A gas-detection image processing device includes first, second, and third processors. The first processor generates a plurality of first images by applying processing to extract a gas candidate region to each of a plurality of infrared images captured in time series during a predetermined period. The second processor generates a second image, while using the first images, by applying processing to extract an appearance region indicating that a gas candidate region has appeared in at least a part of the predetermined period. The second processor generates two or more second images by applying the processing to extract the appearance region to the first images generated in a manner corresponding to two or more of the predetermined periods respectively. The third processor generates a third image by applying processing to extract a common region of the appearance regions while using the two or more of the second images.

A method includes receiving the latest position information from at least one position-changeable apparatus of a plurality of apparatuses of the vehicle; acquiring a first image for the interior of the vehicle; generating a background image based on the latest position information and the first image; acquiring a second image for the interior of the vehicle in a state in which the user rides in the vehicle; and segmenting an image for the user riding in the vehicle based on a differential image between the background image and the second image. The background image may be generated by reflecting a position for an apparatus changed by the user. One or more of an autonomous vehicle, a user terminal and a server of the present invention may be associated with an artificial intelligence module, a drone robot, augmented reality device, a virtual reality device, and a device related to 5G services.

An image processing apparatus according to this embodiment performs, based on a job setting, conversion of a pixel value of a partial region in an input original image into a predetermined value on image data of the original image, as needed. Subsequently, this image processing apparatus generates a histogram representing the density signal distribution of the image data of the original image or the converted image data. Note that if the above-described conversion is performed, this image processing apparatus corrects the generated histogram by subtracting, from a count of the predetermined number of the generated histogram, the number of sampling points counted in the above-described partial region when the histogram is generated.

An image processing apparatus has a color image, the image data being constituted by multiple pixels, each of the multiple pixels having a gradation value, and a controller, which is configured to generate a histogram of index values corresponding to brightness values of the multiple pixels constituting the image data, set an original threshold value based on the histogram which is referred to for binarization, detect a mound-shaped part, in the histogram, satisfying a particular condition, set an adjusting direction in which the original threshold value is to be adjusted, set the index value at a base on a particular direction side of a particular mound-shaped part which is one of mound-shaped parts existing on the adjusting direction side with respect to the original threshold value in the histogram as an adjusted threshold value, and apply a binarizing process to the image data using the adjusted threshold value.

An image processing system includes: a receiving unit configured to receive an input of a plurality of image frames constituting a video from an imaging apparatus; a detection unit configured to detect a feature point included in an image frame to be processed in the plurality of image frames; and an output unit configured to output an output image obtained by superimposing an image frame to be processed of an area detected as a feature point on a background image generated from at least some of a plurality of image frames.

System that facilitates rapid onboarding of an autonomous (cashier-less) store by capturing images of items in the store's catalog from different angles, with varying backgrounds and lighting conditions, and that automatically builds a classifier training dataset from these images. The system may have cameras in different positions, lights supporting variable illumination, and monitor screens that generate different background colors. It may have an input device such as a barcode reader, and an operator terminal that prompts operators to place items into the imaging system in the necessary orientations. Once an item is placed in the imaging system, a fully automated process may generate a sequence of background colors, a sequence of lighting conditions, and may capture and process images from all of the cameras to create training images. Training images for an item may be generated in seconds, compared to many minutes per item using manual image capture and processing.

Techniques are described for automated analysis and filtering of image data. Image data is analyzed to identify regions of interest (ROIs) within the image content. The image data also may have depth estimates applied to content therein. One or more of the ROIs may be designated to possess a base depth, representing a depth of image content against which depths of other content may be compared. Moreover, the depth of the image content within a spatial area of an ROI may be set to be a consistent value, regardless of depth estimates that may have been assigned from other sources. Thereafter, other elements of image content may be assigned content adjustment values in gradients based on their relative depth in image content as compared to the base depth and, optionally, based on their spatial distance from the designated ROI. Image content may be adjusted based on the content adjustment values.

Immunohistochemistry (IHC) and in situ hybridization (ISH) have the aim of detecting, localizing and quantifying certain analytes for various diagnostic purposes. The quality of the stains which are analyzed may deviate for various reasons. Therefore, the present invention provides a method and system for assessing the stain quality and for establishing objective criteria for assessing the stain quality for application in the fields of in-situ hybridization and immunohistochemistry. In one possible embodiment, the invention comprises the steps of unmixing multi-spectral image data of a tissue specimen to obtain analyte intensity images, each analyte intensity image comprising signals from a single stain, computing metrics based on the analyte intensity images, wherein the metrics are uniformity, distribution and/or dispersion of pixel intensity values in the analyte intensity images and assessing a stain quality of a slide by comparing the computed metrics to pre-determined cutoff values regarding uniformity, distribution and/or dispersion of pixel intensity, wherein the stain quality of the slide is assessed as acceptable if the computed metrics meet or exceed the pre-determined cutoff values, and wherein the stain quality of the slide is assessed as unacceptable if the computed metrics do not meet the pre-determined cutoff values. In order to establish objective criteria for assessing stain quality, in one possible embodiment, the method and system includes the step of deriving cut-off values for uniformity, distribution and/or dispersion of pixel intensity by combining the computed metrics based on the analyte intensity images with pre-established data quantifying the stain quality.

A calibration method and a calibration device of a vehicle-mounted camera, a vehicle and a storage medium are provided. The calibration method includes: obtaining an original image including a plurality of first lane lines and captured by the vehicle-mounted camera; determining a region of interest (ROI) including the plurality of first lane lines in the original image; adjusting a pitch angle of the vehicle-mounted camera by detecting a plurality of second lane lines in a first inverse perspective mapping (IPM) image corresponding to the ROI, the second lane lines corresponding to the first lane lines; and adjusting a yaw angle of the vehicle-mounted camera by detecting an IPM binary image of a second IPM image corresponding to the ROI.

A key frame detector and method having input interface accepting multiple frames including a previous frame and a current frame, a programmable logic chip coupled to the input interface that receives a background frame, determines a similarity based on a comparison of the current frame with the background frame and discards the current frame if the similarity is greater than a predetermined similarity threshold, determines an absolute difference on a pixel by pixel basis between the current frame and the previous frame and discards the current frame if the absolute difference is less than a predetermined absolute difference threshold, determines a wholeness of an object in the current frame and discards the current frame if the object is not whole, determines whether the current frame with the whole object has been previously provisionally selected and discards if it has and analyzes the Whole object via an object detector.