Disclosed is a method for inspecting a food product, the method includes: receiving image data representing the food product captured with an X-ray imaging unit; performing a texture analysis to image data for generating a first set of detections; performing a pattern analysis to at least part of the image data, the pattern analysis performed with a machine-learning component trained to identify objects with predefined pattern, for generating a second set of detections; generating an indication of an outcome of an inspection of the food product in accordance with a combination of the generated first set of detections and the second set of detections. Also disclosed is an apparatus and a computer program product.

Systems and methods for using quantitative ultrasound (“QUS”) techniques to generate imaging biomarkers that can be used to assess a prediction of tumor response to different chemotherapy treatment regimens are provided. For instance, the imaging biomarkers can be used to subtype tumors that have resistance to certain chemotherapy regimens prior to drug exposure. These imaging biomarkers can therefore be useful for predicting tumor response and for assessing the prognostic value of particular treatment regimens.

The present application discloses a method and a device for analyzing the water content of skin by means of a skin image. The method comprises performing skin feature analysis on an acquired skin image, and obtaining the water content of skin on the basis of the skin features, wherein the skin features comprise a glossiness level and a smoothness level. The present invention clearly shows a reduction in implementation costs when compared to the prior art. The invention also achieves rapid testing.

A system 100 and computerized method for real time videographic digital and production ready art 118 having a computer capable of: retrieving video files and photographic files from storage 114; automatically selecting patterns or prints, deriving patterns or prints from the files; extracting complementary and/or similar patterns and/or prints as videographic digital and production ready art 118 from files; applying the extracted patterns, prints or both patterns and prints to real world products; and creating targeted still images from the production ready art 118 for commercial use and licensing.

A system 100 and computerized method for real time videographic digital and production ready art 118 having a computer capable of: retrieving video files and photographic files from storage 114; automatically selecting patterns or prints, deriving patterns or prints from the files; extracting complementary and/or similar patterns and/or prints as videographic digital and production ready art 118 from files; applying the extracted patterns, prints or both patterns and prints to real world products; and creating targeted still images from the production ready art 118 for commercial use and licensing.

An apparatus for processing image data may include: a memory storing instructions; and a processor configured to execute the instructions to: extract a target image patch including a target object, from a captured image; obtain a plurality of landmark features from the target image patch; align the plurality of landmark features of the target image patch with a plurality of reference landmark features in a template image patch including the same target object; and when the plurality of landmark features are aligned with the plurality of reference landmark features, transfer texture details of the target object in the template image patch to the target object in the target image patch.

An identification method executed by a computer, the identification method includes receiving a face image; generating each of a plurality of first estimated values regarding an attribute of a face image by using a plurality of estimation models that generates a first estimated value regarding the attribute of the face image from the face image; generating a plurality of pieces of similarity information that indicates a similarity between feature information of the face image and a plurality of pieces of feature information respectively associated with the plurality of estimation models; and generating a second estimated value regarding the attribute of the face image, based on the plurality of first estimated values and the plurality of pieces of similarity information.

An identification method executed by a computer, the identification method includes receiving a face image; generating each of a plurality of first estimated values regarding an attribute of a face image by using a plurality of estimation models that generates a first estimated value regarding the attribute of the face image from the face image; generating a plurality of pieces of similarity information that indicates a similarity between feature information of the face image and a plurality of pieces of feature information respectively associated with the plurality of estimation models; and generating a second estimated value regarding the attribute of the face image, based on the plurality of first estimated values and the plurality of pieces of similarity information.

Embodiments of the present disclosure provide for improved generation and outputting of object identification data indicating object classifications for object representations. Such objects representations may correspond to depictions of objects in images captured using digital holographic microscopy. Some embodiments generate object identification data by comparing object representations in focused image(s) with specially configured annotated focused images, for example using a specially trained neural network or other machine learning model trained based on such annotated focused images. The annotated focused images are generated including a plurality of channels, each associated with a different grayscale focused image at a different target focal length of a range of target focal lengths. In this regard, model(s), algorithm(s), and/or other specially configured implementations may learn the spatial features of particular object representations and associated object identification data. The trained models may be used to perform accurate comparisons with the annotated focused images.

An image classifier for classifying an input image x with respect to combinations of an object value o and an attribute value. The image classifier includes an encoder network that is configured to map the input image to a representation comprising multiple independent components; an object classification head network configured to map representation components of the input image to one or more object values; an attribute classification head network configured to map representation components of the input image to one or more attribute values; and an association unit configured to provide, to each classification head network, a linear combination of those representation components of the input image x that are relevant for the classification task of the respective classification head network. A method for training the image classifier is also provided.