A method for process control of a semiconductor structure fabricated by a series of fabrication steps, the method comprising obtaining an image of the semiconductor structure indicative of at least two individual fabrication steps; wherein the image is generated by scanning the semiconductor structure with a charged particle beam and collecting signals emanating from the semiconductor structure; and processing, by a hardware processor, the image to determining a parameter of the semiconductor structure, wherein processing includes measuring step/s from among the fabrication steps as an individual feature.

There are provided a method for segmenting a sign language video by gloss to recognize a sign language sentence, and a method for training. According to an embodiment, a sign language video segmentation method receives an input of a sign language sentence video, and segments the inputted sign language sentence video by gloss. Accordingly, there is suggested a method for segmenting a sign language sentence video by gloss, analyzing various gloss sequences from the linguistic perspective, understanding meanings robustly in spite of various changes in sentences, and translating sign language into appropriate Korean sentences.

A training label image correction method includes performing a segmentation process on an input image (11) of training data (10) by a trained model (1) using the training data to create a determination label image (14), comparing labels of corresponding portions in the determination label image (14) and a training label image (12) with each other, and correcting label areas (13) included in the training label image based on label comparison results.

Apparatus for an automatic identification of medically relevant video elements, the apparatus comprising a data input, configured to receive a data stream of image slices, wherein the data stream of image slices represents a temporal course of a view of image slices defined by a masking strip of video images from a video which has been recorded during a medical surgery on a patient an analysis apparatus configured to analyze the data stream of image slices via an analysis comprising at least one predefined analysis step for the presence of at least one sought-for feature and to output a result of the presence, and a processing device configured to output a start mark which indicates a correspondence between the presence and a position in the data stream of image slices if the result indicates the presence of the sought-for feature. Also, a corresponding method is disclosed.

The invention concerns a method implemented by a device for displaying strokes of digital ink in a display area and for performing text line extraction to extract text lines from the strokes. In particular, the text line extraction may involve slicing the display area into strips, ordering for each strip the strokes into ordered lists which form collectively a first set of ordered lists, forming for each strip a second set of ordered lists by filtering out from the ordered lists of the first set strokes which are below a given size threshold, and performing a neural net analysis based on said first and second sets to determine for each stroke a respective text line to which it belongs.

This invention provides an information processing apparatus which comprises an image acquiring unit configured to acquire images captured by a plurality of image capturing devices, a contour extracting unit configured to extract a contour of an object from the image acquired by the image capturing device, a matching unit configured to match corresponding points on the contours between the images, a depth calculating unit configured to calculate a depth of the matched point, a reliability extracting unit configured to acquire a reliability of the depth based on an angle formed by an epipolar line and a tangential direction of the contour, and a depth correcting unit configured to correct the depth based on the depth and the reliability.

A vehicle communication and control system includes a first vehicle in signal communication with a remote computing system and/or a second vehicle. The first vehicle includes a sensor configured to capture a raw image having a first image volume and including at least one target object. An image encoder included in the vehicle converts the raw image into a masked image having a second image volume that is less than the first image volume. A segmentation unit included in the remote computing system and/or the second vehicle determines the at least one target object from the masked image, generates a masked segmented image including a sparse segmentation of the at least one target object, and converts the sparse segmentation of the at least one target object into at least one recovered segmented target object indicative of the at least one target object.

An image jaggedness filter is disclosed that can be used to detect the presence of ungrounded objects such as water droplets or coins, and delay periodic baseline adjustments until these objects are no longer present. To do otherwise could produce inaccurate normalized baseline sensor output values. The application of a global baseline offset is also disclosed to quickly modify the sensor offset values to account for conditions such as rapid temperature changes. Background pixels not part of any touch regions can be used to detect changes to no-touch sensor output values and globally modify the sensor offset values accordingly. The use of motion dominance ratios and axis domination confidence values is also disclosed to improve the accuracy of locking onto dominant motion components as part of gesture recognition.

A computing device of a first vehicle may receive a first image and a second image of a second vehicle having flashing light signals. The computing device may determine, in the first image and the second image, an image region that bounds the second vehicle such that the image region substantially encompasses the second vehicle. The computing device may determine a polar grid that partitions the image region in the first image and the second image into polar bins, and identify portions of image data exhibiting a change in color and a change in brightness between the first image and the second image. The computing device may determine a type of the flashing light signals and a type of the second vehicle; and accordingly provide instructions to control the first vehicle.

An automated segmentation and identification system/method for identifying geographic atrophy (GA) phenotypic patterns in fundus autofluorescence images. A hybrid process combines a supervised pixel classifier with an active contour algorithm. A trained, machine learning model (e.g., SVM or U-Net) provides initial GA segmentation/classification, and this is followed by Chan-Vese active contour algorithm. The junctional zones of the GA segmented area are then analyzed for geometric regularity and light intensity regularity. A determination of GA phenotype is made, at least in part, from these parameters.