A solution is proposed for assisting a medical procedure. A corresponding method comprises acquiring a luminescence image (205F), based on a luminescence light, and an auxiliary image (205R), based on an auxiliary light different from this luminescence light, of a field of view (103); the field of view (103) contains a region of interest comprising a target body of the medical procedure (containing a luminescence substance) and one or more foreign objects. An auxiliary informative region (210Ri) representative of the region of interest without the foreign objects is identified in the auxiliary image (205R) according to its content, and a luminescence informative region (210Fi) is identified in the luminescence image (205F) according to the auxiliary informative region (210Ri). The luminescence image (205F) is processed limited to the luminescence informative region (210Fi) for facilitating an identification of a representation of the target body therein. A computer program and a corresponding computer program product for implementing the method are also proposed. Moreover, a computing device for performing the method and an imaging system comprising it are proposed. A medical procedure based on the same solution is further proposed.

An apparatus is proposed for identifying respective cover slip regions of respective cover slips having respective tissue sections on a specimen slide, which has multiple optical identifiers. The apparatus includes a planar light source, an image acquisition unit, a holding unit for positioning the specimen slide between the planar light source and the image acquisition unit, a slit diaphragm, which has multiple opening slits, reversibly positionable between the planar light source and the specimen slide, and an illumination unit, which is designed to illuminate that surface of the specimen slide which faces toward the image acquisition unit. Furthermore, the apparatus includes a monitoring unit which is designed, on the basis of a completely illuminated transmitted light image, an incident light image, and a partially darkened transmitted light image, to assign respective tissue sections to respective optical identifiers.

A method of classifying an object according to an embodiment includes extracting a first feature by transforming rectangular coordinates of points included in the box of the object, obtained from a point cloud acquired using a LiDAR sensor, into complex coordinates and performing Fast Fourier Transform (FFT) on the complex coordinates, obtaining an average and a standard deviation as a second feature, the average and the standard deviation being parameters of a Gaussian model for the points included in the box of the object, and classifying the type of object based on at least one of the first feature or the second feature.

The disclosure relates, in part, to computer-based visualization of stent position within a blood vessel. A stent can be visualized using intravascular data and subsequently displayed as stent struts or portions of a stent as a part of a one or more graphic user interface(s) (GUI). In one embodiment, the method includes steps to distinguish stented region(s) from background noise using an amalgamation of angular stent strut information for a given neighborhood of frames. The GUI can include views of a blood vessel generated using distance measurements and demarcating the actual stented region(s), which provides visualization of the stented region. The disclosure also relates to display of intravascular diagnostic information such as indicators. An indicator can be generated and displayed with images generated using an intravascular data collection system. The indicators can include one or more viewable graphical elements suitable for indicating diagnostic information such as stent information.

According to an aspect of the present invention, an information processing apparatus includes an object region detecting unit, a local region detecting unit, an object specifying unit. The object region detecting unit is configured to detect an object region based on one of distance information and luminance information. The local region detecting unit is configured to, when a divided area obtained by dividing the detected object region meets a predetermined condition, detect the divided area as a local region. The object specifying unit is configured to specify, as a specification target object, the object region in which the local region is continuously detected.

In various examples, a low-latency variable backlight liquid crystal display (LCD) system is disclosed. The LCD system may reduce latency and video lag by performing an analysis of peak pixel values within subsets of pixels using a rendering device, prior to transmitting the frame to a display device for display. As a result, the display device may receive the peak pixel value data prior to or concurrently with the frame data, and may begin updating the backlight settings of the display without having to wait for a substantial portion of the frame to be received. In this way, the LCD system may avoid the full frame delay of conventional systems, allowing the LCD system to more reliably support high-performance applications such as gaming.

An image capture device may automatically capture images. An image sensor may generate visual content based on light that becomes incident thereon. A depiction of interest within the visual content may be identified, and one or more images may be generated to include one or more portions of the visual content including the depiction of interest.

An on-board outside recognition apparatus extracts a feature point from an image including an environment around a user's own vehicle, measures a three-dimensional position of the feature point based on movement of the feature point tracked in time series on the image and calculates a foot position of the feature point on the image from the three-dimensional position of the feature point is performed. Then, the on-board outside recognition apparatus extracts a road surface area where the user's own vehicle can travel, from the image using a degree of similarity of a texture of the image, and judges a feature point the foot position of which is judged not to be included in the road surface area to be highly reliable.

A method of recognizing an image is provided, which relates to a field of artificial intelligence technology, in particular to a field of image recognition. The method includes: recognizing a plurality of target object groups of different categories from an image to be recognized; intercepting an area of each target object group from the image to be recognized, so as to obtain a target image of the each target object group; recognizing a number of at least one target object in the each target object group from the target image of the each target object group; and generating a scheduling information for the each target object group according to the category of the each target object group and the number of the at least one target object in the each target object group. An electronic device and a storage medium are further provided.

The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.