The disclosure generally relates to a system for creating color-customizable customized beverages (e.g., adding various color ingredients to a beverage to customize the color of the beverage). The system may obtain color mappings (e.g., manual color mappings) that map particular color ingredients to particular colors. The system can generate a three-dimensional color map based on the color mappings to identify additional color mappings. The system can receive a request for a particular beverage with a particular color, and based on the three-dimensional color map, the system can identify a color mapping for the particular color. The system can output an instruction set for the color mapping and/or can cause an automated dispensing platform to dispense one or more particular color ingredients for the beverage based on the color mapping.

Disclosed are an image processing method and apparatus, a computer device and a storage medium, which relate to the field of artificial Intelligence. The method includes receiving an original image; performing image photographing defect detection and color deviation detection on the original image, the image photographing defect detection determining whether there exists a photographing defect that is irreparable through image processing, the color deviation detection determining whether there exists color cast in the original image; performing color correction on the original image if the original image passes the image photographing defect detection and does not pass the color deviation detection; and generating a target image based on the color-corrected original image.

Disclosed are an image processing method and apparatus, a computer device and a storage medium, which relate to the field of artificial Intelligence. The method includes receiving an original image; performing image photographing defect detection and color deviation detection on the original image, the image photographing defect detection determining whether there exists a photographing defect that is irreparable through image processing, the color deviation detection determining whether there exists color cast in the original image; performing color correction on the original image if the original image passes the image photographing defect detection and does not pass the color deviation detection; and generating a target image based on the color-corrected original image.

A colon cleanliness indicating device including a forward toilet-secured receiving section configured with a recessed receptacle for receiving colonic effluent and a rearward indicating section for indicating a degree of colon cleanliness. The indicating section is formed with one or more channels in fluid communication with the receptacle through which the received colonic effluent is flowable and by which the received colonic effluent can be visualized in order to assess a degree of colon cleanliness.

A colon cleanliness indicating device including a forward toilet-secured receiving section configured with a recessed receptacle for receiving colonic effluent and a rearward indicating section for indicating a degree of colon cleanliness. The indicating section is formed with one or more channels in fluid communication with the receptacle through which the received colonic effluent is flowable and by which the received colonic effluent can be visualized in order to assess a degree of colon cleanliness.

In various embodiments, a training application generates a perceptual video model. The training application computes a first feature value for a first feature included in a feature vector based on a first color component associated with a first reconstructed training video. The training application also computes a second feature value for a second feature included in the feature vector based on a first brightness component associated with the first reconstructed training video. Subsequently, the training application performs one or more machine learning operations based on the first feature value, the second feature value, and a first subjective quality score for the first reconstructed training video to generate a trained perceptual quality model. The trained perceptual quality model maps a feature value vector for the feature vector to a perceptual quality score.

In various embodiments, a training application generates a perceptual video model. The training application computes a first feature value for a first feature included in a feature vector based on a first color component associated with a first reconstructed training video. The training application also computes a second feature value for a second feature included in the feature vector based on a first brightness component associated with the first reconstructed training video. Subsequently, the training application performs one or more machine learning operations based on the first feature value, the second feature value, and a first subjective quality score for the first reconstructed training video to generate a trained perceptual quality model. The trained perceptual quality model maps a feature value vector for the feature vector to a perceptual quality score.

An improved method for examining an article by using a vision system is presented. Also presented is a vision system for use within such a method.

An improved method for examining an article by using a vision system is presented. Also presented is a vision system for use within such a method.

Various client devices include displays and one or more image capture devices configured to capture video data. Different users of an online system may authorize client devices to exchange information captured by their respective image capture devices. Additionally, a client device modifies captured video data based on users identified in the video data. For example, the client device changes parameters of the image capture device to more prominently display a user identified in the video data and may further change parameters of the image capture device based on gestures or movement of the user identified in the video data. The client device may apply multiple models to captured video data to modify the captured video data or subsequent capturing of video data by the image capture device.