Abstract: Unlike existing methods, using LIDAR and 2D-cameras for detection and limit in providing robust tracking of objects, the method disclosed addresses the technical challenge by utilizing 3D-LIDAR points clouds, 2D-camera images and additionally 2D-BEV from 3D LIDAR point clouds to provide, robust, seamless, 360 tracking of objects. The method independently detects, and tracks objects captured by each of the LIDAR, 2D-camera set up mounted on an autonomous vehicle. Detection and tracking is performed on 2D-Camera and 2D-BEV and fused with 3D-Lidar tracker, Additionally, non-occluded area of an object in 2D-bounding boxes, is identified by superimposing panoptic segmentation output with 2D-BB, that helps to eliminate the lidar points falling on the irrelevant objects and provides accurate real world position of the detected objects in the point cloud. An enhanced 2D object detector based on a customized NN architecture employing MISH activation function is also introduced.

Abstract: An information processing device obtains each piece of image data captured within a period of time from entering until exiting of a person at a store. The information processing device identifies joint positions of a skeleton related to the person by analyzing each piece of the image data. The information processing device identifies, as an action which indicates a degree of interest of the person in the product, an action performed by the person to a product in the store from the entering until the exiting, on a basis of the joint positions of the skeleton. The information processing device generates a detection rule that correlates the identified action and the product with each other.

Abstract: A temporal sequence alignment method includes the following steps: receiving gesture training data and gesture sample data; wherein the gesture training data includes multiple training frames and multiple training soft labels, and the gesture sample data includes multiple sample frames and multiple sample soft labels; compressing the training frames to generate a compressed training frame; compressing the sample frames to generate a compressed sample frame; calculating an alignment model of the compressed training frame and the compressed sample frame; aligning the sample soft labels to multiple aligned soft labels according to the alignment model; generating an aligned training data according to the gesture sample data and the aligned soft labels. The present invention uses the aforementioned steps to calibrate the sample soft labels of the gesture sample data, allowing a gesture recognition system to minimize time discrepancy for recognizing a gesture.

Abstract: An image processing system includes a detection unit, an identification unit, and a feature extraction unit. The detection unit detects, from a first image in which a target person is captured, a candidate region being an image region estimated to represent an eye of the target person, based on a first evaluation value. The identification unit identifies, from the detected candidate region, an eye region being an image region that represents the eye, based on a second evaluation value. The feature extraction unit extracts a feature value of the identified eye region. The first evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the first image. The second evaluation value indicates a likelihood of the eye, and is calculated for an image region being set based on the detected candidate region.

Abstract: A method may include generating views materializing tensors generated by a convolutional neural network operating on an image. Determining the outputs of the convolutional neural network operating on the image with a patch occluding various portions of the image. The outputs being determined by generating queries on the views that performs, based at least on the changes associated with occluding different portions of the image, partial re-computations of the views. A heatmap may be generated based on the outputs of the convolutional neural network. The heatmap may indicate the quantities to which the different portions of the image contribute to the output of the convolutional neural network operating on the image. Related systems and articles of manufacture, including computer program products, are also provided.

Abstract: The invention relates to the technical field of intelligent construction and maintenance, and relates to a non-contact visual monitoring system and method for a flexible protective structure against rockfall disasters; it comprises a hardware system and a data analysis system, and the hardware system uses a multipoint distributed high-resolution high-speed camera to capture dynamic image sequences of a protective structure under the rockfall impact in a non-contact mode; the data analysis system comprises an impact deformation state full-field tracking module and a multipoint impact large deformation extraction module, and the impact deformation state full-field tracking module captures spatio-temporal changes of impact deformation of the protective system by adopting a full-field optical flow method, constructs a two-dimensional velocity amplitude distribution diagram and performs full-field spatio-temporal tracking of large deformation of the system.

Abstract: One or more processing devices access a scene depicting a reference object that includes an annotation identifying a target region to be modified in one or more video frames. The one or more processing devices determine that a target pixel corresponds to a sub-region within the target region that includes hallucinated content. The one or more processing devices determine gradient constraints using gradient values of neighboring pixels in the hallucinated content, the neighboring pixels being adjacent to the target pixel and corresponding to four cardinal directions. The one or more processing devices update color data of the target pixel subject to the determined gradient constraints.

Abstract: Disclosed frameworks for generating an image including a salient object and a staged background include extracting a salient object from a source image and applying a generative model to the salient object to generate the image. According to some embodiments, extracting a salient object from a source image involves using salient object detection method to identify the relevant portions of the source image corresponding to the salient object. In some embodiments, the generative model is a generative adversarial network trained using a domain relevant dataset.

Abstract: This disclosure describes entropy coding techniques for media data coded using neural-based techniques. A media coder is configured to determine a probability distribution function parameter for a data element of a data stream coded by a neural-based media compression technique, wherein the probability distribution function parameter is a logarithmic function of a standard deviation of a probability distribution function of the data stream, determine a code vector based on the probability distribution function parameter, and entropy code the data element using the code vector.

Abstract: A method for depth estimation performed by a depth estimation system associated with an agent includes determining a first depth of a first image and a second depth of a second image, the first image and the second image being captured by a sensor associated with the agent. The method also includes generating a first 3D image of the first image based on the first depth, a first pose associated with the sensor, and the second image. The method further includes generating a warped depth image based on transforming the first depth in accordance with the first pose. The method also includes updating the first pose based on a second pose associated with the warped depth image and the second depth, and updating the first 3D image based on the updated first pose. The method further includes controlling an action of the agent based on the updated first 3D image.

Abstract: Synthesizing training data for training a change detection model includes receiving a patched image comprising a background image with a patch pasted into the background image. It further includes synthesizing a harmonized patched image at least in part by harmonizing the patched image using a machine learning model trained on the background image. It further includes providing as output a synthetic training sample usable to train a change detection model. The synthetic training sample includes a reference image, at least a portion of the harmonized patched image, and a corresponding mask.

Abstract: There is provided a method and a system configured obtain an image of a semiconductor specimen including one or more arrays, each including repetitive structural elements, and one or more regions, each region at least partially surrounding a corresponding array and including features different from the repetitive structural elements, wherein the PMC is configured to, during run-time scanning of the semiconductor specimen, perform a correlation analysis between pixel intensity of the image and pixel intensity of a reference image informative of at least one of the repetitive structural elements, to obtain a correlation matrix, use the correlation matrix to distinguish between one or more first areas of the image corresponding to the one or more arrays and one or more second areas of the image corresponding the one or more regions, and output data informative of the one or more first areas of the image.

Abstract: A method and device for image storage control, and a storage medium are disclosed. The method may include: collecting biometric information of a user during capturing an image; and performing storage and clearing processing on image data obtained by capturing according to storage control information corresponding to the biometric information.

Abstract: A method, an apparatus, and a device for image processing and a training method thereof are provided. The training method includes obtaining a sample image set, the sample image set comprising a first number of sample images; constructing an image feature set based on the sample image set, the image feature set comprising an image feature extracted from each of the sample images in the sample image set; obtaining a training image set, the training image set comprising a second number of training images; constructing multiple training image pairs based on the training image set and the image feature set; and training the image processing model based on the multiple training image pairs.

Abstract: The present technology relates to an information processing apparatus, an information processing method, a program, a mobile-object control apparatus, and a mobile object that make it possible to improve the accuracy in recognizing a target object. An information processing apparatus includes a geometric transformation section that transforms at least one of a captured image or a sensor image to match coordinate systems of the captured image and the sensor image, the captured image being obtained by an image sensor, the sensor image indicating a sensing result of a sensor of which a sensing range at least partially overlaps a sensing range of the image sensor; and an object recognition section that performs processing of recognizing a target object on the basis of the captured image and sensor image of which the coordinate systems have been matched to each other. The present technology is applicable to, for example, a system used to recognize a target object around a vehicle.

Abstract: A method includes receiving a three-dimensional image dataset of a surgical site of a patient. The method also includes segmenting one or more anatomical features of the surgical site based on the three-dimensional image dataset. The method also includes receiving a two-dimensional image of the surgical site of the patient and registering the two-dimensional image to an image from the three-dimensional image dataset. The method also includes displaying a two-dimensional representation of the segmented one or more anatomical features based on the registered two-dimensional image and the image from the three-dimensional image dataset.

Abstract: A method may process an electronic image corresponding to a medical sample associated with a patient. The method may include receiving a selection of one or more artificial intelligence (AI) algorithms, receiving one or more whole slide images of a medical sample associated with a patient, performing a task on the whole slide images, using the one or more selected AI algorithms, the whole slide images being stored in a first container, the whole slide images being originated from a first user, the task comprising determining a characteristic of the medical sample in the whole slide images, based on the characteristic of the whole slide image, generating metadata associated with the whole slide image, and storing the metadata in a second container.

Abstract: Various methods and systems are provided for increasing an efficiency of operations for improving digital image quality. In an embodiment, a method for processing a digital image includes generating a higher resolution histogram from the digital image, the higher resolution histogram generated from a lower resolution histogram of raw image data of the digital image, filtering the higher resolution histogram, downsampling the filtered, higher resolution histogram to form a downsampled histogram, generating one or more picture quality adjustments based on the downsampled histogram, applying the one or more picture quality adjustments to the raw image data of the digital image to generate an improved digital image, the improved digital image having a higher picture quality than the digital image, and displaying the improved digital image on a display device.

Abstract: An apparatus for enhancing an input phase distribution (I(xi)) is configured to retrieve the input phase distribution (I(xi)) and compute a baseline estimate (ƒ(xi)) as an estimate of a baseline (I2 (xi)) in the input phase distribution (I(xi)). The apparatus is further configured to obtain an output phase distribution (O(xi)) based on the baseline estimate (ƒ(xi)) and the input phase distribution (I(xi)).

Abstract: A method for preparing a deck for a process is disclosed. The deck can be prepared with any necessary components, and then an imaging device can capture an image of the deck. This image can be compared with a reference image and any differences identified. The differences can be indicated in the image and shown to an operator, such that the operator can correct any errors associated with the differences.