A visible light sensor may be configured to sense environmental characteristics of a space using an image of the space. The visible light sensor may be controlled in one or more modes, including a daylight glare sensor mode, a daylighting sensor mode, a color sensor mode, and/or an occupancy/vacancy sensor mode. In the daylight glare sensor mode, the visible light sensor may be configured to decrease or eliminate glare within a space. In the daylighting sensor mode and the color sensor mode, the visible light sensor may be configured to provide a preferred amount of light and color temperature, respectively, within the space. In the occupancy/vacancy sensor mode, the visible light sensor may be configured to detect an occupancy/vacancy condition within the space and adjust one or more control devices according to the occupation or vacancy of the space. The visible light sensor may be configured to protect the privacy of users within the space via software, a removable module, and/or a special sensor.

A visible light sensor may be configured to sense environmental characteristics of a space using an image of the space. The visible light sensor may be controlled in one or more modes, including a daylight glare sensor mode, a daylighting sensor mode, a color sensor mode, and/or an occupancy/vacancy sensor mode. In the daylight glare sensor mode, the visible light sensor may be configured to decrease or eliminate glare within a space. In the daylighting sensor mode and the color sensor mode, the visible light sensor may be configured to provide a preferred amount of light and color temperature, respectively, within the space. In the occupancy/vacancy sensor mode, the visible light sensor may be configured to detect an occupancy/vacancy condition within the space and adjust one or more control devices according to the occupation or vacancy of the space. The visible light sensor may be configured to protect the privacy of users within the space via software, a removable module, and/or a special sensor.

An optical neural network apparatus that optically implements an artificial neural network includes an input layer, a hidden layer, and an output layer sequentially arranged in a traveling direction of light, wherein the output layer includes an image sensor including a plurality of light sensing pixels arranged in two dimensions, and wherein the input layer or the hidden layer includes at least one passive phase modulator configured to locally modulate a phase of incident light depending on positions on a two dimensional plane.

An optical neural network apparatus that optically implements an artificial neural network includes an input layer, a hidden layer, and an output layer sequentially arranged in a traveling direction of light, wherein the output layer includes an image sensor including a plurality of light sensing pixels arranged in two dimensions, and wherein the input layer or the hidden layer includes at least one passive phase modulator configured to locally modulate a phase of incident light depending on positions on a two dimensional plane.

An image stitching method includes: receiving a first image and a second image; determining that both the first image and the second image include a target object; obtaining a first brightness value and a second brightness value, the first brightness value being a brightness value of the target object in the first image, and the second brightness value being a brightness value of the target object in the second image; adjusting a brightness value of the first image and a brightness value of the second image according to the first brightness value and the second brightness value, so as to obtain a first image to be stitched and a second image to be stitched; and stitching the first image to be stitched and the second image to be stitched to obtain a first stitched image.

An image stitching method includes: receiving a first image and a second image; determining that both the first image and the second image include a target object; obtaining a first brightness value and a second brightness value, the first brightness value being a brightness value of the target object in the first image, and the second brightness value being a brightness value of the target object in the second image; adjusting a brightness value of the first image and a brightness value of the second image according to the first brightness value and the second brightness value, so as to obtain a first image to be stitched and a second image to be stitched; and stitching the first image to be stitched and the second image to be stitched to obtain a first stitched image.

Examples of the disclosure describe systems and methods for identifying, quantifying, and/or characterizing plant stomata. In an example method, a first set of two or more images of a plant leaf representing two or more focal distances is captured via an optical sensor. A reference focal distance is determined based on the first set of images. A second set of two or more images of the plant leaf is captured via the optical sensor, including at least one image captured at a focal distance less than the reference focal distance, and at least one image captured at a focal distance greater than the reference focal distance. A composite image is generated based on the second set of images. The composite image is provided to a trainable feature detector in order to determine a number, density, and/or distribution of stomata in the composite image.

A computer system is configured to remove motion artifacts in medical images using a generative adversarial network (GAN). The computer system instantiates the GAN having one or more generative network(s) and one or more discriminative network(s) that are pitted against each other to train a generative model and a discriminative model. The training uses a training dataset including a plurality of medical images that are previously classified as without significant motion artifacts for diagnostic purposes. The discriminative model is trained to classify medical images as real or artificial. The generative model is trained to enhance the quality of a medical image and remove motion artifacts by producing a medical image directly from a post-contrast image, without using a pre-contrast mask.

Concepts and technologies disclosed herein are directed to pictograms as digitally recognizable tangible controls. According to one aspect disclosed herein, a user system can include a processing component and a memory component. The memory component can include instructions of a pictogram digitization module. The user system can capture, via a camera component, an image containing a pictogram that is a digitally recognizable tangible manifestation of a digital control. The user system can determine, via the pictogram digitization module, the digital control associated with the pictogram. The user system can implement, via the pictogram digitization module, the digital control. The digital control can include a digital content, an action, or a context. The user system can create, via the pictogram digitization module, a digital interface that includes the digital control. In some embodiments, the pictogram includes a formal pictogram. In other embodiments, the pictogram includes an informal pictogram.

A refrigerator appliance is provided including a cabinet defining a chilled chamber, a door rotatably hinged to the cabinet to provide selective access to the chilled chamber, and an inventory management system mounted within the chilled chamber for monitoring objects positioned within the chilled chamber. The inventory management system includes a camera assembly that obtains a plurality of images of food items as they are being added to or removed from the chilled chamber. A controller of the appliance analyzes the images using a machine learning image recognition process to identify an object and monitor the object between different images to determine a motion vector associated with its movement.