Abstract: Described are systems and methods for determining an agent that performed an event within a materials handling facility. A series of overhead images that include representations of the event location and one or more agents are processed to determine a motion or movement of the agent over a period of time. For example, a motion model representative of a motion of the agent over a period of time is generated from the images. A distance between the motion model and the event location is also determined. An association between the agent and the event may be determined based on the motion model and the distance between the motion model and the event location.

Abstract: A measuring apparatus that measures an optical characteristic of a surface in accordance with a standard selected from a plurality of standards is provided. The apparatus includes an illumination device configured to illuminate the surface with light from a light source, an imaging device configured to image the light source with reflected light from the surface illuminated by the illumination device, and a processor configured to process image data having number of pixels obtained by reducing number of pixels of the imaging device by a reduction rate to obtain the optical characteristic of the surface. The processor is configured to determine the reduction rate based on the selected standard.

Abstract: A method includes identifying one or more objects in one or more images of real-world scenes associated with a user, adding the identified one or more objects to a list of real-world objects associated with the user, assigning each object in the list of real-world objects to an object class based on object recognition, and providing a notification to the user that a content item has been associated with an object class assigned to one of the objects on the list of real-world objects associated with the user. A computer readable storage medium stores one or more computer programs, and an apparatus includes a processor-based device.

Abstract: Devices, systems and methods are disclosed for validating an electronic publication and determining a source of identified errors in a rendering of the electronic publication. The rendering may be captured as a rendered image and rendered data may be extracted from the rendering. The rendered data may be compared to actual input data to the renderer used to generate the rendered image. If errors are visible in the rendering, a source of the errors may be identified based on the comparison between the extracted rendered data to the actual input data. If errors are not visible in the rendering, the rendering may be validated.

Abstract: Image feature trackability ranking systems and methods are disclosed. A method of establishing a trackability ranking order from tracked image features within a training video sequence at a tracking analysis device includes establishing a tracking region within the training video sequence using a feature detection algorithm. Trajectories of tracked image features within the tracking region are compiled using a feature tracking algorithm. Saliency metrics are assigned to each one of the trajectories of tracked image features based on one or more feature property measurements within the tracking region, and a trackability ranking algorithm that is a function of the saliency metrics and a defined feature trajectory ranking associated with the training video sequence is determined, the trackability ranking algorithm being usable for ranking, based on trackability, tracked image features within another video sequence.

Abstract: The present disclosure provides for a method, device, and computer-readable storage medium for performing a method for discerning a vehicle at an access control point. The method including obtaining a video sequence of the access control point; detecting an object of interest from the video sequence; tracking the object from the video sequence to obtain tracked-object data; classifying the object to obtain classified-object data; determining that the object is a vehicle based on the classified-object data; and determining that the vehicle is present in a predetermined detection zone based on the tracked-object data.

Abstract: There is provided an apparatus for supporting Computer Aided Diagnosis (CAD) based on a speed of a probe. The apparatus includes a region of interest (ROI) detector configured to detect an ROI from a current image acquired from a probe; and an ROI classifier configured to determine whether to classify the ROI using a determined state of a speed, and classify the ROI according to a result of the determination.

Abstract: The position of a next object is estimated efficiently according to the observed value of physical quantity related to the object in an actual environment. An image processing apparatus extracts a first object image of state prediction from a first image and identifies the type of the first object. A model appropriate to the type of the object of state prediction is merged with the first object image and a feature point of the first object image is associated with the model. A plurality of motion sequences are selected from a finite state sequence corresponding to the type of the first object according to the position of the first object and the initial observed state.

Abstract: A method and system are proposed to obtain a reduced speckle noise image of a subject from optical coherence tomography (OCT) image data of the subject. The cross sectional images each comprise a plurality of scan lines obtained by measuring the time delay of light reflected, in a depth direction, from optical interfaces within the subject. The method comprises two aligning steps. First the cross sectional images are aligned, then image patches of the aligned cross sectional images are aligned to form a set of aligned patches. An image matrix is then formed from the aligned patches; and matrix completion is applied to the image matrix to obtain a reduced speckle noise image of the subject.

Abstract: A system and method for reconstructing an image using a cone-beam computed tomography (CT) imaging system includes acquiring data from a subject with the CT imaging system using a limited scan range that is less than 360 degrees. The process also includes reconstructing at least one image of the subject having a first temporal resolution from the data acquired, performing a temporal deconvolution of the at least one image using a finite temporal window to generate at least one image of the subject with a second temporal resolution that is greater than the first temporal resolution, and subtracting the at least one image of the subject with the second temporal resolution and a mask image of the subject to generate a time-resolved CT angiogram of the subject.

Abstract: In a lane boundary line information acquiring device, a detection unit detects lane boundary lines. A driving environment acquiring unit acquires a driving environment. A probability information acquiring unit acquires probability information containing a probability of presence of a lane boundary line, etc. based on the detected lane boundary lines and the acquired driving environment. A position information acquiring unit acquires position information of the own vehicle. A memory unit associates the probability information with the position information of the own vehicle. Where the position information is acquired by the position information acquiring unit at a time when the probability information acquiring unit acquires the probability information, and stores the probability information associated with the position information into the memory unit. A readout unit reads out the probability information associated with the position information at a location in front of the own vehicle.

Abstract: An image processing apparatus is provided with a spatial information calculation unit for calculating spatial information of a subject, which is the information of an area in which the subject in an image is predicted to be present, a first area setting unit for setting a first area in the image based on the spatial information, a second area setting unit for setting a second area outside the first area, a first feature amount calculation unit for calculating a first feature amount of the first area, a second feature amount calculation unit for calculating a second feature amount of the second area, the second feature amount being a feature amount of the same type as the first feature amount, and an saliency calculation unit for calculating a degree of visual saliency of the subject.

Abstract: Methods, apparatus, and computer readable storage media related to defining a planar model that approximates a plurality of surfaces of an object and/or applying the planar model to detect the object and/or to estimate a pose for the object. For example, the planar model may be compared to data points sensed by the a three-dimensional vision sensor of a robot to determine the object is present in the field of view of the sensor and/or to determine a pose for the object relative to the robot. A planar model comprises a plurality of planar shapes modeled relative to one another in a three-dimensional space and approximates an object by approximating one or more surfaces of the object with the planar shapes.

Abstract: Disclosed is an apparatus for hand gesture recognition based on a depth image, which includes a depth image acquiring unit configured to acquire a depth image including a hand region, a depth point classifying unit configured to classify depth points of a hand region in the depth image according to a corresponding hand portion by means of a machine studying method, and a hand model matching unit configured to match a three-dimensional hand model with the classified depth points by using distances between the classified depth points and a hand portion respectively corresponding to the depth points. A recognition method using the apparatus is also disclosed.

Abstract: A parking assist system of a vehicle includes a camera that, when disposed at the vehicle, has a field of view exterior of the vehicle. An image processor is operable to process image data captured by the camera to detect parking space markers indicative of a parking space and to identify empty or available parking spaces. The image processor includes a parking space detection algorithm that detects parking space markers by (i) extracting low level features from captured image data, (ii) classifying pixels as being part of a parking space line or not part of a parking space line, (iii) performing spatial line fitting to find lines in the captured images and to apply parking space geometry constraints, and (iv) detecting and selecting rectangles in the captured images.

Abstract: Systems and methods for processing distribution items having unique identifiers affixed thereon. The unique identifiers can include computer readable codes and written alphanumeric strings generated by a user. A system receives the unique identifier and requests payment information from a user, and stores an association between the unique identifier and the payment information, and processes the item according to the payment information.

Abstract: The invention provides a system and a method for an otitis media database constructing. The steps of method comprise: First, receiving a plurality of tympanic membrane images, wherein the tympanic membrane images are ear infection in different types. Second, choosing one of the tympanic membrane images, then classifying into a plurality of anatomic regions based on a plurality of tissue types. Coding each of anatomic regions with a numerical number to describe its morbid condition, and obtaining an eigenvalue through collecting the numerical number of each anatomic region. Furthermore, repeatedly choosing other one of tympanic membrane images until each tympanic membrane image obtaining the eigenvalue. And obtaining a matrix through collecting the eigenvalue of each tympanic membrane image, then generating an otitis media database.

Abstract: The present invention is directed to a novel tomographic reconstruction framework that explicitly models the covariance of the measurements in the forward model using a mean measurement model and a noise model. This more accurate model can result in improved image quality, increased spatial resolution, and enhanced detectability—in particular, for imaging scenarios where there are features on the order of the correlation length in the projection data. Applications where these methods might have particular benefit include high resolution CBCT applications as in CBCT mammography (where very fine calcifications are difficult to resolve due to detector blur and correlation), musculoskeletal imaging (where fine bone details are important to the imaging task), or in temporal bone imaging where the fine detail structures of the inner ear are also difficult to resolve with standard imaging techniques.

Abstract: The present disclosure provides for a method, device, and computer-readable storage medium for performing a method for discerning a vehicle at an access control point. The method including obtaining a video sequence of the access control point; detecting an object of interest from the video sequence; tracking the object from the video sequence to obtain tracked-object data; classifying the object to obtain classified-object data; determining that the object is a vehicle based on the classified-object data; and determining that the vehicle is present in a predetermined detection zone based on the tracked-object data.

Abstract: Velocity controllers in accordance with embodiments of the invention enable velocity estimation for tracked objects. One embodiment includes a tracker controller, including: a processor; and a memory containing: a velocity tracker application; a state space describing relationships between measured locations, calculated locations, and changes in locations, where the calculated locations in the state space correspond to unoccluded points on the surface of the tracked object; wherein the processor is configured by the velocity tracker application to: pre-process the state space to identify a tracked object; estimate a velocity of the tracked object using a location history calculated from the measured locations of the tracked object within the state space and a motion model calculated from the state space; and return the velocity of the tracked object.