A computation unit calculates luminance differences of individual pixel pairs in a feature area and calculates, based thereon, a local feature value formed from bit values respectively corresponding to the pixel pairs. Specifically, the computation unit calculates a specific luminance difference for a specific pixel pair corresponding to a specific bit value and then compares the result with a specified range including a zero point of luminance difference. Then a first value is assigned to the specific bit value when the specific luminance difference is greater than the upper bound of the specified range. A second value is assigned to the same when the specific luminance difference is smaller than the lower bound of the specified range. A predetermined one of the first and second values is assigned to the same when the specific luminance difference falls in the specified range.

A system for digitizing gauges, lights and other human-readable machine gauges and functions and status without interfering with the operation of the machine or requiring re-working or interfering with the existing machine wiring, signaling, electrical or mechanical elements or operating modes, or adding new digitizing equipment to the machine.

A data analysis system according to the present invention includes: a training data acquisition unit that acquires a combination of training data including information about a medicinal drug and a plurality of pieces of classification information for classifying the training data on the basis of a plurality of classification standards; a learning unit that learns a pattern of the information about the medicinal drug from distribution of data elements which constitute at least part of the training data and appear according to the classification information; an unknown data acquisition unit that acquires unknown data from a specified information source; a data evaluation unit that evaluates the acquired unknown data on the basis of the learned pattern with respect to each of the plurality of classification standards; and a presentation unit that presents the information about the medicinal drug included in the unknown data to a user according to evaluation by the data evaluation unit.

Processes, systems, and devices for occlusion detection for video-based object tracking (VBOT) are described herein. Embodiments process video frames to compute histogram data and depth level data for the object to detect a subset of video frames for occlusion events and generate output data that identifies each video frame of the subset of video frames for the occlusion events. Threshold measurement values are used to attempt to reduce or eliminate false positives to increase processing efficiency.

A system and method are provided for capturing an image with correct skin tone exposure. In use, one or more faces having threshold skin tone are detected within a scene. Based on the detected one or more faces, a high dynamic range (HDR) capture mode is enabled. Further, the scene image is captured using the HDR capture mode.

Methods and systems for generating dynamic picture metadata are presented. Given an input picture generated on a mastering display, characteristics of a target display which is different than the mastering display, and an initial set of dynamic metadata for the input picture, an iterative algorithm: maps the input image to a mapped image for the target display according to a display management process and the image metadata, compares the input image to the mapped image according to a visual appearance-matching metric, and updates the image metadata using an optimization technique until a visibility difference value between the input image and the mapped image according to the visual appearance-matching metric is below a threshold.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

A histogram is calculated based on a road surface image of a portion around a vehicle, a running-allowed region in which the vehicle can run is detected based on the histogram, an obstacle region is extracted based on the running-allowed region, and a position of an obstacle in the obstacle region is detected, to further enhance the accuracy of detecting an obstacle around the vehicle as compared with conventional art.

A system may be configured to accurately track shelf activity in real-time with support for dynamic shelf size, configuration, and item containment. In some aspects, the system may parse regions of a video frame to determine a region of interest representation corresponding to a physical location (e.g., a shelf compartment), determine an enhanced region of interest representation based at least in part on the region of interest representation and an image enhancement pipeline, determine edge information of one or more objects based on the enhanced region of interest representation, compare a reference representation of the physical location to the edge information, and determine the amount of available space for the physical location based on the comparing.

An image segmentation method according to an embodiment of the present invention is performed in a computing device having one or more processors and memory for storing one or more programs executed by means of the one or more processors, and includes the steps of: (a) receiving the input of an image; (b) generating a first-generation image segment set by dividing the input image in an overlapped manner; and (c) generating a second or higher-generation image segment set from the first-generation image segment set, wherein a subsequent-generation image segment set is generated by dividing in an overlapped manner at least one of a plurality of image segments included in the previous-generation image segment set.