Abstract: In certain embodiments, the systems, apparatus, and methods disclosed herein relate to robotic surgical systems with built-in navigation capability for patient position tracking and surgical instrument guidance during a surgical procedure, without the need for a separate navigation system. Robotic based navigation of surgical instruments during surgical procedures allows for easy registration and operative volume identification and tracking. The systems, apparatus, and methods herein allow re-registration, model updates, and operative volumes to be performed intra-operatively with minimal disruption to the surgical workflow. In certain embodiments, navigational assistance can be provided to a surgeon by displaying a surgical instrument's position relative to a patient's anatomy.

Abstract: Methods, systems, and apparatuses related to autonomous vehicle object detection are described. An autonomous vehicle can capture an image corresponding to an unknown object disposed within a sight line of the autonomous vehicle. Processing resources available to a plurality of memory devices associated with the autonomous vehicle can be reallocated in response to capturing the image and an operation involving the image corresponding to the unknown object to classify the unknown object can be performed using the reallocated processing resources.

Abstract: A vehicle light signal detection and recognition method, system, and computer program product include bounding, using a coarse attention module, one or more regions of an image of an automobile including at least one of a brake light and a signal light generated by automobile signals which include illuminated sections to generate one or more bounded region, removing, using a fine attention module, noise from the one or more bounded regions to generate one or more noise-free bounded regions, and identifying the at least one of the brake light and the signal light from the one or more noise-free bounded regions.

Abstract: Disclosures herein teach applying a set of sections spanning a down-sampled version of an image of a road-scene to a low-fidelity classifier to determine a set of candidate sections for depicting one or more objects in a set of classes. The set of candidate sections of the down-sampled version may be mapped to a set of potential sectors in a high-fidelity version of the image. A high-fidelity classifier may be used to vet the set of potential sectors, determining the presence of one or more objects from the set of classes. The low-fidelity classifier may include a first Convolution Neural Network (CNN) trained on a first training set of down-sampled versions of cropped images of objects in the set of classes. Similarly, the high-fidelity classifier may include a second CNN trained on a second training set of high-fidelity versions of cropped images of objects in the set of classes.

Abstract: An onboard camera image application system includes multiple vehicles, an information providing system, and a PC. In the information providing system, a collection server collects image data captured by onboard cameras of the vehicles. An analysis server extracts, from the image data captured around one location, feature information associated with an extraction item for a person or a vehicle around the location. The analysis server also classifies the extracted feature information by classification item. A providing server sends the classified feature information to the PC of a user. The user is able to set a location to be processed, through the PC, and is also able to set the extraction item or the classification item.

Abstract: A system and method detect a change within an agricultural field during a growing season. The system and method involve the computation of correlation coefficients between pairs of images in a time series of images over an area, for example an agricultural field. Through comparisons of correlation coefficients over time, sudden field changes can be detected. When these field changes meet specified criteria (such as a minimum value threshold), a notification (alert) can be sent to an agricultural grower, informing them of the change in crop condition, and allowing them to view the changes in the field.

Abstract: The image processing device acquires feature quantities (maximum value, minimum value, average value, histogram, etc.) of the entire area (GA) of the image and feature quantities of each local area (LA) of the image from the input image, and calculates a plurality of modulation gain values (gamma correction curves) for GA and each LA. Furthermore, the image processing device determines the correction intensity for each LA from the feature quantity of the GA and the feature quantity of each LA, and creates the LA correction intensity map. Finally, the image processing device finally applies the result of combining a plurality of modulation gain values based on the LA correction intensity map to the input image.

Abstract: In a road parameter estimation apparatus, a marker-based estimator extracts, based on markers extracted by a marker extractor, at least one lane line that demarcates a road into plural regions in a width direction of the road, and estimates, based on the extracted at least one lane line, a value of at least one feature parameter of the road as a maker-based estimation result. The at least one feature parameter of the road represents at least one feature of the road. A model-based estimator estimates, based on the at least one model-based demarcation line, a value of the at least one feature parameter of the road parameter as a model-based estimation result. A determiner compares the at least one lane line with the model-based demarcation line to accordingly determine whether to use the marker-based estimation result or the model-based estimation result.

Abstract: According to some embodiments, disclosed are systems and methods for a novel framework of real-time event alert detection and communication. The disclosed framework operates by analyzing live-feeds of captured video at location and determining whether events lend towards a dangerous activity, then automatically alerting the users involved as to potential and/or imminent harm awaiting their actions. Rather than alerting one user, or a manger, as in conventional systems, the disclosed technology may evidence a communication relay among devices at a location, devices of users involved, as well as devices (and devices of users) overseeing operations within which the dangerous activity is anticipated or detected. This may lead to improved safety at and/or around workplace environments, as well as improved operational efficiency, thereby leading to reduced costs, reduced overhead and a reduction in resource expenditure.

Abstract: Aspects of the present disclosure relate to multi-spectrum visual object recognition. A first image corresponding to visible light and a second image corresponding to invisible light with respect to an object can be obtained. A first contour of the object can be identified based on the first image. A second contour of the object can be identified based on the second image. The first contour of the object and the second contour of the object can be integrated to generate a multi-spectrum contour of the object. The object can be recognized using the multi-spectrum contour of the object.

Abstract: A tiptoe position estimating device includes: a camera configured to capture an image of an interior of a vehicle; and a human body information acquisition unit configured to acquire, from a captured image obtained by capturing an image of a person seated on a seat in the interior by the camera, position information of skeleton points at a plurality of positions of a body of the person. The human body information acquisition unit is configured to acquire thigh information which is position information of a skeleton point of a thigh of the person, and lower leg information which is position information of a skeleton point of a lower leg of the person. The human body information acquisition unit includes a tiptoe position estimating part configured to estimate a tiptoe position of a foot of the person based on the acquired thigh information and the acquired lower leg information.

Abstract: The present disclosure is directed to apparatuses, systems and methods for automatically classifying images of occupants inside a vehicle. More particularly, the present disclosure is directed to apparatuses, systems and methods for automatically classifying images of occupants inside a vehicle by comparing current image feature data to previously classified image features.

Abstract: A method for determining a safe state for a vehicle includes disposing a camera at a vehicle and disposing an electronic control unit (ECU) at the vehicle. Image data is captured via the camera and provided to the ECU. An image processor of the ECU processes captured image data. A condition is determined via processing at the image processor of the ECU captured image data. The condition comprises a shadow present in the field of view of the camera within ten frames of captured image data or a damaged condition of the imager within two minutes of operation of the camera. The condition is indicative of a condition where processing of captured image data degrades in performance. The ECU determines a safe state for the vehicle responsive to determining the condition.

Abstract: A driving assistance device includes: a line-of-sight direction detection unit that detects a direction of a line of sight of a driver of a moving body; an obstacle detection unit that detects a position of an obstacle in environs of the moving body; an assessment criteria determination unit that determines assessment criteria of a look at the obstacle by the driver based on at least one of time, location, weather, a state of the moving body, and a state of the driver; and a warning processing unit that obtains an assessment result by applying, to the assessment criteria, a score computed based on the direction of the line of sight of the driver and the position of the obstacle, the warning processing unit determining at least one of whether or not a warning needs to be issued to the driver and a level of the warning, based on the assessment result.

Abstract: A sequence of images depicting an object is captured, e.g., by a camera at a point-of-sale terminal in a retail store. The object is identified, such as by a barcode or watermark that is detected from one or more of the images. Once the object's identity is known, such information is used in training a classifier (e.g., a machine learning system) to recognize the object from others of the captured images, including images that may be degraded by blur, inferior lighting, etc. In another arrangement, such degraded images are processed to identify feature points useful in fingerprint-based identification of the object. Feature points extracted from such degraded imagery aid in fingerprint-based recognition of objects under real life circumstances, as contrasted with feature points extracted from pristine imagery (e.g., digital files containing label artwork for such objects).

Abstract: An anomaly detection apparatus having a function of an autoencoder that includes an input layer, hidden layers, and an output layer, and that learns parameters such that data of the input layer is reproduced in the output layer, the anomaly detection apparatus including: input means that input normal data of a plurality of types; learning means that learn parameters such that normal data of the input layer is reproduced in the output layer by learning a feature across data types using data of a dimension which is smaller than a dimension of the normal data; and anomaly degree calculation means that input test data to the autoencoder using the parameters learned by the learning means, and calculate anomaly degree of the test data based on output data of the autoencoder and the test data.

Abstract: The present application discloses a method and apparatus for extracting a geographic location point spatial relationship, and relates to the field of big data technologies. A specific implementation solution is as follows: determining geographic location point pairs included in real-scene images by performing signboard recognition on the real-scene images collected by terminal devices; acquiring at least two real-scene images collected by the same terminal device and including the same geographic location point pair; and determining a spatial relationship of the same geographic location point pair by using shooting parameters of the at least two real-scene images. The geographic location point spatial relationship extracted through the present application has higher accuracy and a coverage rate.

Abstract: A cascaded machine learning inference system and method is disclosed. The cascaded system and method may be designed to be employed in resource restricted environments. The cascaded system and method may be applicable for applications that operate with limited power (e.g., a wearable smart watch). The cascaded system and method may employ two or more subsystems that are operable to classify an input signal provided by any number or types of sensors suitable for a given application. For instance, the sensors used may include gyroscopes, accelerometers, magnetometers, or barometric altimeters. The system and method may also be further split functionality across additional or new subsystems. By splitting operations and functionality across additional subsystems, the overall power consumption may further be reduced.

Abstract: A prism of an approximately quadrangle-frustum shape is arranged so that a bottom side, out of two parallel surfaces of the prism, is a placing surface side for a finger. A first imaging unit arranged below a top surface parallel to the bottom surface images an image of the finger transmitted through the top surface. A light source radiates light to at least one side surface of a first set of side surfaces, out of two sets of side surfaces of the approximately quadrangle-frustum shape that face each other. A second imaging unit images the image of the finger transmitted through a second set of side surfaces, out of the two sets of side surfaces. An infrared ray light source radiates infrared ray light into the finger so that the infrared ray light is scattered inside the finger and is received by the imaging unit.

Abstract: Tracking units for facial features with advanced training for natural rendering of human faces in real-time are provided. An example device receives a video stream, and upon detecting a visual face, selects a 3D model from a comprehensive set of head orientation classes. The device determines modifications to the selected 3D model to describe the face, then projects a 2D model of tracking points of facial features based on the 3D model, and controls, actuates, or animates hardware based on the facial features tracking points. The device can switch among an example comprehensive set of 35 different head orientation classes for each video frame, based on suggestions computed from a previous video frame or from yaw and pitch angles of the visual head orientation. Each class of the comprehensive set is trained separately based on a respective collection of automatically marked images for that head orientation class.