A method for non-destructive ripeness identification of kiwifruit based on machine vision learning may include: collecting kiwifruit data to obtain an original data set by collecting images of 40-80 kiwifruits in the same period of time over 3-6 days, recording a label, which comprises ripeness information for each of the images, and saving each of the images with the label; extracting the color and the texture of a kiwifruit skin from each of the images in the original data set; and training a deep learning model to learn a connection between the color and the texture of the kiwifruit skin and the ripeness information of the corresponding kiwifruit using the color and the texture of the kiwifruit skin extracted from each of the images and the label.

A computer implemented authentication method comprising the following steps: determining the spatial position of the user's face using the image obtained by the device's camera; determining a circumference circumscribed around the user's face and displaying it in the user interface; determining the horizontal and vertical lines passing through the center of the circumference characterizing the turn of the user's face; performing at least two rotation user checks comprising the following steps: determining a point on the circumference circumscribed around the user's face and displaying it in the user interface; prompting the user to change the position of the face so the line intersection point is aligned with the set point; obtaining an image of the user's face during the check; determining the correlation between the model generated on the basis of the user's face images during the rotation checks and the previously generated authorized user parameter-based model.

A computer implemented authentication method comprising the following steps: determining the spatial position of the user's face using the image obtained by the device's camera; determining a circumference circumscribed around the user's face and displaying it in the user interface; determining the horizontal and vertical lines passing through the center of the circumference characterizing the turn of the user's face; performing at least two rotation user checks comprising the following steps: determining a point on the circumference circumscribed around the user's face and displaying it in the user interface; prompting the user to change the position of the face so the line intersection point is aligned with the set point; obtaining an image of the user's face during the check; determining the correlation between the model generated on the basis of the user's face images during the rotation checks and the previously generated authorized user parameter-based model.

Systems and methods for analyzing image data to assess property damage are disclosed. According to certain aspects, a server may analyze segmented digital image data of a roof of a property using a convolutional neural network (CNN). The server may extract a set of features from a set of regions output by the CNN. Additionally, the server may analyze the set of features using an additional image model to generate a set of outputs indicative of a confidence level that actual hail damage is depicted in the set of regions.

Systems and methods for analyzing image data to assess property damage are disclosed. According to certain aspects, a server may analyze segmented digital image data of a roof of a property using a convolutional neural network (CNN). The server may extract a set of features from a set of regions output by the CNN. Additionally, the server may analyze the set of features using an additional image model to generate a set of outputs indicative of a confidence level that actual hail damage is depicted in the set of regions.

The present invention relates to an image analysis method for providing information for supporting illness development prediction regarding a neoplasm in a human or animal body. The method includes receiving for the neoplasm first and second image data at a first and second moment in time, and deriving for a plurality of image features a first and a second image feature parameter value from the first and second image data. These feature parameter values being a quantitative representation of a respective image feature. Further, calculating an image feature difference value by calculating a difference between the first and second image feature parameter value, and based on a prediction model deriving a predictive value associated with the neoplasm for supporting treatment thereof. The prediction model includes a plurality of multiplier values associated with image features. For calculating the predictive value the method includes multiplying each image feature difference value with its associated multiplier value and combining the multiplied image feature difference values.

A product defect detection method, device and system are disclosed. The product defect detection method comprises: constructing a defect detection framework including a classification network, a localization and detection network, and a judgment network, and setting a quantity of the localization and detection network and judgment rules of the judgment network according to classification results of the classification network, wherein each localization and detection network is associated with a classification result, and each judgment rule is associated with a detection result of the localization and detection network; when performing product defect detection, inputting a product image acquired into the defect detection framework, using the classification network to classify defect types in the product image, detecting defects of the product image according to a localization and detection network associated with a classification result, then judging whether the product has a defect, and detecting a defect type and a defect position.

A product defect detection method, device and system are disclosed. The product defect detection method comprises: constructing a defect detection framework including a classification network, a localization and detection network, and a judgment network, and setting a quantity of the localization and detection network and judgment rules of the judgment network according to classification results of the classification network, wherein each localization and detection network is associated with a classification result, and each judgment rule is associated with a detection result of the localization and detection network; when performing product defect detection, inputting a product image acquired into the defect detection framework, using the classification network to classify defect types in the product image, detecting defects of the product image according to a localization and detection network associated with a classification result, then judging whether the product has a defect, and detecting a defect type and a defect position.

The present invention provides a new method and a device for the contactless human identification using biometrics images. The present invention develops a robust feature extraction algorithm to recover three-dimensional (3D) shape information from biometrics images. Further, it provides significantly improved performance than what is possible from the state-of-art methods, adding practicality for real applications on mobile platform, smartphones, and also as add-on system for conventional fingerprint system. The present invention's unique advantages are based on its computational simplicity, efficient matching and requiring least storage. Experiments were conducted to confirm very high accuracy and reliability on a number of biometric modalities including iris, palmprint, and finger knuckle images.

A method and an apparatus for obtaining information are provided. The method may include: obtaining at least one image feature from a to-be-inspected image, where the to-be-inspected image includes an image of a to-be-inspected item, and the image feature is used to represent surface feature information of the to-be-inspected item; and importing the to-be-inspected image and the at least one image feature into a pre-trained defect detection model to obtain defect information corresponding to the to-be-inspected item, where the defect detection model is obtained by training using a sample image, a sample image feature and sample defect information, and configured to represent a corresponding relationship between the to-be-inspected image and the at least one image feature.