Abstract:
An image processing device includes a processor; and a memory which stores a plurality of instructions, which when executed by the processor, cause the processor to execute: obtaining a first image in which a user is included, a second image which is imaged in an imaging condition different from that of the first image, a third image which is continuously imaged at a different point of time from that of the first image, and in the same imaging condition as that of the first image, and a fourth image which is continuously imaged at a different point of time from that of the second image, and in the same imaging condition as that of the second image; extracting a first feature amount of a user which is included in the first image, a second feature amount of the user which is included in the second image, a third feature amount.
Abstract:
A user detecting apparatus includes: a memory that stores a program including a procedure; and a processor that executed the program, the procedure including: obtaining an image captured by a camera, switching between a first mode in which a first user-associated area, which is associated with a user, is detected from the image according to a similarity between a color histogram of the image and a reference color histogram and a second mode in which a second user-associated area, which is associated with the user, is detected from the image according to a feature point extracted from the image, according to luminance of the image, and detecting, from the image, the first user-associated area in the first mode or the second user-associated area in the second mode.
Abstract:
In an example, a method may include obtaining, from a data source, first data including multiple frames each including a human face. The method may include automatically detecting, in each of the multiple frames, one or more facial landmarks and one or more action units (AUs) associated with the human face. The method may also include automatically generating one or more semantic masks based at least on the one or more facial landmarks, the one or more semantic masks individually corresponding to the human face. The method may further include obtaining a facial hyperspace using at least the first data, the one or more AUs, and the semantic masks. The method may also include generating a synthetic image of the human face using a first frame of the multiple frames and one or more AU intensities individually associated with the one or more AUs.
Abstract:
A recording medium on which a user assistance program is recorded which makes a computer perform: based on image information obtained by photographing a plurality of users who use a given service, calculating state quantities of the plurality of respective users corresponding to the image information; counting, for each time period, a number of users whose amounts of change in the calculated respective state quantities are equal to or more than a given threshold value among the plurality of users; and detecting a time period in which the counted number of users satisfies a given condition.
Abstract:
A method of extracting a region in a distance image including pixels, the method includes: for each of adjacent pixel pairs in the distance image, generating a third pixel group that includes a first pixel group to which a first pixel belongs and a second pixel group to which a second pixel belongs based on a difference between pixel values of the first pixel and the second pixel included in the adjacent pixel pair; dividing the distance image into regions by determining whether to generate a third region represented by the third pixel group by merging a first region represented by the first pixel group and a second region represented by the second pixel group, based on a positional relationship of points represented by pixels included in the third pixel group; and selecting a region that satisfies a predetermined condition from among the regions.
Abstract:
An image processing device includes, a processor; and a memory which stores a plurality of instructions, which when executed by the processor, cause the processor to execute, acquiring an image including a first region of a user; extracting a color feature quantity or an intensity gradient feature quantity from the image; detecting the first region based on the color feature quantity or the intensity gradient feature quantity; and selecting whether the detecting is detecting the first region using either the color feature quantity or the intensity gradient feature quantity, based on first information related to the speed of movement of the first region calculated from a comparison of the first regions in a plurality of images acquired at different times.
Abstract:
Operations include extracting a depiction of a person and associated movement of the person from a first video clip of a first training video included in a first domain dataset. The operations further include superimposing the depiction of the person and corresponding movement into a second video clip of a second training video included in a second domain dataset to generate a third video clip. The operations also include annotating the third video clip to indicate that the movement of the person corresponds to a particular type of behavior, the annotating being based on the first video clip also being annotated to indicate that the movement of the person corresponds to the particular type of behavior. Moreover, the operations include training a machine learning model to identify the particular type of behavior using the second training video having the annotated third video clip included therewith.
Abstract:
A method may include obtaining a facial image of a subject and identifying a number of new images to be synthesized with target AU combinations and categories of intensity. The method may also include synthesizing the number of new images using the facial image of the subject as the base image with the number of target AU combinations and categories of intensity with a number of new images that have different AU combinations than the facial image of the subject. The method may additionally include adding the number of new images to a dataset and training a machine learning system using the dataset to identify a facial expression of the subject.
Abstract:
A method may include obtaining a base facial image, and obtaining a first set of base facial features within the base facial image, the first set of base facial features associated with a first facial AU to be detected in an analysis facial image. The method may also include obtaining a second set of base facial features within the base facial image, the second set of facial features associated with a second facial AU to be detected. The method may include obtaining the analysis facial image, and applying a first image normalization to the analysis facial image using the first set of base facial features to facilitate prediction of a probability of the first facial AU. The method may include applying a second image normalization to the analysis facial image using the second set of base facial features to facilitate prediction of a probability of the second facial AU.
Abstract:
A display control method includes: based on information acquired from an information processing terminal that accesses content provided by an information processing device, computing a degree of interest and a degree of perplexity, with respect to the content, of a user using the information processing terminal; and, based on the computed degree of interest and the computed degree of perplexity, displaying a symbol corresponding to the information processing terminal at a corresponding position in a region that has degree of interest and degree of perplexity as axes.