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 method according to the present disclosure includes (a) generating N pieces of input data from one target object, (b) inputting the input data to a machine learning model and obtaining M classification output values, one determination class, and a feature spectrum, (c) obtaining a similarity degree between a known feature spectrum group and the feature spectrum for the input data, and obtaining a reliability degree with respect to the determination class as a function of the reliability degree, and (d) executing a vote for the determination class, based on the reliability degree with respect to the determination class, and determining a class determination result of the target object, based on a result of the vote.

A sensor test system may include a controller coupled to at least one test device and a sensor, the controller configured to receive test instructions including a plurality of test sequences, instruct at least one display unit to display an item based on the test sequences, receive response data from the test device indicative of a driver behavior, the response data including timing information and test device information, compile the response data based on the timing information and the test device information, receive sensor data acquired by the sensor during the test sequences, compare the compiled response data to the sensor data, and determine an accuracy of the sensor based on the comparison.

The subject disclosure presents systems and computer-implemented methods for providing reliable risk stratification for early-stage cancer patients by predicting a recurrence risk of the patient and to categorize the patient into a high or low risk group. A series of slides depicting serial sections of cancerous tissue are automatically analyzed by a digital pathology system, a score for the sections is calculated, and a Cox proportional hazards regression model is used to stratify the patient into a low or high risk group. The Cox proportional hazards regression model may be used to determine a whole-slide scoring algorithm based on training data comprising survival data for a plurality of patients and their respective tissue sections. The coefficients may differ based on different types of image analysis operations applied to either whole-tumor regions or specified regions within a slide.

Techniques are provided for increasing the accuracy of automated classifications produced by a machine learning engine. Specifically, the classification produced by a machine learning engine for one photo-realistic image is adjusted based on the classifications produced by the machine learning engine for other photo-realistic images that correspond to the same portion of a 3D model that has been generated based on the photo-realistic images. Techniques are also provided for using the classifications of the photo-realistic images that were used to create a 3D model to automatically classify portions of the 3D model. The classifications assigned to the various portions of the 3D model in this manner may also be used as a factor for automatically segmenting the 3D model.

Human mesh model recovery may utilize prior knowledge of the hierarchical structural correlation between different parts of a human body. Such structural correlation may be between a root kinematic chain of the human body and a head or limb kinematic chain of the human body. Shape and/or pose parameters relating to the human mesh model may be determined by first determining the parameters associated with the root kinematic chain and then using those parameters to predict the parameters associated with the head or limb kinematic chain. Such a task can be accomplished using a system comprising one or more processors and one or more storage devices storing instructions that, when executed by the one or more processors, cause the one or more processors to implement one or more neural networks trained to perform functions related to the task.

Systems and methods for lap timing and counting in athletic events are disclosed. The systems and methods do not require the athlete to wear a counter/timer, a transmitter, a reflector or another kind of marker. A portable computing device with a sensor, such as a tablet computer with a camera, is positioned in an appropriate location. Data from the sensor is transformed into a time series of data, and one or more learned statistics are calculated in real time as benchmark ambient conditions. The learned statistics are essentially continuously updated and data that indicates irrelevant volatility is excluded. A detection threshold is determined and essentially continuously updated based on the learned statistics, and lap completion is determined based on the threshold. Times, lap counts, and other data are displayed on the portable device in real time.

Provided is an examination apparatus including a target image acquiring section that acquires a target image obtained by capturing an examination target; a target image masking section that masks a portion of the target image; a masked region predicting section that predicts an image of a masked region that is masked in the target image; a reproduced image generating section that generates a reproduced image using a plurality of predicted images predicted respectively for the plurality of masked regions; and a difference detecting section that detects a difference between the target image and the reproduced image.

A multi-label object detection method based on an object detection network includes: selecting an image of an object to be detected as an input image; based on a trained object detection network, obtaining a class of the object to be detected, coordinates of a center of the object to be detected, and a length and a width of a detection rectangular box according to the input image; and outputting the class of the object to be detected, the coordinates of the center of the object to be detected, and the length and the width of the detection rectangular box. The method of the present invention can perform real-time and accurate object detection on different classes of objects with improved detection speed and accuracy, and can solve the problem of object overlapping and occlusion during the object detection.

A patient's healthcare experience may be enhanced utilizing a system that automatically recognizes the patient based on one or more images of the patient and generates personalized medical assistance information for the patient based on electronic medical records stored for the patient. Such electronic medical records may comprise imagery data and/or non-imagery associated with a medical procedure performed or to be performed for the patient. As such, the imagery and/or non-imagery data may be incorporated into the personalized medical assistance information to provide positioning and/or other types of diagnostic or treatment guidance to the patient or a service provider.