Abstract: The present disclosure provides a wearable display device that relates to the field of virtual reality technology. Because the wearable display device has a high efficiency in processing electric signals transmitted via each of the photoelectric sensor assemblies, the wearable display device can quickly determine a gaze position of eyes of a user on the display panel based on an electric signals transmitted via each of the photoelectric sensor assemblies. In this way, the efficiency of the display panel in displaying images is improved, and the refresh rate of the display panel is higher.

Abstract: This disclosure provides an array substrate, including: a display region and a peripheral region. The peripheral region includes at least one first sensor. The first sensor includes a photodiode and a driving circuit which are electrically connected to each other. The photodiode includes: an anode, a cathode and a photosensitive material layer. The array substrate includes: a base substrate, a plurality of thin film transistors, a common electrode and a pixel electrode. The common electrode is reused as an anode of the photodiode. The pixel electrode is reused as a cathode of the photodiode. One of the plurality of thin film transistors is reused as a first transistor of the driving circuit.

Abstract: System and method for reducing false negatives in object detection, including: extracting an object of interest from a respective image in a first set of training data that includes in distribution (ID) data. For each of the object of interest extracted from the respective image in the first set of training data: fusing the object of interest with an image from a second set of data that does not include any objects of interest to form a fused image; adding the fused image to the training data; and using the training data to train a detection model for object detection.

Abstract: System and method for reducing false negatives in object detection, including: extracting an object of interest from a respective image in a first set of training data that includes in distribution (ID) data. For each of the object of interest extracted from the respective image in the first set of training data: fusing the object of interest with an image from a second set of data that does not include any objects of interest to form a fused image; adding the fused image to the training data; and using the training data to train a detection model for object detection.

Abstract: System and method for providing visual-aided placement recommendation includes obtaining images of a rack configured to hold objects inside a chamber, placement of the plurality of objects on the rack being subject to preset constraints corresponding to characteristics of respective objects of the plurality of objects relative to physical parameters of respective locations on the rack; analyzing the images to determine whether the preset constraints have been violated by placement of objects on the rack; and in accordance with a determination that at least one preset constraint has been violated, generating a first output providing a guidance on proper placement of the first object on the rack that complies with the one or more preset constraints, in accordance with the physical characteristics of the first object relative to the physical parameters of the respective locations on the rack, taking into account of other objects already placed on the rack.

Abstract: A method includes: capturing one or more images of an unorganized collection of items inside a first machine; determining one or more item types of the unorganized collection of items from the one or more images, comprising: dividing a respective image in the one or more images into a respective plurality of sub-regions; performing feature detection on the respective plurality of sub-regions to obtain a respective plurality of regional feature vectors, wherein a regional feature vector for a sub-region indicates characteristics for a plurality of predefined local item features for the sub-region; generating an integrated feature vector by combining the respective plurality of regional feature vectors; and applying a plurality of binary classifiers to the integrated feature vector; and selecting a machine setting for the first machine based on the determined one or more clothes type in the unorganized collection of items.

Abstract: A method includes: capturing one or more images of an unorganized collection of items inside a first machine; determining one or more item types of the unorganized collection of items from the one or more images, comprising: dividing a respective image in the one or more images into a respective plurality of sub-regions; performing feature detection on the respective plurality of sub-regions to obtain a respective plurality of regional feature vectors, wherein a regional feature vector for a sub-region indicates characteristics for a plurality of predefined local item features for the sub-region; generating an integrated feature vector by combining the respective plurality of regional feature vectors; and applying a plurality of binary classifiers to the integrated feature vector; and selecting a machine setting for the first machine based on the determined one or more clothes type in the unorganized collection of items.

Abstract: System and method for providing visual-aided placement recommendation includes obtaining images of a rack configured to hold objects inside a chamber, placement of the plurality of objects on the rack being subject to preset constraints corresponding to characteristics of respective objects of the plurality of objects relative to physical parameters of respective locations on the rack; analyzing the images to determine whether the preset constraints have been violated by placement of objects on the rack; and in accordance with a determination that at least one preset constraint has been violated, generating a first output providing a guidance on proper placement of the first object on the rack that complies with the one or more preset constraints, in accordance with the physical characteristics of the first object relative to the physical parameters of the respective locations on the rack, taking into account of other objects already placed on the rack.