apparatus for classifying with a classification model are disclosed. The apparatus for training a classification model comprises: a local area obtainment unit to, obtain predetermined local area of each sample image; a feature extraction unit to, with respect to each sample image, set corresponding numbers of feature extraction layers for the global area and each predetermined local area, to extract a global feature of the global area and a local feature of each predetermined local area, wherein the global area and the predetermined local areas share at least one feature extraction layer in which the global feature and each local feature are combined; and a loss determination unit to calculate a loss function of the sample image based on combined features of each sample image, and to train the classification model based on the loss function.

Abstract: A method for creating point cloud representations for a point cloud generated based on images, wherein the method comprises receiving a point cloud generated based on images, retrieving, from a user, selection criterion information, wherein the selection criterion information is related to the quality information comprised by the points of the point cloud, and filtering the generated point cloud and creating a filtered-point-cloud representation to be displayed while leaving volume and substance of the generated point cloud unchanged, wherein for each point of the point cloud its quality information is compared with the retrieved selection criterion information and only those points of the point cloud are used for the creation of the filtered-point-cloud representation to be displayed, which have a quality information fulfilling the retrieved selection criterion information, and displaying the filtered-point-cloud representation to the user.

Abstract: A system and associated processes may generate new modalities by receiving content associated with an original modality. An appropriateness of an alternative modality may be determined based on at least one of a device available to present the content, a user profile, and a contextual factor regarding an environment of a user intended to receive the content. Source content semantics associated with the content may be automatically determined, and an alternative modality based on the determined source content semantics may be generated and used to present the content to the user.

Abstract: A system and method for proximate vehicle intention prediction for autonomous vehicles are disclosed. A particular embodiment is configured to: receive perception data associated with a host vehicle; extract features from the perception data to detect a proximate vehicle in the vicinity of the host vehicle; generate a trajectory of the detected proximate vehicle based on the perception data; use a trained intention prediction model to generate a predicted intention of the detected proximate vehicle based on the perception data and the trajectory of the detected proximate vehicle; use the predicted intention of the detected proximate vehicle to generate a predicted trajectory of the detected proximate vehicle; and output the predicted intention and predicted trajectory for the detected proximate vehicle to another subsystem.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement defect solutions. In one aspect, a method includes receiving a log file and testing results generated from a code base for an application; processing the log file through a pattern-mining algorithm to determine a usage pattern of code modules within the code base; clustering defects from the testing results based on a respective functionality of the application reported within each of the defects; generating testing prioritizations for test cases for the application by assigning weightages to the test cases based on the clusters of defects and the usage pattern of the code modules within the code base; sequencing a set of the test cases based on the test prioritizations; and transmitting the sequence to a test execution engine.

Abstract: A system and method of selecting a frame capturing a short duration action from a video. The method including receiving a sequence of frames of the video; and determining a reference frame in the sequence of frames by detecting a predetermined action captured in one of a first plurality of frames from the sequence of video frames, the first plurality of frames being sampled from the sequence of frames at a first sampling rate lower than that of the sequence of frames. The method further comprises selecting a second plurality of frames from the sequence of frames, the second plurality of frames having a location associated with the reference frame in the sequence and being selected at a second sampling rate, the second sampling rate being higher than the first sampling rate; and selecting the frame capturing the short duration action from the second plurality of frames.

Abstract: A location of an object of interest (205) is determined using both observations and non-observations. Numerous images (341-345) are stored in a database in association with image capture information, including an image capture location (221-225). Image recognition is used to determine which of the images include the object of interest (205) and which of the images do not include the object of interest. For each of multiple candidate locations (455) within an area of the captured images, a likelihood value of the object of interest existing at the candidate location is calculated using the image capture information for images determined to include the object of interest and using the image capture information for images determined not to include the object of interest. The location of the object is determined using the likelihood values for the multiple candidate locations.

Abstract: Disclosed herein are systems and methods for blocking network connections. In one aspect, an exemplary method comprises, intercepting a certificate from the server when establishing a protected connection between a server and a client, determining whether the intercepted certificate is similar to one or more forbidden certificates, the determination of whether the intercepted certificate is similar to one or more forbidden certificates comprising transforming the intercepted certificate in accordance with a method of determining similarities between certificates and a method of saving forbidden certificates in a database of forbidden certificates, and blocking the connection when the intercepted certificate is similar to the one or more forbidden certificates.

Abstract: An article type adjustment approximation value for each physical article type of a plurality of physical article types of limited quantities is predetermined. A determination is made to assign to a selected client among a set of clients, a set of physical article types among the plurality of physical article types. A group of eligible physical article types for the selected client among the plurality of physical article types is identified. Using one or more machine learning prediction models, a desirability prediction value for each physical article type in the group of eligible physical article types is determined. The desirability prediction values are adjusted using the corresponding predetermined article type adjustment approximation value and a corresponding predetermined client adjustment approximation value to determine corresponding adjusted prediction values. The corresponding adjusted prediction values are used to determine the set of physical article types to be assigned to the selected client.

Abstract: A system for managing an unmanned aerial vehicle (UAV) include one or more storage media storing offline data that comprises verified information associated with a user, an input device configured to receive an input from the user, and one or more processors, individually or collectively configured to determine whether a connection to an online database is established and, if the connection to the database is not established, process the input and the offline data; and manage a flight of the UAV according to the processing of the input and the offline data.

Abstract: A control circuit access a source image that includes a source face and an image to be modified that includes a face to be modified. The control circuit then employs a trained identity encoder to transform at least a part of the source image into a corresponding identity vector and then also employs an attribute encoder to form an attribute feature vector that represents characterizing information for a plurality of features of the image to be modified. The control circuit then employs an image generator to decode the attribute feature vector to obtain the characterizing information for the plurality of features of the image to be modified and to then modify the identity vector as a function of the characterizing information for the plurality of features of the image to be modified to provide a resultant image that includes a resultant face that is essentially the face of the image to be modified but with facial features that emulate corresponding facial features of the source face.

Abstract: An electronic device and a method for setting image quality are provided. An electronic device includes a camera, a memory, and a processor configured to obtain, using the camera, a plurality of images for one or more external objects, identify a region of interest and a background region using one or more images of the plurality of images, recognize a first object included in the region of the interest, identify a type of the background region by recognizing a second object included in the background region, determine a scene corresponding to the one or more images, based on the recognized first object and the identified type of the background region, and adjust at least one of an image quality setting associated with the camera or an image quality setting associated with the one or more images using a specified image quality setting corresponding to the determined scene.

Abstract: A method of training an image classification model includes obtaining training images associated with labels, where two or more labels of the labels are associated with each of the training images and where each label of the two or more labels corresponds to an image classification class. The method further includes classifying training images into one or more classes using a deep convolutional neural network, and comparing the classification of the training images against labels associated with the training images. The method also includes updating parameters of the deep convolutional neural network based on the comparison of the classification of the training images against the labels associated with the training images.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems provide user interfaces that allow users to review search results, pinpoint relevant portions of media content items where the identified objects are determined to be present, review detector performance and retrain detectors, providing search result feedback, and/or reviewing video monitoring results and analytics.

Abstract: To have convenient and highly precise prediction of the shape of a human body after a treatment by calculation processing that includes extracting a feature vector Fnew from face data of a patient as an evaluation subject, selecting a plurality of case patients having feature vectors Fpre(i), extracted from the face data of a plurality of previous patients, obtaining pre-orthodontic facial shape models Hpre(i) and a post-orthodontic facial shape models Hpost(i) in which the faces of the selected previous case patients before and after a treatment have been normalized, obtaining a facial shape model Hnew, and obtaining a three-dimensional predicted facial shape model Hprd, by modifying the facial shape model Hnew of the patient as an evaluation subject, using a vector average difference AVEpost?AVEpre between the pre-treatment and post-treatment facial shape models.

Abstract: A method for detecting a target object in an image is described. The method includes obtaining an image to be detected, determining a first layer of an image pyramid as a current layer, building an image feature data set of the current layer according to the image to be detected, and detecting the target object therefrom. In response to detecting the target object, the method includes stopping the detection and outputting the detected target object; in response to not detecting the target object and the number of layers of the image pyramid not reaching a threshold for the number of layers, determining a next layer of the current layer as the current layer and returning to the building the image feature data set of the current layer according to the image to be detected and detecting the target object therefrom.

Abstract: A video image encoding apparatus includes an in-picture motion vector calculation unit and a vector encoding unit. The in-picture motion vector calculation unit calculates an in-picture motion vector indicating a spatial movement amount between a block to be encoded and a reference block in an encoded region in the picture to be encoded. The vector encoding unit obtains a prediction vector for predicting the in-picture motion vector of the block to be encoded based on at least one of a first in-picture motion vector calculated in a first reference available region including a block encoded before the block to be encoded is encoded and a second in-picture motion vector calculated in a second reference available region including a block in a position corresponding to the block to be encoded in an already encoded reference picture, and performs prediction encoding on the in-picture motion vector based on the prediction vector.

Abstract: A human face detection method, falling within the technical field of image detection. The method comprises: respectively determining a plurality of pieces of human face characteristic information in a plurality of pre-input human face training samples, and training and forming a characteristic prediction model according to all the pieces of human face characteristic information in each of the human face training samples.

Abstract: Filtering a data set including a plurality of image frames to form a reduced “key frame” data set including a reduced plurality of “key” image frames that is suitable for use in training an artificial intelligence (AI) or machine learning (ML) system, including: removing an image frame from the plurality of image frames of the data set if a structural similarity metric of the image frame with respect to another image frame exceeds a predetermined threshold, thereby forming a reduced data set including a reduced plurality of image frames; and analyzing an object/semantic content of each of the reduced plurality of images using a plurality of dissimilar expert models and designating any image frames for which the plurality of expert models disagree related to the object/semantic content as “key” image frames, thereby forming the reduced “key frame” data set including the reduced plurality of “key” image frames.

Abstract: The present disclosure relates to an object selection system that automatically detects and selects objects in a digital image utilizing a large-scale object detector. For instance, in response to receiving a request to automatically select a query object with an unknown object class in a digital image, the object selection system can utilize a large-scale object detector to detect potential objects in the image, filter out one or more potential objects, and label the remaining potential objects in the image to detect the query object. In some implementations, the large-scale object detector utilizes a region proposal model, a concept mask model, and an auto tagging model to automatically detect objects in the digital image.

Abstract: Disclosed is a method for authenticating and/or checking the integrity of a subject, including:—visually superimposing, optically or electronically:—at least one so-called authentication image of at least one authentication region of an authentic subject, the authentication image including at least one texture of random composition,—and, also, the authentication region per se of a candidate subject or at least one verification image of the authentication region of the candidate subject,—in the event of observing, on the image resulting from the superimposition, a Glass pattern optical phenomenon in the authentication region, concluding that the candidate subject is the authentic subject and/or the at least partial integrity of the authentication region of the authentic subject.

Abstract: [Problem] To provide an action-estimating device which is capable of estimating an action of a subject appearing in a plurality of time-series images with high precision. [Solution] Provided is an action-estimating device 1 comprising: an estimating-side obtaining unit 12 for obtaining a plurality of time-series images Y in which a subject Z appears; an estimating-side detecting unit 13 for detecting a plurality of articulations A appearing in each time-series image Y; an estimating-side measuring unit 14 for measuring coordinates of the detected plurality of articulations A in each time-series image Y; an estimating unit 15 for estimating an action of the subject Z based on displacement of the coordinates of the measured plurality of articulations A in the plurality of time-series images Y; and a storing unit 3 for storing a plurality of choices of the action to be estimated. The estimating-side detecting unit 13 further detects a background appeared in each time-series image Y.

Abstract: An autonomous vehicle is described herein. The autonomous vehicle includes several different types of sensor systems, such as image, lidar, and radar. The autonomous vehicle additionally includes a computing system that executes several different object classifier modules, wherein the object classifier modules are configured to identify types of objects that are in proximity to the autonomous vehicle based upon outputs of the sensor systems. The computing system additionally executes a Bayesian object classifier system that is configured to receive outputs of the object classifier modules and assign labels to objects captured in sensor signals based upon the outputs of the object classifier modules.

Abstract: A computer vision neural network system is provided. The computer vision neural network system includes a computer with a multi-attribute loss module that concatenates first and second normalized feature vector descriptors to generate a master feature vector descriptor that describes attributes and classes of a facial image. The multi-attribute loss module estimates an error distance between the master feature vector descriptor and a plurality of class center vector descriptors. Each class center vector descriptor is a mean of a plurality of master feature vector descriptors associated with a soft-biometric class in a plurality of faces in a plurality of facial images.

Abstract: A method for managing data includes obtaining, by a virtual machine (VM) executing on a computing node, a data computation request from a client, and in response to the data computation request: sending a metadata request to a catalog node based on the computation request, obtaining a plurality of data set entries from the catalog node based on the metadata request, performing a confidence score analysis on the plurality of data set entries to obtain selected data set entries, initiating, on a local data system, a data computation using a local data set associated with at least one of the selected of data set entries, obtaining, in response to the initiating, a result from the local data system, and sending a data computation result to the client based on the data computation, wherein the data computation result is based on the result.

Abstract: A method implemented by a computer to output digital ink includes: repeatedly generating a stroke object each time a pointer is removed from a panel, where the stroke object includes control points used to reproduce a stroke made by the pointer on the panel, the stroke starting when the pointer contacts the panel and ending when the pointer is removed from the panel; each time the stroke object is generated, determining a generation time of the stroke object and generating metadata including the generation time of the stroke object; and serializing one piece of ink data, in which the stroke object is associated with the corresponding metadata, into a binary sequence, and outputting the binary sequence.

Abstract: This application relates to microphone authentication apparatus for verifying whether or not an audio signal originated at a microphone (101, 102). The microphone authentication apparatus (300) has a comparison block (301) configured to receive a first signal (A*) indicative of one or more spectral parameters of at least part of an audio signal to be verified, and compare the one or more spectral parameters to one or more predetermined characteristic microphone parameters relating to a characteristic resonance associated with an acoustic port of a microphone. The first signal (A*) may be an audio signal (A) and the microphone authentication apparatus may have a feature extract module (303) for determining the spectral parameters. Based on the comparison determination block (304) may whether the audio signal originated from a microphone and may send a verification signal (VMIC) to a voice biometric module 111.

Abstract: A method and system for profiling interests of users based on multimedia content analysis and creating users' profiles respective thereof is provided. The method comprises receiving a tracking information gathered with respect to an interaction of a user with at least one multimedia element displayed on a user node; determining a user impression respective of at least one multimedia content element using the received tracking information; generating at least one signature for the at least one multimedia element; determining at least a concept of the at least one multimedia element using the at least one generated signature, wherein an interest of the user is determined respective of the concept; creating a user profile to include at least the user interest; and storing the user profile in a data warehouse.

Abstract: Aspects relate to digital imaging. An example method includes receiving a first image frame of a scene captured by a first image sensor. The method also includes receiving a second image frame of the scene captured by a second image sensor. The second image frame is captured concurrently with capture of the first image frame. The method also includes receiving a third image frame of the scene captured by a third image sensor. The third image frame is captured concurrently with capture of the first image frame. The method also includes generating a combined image from the first image frame, the second image frame, and the third image frame. The combined image includes a first field of view that is wider than a field of view of the first image frame.

Abstract: An object authentication device includes a speech recognition unit configured to obtain candidates for a speech recognition result for an input speech and a likelihood of the speech as a speech likelihood and an image model generation unit configured to obtain image models of a predetermined number of candidates for the speech recognition result in descending order of speech likelihoods, wherein the image model generation unit initially performs retrieval from an image model database storing the image models when the image models for the candidates for the speech recognition result are generated and generates an image model from information acquired from a network if the image model is not stored in the image model database.

Abstract: The present invention is a system and method for providing an online game implemented by means of a computer system. The method includes the step of storing a plurality of images in at least one computing device. The method then includes the step of obtaining content data from the plurality of images stored in the computing device through a content extraction module. The method includes the step of facilitating the users to initiate a challenge for each other to create a game session over a game networking system through a challenge creation module. The game session includes a friend game session and a date game session. The method includes the step of initiating the game session created by the challenge creation module over the game networking system through a gaming module.

Abstract: A method for tag-based search includes capturing an image, extracting a tag from the image, identifying a location associated with the captured image, and querying stored content for information that matches the location and the tag. Local storage is checked for the information first, and remote storage may be checked subsequently. Any located information may be used to augment the image. Information located in the remote storage may be saved in the local storage until it reaches a certain age, until it fails to be accessed for a threshold period of time, or until the location moves outside a threshold radius associated with a location of the information located in the remote storage.

Abstract: A server device may receive a series of at least two screen capture representations of a graphical user interface on a client device. A first active window for a first screen capture representation of the series and a second active window for a second screen capture representation of the series may be determined. The first screen capture representation may have been screen captured by the client device at a first time and the second screen capture representation may have been screen captured by the client device at a second time. A first application associated with the first active window and a second application associated with the second active window may be identified, at least one of which may be a pre-determined target application. Based on the identified applications, an activity log for the client device may be determined.

Abstract: Font recognition and similarity determination techniques and systems are described. In a first example, localization techniques are described to train a model using machine learning (e.g., a convolutional neural network) using training images. The model is then used to localize text in a subsequently received image, and may do so automatically and without user intervention, e.g., without specifying any of the edges of a bounding box. In a second example, a deep neural network is directly learned as an embedding function of a model that is usable to determine font similarity. In a third example, techniques are described that leverage attributes described in metadata associated with fonts as part of font recognition and similarity determinations.

Abstract: A web scaping system configured with artificial intelligence and image object detection. The system processes a web page with a neural network to perform object detection to obtain structured data, including text, image and other kinds of data, from web pages. The neural network allows the system to efficiently process visual information (including screenshots), text content and HTML structure to achieve good quality and decrease extraction time.

Abstract: The present disclosure provides a method, an apparatus and a device for detecting a fatigue state. In this method, by receiving an initial image sent by a 3D sensor, where the initial image includes depth information containing an eye contour, extracting eye state information from the initial image, and determining whether a user is currently in the fatigue state according to the eye state information, detecting whether the user is in the fatigue state through the eye state information is thereby achieved, with high detection efficiency and more accurate detection results.

Abstract: Image analysis includes obtaining, from an image signal processor, image processing information corresponding to a previously processed image, obtaining scene classification information for an input image based on the image processing information, generating a processed image by processing the input image based on the scene classification information, and outputting the processed image. The image processing information includes automatic white balance correction information and obtaining the scene classification information includes obtaining the scene classification information based on the automatic white balance correction information.

Abstract: An image generating method and an electronic apparatus are provided. The method includes: determining whether at least one object in a plurality of first images to be captured will generate a blur to generate a determination result; determining a first amount of at least one second image in the plurality of first images and a second amount of at least one third image in the plurality of first images according to the determination result, wherein a first setting for capturing the second image is different from a second setting for capturing the third image; capturing the second image according to the first setting; capturing the third image according to the second setting; using the first amount of the second image and the second amount of the third image to perform an image superposition operation to generate an output image; and outputting the output image.

Abstract: An information processing apparatus includes a processor; and a memory storing a program which, when executed by the processor, causes the information processing apparatus to: store a plurality of combinations of associated subject information and layout information in a storage unit, the subject information relating to a subject appearing on an image and the layout information indicating a layout in which the image is to be arranged; perform image analysis on a target image and obtain the subject information relating to the subject appearing on the target image according to a result of the image analysis; select layout information that corresponds to the obtained subject information in relation to the target image from the plurality of layout information stored in the storage unit, and arrange the target image according to the selected layout information, wherein the subject information includes a type of the subject appearing on the image.

Abstract: An apparatus for recognizing a license plate of a vehicle, the apparatus having an interface for reading-in an image of a surrounding of an optical sensor, the image originating from the optical sensor picturing at least the vehicle having a detectable license plate. The apparatus further comprises a unit for analyzing the image using a convolutional neural network, the convolutional neural network having at least two separated symbol identification branches, each of the separated symbol identification branches being configured for identifying one of several symbols of the license plate and/or the convolutional neural network having a country identification branch being separated from the symbol identification branches, the country identification branch being configured for identifying the country having issued the license plate.

Abstract: A clothes and accessories fitting method, a display system and a computer-readable recording medium thereof are provided, where the method is applicable to a display system having an image capturing device and a screen and includes the following steps. Images of a user wearing a first apparel appearing in front of the screen are continuously or intermittently captured by using the image capturing device. When a first image of the user in a particular pose is determined to be included in the images, the first image is shot by using the image capturing device. A foreground region and a background region in the first image are segmented, and the background region is processed to generate a processed background region. A first preview image is generated according to the foreground region and the processed background region and displayed on the screen.

Abstract: An apparatus includes a capture device and a processor. The capture device may be configured to generate a plurality of video frames corresponding to an interior view of a vehicle. The processor may be configured to perform operations to detect objects in the video frames, detect occupants of the vehicle and seats of the vehicle based on the objects detected in the video frames, determine an age of the occupants based on characteristics of the occupants and select a reaction if the age of the occupant is below a threshold for the seat. The threshold may be based on a location of the seat within the vehicle. The characteristics may be determined by performing the operations on each of the occupants.

Abstract: A predetermined image correction process is automatically performed on an image, and information for identifying that the predetermined image correction process has been performed is displayed in a state that an image having undergone the predetermined image correction process is being displayed.