Abstract: Apparatus and associated methods relate to a method of non-contact motion detection. A one-dimensional optical sensor detects motion of a target or objects on a conveyor belt through a continuous measurement of targets or objects and a real-time comparison of the pixel images captured by the one-dimensional optical sensor. In an illustrative embodiment, a one-dimensional sensor may be configured to determine motion of objects based on changes to the captured intensities of pixel images over time. The sensor may continually capture photoelectric pixel images and compare a current pixel image with a previous pixel image to determine a frame differential image value. The frame differential image value is evaluated against a predetermined threshold over a predetermined time period. Based on the evaluation, a signal is output indicating whether the objects on the conveyor belt are moving or jammed.

Abstract: A medical information processing apparatus according to an embodiment includes processing circuitry. The processing circuitry obtains image data rendering a blood vessel of a patient. The processing circuitry performs a fluid analysis on the obtained image data and calculates an index value related to a blood flow in the blood vessel with respect to each of a plurality of positions in the blood vessel. With respect to the index values to be calculated, the processing circuitry selects a position in which a first value is to be obtained from among the plurality of positions or selects a value serving as the first value from among the index values exhibited in positions. The processing circuitry causes a display to display the first value in a predetermined display region thereof used for displaying the first value.

Abstract: Some embodiments are directed to an image director of a patient monitoring system to obtain calibration images of a calibration sheet or other calibration object at various orientations and locations. The images are then stored and processed to calculate camera parameters defining the location and orientation of the image detector and identifying internal characteristics of the image detector, and the information are stored. The patient monitoring system can be re-calibrated by using the image detector to obtain an additional image of a calibration sheet or calibration object. The additional image and the stored camera parameters are then used to detect any apparent change in the internal characteristics of the image detector (10)(S6-4).

Abstract: An information processing device includes: a distinction unit that distinguishes a captured person on a basis of feature information of the captured person, on a basis of staff registration information containing feature information of staff members, and on a basis of customer registration information containing feature information of customers, the person captured by a camera; a staff-smile determination unit that determines, if one staff member of the staff members is distinguished as corresponding to the captured person, whether the one staff member corresponding to the captured person has smiled on a basis of a smile index of the captured person; a staff-smile counting unit that counts by which the staff members are determined to have smiled in a preset time period; a customer-revisit detection unit that detects that one customer of the customers has revisited; and a customer-revisit counting unit.

Abstract: A computer-implemented method is for identifying the presence of an image trigger in a digitally captured scene. The method includes, at a component installed on a client device and upon commencement of an image trigger matching operation: a) obtaining, from a multiplicity of image triggers, a subset of image triggers; b) subdividing the subset of image triggers into a plurality of image trigger sub-subsets; and c) for each sub-subset of image triggers in turn, and at predefined time intervals, submitting the sub-subset of image triggers to an Augmented Reality (AR) core Application Programming Interface (API), to cause the client device to cache the received sub-subsets of image triggers, search the digitally captured scene for the presence of one or more of the cached image triggers, and identify a positive match of an image trigger to said component using said AR core API.

Abstract: There are provided machine learning device and method which can prepare divided data suitable for machine learning from volume data for learning. A machine learning unit (15) calculates detection accuracy of each organ O(j,i) in a predicted mask Pj using a loss function Loss. However, the detection accuracy of the organ O(k,i) with a volume ratio A(k,i)<Th is not calculated. That is, in the predicted mask Pk, the detection accuracy of the organ O(k,i) with a volume ratio that is small to some extent is ignored. The machine learning unit (15) changes each connection load of a neural network (16) from an output layer side to an input layer side according to the loss function Loss.

Abstract: A jump counting method for jump rope is provided. The jump counting method comprises: S1, obtaining an original video data of a jump rope movement, and extracting an audio data and an image data from the original video data; S2, calculating the number of jumps of the rope jumper according to an audio information and an image information extracted from the audio data and the image data; and S3, outputting and displaying the calculation result.

Abstract: The invention refers to the field of processing and analyzing video data received from video surveillance cameras, and more specifically, to technologies aimed at detecting a human in a frame and at analyzing their posture for subsequent detection of potentially dangerous situations by video data. The system for detecting potentially dangerous situations contains video cameras, a memory, a graphical user interface (GUI), and a data processing device.

Abstract: A coding efficiency improvement is achieved by performing bit-plane coding in a manner so that coefficient groups for which the set of coded bit-planes is predictively signaled in the datastream, are grouped in two group sets and if a signal is spent in the datastream which signals, for a group set, whether the set of coded bit-planes of all coefficient groups of the respective group set are empty, i.e. all coefficients within the respective group sets are insignificant. In accordance with another aspect, a coding efficiency improvement is achieved by providing bit-plane coding with group-set-wise insignificant signalization according to the first aspect as a coding option alternative relative to the signalization for group sets for which it is signaled that there is no coded prediction residual for the coded bit-planes for the claim groups within the respective group set.

Abstract: The present disclosure relates generally improving visibility artifacts associated with encoded signals. A visibility change for local image areas associated with an encoded signal can be determined through use of a plurality of channel-specific contrast sensitivity functions. Of course, other features, and related claims and combinations are provided as well.

Abstract: A vehicle image processing device includes: a plurality of buffers configured to accumulate pieces of image data input individually and sequentially from a plurality of cameras installed in a vehicle so as to associate the pieces of image data with the cameras; a processor configured to select the buffer based on the state information of the vehicle and acquire the piece of image data from the selected buffer so as to perform image processing thereon; a signal line for transferring the pieces of image data in the buffers to the processor; and a transfer controller configured to output the piece of image data in the buffer required from the processor to the signal line.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing point cloud data representing a sensor measurement of a scene captured by one or more sensors to generate an object detection output that identifies locations of one or more objects in the scene. When deployed within an on-board system of a vehicle, the object detection output that is generated can be used to make autonomous driving decisions for the vehicle with enhanced accuracy.

Abstract: A road obstacle detection device which uses a pre-learned first identifier to associate a semantic label with each pixel of an image, uses a pre-learned second identifier to estimate a statistical distribution of a semantic label of a predetermined region of interest of the image from a statistical distribution of a semantic label of a peripheral region that surrounds the region of interest, and uses the statistical distribution of the semantic label associated with the region of interest and the statistical distribution of the semantic label estimated for the region of interest to estimate a likelihood that an object is a road obstacle.

Abstract: According to an embodiment, a calculation device includes a registerer, a first calculator, a receiver, and a second calculator. The registerer registers a plurality of patterns. The first calculator calculates a degree of closeness between the registered patterns. The receiver receives an input pattern. The second calculator calculates a first similarity value between the input pattern and a first registered pattern among the plurality of registered patterns, calculates second similarity values between the input pattern and one or more second registered patterns having a neighbor relationship with the first registered pattern among the plurality of registered patterns, and calculates a combined similarity value in which the first similarity value and the one or more second similarity values are combined.

Abstract: In example implementations, a method is provided. The method may be executed by a processor. The method includes receiving an image. The image is analyzed to obtain facial features. Contextual information is obtained and a vector including a facial feature class of the facial features and contextual feature classes of the contextual information is generated. A facial recognition is then performed based on the vector.

Abstract: A product analysis system 800 includes a detection means 810, a classification means 820, and a specification means 830. The detection means 810 detects an area of change in a product shelf from a video of the product shelf. The classification means 820 classifies the change in the product shelf in the detected area of change. The specification means 830 specifies the frequency at which a customer was interested in but did not purchase a product on the basis of the classification of the change.

Abstract: A coding efficiency improvement is achieved by performing bit-plane coding in a manner so that coefficient groups for which the set of coded bit-planes is predictively signaled in the datastream, are grouped in two group sets and if a signal is spent in the datastream which signals, for a group set, whether the set of coded bit-planes of all coefficient groups of the respective group set are empty, i.e. all coefficients within the respective group sets are insignificant. In accordance with another aspect, a coding efficiency improvement is achieved by providing bit-plane coding with group-set-wise insignificant signalization according to the first aspect as a coding option alternative relative to the signalization for group sets for which it is signaled that there is no coded prediction residual for the coded bit-planes for the claim groups within the respective group set.

Abstract: An abnormality determination device includes: an analysis unit that analyzes at least a feature amount related to a pattern of a captured image of a manhole cover, the feature amount being included in coded information obtained by coding the captured image; and a determination unit that determines based on an analysis result of the analysis unit whether the manhole cover is abnormal.

Abstract: A lane line recognition method, a lane line recognition device and a non-volatile storage medium. The method includes obtaining a first image which is a road image; dividing the first image from a middle of the first image to determine a first sub-image at a left part of the first image and a second sub-image at a right part of the first image; and performing respectively a recognition operation on the first sub-image and the second sub-image to determine a first lane line in the first sub-image and a second lane line in the second sub-image.

Abstract: A computer vision (CV) training system, includes: a supervised learning system to estimate a supervision output from one or more input images according to a target CV application, and to determine a supervised loss according to the supervision output and a ground-truth of the supervision output; an unsupervised learning system to determine an unsupervised loss according to the supervision output and the one or more input images; a weakly supervised learning system to determine a weakly supervised loss according to the supervision output and a weak label corresponding to the one or more input images; and a joint optimizer to concurrently optimize the supervised loss, the unsupervised loss, and the weakly supervised loss.