An intelligent entrance guard unlocking method includes: transmitting electric energy by wireless electromagnetic radiation via a transmit coil; receiving the electric energy via a receive coil; capturing a vein image of a part of a user via a number of small cameras by Infrared scanning technology and filtering noises from the vein image to get a filtered vein image; transmitting a wireless signal containing the filtered vein image to an intelligent entrance guard; comparing the filtered vein image with a predefined vein image; unlocking the intelligent entrance guard when the filtered vein image matches with the predefined vein image and not unlocking the intelligent entrance guard when the filtered vein image does not match with the predefined vein image.

The invention provides a method of processing an image in a diagnostic apparatus 100 of diagnosing a disease using a captured image of an affected area, comprising: a separating step of separating the captured image into a brightness component and a color information component; and an extracting step of extracting a region to be diagnosed based on the brightness component or the color information component of the configured image to highlight likeness of the region.

.[.The present invention relates to an.]. .Iadd.An .Iaddend.image processing apparatus which can restore, from a color and sensitivity mosaic image acquired using a CCD image sensor of the single plate type or the like, a color image signal of a wide dynamic range wherein the sensitivity characteristics of pixels are uniformized and each of the pixels has all of a plurality of color components .Iadd.is provided.Iaddend.. A sensitivity uniformization section uniformizes the sensitivities of pixels of a color and sensitivity mosaic image to produce a color mosaic image, and a color interpolation section interpolates color components of the pixels of the color mosaic image M to produce output images R, G and B. The .[.present invention.]. .Iadd.image processing apparatus .Iaddend.can be applied to a digital camera which converts a picked up optical image into a color image signal of a wide dynamic range.

A pattern extracting device, an image projecting device, a pattern extracting method, and a program capable of extracting all the feature points by interpolating defective feature points even when there are defective feature points of an image pattern. The pattern extracting device extracts feature points to be interpolated based on a captured image of a projected image pattern, and interpolates the feature point to be interpolated by using near-by feature points that are located near the feature point, divides the near-by feature points into groups, calculates extrapolation coordinates of the groups, and calculates coordinates of the feature point to be interpolated in view of significance of the extrapolation.

A method of providing a medical image of a region of interest (ROI) of a patient, the method comprising: acquiring a time ordered sequence of measurements of radiation used to provide the medical image; generating a Fourier transform of the time ordered sequence or a function thereof; using the Fourier transform to determine which of the frequencies characterize real motion of tissue in the ROI and which characterize noise; generating a corrected Fourier transform that is exclusive of frequencies that characterize noise; and providing a medical image of the ROI using the corrected Fourier transform.

Techniques for processing imaging data contaminated by sensor-dependent noise. An imaging method is described. In the imaging method, imaging data corresponding to an imaged region and acquired by at least first and second sensor elements is obtained. A parameterized model is fitted to the imaging data. The parameterized model includes a first sensor-dependent model of noise generated by the first sensor element in a first portion of the imaging data acquired by the first sensor element, and a second sensor-dependent model of noise generated by a second sensor element in a second portion of the imaging data acquired by the second sensor element. The first sensor-dependent noise model differs, at least in part, from the second sensor-dependent noise model.

An image capture application captures a sequence of images via a digital camera. The sequence of images may have undesirable levels of blurriness due to the motion of objects in the field of view of the digital camera or due to movement of the digital camera itself. A deblur engine within the image capture application generates image segments within one of the captured images, where a given image segment includes pixel values that move coherently between different images in the sequence. The deblur engine then deblurs each image segment based on the coherent motion of each different image segment and combines the resultant, deblurred image segments into a deblurred image. Advantageously, blurriness caused by the combined effects of moving objects and camera motion may be reduced, thereby improving the ability of a digital camera to provide high-quality images. As such, the user experience of digital photography may be enhanced.

A lane departure warning method includes steps of: collecting a road image by an imaging device; detecting a lane marking in accordance with the road image, so as to extract a position of the lane marking and an angle of the lane marking relative to a running direction of the vehicle; acquiring steering information and a movement speed of the vehicle by an OBD system; judging whether or not the vehicle is unconsciously running on the lane marking in accordance with the position of the lane marking, the angle of the lane marking relative to the running direction of the vehicle and the steering information of the vehicle; in the case that the vehicle is unconsciously running on the lane marking, recording a duration within which the vehicle is unconsciously running on the lane marking; and judging whether or not to send a lane departure warning to the vehicle.

A method generates bounding-boxes within frames of a sequence of frames. The bounding-boxes may be generated via a recurrent neural network (RNN) such as a long short-term memory (LSTM) network. The method includes receiving the sequence of frames and generating an attention feature map for each frame of the sequence of frames. Each attention feature map indicates at least one potential moving object. The method also includes up-sampling each attention feature map to determine an attention saliency for pixels in each frame of the sequence of frames. The method further includes generating a bounding-box within each frame based on the attention saliency and temporally smoothing multiple bounding-boxes along the sequence of frames to obtain a smooth sequence of bounding-boxes. The method still further includes localizing an action location within each frame based on the smooth sequence of bounding-boxes.

Known methods for generating super-resolved images from single input images have various disadvantages. An improved method for generating a super-resolved image from a single low-resolution input image comprises up-scaling the input image to generate an initial version of the super-resolved image, searching, for each patch of the low-resolution input image, similar low-resolution patches in first search windows within down-sampled versions of the input image, and determining, in less down-sampled versions of the input image, high-resolution patches that correspond to the similar low-resolution patches. The determined high-resolution patches are cropped, a second search window is determined in the initial version of the super-resolved image, and a best-matching position for each cropped high-resolution patch is searched within the second search window. Finally, each cropped high-resolution patch is added to the super-resolved image at its respective best-matching position.