Abstract: Systems and methods for constructing high resolution panoramic imagery for feature identification on robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot collects a plurality of images of an environment, these images include large overlap in their visual scenes. Using additional image data from the overlapping images, resolution of labels, price tags, and other inventory tags may be enhanced when constructing panoramic imagery to improve feature identification.

Abstract: To enable high-speed autonomous flight of a flight object. A three-dimensional real-time observation result is generated on the basis of self-position estimation information and three-dimensional distance measurement information. A prior map corresponding to a three-dimensional real-time observation result is acquired. The three-dimensional real-time observation result and the prior map are aligned. After the alignment, the three-dimensional real-time observation result is expanded on the basis of the prior map. A flight route is set on the basis of the three-dimensional real-time observation result having been expanded. In the flight object such as a drone, a somewhat long flight route can be accurately calculated at a time in a global behavior plan, which enables high-speed autonomous flight of the flight object.

Abstract: In a case where the detection target is detected, an actual position display control process of displaying a detected position display circle on a display in an actual position display mode (single line) is performed. In a case where the detection target is not detected, an estimated position display control process of displaying an estimated position display circle on the display in an estimated position display mode (double line) different from the actual position display mode is performed.

Abstract: A system and method for dense depth map determination from image data, including: predicting a standard disparity map for the image using a standard disparity model based on the image; and converting the standard disparity map to a depth map using a focal length in pixels associated with the image.

Abstract: There is provided a method for training a ground truth generator machine learning model, comprising: creating a ground truth multi-record training dataset wherein a record comprises: a first image of a sample of tissue of a subject depicting a first group of biological objects, a second image of the sample depicting a second group of biological objects presenting at least one biomarker, and ground truth labels indicating a respective biological object category of a plurality of biological object categories for biological object members of the first group and the second group; and training the ground truth generator machine learning model on the ground truth multi-record training dataset for automatically generating ground truth labels selected from the plurality of biological object categories for biological objects depicted in an input set of images of a first type corresponding to the first image and a second type corresponding to the second image.

Abstract: A method of object detection in paired imaging includes detecting areas of interest for each image of a set of multi-view images, each detected area of interest having a corresponding initial probability of being an area of interest; determining a matching probability for each detected area of interest across the set of multi-view images such that detected areas of interest from one image of the set of multi-view images are assigned matching probabilities with respect to detected areas of interest of other images of the set of multi-view images; generating a modified probability for each detected area of interest according to one or more object-specific weighting factors and one or more of the matching probabilities for that detected area of interest; adjusting the initial probability of each detected area of interest using the modified probability to generate a refined probability for each detected area of interest; and identifying the detected areas of interest in each image that have refined probabilities

Abstract: A method for three-dimensionally modeling an eyeball and optical nerves using the merging of MRI images and OCT images, comprises: (a) a step for three-dimensionally modeling an eyeball model on the basis of the shapes of an eyeballs in a first MRI head image and a second MRI head image; (b) a step for three-dimensionally modeling an ASCO model in a corrected OCT eyeball cross-sectional image; (c) a step for three-dimensionally modeling the lamina cribrosa in the corrected OCT eyeball image; and (d) a step for generating an optic nerve model by three-dimensionally modeling an optic tract connected to the three-dimensionally modeled eyeball model.

Abstract: The present disclosure relates to a meter recognition method, which includes: determining embedded features of pixels in a target image of a meter, and encoding position information of the pixels to obtain encoded position features; inputting superimposed features obtained by superimposing the encoded position features and the embedded features into an encoder of a target model; wherein an input of the target model includes the labels, and an output of the target model includes coordinates of key points in a sample image of the meter. According to the present disclosure, the image of the meter can be processed by the trained target model, the coordinates of key points in the target image of the meter are outputted. It can reduce manual operations and improve efficiency, and on the other hand, it can avoid possible misoperations during manual operations, which is beneficial for improving accuracy.

Abstract: Systems, methods, and computer program products are provided for removing noise and/or artifacts from an image. The method includes training a generative adversarial network (GAN) based on a plurality of images, the plurality of images comprising at least one undesired element comprising at least one of the following: noise, speckle patterns, artifacts, or any combination thereof, and generating a modified image based on processing an image of an eye or other object with the GAN to remove the at least one undesired element from the image that is above an outer surface of the eye or other object.

Abstract: A system and method are provided for denoising images. A standard image module is configured to generate a standard anatomy feature and a standard noise feature from a standard image and reconstruct the standard image from the standard anatomy feature and the standard noise feature. A reduced quality image module is configured to generate a reduced quality anatomy feature and a reduced quality noise feature from a reduced quality image, and reconstruct the reduced quality image from the reduced quality anatomy feature and the reduced quality noise feature. A loss calculation module is provided for calculating loss metrics at least partially based on a comparison between 1) the reconstructed standard image and the standard image, and 2) the reconstructed reduced quality image and the reduced quality image. Upon providing the standard image module with the reduced quality anatomy feature, the standard image module outputs a reconstructed standard transfer image.

Abstract: This paper discloses a careless driving determination apparatus. The careless driving determination apparatus captures a plurality of face images of a driver of a vehicle in a predetermined period using a driver monitor camera. Then, it determines whether each of the plurality of face images shows first status in which the driver wears a wearing object or second status in which the driver does not wear the wearing object, the wearing object covering a part of a face of the driver. Then, it calculates a distribution range of angles of face directions or angles of gaze directions of the driver. Then, it sets a threshold range based on ratio of face images that show the first status and ratio of face images that show the second status. Then, it determines that the driver is in a careless driving state when the distribution range is included in the threshold range.

Abstract: An embodiment of the present invention provides a learning method, a learning apparatus, and a learning program for decomposing a two-dimensional X-ray image into a variety of regions, and an image processing method, an image processing apparatus, and an image processing program using a result of learning. A learning method according to an aspect of the present invention includes generating a masked CT image from a three-dimensional CT image, and generating a third pseudo X-ray image from the masked CT image. Then, a second pseudo X-ray image generated by projecting an original CT image is compared with the third pseudo X-ray image as a reference, and a parameter for use in generating the second pseudo X-ray image is updated based on a comparison result. A processor repeats the processing of each step until a predetermined condition is satisfied.

Abstract: The present disclosure achieves accurate tracking of sunlit orbital objects, including small, dim orbital debris, using a ground-based telescope and detector which tracks the orbital object. The angular position of the orbital object relative to background celestial objects is accurately determined over a trajectory, allowing accurate determination of the orbit parameters.

Abstract: In a method for enhancing accuracy and efficiency in automated visual inspection of vessels, a vessel containing a sample is oriented such that a line scan camera has a profile view of an edge of a stopper of the vessel. A plurality of images of the edge of the stopper is captured by the first line scan camera while spinning the vessel, where each image of the plurality of images corresponds to a different rotational position of the vessel. A two-dimensional image of the edge of the stopper is generated based on at least the plurality of images, and pixels of the two-dimensional image are processed, by one or more processors executing an inference model that includes a trained neural network, to generate output data indicative of a likelihood that the sample is defective.

Abstract: An output device including: a memory; and a processor coupled to the memory, the processor configured to: acquire a satellite image in which a target region is photographed and event information relating to events that have occurred in the target region; and output, from among roads in the target region, a road for which a safety degree calculated based on the satellite image and the event information is less than a threshold value.

Abstract: In an embodiment, an apparatus comprises one or more processors and one or more memories communicatively coupled to the one or more processors and storing instructions which, when processed by the one or more processors, cause: receiving a digital image of a particular item of mail and storing the digital image; causing data to be extracted from the digital image using Optical Character Recognition (OCR) or by processing encoded data; based on the extracted data, matching the particular item of mail to particular mailing data from the data stream; determining, based, at least in part, on the extracted data and the particular mailing data, one or more actions to take with respect to the particular item of mail and, in response, causing performance of the one or more actions.

Abstract: A method for predicting a scattered signal of an X-ray for an examination object includes: scanning each phantom in a scanning manner in which a scattering degree of each phantom in a plurality of phantoms is less than a reference scattering degree, so as to obtain first projection data of each phantom; scanning each phantom in a scanning manner in which the scattering degree of each phantom in the plurality of phantoms is equal to the reference scattering degree, so as to obtain second projection data of each phantom; obtaining a real scattered signal of each phantom by subtracting the first projection data of the phantom from the second projection data of the phantom; training a learning model based on the second projection data of each phantom and the real scattered signal of each phantom to obtain a trained learning model; and applying the trained learning model to projection data of the X-ray for the examination object to predict the scattered signal of the projection data of the X-ray.

Abstract: A facial expression-based detection method for deepfake by generative artificial intelligence (AI) constructs an AIR-Face facial dataset for generative AI-created face detection training, and uses an untrained information feature space for real and fake classification. Nearest linear detection is performed in this space to significantly improve the generalization ability of detecting fake images, especially those created by new methods such as diffusion models or autoregressive models. The detection method improves the performance of extracting features of generative AI-created faces through phased trainings, and detects generative AI-created faces through the feature space. Compared with other methods, the detection method scientifically and effectively improves the accuracy of generative AI-created face recognition, and fully mines the potential semantic information of generative AI-created faces through phased trainings.

Abstract: Systems and methods for image segmentation are described. Embodiments of the present disclosure receive a training image and a caption for the training image, wherein the caption includes text describing an object in the training image; generate a pseudo mask for the object using a teacher network based on the text describing the object; generate a mask for the object using a student network; compute noise information for the training image using a noise estimation network; and update parameters of the student network based on the mask, the pseudo mask, and the noise information.

Abstract: Disclosed herein is an image analysis system including a machine learning model configured to receive input of an image and to output an image feature quantity and a map source for image analysis, a map generation section configured to generate, on the basis of a vector corresponding to an object and the output map source, an attention map indicative of a region associated with the object in the image, and a token generation section configured to generate a token indicative of a feature associated with an event of the object on the basis of the generated attention map and the image feature quantity.