An object is to provide an endoscopic image learning device, an endoscopic image learning method, an endoscopic image learning program, and an endoscopic image recognition device that appropriately learn a learning model for image recognition for recognizing an endoscopic image in which a treatment tool for an endoscope appears. The object is achieved by an endoscopic image learning device including an image generation unit and a machine learning unit. The image generation unit generates a superimposed image where a foreground image in which a treatment tool for an endoscope is extracted is superimposed on a background-endoscopic image serving as a background of the foreground image, and the machine learning unit performs the learning of a learning model for image recognition using the superimposed image.

Apparatus and methods for ascribing one of multiple predetermined sub-classes to multiple pixels of an image of an unknown rock sample retrieved from a geological formation. The ascription utilizes a deep learning model trained with an annotated training dataset. The annotated training dataset includes multi-pixel images of known rock samples and, for each known rock sample image, which sub-class corresponds to at least a subset of pixels of that image. For each pixel of the unknown rock sample image having an ascribed sub-class, which one of predetermined meta-classes is associated with that pixel is derived based on the sub-class ascribed to that pixel. The meta-classes represent different predetermined rock types. At least one property of the formation is predicted utilizing the ascription-derived meta-classes, including which rock type(s) are present in the formation.

The present application discloses a camera device for collecting vehicle information. In the present application, the camera device uses a dual-lens configuration including a first camera assembly and a second camera assembly, wherein the lens field of view of the first camera assembly intersects with the lens field of view of the second camera assembly, so as to form a layout mode that two areas on both sides of the camera device are photographed in a staggered manner, thereby avoiding the occurrence of a blind area between the lens fields of view of the first camera assembly and the second camera assembly and improving the overall photographing range of the camera device. Moreover, the camera device may introduce at least one auxiliary function such as photosensitive detection, light supplementing, and target detection, so as to improve the quality of image photographing and the accuracy of photographing moment, thereby facilitating improving the accuracy of vehicle information collection. In addition, the camera device is powered by an exchangeable dry battery module, and can also support antenna-based wireless transmission, thereby omitting cable connection and network wiring.

An optical dimensioning system includes one or more light emitting assemblies configured to project one or more predetermined patterns on an object; an imaging assembly configured to sense light scattered and/or reflected off the object, and to capture an image of the object while the patterns are projected; and a processing assembly configured to analyze the image of the object to determine one or more dimension parameters of the object. The light emitting assembly may include a single piece optical component configured for producing a first pattern and second pattern. The patterns may be distinguishable based on directional filtering, feature detection, feature shift detection, or the like. A method for optical dimensioning includes illuminating an object with at least two detectable patterns; and calculating dimensions of the object by analyzing pattern separate of the elements comprising the projected patterns. One or more pattern generators may produce the patterns.

An access point barrier system having a barrier that extends in such a way as to be movable between an enabled position and a non-enabled position preventing a vehicle from crossing the access point is provided. The image-capture device is integrated to the arm of the barrier and a computing device coupled to a database of registered vehicles, wherein the computing device is adapted to establish a match between the vehicle adjacent the barrier against the database of registered vehicles based on in part at least one image captured by the image-capture device so that an established match moves the barrier to the enabled position.

A system for monitoring for a gas leak from a gas containing structure is disclosed. The system includes a lens that directs an image of a scene of interest through an optical filter onto a detector. The filter is associated with the lens and the filter has one or more passbands that passes wavelengths which match one or more emission or reflectively wavelengths of the gas being monitored. A detector receives the image after the image passes through the lens and the filter. The detector generates image data representing the scene including the gas containing structure. A processor is configured to process the image data by executing machine executable code stored on a memory. The machine executable code processes the image data to identify turbulence flows in the image data such that turbulence flow indicates a gas leak, and generate and send an alert in response to the identification of a turbulence flow.

A system for monitoring for a gas leak from a gas containing structure is disclosed. The system includes a lens that directs an image of a scene of interest through an optical filter onto a detector. The filter is associated with the lens and the filter has one or more passbands that passes wavelengths which match one or more emission or reflectively wavelengths of the gas being monitored. A detector receives the image after the image passes through the lens and the filter. The detector generates image data representing the scene including the gas containing structure. A processor is configured to process the image data by executing machine executable code stored on a memory. The machine executable code processes the image data to identify turbulence flows in the image data such that turbulence flow indicates a gas leak, and generate and send an alert in response to the identification of a turbulence flow.

An image sensor is positioned such that a field-of-view of the image sensor encompasses at least a portion of a rack storing items. The image sensor generates angled-view images of the items stored on the rack. A tracking subsystem determines that a person is within a threshold distance of the rack and receives image frames of the angled-view images. A pixel position of a wrist of the person is determined in at least a subset of the received image frames, thereby determining a set of pixel positions of the wrist. An aggregated wrist position is determined based on the set of pixel positions. If the aggregated wrist position is determined to correspond to a position on a shelf of the rack, a trigger signal is provided indicating a shelf-interaction event has occurred.

A method of detecting biometric feature with an electronic device and the method includes the following steps. Firstly, a detection region is provided on the electronic, the electronic device includes a plurality of first sensor units and a plurality of second sensor units. Then, a first scanning light is generated in the detection region, and the first scanning light is sensed by the plurality of first sensor units. Next, a second scanning light is generated in the detection region, and the second scanning light is sensed by the plurality of second sensor units. Finally, a biometric feature is determined.

Method and computing device using a neural network to determine whether or not to process images of an image flow. A predictive model of the neural network is generated and stored at a computing device. The computing device receives (b) an image of the image flow and executes (c) the neural network, using the predictive model for generating an indication of whether or not to process the image based on input(s) of the neural network, the input(s) comprising the image. The computing device determines (d) whether or not to process the image by an image processing module, based on the indication of whether or not to process the image. The image is processed by the image processing module if the determination is positive and not processed if the determination is negative. Steps (b), (c), (d) are repeated for consecutive images of the image flow.