A method for detecting boxes includes receiving a plurality of image frame pairs for an area of interest including at least one target box. Each image frame pair includes a monocular image frame and a respective depth image frame. For each image frame pair, the method includes determining corners for a rectangle associated with the at least one target box within the respective monocular image frame. Based on the determined corners, the method includes the following: performing edge detection and determining faces within the respective monocular image frame; and extracting planes corresponding to the at least one target box from the respective depth image frame. The method includes matching the determined faces to the extracted planes and generating a box estimation based on the determined corners, the performed edge detection, and the matched faces of the at least one target box.

A method for concept based segmentation, the method may include (a) detecting an object within a region of an image; wherein the object is associated with characteristic pixels metadata that indicative of multiple examples of pixels properties of pixels that are included in at least one appearance of the object within at least one image; and (b) finding, within the region, one or more object boundaries, based on the characteristic pixels metadata.

A learning device 10 includes a first learning unit 20. The first learning unit 20 includes a first supervised learning unit 22 and a first self-supervised learning unit 24. The first supervised learning unit 22 learns a first object detection network 30 using learning data 40 so as to reduce a first loss between an output of the first object detection network 30 for detecting an object from target image data and supervised data 40B. Using image data 40A and self-supervised data 40C generated from the image data 40A, the first self-supervised learning unit 24 learns the first object detection network 30 so as to reduce a second loss of a feature amount of a corresponding candidate area P between the image data 40A and the self-supervised data 40C, the second loss being derived by the first object detection network 30.

An annotation device includes an image-capturing device, a robot, a control unit, a designation unit, a coordinate processing unit, and a storage unit. The control unit controls the robot so as to acquire a learning image of a plurality of objects, each having a different positional relationship with the image-capturing devices. Furthermore, the storage unit converts a position of the object in a robot coordinate system into a position of the object in an image coordinate system at the time of image-capturing or a position of the object in a sensor coordinate system, and stores the position thus converted together with the learning image.

Among other things, the techniques described herein include a method for receiving a plurality of images of one or more containers while the one or more containers are being emptied, the plurality of images comprising a training set of images and a validation set of images; labeling each image of the plurality of images as including either an overfilled container or a not-overfilled container; processing each image of the plurality of images to reduce bias of a machine learning model; training, and based on the labeling, the machine learning model using the plurality of images; and optimizing the machine learning model by performing learning against the validation set, the optimized machine learning model being used to generate a prediction for a new image of a container, the prediction indicating whether the container in the new image was overfilled prior to the new container being emptied.

A method, system, and computer-readable media of generating a display on a device, including combining content from a plurality of sources into a display, the content from each of the plurality of sources being presented as a layer of the display, and further, each layer of the display being of substantially the same dimensions, detecting one or more objects in each layer of the generated display, and for one or more of the detected objects determining an object type or classification, determining if the object is overlapping or obscuring an object in a different layer of the generated display, and determining if the object will appear to a viewer as if it will overlap or obscure an object in a different layer of the generated display as a result of the motion, orientation, or gaze of the viewer.

An information processing apparatus (2000) detects one or more candidate regions (22) from a captured image (20) based on an image feature of a target object. Each candidate region

(22) is an image region that is estimated to represent the target object. The information processing apparatus (2000) detects a person region (26) from the captured image (20) and detects an estimation position (24) based on the detected person region (26). The person region
(26) is a region that is estimated to represent a person. The estimation position (24) is a position in the captured image (20) where the target object is estimated to be present. Then, the information processing apparatus (2000) determines an object region (30), which is an image region representing the target object, based on each candidate region (22) and the estimation position (24).

An image processing apparatus extracts a foreground image corresponding to an object included in a processing image using a background image corresponding to the processing image, and generates the background image from the processing image. The image processing apparatus determines whether it is allowed to update the background image for use in the extraction, and based on a result of the determination, updates the background image for use in the extraction using the generated background image.

A method of preprocessing incoming video data of at least one region of interest from a camera collecting video data having a first field of view is disclosed herein that includes receiving the incoming video data from the camera; preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file, with the preprocessing including formatting the incoming video data to create first video data of a first region of interest with a second field of view that is less than the first field of view; and publishing the first video data of the first region of interest to an endpoint to allow access by a first subscriber.

An apparatus for feature recognition of two-dimensional prints is illustrated. The apparatus comprise a processor and a memory communicatively connected to the processor. The memory contains instructions configuring the processor to receive a two-dimensional print of a part for manufacture, scale two-dimensional print so that the two-dimensional print is within a predetermined area, identify a curve feature of the two-dimensional print as a function of scaling of the two-dimensional print, wherein the curve feature comprises a plurality of line segments, and classify a line type of the curve feature using line observations as a function of the curve feature identification.