Methods and apparatuses are described for automated cross-browser user interface testing. A computing device captures (i) a first image file corresponding to a first current user interface view of a web application on a first testing platform and (ii) a second image file corresponding to a second current user interface view of a web application on a second testing platform. The computing device prepares the image files, and compares the prepared image files using a structural similarity index measure. The computing device determines that the prepared first image file and the prepared second image file represent a common user interface view when the structural similarity index measure is within a predetermined range. The computing device highlights corresponding regions that visually diverge from each other in each of the prepared image files and transmits a notification message comprising the highlighted image files.

A camera with a field of view toward an external environment of an aircraft is disposed within an aircraft door such that an evacuation slide deployment path is within the field of view of the camera. A display device is disposed within an interior of the aircraft. A processor is operatively coupled to the camera and to the display device. The processor analyzes image data captured by the camera to identify a region within the captured image data that corresponds to the evacuation slide deployment path, determine whether the evacuation slide deployment path will generate a failed deployment outcome, and produce a warning associated with the evacuation slide deployment path in response to determining that the evacuation slide deployment path will generate the failed deployment outcome.

A camera with a field of view toward an external environment of an aircraft is disposed within an aircraft door such that a wheel of a main landing gear of the aircraft is within the field of view of the camera. A display device is disposed within an interior of the aircraft. A processor is operatively coupled to the camera and to the display device. The processor analyzes image data captured by the camera to monitor the landing gear.

A camera with a field of view toward an external environment of an aircraft is disposed within an aircraft door such that a jet bridge cabin is within the field of view of the camera when the aircraft is within a docking distance of the jet bridge cabin. A display device is disposed within an interior of the aircraft. A processor is operatively coupled to the camera and to the display device. The processor analyzes image data captured by the camera by identifying physical characteristics of the jet bridge cabin, extracting, using the identified physical characteristics of the jet bridge cabin, alignment features corresponding to the physical characteristics of the jet bridge cabin that are indicative of alignment between the jet bridge cabin and the aircraft door, determining, based on the alignment features, whether the physical characteristics of the jet bridge cabin within the captured image data satisfy threshold alignment criteria to produce an alignment state, and outputting an indication of the alignment state.

A camera with a field of view toward an external environment of an aircraft is disposed within an aircraft door such that a leading edge of a wing of the aircraft is within the field of view of the camera. A display device is disposed within an interior of the aircraft. A processor is operatively coupled to the camera and to the display device. The processor analyzes image data captured by the camera to predict a likelihood of foreign object collision with the leading edge of the wing, or detect damage or deformation to the leading edge.

A camera with a field of view toward an external environment of an aircraft is disposed within an aircraft door such that an engine inlet of an engine of the aircraft is within the field of view of the camera. A display device is disposed within an interior of the aircraft. A processor is operatively coupled to the camera and to the display device. The processor analyzes image data captured by the camera to determine whether persons or foreign objects are present near the engine inlet, or detect damage or deformation of the engine inlet.

A wearable device for use in immersive reality applications is provided. The wearable device has a frame including an eyepiece to provide a forward-image to a user, a first forward-looking camera mounted on the frame, the first forward-looking camera having a field of view within the forward-image, a sensor configured to receive a command from the user, the command indicative of a region of interest within the field of view, and an interface device to indicate to the user that a field of view of the first forward-looking camera is aligned with the region of interest. Methods of use of the device, a memory storing instructions and a processor to execute the instructions to cause the device to perform the methods of use, are also provided.

The present disclosure accurately detects the position of a shelf label disposed on a display shelf. A shelf label detection device may be provided with a shelf label position correction unit and a shelf label position identification unit. The shelf label position correction unit corrects a manual shelf label position set including a shelf label position that has been set in advance for a reference camera image, using an automatic shelf label position set which includes a shelf label position detected from a monitoring camera image using image recognition, thereby generating a corrected shelf label position set. The shelf label position identification unit uses the corrected shelf label position set to identify the shelf label position in the monitoring camera image.

The invention provides for a medical imaging system comprising: a memory for storing machine executable instructions; a processor for controlling the medical instrument. Execution of the machine executable instructions causes the processor to: receive MRF magnetic resonance data acquired according to an MRF magnetic resonance imaging protocol of a region of interest; reconstruct an MRF vector for each voxel of a set of voxels descriptive of the region of interest using the MRF magnetic resonance data according to the MRF magnetic resonance imaging protocol; calculate a preprocessed MRF vector (126) for each of the set of voxels by applying a predetermined preprocessing routine to the MRF vector for each voxel, wherein the predetermined preprocessing routine comprises normalizing the preprocessed MRF vector for each voxel; calculate an outlier map for the set of voxels by assigning an outlier score to the preprocessed MRF vector using a machine learning algorithm.

Predicting patch displacement maps using a neural network is described. Initially, a digital image on which an image editing operation is to be performed is provided as input to a patch matcher having an offset prediction neural network. From this image and based on the image editing operation for which this network is trained, the offset prediction neural network generates an offset prediction formed as a displacement map, which has offset vectors that represent a displacement of pixels of the digital image to different locations for performing the image editing operation. Pixel values of the digital image are copied to the image pixels affected by the operation.