A method for capturing motion of an object, the method comprising: installing at least one marker on the object; bringing the object having the at least one marker installed thereon in an acquisition volume; arranging at least two event-based light sensors such that respective fields of view of the at least two event-based light sensors cover the acquisition volume, wherein each event-based light sensor has an array of pixels; receiving events asynchronously from the pixels of the at least two event-based light sensors depending on variations of incident light from the at least one marker sensed by the pixels; and processing the events to position the at least one marker within the acquisition volume and capture motion of the object.

A method for capturing motion of an object, the method comprising: installing at least one marker on the object; bringing the object having the at least one marker installed thereon in an acquisition volume; arranging at least two event-based light sensors such that respective fields of view of the at least two event-based light sensors cover the acquisition volume, wherein each event-based light sensor has an array of pixels; receiving events asynchronously from the pixels of the at least two event-based light sensors depending on variations of incident light from the at least one marker sensed by the pixels; and processing the events to position the at least one marker within the acquisition volume and capture motion of the object.

Computer vision systems and methods for localizing image forgery are provided. The system generates a constrained convolution via a plurality of learned rich filters. The system trains a convolutional neural network with the constrained convolution and a plurality of images of a dataset to learn a low level representation of each image among the plurality of images. The low level representation is indicative of a statistical signature of at least one source camera model of each image. The system can determine a splicing manipulation localization by the trained convolutional neural network.

A system and process to determine whether a digital image has been manipulated involves determining expected locations of non-standard pixels in the digital image, and determining a feature for evaluating the non-standard pixels. The feature in pixels of the digital image that are located at the expected locations of non-standard pixels is then measured, and a statistical measure of the feature of pixels in the digital image that are located at the expected locations of non-standard pixels is evaluated. The digital image is assessed to determine a probability that the digital image includes a manipulated portion, based on the statistical measure. The system and process can also determine a make and model of an image sensing device via an examination of the non-standard pixels.

This application discloses a secure face image transmission method performed by an electronic device. In this application, when a camera component of the electronic device acquires any face image, face image information can be read from a buffer. The face image information is used for indicating a quantity of all face images historically acquired by the camera component. Identification information is embedded in the face image to obtain the face image carrying the identification information. The identification information is used for indicating the face image information but not perceivable by a human being. The face image carrying the identification information is transmitted to a remote server for authenticating the face image using the identification information. Therefore, the security of the face image acquired by the camera component is improved, and the security of the face image transmission process is effectively ensured.

In various embodiments, a defective pixel detection application automatically detects defective pixels in video content. In operation, the defective pixel detection application computes a first set of pixel intensity gradients based on a first frame of video content and a first neighborhood of pixels associated with a first pixel. The defective pixel detection application also computes a second set of pixel intensity gradients based on the first frame and a second neighborhood of pixels associated with the first pixel. Subsequently, the defective pixel detection application computes a statistical distance between the first set of pixel intensity gradients and the second set of pixel intensity gradients. The defective pixel detection application then determines that the first pixel is defective based on the statistical distance.

A method includes acquiring a first data column output from a plurality of sensors, generating a model for estimating data from the plurality of sensors on the basis of the first data column, acquiring a second data column output from the plurality of sensors, obtaining an estimated data column corresponding to the second data column based on the model by using regularization for making an error between the second data column and the estimated data column sparse, and identifying a sensor in which a change occurred between the first data column and the second data column on the basis of the error between the second data column and the estimated data column. A corresponding computer program product and apparatus are also disclosed herein.

The invention relates to an artificial intelligence cataract analysis system, including a pattern recognition module for recognizing a photo mode of an input eye image, wherein the photo mode is divided according to the slit width of the illuminating slit during photographing of the eye image and/or whether a mydriatic treatment is carried out; a preliminary analysis module used for selecting a corresponding deep learning model for eye different photo modes, analyzing the characteristics of lens in the eye image by using a deep learning model, and further performing classification in combination with cause and severity degree of a disease. The invention can perform cataract intelligent analysis on eye images with different photo modes by using deep learning models, so that the analysis accuracy is improved.

A method and system that efficiently selects sensors without requiring advanced expertise or extensive experience even in a case of new machines and unknown failures. An abnormality detection system includes a storage unit for storing a latent variable model and a joint probability model, an acquisition unit for acquiring sensor data that is output by a sensor, a measurement unit for measuring the probability of the sensor data acquired by the acquisition unit based on the latent variable model and the joint probability model stored by the storage unit, a determination unit for determining whether the sensor data is normal or abnormal based on the probability of the sensor data measured by the measurement unit, and a learning unit for learning the latent variable model and the joint probability model based on the sensor data output by the sensor.

Computer vision systems and methods for localizing image forgery are provided. The system generates a constrained convolution via a plurality of learned rich filters. The system trains a convolutional neural network with the constrained convolution and a plurality of images of a dataset to learn a low level representation of each image among the plurality of images. The low level representation is indicative of a statistical signature of at least one source camera model of each image. The system can determine a splicing manipulation localization by the trained convolutional neural network.