A method for selecting samples of reticle design data patterns in order to calibrate the parameters based on which the reference image used in a die-to-database reticle inspection method is rendered, the method comprising the steps of applying local binary pattern (LBP) analysis to a plurality of samples to obtain a p-dimensional vector output for each of the plurality of samples, clustering the q-D data points to M groups, selecting one sample from each clustered group, calculating evaluation scores for the samples selected, and, selecting a portion of the M samples on the representativeness score and the diversity score.

An object recognition method performed by an object recognition system includes: setting a first binarization area in a portion of a binarization target area of an image, and a first coefficient calculation area including the first binarization area and having an area wider than the first binarization area by a predetermined ratio; determining a first binarization coefficient on the basis of pixel values included in the first coefficient calculation area; performing binarization on the first binarization area through the determined first binarization coefficient; setting a second binarization area in a portion other than the first binarization area, and a second coefficient calculation area including the second binarization area and having an area wider than the second binarization area; determining a second binarization coefficient on the basis of pixel values included in the second coefficient calculation area; and performing binarization on the second binarization area through the determined second binarization coefficient.

Various disclosed embodiments relate to video content analysis based in part upon the detection of shot transitions. In some embodiments, a process and computer system for detecting shot transitions in a video is used to separate a video sequence into a series of “shots” having multiple frames. These shots may then be used for additional processing, e.g., content detection within the video frames.

This disclosure describes techniques to detect an object. The techniques include operations comprising: receiving an image captured by overhead camera; identifying a region of interest (ROI) of a plurality of regions within the image; selecting an object classifier from a plurality of object classifiers based on a position of the identified ROI relative to the overhead camera; and applying the selected. object classifier to the identified ROI; and detecting presence of the object within the ROI in response to applying the selected object classifier to the identified ROI.

A pedestrian detecting system includes a depth capturing unit, an image capturing unit and a composite processing unit. The depth capturing unit is configured to detect and obtain spatial information of a target object. The image capturing unit is configured to capture an image of the target object and recognize the image, thereby obtaining image feature information of the target object. The composite processing unit is electrically connected to the depth capturing unit and the image capturing unit, wherein the composite processing unit is configured to receive the spatial information and the image feature information and to perform a scoring scheme to detect and determine if the target object is a pedestrian. The scoring scheme performs weighted scoring on a spatial confidence and an appearance confidence to obtain a composite scoring value to determine if the target object is the pedestrian.

A super-resolution method according to the inventive concept may include receiving an input image of low resolution, separating the input image into low resolution (LR) unit patches, classifying a texture type of each of pixels included in the input image using a local binary pattern for the LR unit patches, generating high resolution (HR) unit patches corresponding to each of the pixels based on a mapping kernel corresponding to the texture type, and combining the HR unit patches based on a predetermined setting to generate an output image of high resolution.

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical feature identification algorithm to identify one or more patient specific anatomical features within the medical images by exploring an anatomical knowledge dataset. A 3D surface mesh model may be generated representing the one or more classified patient specific anatomical features, such that information may be extracted from the 3D surface mesh model based on the selected pathology. Physiological information associated with the selected pathology for the 3D surface mesh model may be generated based on the extracted information.

At least one example embodiment discloses an image feature extracting method. The method includes determining a probabilistic model based on pixel values of pixels in a kernel, determining image feature information of a current pixel of the pixels in the kernel and determining whether to change the image feature information of the current pixel based on a random value and a probability value of the current pixel, the probability value being based on the probabilistic model.

An inspection method includes the following steps: identifying a plurality of patterns within an image; and comparing the plurality of patterns with each other for measurement values thereof. The above-mentioned inspection method uses the pattern within the image as a basis for comparison; therefore, measurement values of the plurality of pixels constructing the pattern can be processed with statistical methods and then compared, and the false rate caused by variation of a few pixels is decreased significantly. An inspection system implementing the above-mentioned method is also disclosed.

Methods and systems of an intelligent nanny assistant are described. A processor may receive image-related data from a monitoring system and determine whether a subject of concern is approaching a predefined area of the environment based on the image-related data. In response to a determination that the subject of concern is approaching the predefined area, the processor may control an information output system to provide visual information, audible information, or both the visual information and the audible information in a way that attracts the subject of concern to move away from the predefined area.