In a method for processing microscope images, at least one microscope image is provided as input image for an image processing algorithm. An output image is created from the input image by means of the image processing algorithm. The creation of the output image comprises adding low-frequency components for representing solidity of image structures of the input image to the input image, wherein the low-frequency components at least depend on high-frequency components of these image structures and wherein high-frequency components are defined by a higher spatial frequency than low-frequency components. A corresponding computer program and microscope system are likewise described.

A LIDAR noise removal apparatus and a LIDAR noise removal method thereof are provided. The apparatus includes a LIDAR detection information processor that processes LIDAR detection information received from a LIDAR of a vehicle. A sun position acquirer acquires an azimuth angle and elevation angle of the sun relative to a traveling direction of the vehicle. An ROI selector selects an ROI corresponding to the sun from a front image of the vehicle based on the azimuth angle and elevation angle and compares a brightness of the selected ROI with a threshold value. A noise region selector selects a noise region corresponding to the ROI from the LIDAR detection information based on the azimuth angle and elevation angle when the brightness of the ROI exceeds the threshold value, and a noise remover removes noise points in the selected noise region.

A LIDAR noise removal apparatus and a LIDAR noise removal method thereof are provided. The apparatus includes a LIDAR detection information processor that processes LIDAR detection information received from a LIDAR of a vehicle. A sun position acquirer acquires an azimuth angle and elevation angle of the sun relative to a traveling direction of the vehicle. An ROI selector selects an ROI corresponding to the sun from a front image of the vehicle based on the azimuth angle and elevation angle and compares a brightness of the selected ROI with a threshold value. A noise region selector selects a noise region corresponding to the ROI from the LIDAR detection information based on the azimuth angle and elevation angle when the brightness of the ROI exceeds the threshold value, and a noise remover removes noise points in the selected noise region.

A system for authenticating a user attempting to access a computing device or a software application executing thereon. A data storage device stores one or more digital images or frames of video of face(s) of authorized user(s) of the device. The system subsequently receives from a first video camera one or more digital images or frames of video of a face of the user attempting to access the device and compares the image of the face of the user attempting to access the device with the stored image of the face of the authorized user of the device. To ensure the received video of the face of the user attempting to access the device is a real-time video of that user, and not a forgery, the system further receives a first photoplethysmogram (PPG) obtained from a first body part (e.g., a face) of the user attempting to access the device, receives a second PPG obtained from a second body part (e.g., a fingertip) of the user attempting to access the device, and compares the first PPG with the second PPG. The system authenticates the user attempting to access the device based on a successful comparison of (e.g., correlation between, consistency of) the first PPG and the second PPG and based on a successful comparison of the image of the face of the user attempting to access the device with the stored image of the face of the authorized user of the device.

A system for authenticating a user attempting to access a computing device or a software application executing thereon. A data storage device stores one or more digital images or frames of video of face(s) of authorized user(s) of the device. The system subsequently receives from a first video camera one or more digital images or frames of video of a face of the user attempting to access the device and compares the image of the face of the user attempting to access the device with the stored image of the face of the authorized user of the device. To ensure the received video of the face of the user attempting to access the device is a real-time video of that user, and not a forgery, the system further receives a first photoplethysmogram (PPG) obtained from a first body part (e.g., a face) of the user attempting to access the device, receives a second PPG obtained from a second body part (e.g., a fingertip) of the user attempting to access the device, and compares the first PPG with the second PPG. The system authenticates the user attempting to access the device based on a successful comparison of (e.g., correlation between, consistency of) the first PPG and the second PPG and based on a successful comparison of the image of the face of the user attempting to access the device with the stored image of the face of the authorized user of the device.

In one embodiment, a method includes obtaining an image comprising a plurality of pixels, determining, for a particular pixel of the plurality of pixels, a gradient value, classifying, based on the gradient value, the particular pixel into a flat class or one of a plurality of edge classes, and denoising the particular pixel based on the classification.

A specimen processing system includes a plate for supporting a specimen system, wherein the specimen system includes a container and a specimen contained therein. The specimen processing system further includes a camera disposed above the plate and configured to generate images of the specimen system, a light source disposed beneath the plate for radiating light towards the plate, a light stop for blocking a portion of the light from reaching the specimen system to produce darkfield illumination of the specimen at the camera, and one or more processors electronically coupled to the camera and configured to track a position of the specimen within the specimen container during a specimen processing protocol based on the images.

By use of the characteristics that an analog-to-digital converter sends out data sequentially when it converts data of a two-dimensional analog image into pixel data, a fingerprint image detecting device and method generate digital output data having a plurality of rows of data, generate a plurality of one-dimensional datum segments linearly from the digital output data, and determine whether the two-dimensional analog image is a real fingerprint image according to the plurality of one-dimensional datum segments. Thus, the detection of a fingerprint image is implemented by means of one-dimensional calculation instead of two-dimensional calculation, thereby effectively reducing computational load and computational time.

In an apparatus, it is determined whether a covariance matrix calculated based on a plurality of patches is abnormal. In a case where it is determined that the covariance matrix is not abnormal, the covariance matrix is used to perform first correction on pixels included in the plurality of patches. In a case where it is determined that the covariance matrix is abnormal, second correction, which is different from the first correction, is performed on the pixels included in the plurality of patches.

In an apparatus, it is determined whether a covariance matrix calculated based on a plurality of patches is abnormal. In a case where it is determined that the covariance matrix is not abnormal, the covariance matrix is used to perform first correction on pixels included in the plurality of patches. In a case where it is determined that the covariance matrix is abnormal, second correction, which is different from the first correction, is performed on the pixels included in the plurality of patches.