A processor-implemented method with liveness detection includes: receiving a plurality of phase images of different phases; generating a plurality of preprocessed phase images by performing preprocessing, including edge enhancement processing, on the plurality of phase images of different phases; generating a plurality of differential images based on the preprocessed phase images; generating a plurality of low-resolution differential images having lower resolutions than the differential images, based on the differential images; generating a minimum map image based on the low-resolution differential images; and performing a liveness detection on an object in the phase images based on the minimum map image.

A processor-implemented method with liveness detection includes: receiving a plurality of phase images of different phases; generating a plurality of preprocessed phase images by performing preprocessing, including edge enhancement processing, on the plurality of phase images of different phases; generating a plurality of differential images based on the preprocessed phase images; generating a plurality of low-resolution differential images having lower resolutions than the differential images, based on the differential images; generating a minimum map image based on the low-resolution differential images; and performing a liveness detection on an object in the phase images based on the minimum map image.

The technology disclosed relates to equalizer-based intensity correction for base calling. In particular, the technology disclosed relates to accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters, selecting a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio, applying the pixel coefficients to intensity values of the pixels in the image to produce an output, and base calling the target cluster based on the output.

An image processing apparatus for performing image quality processing on an image includes a feature extraction network and an image quality processing network including one or more modulation blocks, wherein each of the one or more modulation blocks includes a convolution layer, a modulation layer, and an activation layer for processing the image.

Aspects of the present disclosure relate to multi-spectrum visual object recognition. A first image corresponding to visible light and a second image corresponding to invisible light with respect to an object can be obtained. A first contour of the object can be identified based on the first image. A second contour of the object can be identified based on the second image. The first contour of the object and the second contour of the object can be integrated to generate a multi-spectrum contour of the object. The object can be recognized using the multi-spectrum contour of the object.

Aspects of the present disclosure relate to multi-spectrum visual object recognition. A first image corresponding to visible light and a second image corresponding to invisible light with respect to an object can be obtained. A first contour of the object can be identified based on the first image. A second contour of the object can be identified based on the second image. The first contour of the object and the second contour of the object can be integrated to generate a multi-spectrum contour of the object. The object can be recognized using the multi-spectrum contour of the object.

A method for optimizing an image classification model can include determining a first image classification model based on initial training data; in response to model optimization, determining a second image classification model based on the first image classification model and a noise data set; and obtaining a third image classification model by optimizing the second image classification model based on the initial training data, the third image classification model being configured to update the noise data set based on noise data generated within a predetermined time period and the noise data set.

A method for optimizing an image classification model can include determining a first image classification model based on initial training data; in response to model optimization, determining a second image classification model based on the first image classification model and a noise data set; and obtaining a third image classification model by optimizing the second image classification model based on the initial training data, the third image classification model being configured to update the noise data set based on noise data generated within a predetermined time period and the noise data set.

A method and system for tracking visual attention are disclosed. By generating at least one visual stimulus with a characteristic modulation, the characteristic modulation being applied to high spatial frequency, HSF, components of the visual stimulus and displaying the or each visual stimulus via a graphical user interface, GUI, of a display, a neural response may be induced in the user's brain. By receiving neural signals of a user from a neural signal capture device, such as an EEG device, a point of focus of the user (when the user views the visual stimulus) may be determined based on the neural signals, since the neural signals include information associated with the characteristic modulation of a visual stimulus to which the user's visual attention is directed.

The invention provides a technique capable of effectively and appropriately removing noise from various kinds of images including noise and artifacts and images in which a noise pattern changes due to a difference in imaging conditions. Based on a noise removal technique using AI, noise characteristics including artifacts are analyzed for each image, the image is classified based on an analysis result, an optimal neural network for a noise processing is applied for each classification, and the noise and the artifacts are reduced.