Abstract: The present invention provides a word recognition method. The method includes: acquiring an image of a word to be recognized; recognizing edges of each character of the word to be recognized from the image of the word to be recognized; determining a geometric position of the word to be recognized; stretching the geometric position of the word to be recognized to a horizontal position; and recognizing the word to be recognized in the horizontal position.

Abstract: When a user designates a region of interest for a plurality of groups targeted for difference analysis in a microscopic observation image of a sample, an m/z candidate search unit searches for candidates for m/z presumed to differ, based on collected mass spectral data. An intensity histogram creation unit processing unit creates and displays a graph showing a frequency distribution of peak intensities at measurement points included in the ROI of the groups for each of the m/z candidates. If this graph exhibits multimodality, the data distribution is not suitable for a statistical hypothesis test. An intensity range determination unit limits an intensity range in accordance with a user's instruction. Then, ROI correction unit corrects the ROI so as to include only measurement points with peak intensities within the limited intensity range. A test processing unit performs a statistical hypothesis test using the data corresponding to the corrected ROI.

Abstract: An apparatus including a capture device and a processor. The capture device may be configured to generate pixel data of a location. The processor may be configured to generate video frames from said pixel data, perform video operations to detect objects in the video frames, extract data about the objects based on characteristics of the objects determined using the video operations, compare the data to a list of restrictions for the location and generate a notification in response to the data matching an entry of the list of restrictions. The video frames may be discarded after performing the video operations. The video operations may be performed locally by the processor.

Abstract: A driver monitor includes a processor configured to detect the orientation and the position of the face of a driver of a vehicle from each of face images obtained by a camera for capturing the driver, update a reference direction of orientations or a reference position of the driver's face, based on orientations or positions of the driver's face detected from face images obtained in a preceding predetermined period, and detect putting a wearable object on or taking a wearable object off the driver's face in each face image. Upon detection of putting on or taking off the wearable object, the processor updates the reference direction or the reference position, based on only orientations or positions of the driver's face detected from face images obtained after the detection.

Abstract: A system comprising a server and a camera. The server may be configured to receive a rental offer for a location from a first user, receive a rental request for the location from a second user, generate a rental agreement comprising conditions in response to the rental offer and the rental request and generate a feature set based on the conditions. The camera may be configured to receive the feature set from the server, convert the feature set to parameters for the camera, generate a plurality of video frames of the location, perform video operations to detect objects in the video frames, extract data about the objects using the video operations, compare the data to the parameters and generate a notification in response to the data matching the parameters. The video frames may be discarded after performing the video operations. The video operations may be performed locally by the camera.

Abstract: The method of matching digital images of the same article in a data processor unit comprises the steps of: transforming each digital image of an article into a local divergence topographic map of the luminance gradient vector field; detecting singularities or extrema of local divergence in the luminance gradient vector field, such singularities corresponding to points of interest in said digital image; and, for each detected point of interest, encoding the values for the singularity of the gradient field that are located on a plurality of concentric rings centered on the point of interest so as to derive a digital data vector (210); and transforming said vector into a digital hash key (220) by means of a family of hash functions of the cosine Locality-Sensitive Hashing (LSH) type.

Abstract: A method, apparatus, and non-transitory computer readable medium for image processing are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include identifying an original image including a plurality of semantic attributes, wherein each of the semantic attributes represents a complex set of features of the original image; identifying a target attribute value that indicates a change to a target attribute of the semantic attributes; computing a modified feature vector based on the target attribute value, wherein the modified feature vector incorporates the change to the target attribute while holding at least one preserved attribute of the semantic attributes substantially unchanged; and generating a modified image based on the modified feature vector, wherein the modified image includes the change to the target attribute and retains the at least one preserved attribute from the original image.

Abstract: This disclosure includes technologies for action recognition in general. The disclosed system may automatically detect various types of actions in a video, including reportable actions that cause shrinkage in a practical application for loss prevention in the retail industry. Further, appropriate responses may be invoked if a reportable action is recognized. In some embodiments, a three-branch architecture may be used in a machine learning model for action and/or activity recognition. The three-branch architecture may include a main branch for action recognition, an auxiliary branch for learning/identifying an actor (e.g., human parsing) related to an action, and an auxiliary branch for learning/identifying a scene related to an action. In this three-branch architecture, the knowledge of the actor and the scene may be integrated in two different levels for action and/or activity recognition.

Abstract: There is provided a system and method of segmenting an image of a fabricated semiconductor specimen. The method includes: obtaining a first probability map corresponding to the image representative of at least a portion of the fabricated semiconductor specimen and indicative of predicted probabilities of pixels in the image to correspond to one or more first structural elements presented in the image, obtaining a first label map informative of one or more segments representative of second structural elements and labels associated with the segments, performing simulation on the first label map to obtain a second probability map indicative of simulated probabilities of pixels in the first label map to correspond to the one or more segments, and generating a second label map based on the first probability map and the second probability map, the second label map being usable for segmentation of the image with enhanced repeatability.

Abstract: Methods and systems for optimizing one or more imaging settings for a machine vision job are disclosed herein. An example method includes detecting, by one or more processors, an initiation trigger that initiates the machine vision job. The example method further includes, responsive to detecting the initiation trigger, capturing, by an imaging device, a first image of a target object in accordance with a first configuration of the one or more imaging settings. The example method further includes, responsive to capturing the first image of the target object, automatically adjusting, by the one or more processors, the one or more imaging settings to a second configuration that includes at least one different imaging setting from the first configuration; and capturing, by the imaging device, a second image of the target object in accordance with the second configuration of the one or more imaging settings.

Abstract: A geographic object recognition unit (120) recognizes, using image data (192) obtained by photographing in a measurement region where a geographic object exists, a type of the geographic object from an image that the image data (192) represents. A position specification unit (130) specifies, using three-dimensional point cloud data (191) indicating a three-dimensional coordinate value of each of a plurality of points in the measurement region, a position of the geographic object.

Abstract: Radio frequency (RF) generators are disclosed. A RF generator may include a modulator configured to receive an arbitrary waveform and an RF carrier, and generate a pulsed radio frequency (RF). The arbitrary waveform may be generated via an analog signal generator external to the RF generator. Further, the RF generator may include an amplification stage configured to amplify the pulsed RF signal. RF generation systems and methods of generating a pulsed RF signal also disclosed.

Abstract: Identifying activity in an area even during periods of poor visibility using a video camera and an audio sensor are disclosed. The video camera is used to identify visible events of interest and the audio sensor is used to capture audio occurring temporally with the identified visible events of interest. A sound profile is determined for each of the identified visible events of interest based on sounds captured by the audio sensor during the corresponding identified visible event of interest. Then, during a time of poor visibility, a subsequent sound event is identified in a subsequent audio stream captured by the audio sensor. One or more sound characteristics of the subsequent sound event are compared with the sound profiles associated with each of the identified visible events of interest, and if there is a match, one or more matching sound profiles are filtered out from the subsequent audio stream.

Abstract: A sensing flux system includes a memory and a processor in communication with the memory and a sensing device, the memory storing a plurality of capabilities and a plurality of semantic fluxes associated with the plurality of capabilities. The computing system is configured to infer a semantic associated with an indicated user interest based on received inputs and route the inputs to the semantic fluxes based on semantic drift inference between their associated capabilities and inferred semantic in association with a time preference indicated by the user interest.

Abstract: A device includes a primary sector and secondary sectors communicatively coupled to the primary sector. The processor included in the primary sector is configured to down convert a Radio Frequency (RF) signals with a first frequency to an analog baseband (IQ) signal with a second frequency, and receive a second digital baseband signal that comprises a first digital baseband signal and a digital echo signal. The first digital baseband signal comprises a training sequence signal. Further, the processor estimates a plurality of filter taps of the FIR filter based on the digital echo signal and estimate the digital echo signal in the received second digital baseband signal based on the first digital baseband signal and the plurality of filter taps of the FIR filter. The estimated digital echo signal is removed from at least one current digital baseband signal based on the down conversion of the RF signals.

Abstract: A flux sensing system includes a memory and a processor in communication with the memory and at least one sensing device, the memory storing a plurality of capabilities and a plurality of semantic fluxes associated with the plurality of capabilities. Based on inputs from the at least one sensing device, the computing system is configured to determine an active servicing capability associated with a first semantic flux and/or a consumer interest associated with a second semantic flux and match the interest with the capability based on semantic drift inference.

Abstract: A semantic augmentation system includes a sensor with a computing system and a memory in communication with the computing system, the memory storing a plurality of endpoints. The computing system is configured to infer a first and a second semantic identity for an object, based on inputs from the sensor, project a coherent narrative and perform semantic augmentation towards a user. In further examples, the system infers a first narrative comprising two semantic identities and a second narrative wherein the system infers that a user observing view didn't infer the second semantic identity and further doesn't use the second semantic identity in the second narrative. It further, uses the corresponding narrative to remind the user to carry an item and/or credential in order to start an activity.

Abstract: A plurality of robotic posts each include at least one processor, a memory and at least one divider panel secured to the post through a securable latch controlled through the at least one processor. A robotic post may move to a divider location to enforce access control between a first region and a second region divided, at least partially, by its at least one divider panel.

Abstract: A robotic post includes a processor and a memory. The robotic post may include a manipulation arm and a swiveling or otherwise movable trunk or base. One or more sensors provided on the robotic post enable the robotic post to determine the position and location of a piece of luggage. The processor, based on the sensor input, causes the robotic post to rotate, tilt or move toward the luggage to orient and secure a hook or gripper onto the handle of the luggage. The post may move, under control of the processor, to another location. When presented with authorization by a user, the luggage is released at the second location.

Abstract: A computer-implemented method comprises receiving an input associated with the arrival of an entity, performing a classification on the input to determine a purpose of the arrival of the entity, and based on a determined classification of the purpose of the arrival of the entity, invoking an action.