This disclosure can provide a display device and a display method. The display device includes at least one camera configured to capture an environmental scenario image; a display configured to display a user interface; a controller in communicated with the display, configured to receive a command, input by a user, for obtaining a content recommendation resource associated with content currently displayed in the user interface; determine whether an application corresponding to the content currently displayed in the user interface is an application invoking the at least one camera, and if yes, display a first user interface, where the first user interface displays a first image captured by the at least one camera.

A method of reducing false positives and identifying relevant true alerts in a video management system includes analyzing images to look for patterns indicating changes between subsequent images. When a pattern indicating changes between subsequent images is found, the video management system solicits from a user an indication of whether the pattern belongs to one of two or more predefined categories. The patterns indicating changes between subsequent images are saved for subsequent use. Subsequent images received from the video camera are analyzed to look for patterns indicating changes between subsequent images. When a pattern indicating changes between subsequent images is detected by the video management system, the video management system compares the pattern indicating changes between subsequent images to those previously categorized into one of the two or more predefined categories. Based on the comparison, the video management system may provide an alert to the user.

A method for the computer-aided training of an artificial neural network (ANN) for recognizing structural features on objects, by means of which method identified structural features on objects are recognizable rapidly and reliable. That is achieved by virtue of the fact that a convolutional neural network (CNN) having a multiplicity of neurons is used for the training of an ANN for feature recognition on objects. Said network comprises a multiplicity of convolutional and/or pooling layers for the extraction of information from images of individual objects. In this case, the images of the objects are respectively scaled or scaled up and/or down from layer to layer. During the scaling of the images information about the structural features of the objects is maintained, specifically independently of the scaling of the images.

A fingerprint anti-counterfeiting method and an electronic device are provided. The fingerprint anti-counterfeiting method includes: After detecting a fingerprint input action of a user, an electronic device obtains a fingerprint image generated by the fingerprint input action, and obtains a vibration-sound signal generated by the fingerprint input action. The device determines, based on a fingerprint anti-counterfeiting model, whether the fingerprint input action is performed by a true finger. The fingerprint anti-counterfeiting model is a multi-dimensional network model obtained through learning based on fingerprint images for training and corresponding vibration-sound signals. The fingerprint anti-counterfeiting method in embodiments of this application helps improve a protection capability of the electronic device for a fake fingerprint attack.

In a learning phase, a processor of a sample observation device: stores design data on a sample in a storage resource; creates a first learning image as a plurality of input images; creates a second learning image as a target image; and learns a model related to image quality conversion with the first and second learning images. In a sample observation phase, the processor obtains, as an observation image, a second captured image output by inputting a first captured image obtained by imaging the sample with an imaging device to the model. The processor creates at least one of the first and second learning images based on the design data.

Techniques for tuning an image editing operator for reducing a distractor in raw image data are presented herein. The image editing operator can access the raw image data and a mask. The mask can indicate a region of interest associated with the raw image data. The image editing operator can process the raw image data and the mask to generate processed image data. Additionally, a trained saliency model can process at least the processed image data within the region of interest to generate a saliency map that provides saliency values. Moreover, a saliency loss function can compare the saliency values provided by the saliency map for the processed image data within the region of interest to one or more target saliency values. Subsequently, the one or more parameter values of the image editing operator can be modified based at least in part on the saliency loss function.

The technology disclosed implements Human-in-the-loop (HITL) active learning with a feedback look via a user interface that is expressly designed for the suggested images to admit multiple fast feedbacks, including selection, dismissal, and annotation. Then, the downstream selection policy for subsequent sampling iterations is based on the available data interpreted in the context of the previous selections, dismissals, and annotations.

A technique for detecting a glitch in an image is provided. The technique includes providing an image to a plurality of individual classifiers to generate a plurality of individual classifier outputs and providing the plurality of individual classifier outputs to an ensemble classifier to generate a glitch classification.

A method is provided for identifying consumer packaged goods (CPGs). The method includes providing to a machine learning classifier a set of images containing at least one CPG; receiving from the machine learning classifier an indication that the machine learning classifier cannot reliably identify a designated CPG in the set of images; determining whether the designated CPG is a product in a product catalog; if the designated CPG is in the product catalog, then associating the designated CPG with a Global Trade Item Number (GTIN); and if the designated product is not in the product catalog, then designating the CPG as a potentially new product. Notably, this approach allows partially identified products to be treated as full-fledged members of the product catalog, thus allowing data to be collected on these products even before they have been fully identified and their GTINs have been resolved.

A system and method for implementing reward based strategies for promoting exploration that include receiving data associated with an agent environment of an ego agent and a target agent and receiving data associated with a dynamic operation of the ego agent and the target agent within the agent environment. The system and method also include implementing a reward function that is associated with exploration of at least one agent state within the agent environment. The system and method further include training a neural network with a novel unexplored agent state.