A method is provided for watermarking a machine learning model used for object detection. In the method, a first subset of a labeled set of ML training samples is selected. Each of one or more objects in the first subset includes a class label. A pixel pattern is selected to use as a watermark in the first subset of images. The pixel pattern is made partially transparent. A target class label is selected. One or more objects of the first subset of images are relabeled with the target class label. In another embodiment, the class labels are removed from objects in the subset of images instead of relabeling them. Each of the first subset of images is overlaid with the partially transparent and scaled pixel pattern. The ML model is trained with the set of training images and the first subset of images to produce a trained and watermarked ML model.

Systems and methods are provided for remotely detecting a status associated with an autonomous vehicle and generating control actions in response to such detections. In one example, a computing system can access a third-party communication associated with an autonomous vehicle. The computing system can determine, based at least in part on the third-party communication, a predetermined identifier associated with the autonomous vehicle. The computing system can determine, based at least in part on the third-party communication, a status associated with the autonomous vehicle, and transmit one or more control messages to the autonomous vehicle based at least in part on the predetermined identifier and the status associated with the autonomous vehicle.

A row vision system modifies automated operation of a vehicle based on edges detected between surfaces in the environment in which the vehicle travels. The vehicle may be a farming vehicle (e.g., a tractor) that operates using automated steering to perform farming operations that track an edge formed by a row of field work completed next to the unworked field area. A row vision system may access images of the field ahead of the tractor and apply models that identify surface types and detect edges between the identified surfaces (e.g., between worked and unworked ground). Using the detected edges, the system determines navigation instructions that modify the automated steering (e.g., direction) to minimize the error between current and desired headings of the vehicle, enabling the tractor to track the row of crops, edge of field, or edge of field work completed.

A surgical microscope includes an image capture unit having an image sensor, a detection beam path, an image evaluation unit, a connection region for attaching a protective glass module with an objective protective glass. The image sensor has a detection region which has a used detection region for capturing the object region, and a partial detection region, which is not assigned to the used detection region. The image capture unit is configured such that, when the protective glass module with the objective protective glass is arranged at the connection region, a detail of the protective glass module with the objective protective glass is capturable by the partial detection region of the image sensor. The image evaluation unit is configured to generate a signal when an objective protective glass is detectable by the evaluation of the image data of the partial detection region of the image sensor.

A calibration system includes a docking stand fixed within a three-dimensional coordinate system and an end effector supported by the docking stand. The end effector includes a frame received by the docking stand and an adjustable member movable along the frame. The adjustable member includes a clamp and a reference surface. The calibration system includes a computational system including at least one processor and at least one non-transitory computer-readable medium including instructions. The calibration system includes a measurement instrument in electronic communication with the computational system. The measurement instrument is movable and is arranged to interact with the reference surface and transmit a signal to the processor. The processor is programmed to analyze a location of the measurement instrument within the three-dimensional coordinate system and the interaction between the measurement instrument and the reference surface to determine a location of the adjustable member within the three-dimensional coordinate system.

A method including receiving an electronic record including a scan of a physical document. A coordinate system, unique to the electronic record, is established for the scan. A first boundary, defined according to the coordinate system, is generated automatically around a first set of recognized characters in the scan. A second boundary, defined according to the coordinate system, is generated automatically around a second set of recognized characters in the scan. The first set of recognized characters are physically separated in the scan by at least a predetermined distance with respect to the coordinate system. A comparison value is generated automatically by comparing a first location of the first boundary to a second location of the second boundary, relative to the coordinate system. The first set of recognized characters is associated, in storage, with the second set of recognized characters, responsive to the comparison value satisfying a rule.

Image processing device security is provided herein. A method can include assembling, by a first system comprising a processor using a first virtual machine enabled via the first system, raw input data captured by an image capture device from an input image, resulting in assembled input data; generating, by the first system using a second virtual machine that is enabled via the first system and distinct from the first virtual machine, an output image from the assembled input data; reading, by the first system in response to the generating, the output image; and preventing, by the first system, a second system, distinct from the first system, from accessing the output image in response to the reading resulting in execution of unauthorized instructions at the first system.

Systems and methods of image searching include receiving content, receiving a request to select an image from content, selecting a plurality of items in the image, retrieving information about the selected item, and providing display data based on the retrieved information.

Systems and methods for adaptively determining a region of interest (ROI) are disclosed herein. An example device includes an imaging assembly and a controller. The imaging assembly captures image data comprising pixel data from a plurality of pixels. The controller calculates a contrast value for each pixel of the plurality of pixels, generates a histogram of contrast values, calculates an area under the curve of the histogram, and determines a contrast value threshold to delineate between high-contrast value pixels and low-contrast value pixels. The controller also identifies a ROI within the image data by locating a region within the image data that (i) satisfies a pre-determined size threshold and (ii) contains a largest number of high-contrast value pixels relative to all other regions that satisfy the pre-determined size threshold, and adjusts imaging parameters of the imaging assembly based on the ROI to capture at least one subsequent image.

A device for determining an ID card includes an image input unit to acquire an initial image including an ID card image, an image pre-processing unit to generate a processed image by removing a remaining portion of the initial image except for the ID card image, and generate a first training image having a first resolution value and a second training image having a second resolution value, based on the processed image, an image determining unit to determine whether an identifying mark is present on the processed image, based on an artificial intelligence (AI) model based on a neural network trained by training data including the first training image and the second training image, and a model evaluating unit to calculate a plurality of parameters by using a determination result of the image determining unit and to evaluate the AI model based on the plurality of parameters.