A device and a computer implemented method of anomaly detection, including processing a digital representation of a signal or image with a wavelet decomposition to generate a first, second, and third plurality of decomposed representations, processing a first decomposed representation of the third plurality of decomposed representations with a first generative model to determine a first likelihood, processing a second decomposed representation of the third plurality of decomposed representations with a second generative model to determine a second likelihood, processing a second decomposed representation of the second plurality of decomposed representations with a third generative model to determine a third likelihood, processing a second decomposed representation of the first plurality of decomposed representations with a fourth generative model to determine a fourth likelihood, detecting an anomaly when at least one of the first likelihood, the second likelihood, the third likelihood and the fourth likelihood meets a criterium for anomaly detection.

An eye correction system and related techniques are described herein. The eye correction system can automatically detect and correct one or more misaligned eyes in an image. For example, an image of a person can be analyzed to determine whether one or both eyes of the person are misaligned with respect to each other and/or with respect to the entire face of the person. If an eye is determined to be misaligned, the image can be modified so that the eye is adjusted accordingly. For example, an iris of a misaligned eye can be adjusted to align with the face and/or the other eye.

Disclosed herein are system, method, and computer program product embodiments for compliance auditing using cloud based computer vision. In one aspect, a system is configured to receive, from a mobile device, a compliance audit request to at least recognize one or more products within the audit image. The system is further configured to select a first object recognition model having a first associated object recognition model identifier from a model selection list based at least on a required object recognition list, wherein the first object recognition model is configured to recognize a first set of object names within the required object recognition list. The system is further configured to request the computer vision system to perform object recognition using the first object recognition model to recognize the first set of object names within the audit image, and transmit audit result information to the mobile device.

A system is provided for object localization in image data. The system includes an object localization framework comprising a plurality of object localization processes. The system is configured to receive an image comprising unannotated image data having at least one object in the image, access a first object localization process of the plurality of object localization processes, determine first bounding box information for the image using the first object localization process, wherein the first bounding box information comprises at least one first bounding box annotating at least a first portion of the at least one object in the image, and receive first feedback regarding the first bounding box information determined by the first object localization process. The system is further configured to persist the image with the first bounding box information or access a second object localization process based on the first feedback.

Video processing methods and the associated system architecture for measuring transformation in objects, including pupils, entail the following steps: 1. Motion correction; 2. Object (eye) detection; 3. Image correction; and 4. Fourier-based analysis for item (in some embodiments the item is a pupil) motion estimation.

A display apparatus includes a communicator, a display, and a processor. The processor is configured to identify a sign language image region of an input image of content received from the communicator, generate an output image in which the sign language image region is magnified, and control the display to display the output image.

A system is provided for object localization in image data. The system includes an object localization framework comprising a plurality of object localization processes. The system is configured to receive an image comprising unannotated image data having at least one object in the image, access a first object localization process of the plurality of object localization processes, determine first bounding box information for the image using the first object localization process, wherein the first bounding box information comprises at least one first bounding box annotating at least a first portion of the at least one object in the image, and receive first feedback regarding the first bounding box information determined by the first object localization process. The system is further configured to persist the image with the first bounding box information or access a second object localization process based on the first feedback.

Disclosed is a ship identity recognition method based on the fusion of AIS data and video data, comprising: collecting a ship sample to train a ship target classifier; performing, using the ship target classifier, ship target detection on a video frame collected by a gimbal camera; performing a comparison with a recognized ship library to filter a recognized ship; acquiring AIS data and filtering same across time and spatial scales; predicting the current position of an AIS target using a linear extrapolation method and converting the current position to an image coordinate system; performing position matching between a target to be matched and the converted AIS target; and performing feature extraction on the successfully matched target and storing the extracted feature, together with ship identity information, into the recognized ship library. Experimental results show that the present invention can quickly and accurately extract a surveillance video and perform identity recognition on the ship target, effectively reduces labor costs, and has a broad application prospect in the fields such as ship transportation and port management.

The present invention provides a method for tracking face in a video, comprising steps of taking an image sample from a video; extracting and storing a target face feature according to the image sample; dividing the video into one or more scene; and labeling one or more face matching the target face feature. Accordingly, an actor's facial expression and motion can be extracted with ease and used as training materials or subject matters for discussion.

An eye correction system and related techniques are described herein. The eye correction system can automatically detect and correct one or more misaligned eyes in an image. For example, an image of a person can be analyzed to determine whether one or both eyes of the person are misaligned with respect to each other and/or with respect to the entire face of the person. If an eye is determined to be misaligned, the image can be modified so that the eye is adjusted accordingly. For example, an iris of a misaligned eye can be adjusted to align with the face and/or the other eye.