Systems and techniques are disclosed for labeling objects within an image. The objects may be labeled by selecting an option from a plurality of options such that each option is a potential label for the object. An option may have an option score associated with. Additionally, a relation score may be calculated for a first option and a second option corresponding to a second object in an image. The relation score may be based on a frequency, probability, or observance corresponding to the co-occurrence of text associated with the first option and the second option in a text corpus such as the World Wide Web. An option may be selected as a label for an object based on a global score calculated based at least on an option score and relation score associated with the option.

Disclosed is an apparatus and method for calculating a raw-cost necessary for combining images into one image by matching of stereo images. The raw-cost calculation apparatus includes an image acquirer, a window generator, a window mask generator, a window masker, and a raw-cost calculator. In the raw-cost calculation apparatus and method, a raw cost may be calculated by using an adaptive window mask so that accurate 3D information may be obtained on the boundary of thin structures even when stereo images are matched and combined.

An input image showing a same object as an object shown in a reference image is identified more accurately. A difference area in the input image is determined by converting a difference area in the reference image, on a basis of geometric transformation information calculated by an analysis using a local descriptor. By matching a descriptor extracted from the difference area in the input image with the difference area in the reference image, fine differences that cannot be identified by conventional matching using only a local descriptor can be distinguished and images showing a same object can be exclusively identified.

A set of program instructions causes a computer to perform acquiring target image data indicating a target image including an object of an inspection target, inputting the target image data into an image generation model to generate first reproduction image data, generating first difference image data indicating a difference between the target image and a first reproduction image by using the target image data and the first reproduction image data, inputting the first difference image data into a feature extraction model to generate first feature data indicating a feature of the first difference image data, the feature extraction model being a machine learning model including an encoder configured to extract a feature of image data that is input, and detecting a difference between the object of the inspection target and an object of a comparison target by using the first feature data.

Disclosed herein is a detector for determining at least one material property of at least one object. The detector includes at least one projector configured for illuminating the object with at least one illumination pattern including a plurality of illumination features; at least one first camera having at least one first sensor element; at least one second camera having at least one second sensor element; and at least one evaluation device configured for evaluating a first reflection image and the second reflection image.

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 system and method for determining if a first image and a second image are correlated images includes partitioning a first image and a second image into a plurality of corresponding pixel partitions, calculating an average luminance value for each of the plurality of pixel partitions, determining if each of the plurality of pixel partitions of the first image is correlated with each of the corresponding plurality of pixel partitions of the second image, calculating a percentage of correlated pixel partitions of the first image and the corresponding plurality of pixel partitions of the second image and determining that the first image and the second image are correlated images if the percentage of correlated pixel partitions exceeds a predetermined pixel partition correlation threshold. The objective metric of the present invention determines whether two static rendered images are correlated enough to be undetectable by a human observer.

Provided is an image analysis device for specifying an image region of an object being the basis for design similarity determination. An image analysis device 100 includes units 112, 113. The unit 112 moves original images Im1 and Im2 including a designated image region H with a rotationally symmetry on a first point P relative to a reference image Im2(j), calculates a correlation value cim(j) at each relative position, and detects the position of the point P in the images Im1, Im2, according to a geometric model, based on the amount of rotation (j) at which the cim(j) is the maximum and a vector v(j). The unit 113 specifies the range of the region H in the images Im1, Im2 based on the position of the point P and the distribution of the brightness values of pixels in the images Im1, Im2.

A system for managing color consistency in a display including a plurality of panels configured to display a source image includes: a processor; and a memory, and the memory stores instructions that, when executed by the processor, cause the processor to: select a first panel among the plurality of panels as a master panel; capture a first image of the first panel and a second image of a second panel among the plurality of panels; identify a first comparative area within the first image and a second comparative area within the second image; compare the first and second comparative areas to perform a comparison; adjust data of the source image based on the comparison, and the data is adjusted by remapping colors of the source image; and transmit the adjusted data to the second panel.

Systems and techniques for labeling objects within an image. The objects may be labeled by selecting an option from a plurality of options such that each option is a potential label for the object. An option may have an option score associated with. Additionally, a relation score may be calculated for a first option and a second option corresponding to a second object in an image. The relation score may be based on a frequency, probability, or observance corresponding to the co-occurrence of text associated with the first option and the second option in a text corpus such as the World Wide Web. An option may be selected as a label for an object based on a global score calculated based at least on an option score and relation score associated with the option.