Abstract: Summary: The invention relates to a temperature sensor, full-A magnetic element (3) that has at least one magnetic Layer (1), its magnetic properties depend on the temperature characterized in that—The magnetic layer (1) has a vortex-shaped magnetization distribution with a magnetic vortex core (2) has, wherein the vortex-shaped magnetization distribution is formed in a layer plane and the vortex core (2) is formed perpendicular to the layer plane,—An excitation unit (15) which is used to excite the vortex-shaped Magnetization distribution to a gyrotropic magnetization motion is provided,—A detection unit (16) for detecting a resonance frequency the gyrotropic magnetization movement is provided and—An evaluation unit (19) for determining a temperature from the resonance frequency of the gyrotropic magnetization movement is provided.

Abstract: A method for determining the orientation of a surface of an object in a detection region before or behind a vehicle by means of a camera of the vehicle comprises the following steps: detecting a first image of the detection region by means of the camera, detecting a second image of the detection region following in time on the detecting of the first image and by means of the camera, generating of first image data corresponding to the first image and second image data corresponding to the second image, determining of eight image coordinates of four pixels each in the first image and the second image, corresponding to four points on the surface of the object, by means of the first image data and the second image data, determining of a normal vector of the surface of the object by means of the eight image coordinates, determining of the orientation of the surface of the object by means of the normal vector.

Abstract: A computer-implemented method for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the method comprising determining a set of samples characterising a location in an image by sampling scale-space data representative of the image, the scale-space data comprising data representative of the image at a plurality of length scales; and generating a feature descriptor in dependence on the determined set of samples.

Abstract: In implementations of systems for spatially coherent UV packing, a computing device implements a packing system to identify pairs of boundary vertices of different two-dimensional islands included in a set of two-dimensional islands. A first boundary vertex and a second boundary vertex of the pairs of boundary vertices both correspond to a same three-dimensional coordinate of a three-dimensional mesh. The packing system determines transformations for two-dimensional islands included in the set of two-dimensional islands based on distances between the first boundary vertex and the second boundary vertex of the pairs of boundary vertices. A three-dimensional object is generated for display in a user interface based on the transformations and the three-dimensional mesh.

Abstract: A three-dimensional data encoding method includes: encoding three-dimensional points each located in any one of a plurality of regions, on a per region basis, to generate encoded three-dimensional points; generating connectivity information generated based on a relationship between a first region among the plurality of regions and second regions aside from the first region among the plurality of regions, the connectivity information including (i) tile information indicating values each uniquely assigned to a different one of the plurality of regions and (ii) relation information indicating that the first region and one of the second regions are related; and generating a bitstream including the connectivity information generated and the encoded three-dimensional points.

Abstract: An imaging system of the present disclosure includes: an event detection device that detects an event; and a controller that controls the event detection device. The controller then controls the detection sensitivity of event detection being performed by the event detection device, on the basis of external information. Further, an object recognition system of the present disclosure includes: an event detection device that detects an event; a controller that controls the detection sensitivity of event detection being performed by the event detection device, on the basis of external information; and a recognition processing unit that performs object recognition in an event, on the basis of an event signal output from the event detection device.

Abstract: A surface characteristics evaluation method for evaluating a surface characteristic of a painted surface including a glittering material, including: a multi-angle condition image acquisition step S101 for acquiring a multi-angle condition image including multi-angle conditions in a continuous manner by performing an image-capturing process to capture how a reflection condition of the painted surface changes when rotating an illumination device 2 emitting light onto the painted surface, the image-capturing process being performed by the line scan camera 4 while a sample P having the painted surface is moved in a certain direction; an in-plane chromaticity distribution acquisition step S102 for acquiring an in-plane chromaticity distribution of the painted surface from the multi-angle condition image acquired; and a surface characteristics evaluation step S107 for calculating particle characteristics S as surface characteristics evaluation values of the multi-angle conditions, on the basis of the in-plane chrom

Abstract: A method for segmenting a power line image in real time based on self-supervised learning includes: inputting an input power line sample image and power line sample image mask set for the same batch of images into a region growing algorithm to obtain a single power line sub-image and single power line mask set; randomly extracting at least one single power line image pair for combination, and combining the single power line image pair with a random background picture to generate a power line random background fusion image and power line random background mask set; and carrying out random non-repetitive region growing to obtain image inpainting regions, forming a segmentation mask with the image inpainting regions, obtaining power line segmentation images through an image inpainting algorithm, inputting the power line segmentation images into a power line real-time segmentation network for training, and carrying out predicted segmentation.

Abstract: Systems and methods for correlating item data are disclosed. A system for correlating item data may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: receiving text and image data associated with a reference item from a remote device; converting, using a computer-modeled embedding layer, at least one image to an image embedding; comparing the image embedding to reference embeddings stored in a database; selecting a subset of the candidate item text as candidate text data based on the comparison; selecting a subset of the candidate item images as candidate image data based on the comparison; selecting a text correlation model; determining a first similarity score; selecting an image correlation model; determining a second similarity score; calculating a confidence score based on the first and second similarity scores; and performing a responsive action based on the calculated confidence score.

Abstract: Provided is an image capture device capable of achieving both high resolution of images and high frame rate. An image capture device 10 takes images and outputs image data, and includes region of interest compression means 11 for performing lossy compression processing on pixel data of a region of interest and outputting the pixel data.

Abstract: Systems and techniques are described herein for processing media data using a neural network system. For instance, a process can include obtaining a latent representation of a frame of encoded image data and generating, by a plurality of decoder transformer layers of a decoder sub-network using the latent representation of the frame of encoded image data as input, a frame of decoded image data. At least one decoder transformer layer of the plurality of decoder transformer layers includes: one or more transformer blocks for generating one or more patches of features and determine self-attention locally within one or more window partitions and shifted window partitions applied over the one or more patches; and a patch un-merging engine for decreasing a respective size of each patch of the one or more patches.

Abstract: A surveillance system includes an acquisition unit configured to acquire motion information of an object detected from an image captured by an image capturing unit, an association unit configured to associate a recognition processing result of the object with the motion information, and a determination unit configured to determine, from among a plurality of objects detected from the image captured by the image capturing unit, an object to be subjected to recognition processing based on the recognition result associated with the motion information. Thus, even when a plurality of abnormal regions is simultaneously present in a region to be monitored, face recognition can be efficiently performed.

Abstract: A method of determining an optimal target gemstone to be obtained from a rough gemstone comprises obtaining a first series of 2D images of the rough gemstone; providing a 3D model of a target gemstone to be obtained from the rough gemstone; and generating a second series of 2D images of the target gemstone from the 3D model thereof. The method then comprises comparing the first and second series of 2D images to determine an optimal transformation to be applied to the 3D model of the target gemstone.

Abstract: A three-dimensional (3D) sensing device is configured to sense an object. The 3D sensing device includes a flood light source, a structured light source, an image sensor, and a controller. The controller is configured to perform: commanding the flood light source and the structured light source to emit a flood light and a structured light in sequence; commanding the image sensor to sense a first reflective light and a second reflective light in sequence, so as to obtain a first image frame and a second image frame; combining the first image frame and the second image frame into a determination frame; and determining that the object is a specular reflection object in response to determining that the determination frame has at least two spots having gray levels satisfying a predetermined condition. A specular reflection object detection method is also provided.

Abstract: A system for quantitively determining quality for pavement markings disposed along pavement on a roadway includes one or more controllers in wireless communication with a plurality of vehicles. The one or more controllers receive image data represents the pavement markings disposed along the pavement collected by the plurality of vehicles. The one or more controllers execute instructions to determine at least one of a color distance measurement between a mean color space value of the pavement markings and an ideal marking color space value and a marking intensity contrast ratio between the pavement markings and the pavement.

Abstract: A method and system for detecting at least one object depicted in an image may include: receiving an input vector IN, comprising a plurality of location data elements, each representing a location of at least a portion of the at least one depicted object, calculating a 2-dimensional (2D) relation matrix REL_2D, where each entry represents a value of a relation operator, between location data elements i, and j; calculating a 2D mask matrix MASK_2D, where each entry is ‘1’ if the confidence of location data element i is greater than the confidence of location data element j, and ‘0’ otherwise; calculating a penalty vector PEN, based on REL_2D and MASK_2D; elementwise multiplying the confidence levels of vector IN and vector PEN, to produce a selection vector SEL; and selecting at least one location data element based on vector SEL as representing the location of the at least one depicted object.

Abstract: Depth estimation of images using deep learning methods is a wide range of application in Augmented Reality, 3D graphics and robotics. Conventional methods are supervised, which requires explicit ground truth depth information for training and the conventional unsupervised methods fails to provide a generalized solution. The present disclosure estimates accurate depth information and confidence map of a given monocular image in an unsupervised manner. A depth Neural Network receives a monocular image and predicts per-pixel depth map and a confidence map. The depth NN utilizes a negative exponential of photometric loss as ground truth information. The predicted confidence-map is further used to estimate per-pixel uncertainty map. The pose NN predicts a plurality of pose vectors between a plurality of the consecutive monocular images. Finally, the Bayesian inference module is computes the fused depth information and the fused uncertainty map.

Abstract: An Online Machine Learning System (OMLS) including an Online Explanation System (OES), updated continuously, configured to provide instance level explanations and model level explanations to a user; an Online Human Expert Feedback System (OEFS), updated continuously, configured to obtain expert instance level feedback and expert model level feedback, for optimization of operation of the OMLS; an Online Machine Learning Engine (OMLE) for incorporating and utilizing one or more machine learning algorithms or models utilizing features to generate a result, and capable of incorporating and utilizing multiple different machine learning algorithms or models, wherein the OMLS is configured to perform continuous online machine learning.

Abstract: A method for training a machine learning system. The method includes generating an augmented dataset including input images for training the machine learning system, which is for classification and/or semantic segmentation of input images, using a first machine learning system, which is embodied as a decoder of an autoencoder, and a second machine learning system, which is embodied as an encoder of the autoencoder. Latent variables are ascertained from the input images using the encoder. The input images are classified as a function of ascertained feature characteristics of their image data. An augmented input image of the augmented dataset is ascertained from at least one of the input images as a function of average values of the ascertained latent variables in at least two of the classes. The image classes are selected so that the input images classified therein agree in their characteristics in a predefinable set of other features.

Abstract: This disclosure describes, in part, techniques for collecting image data representing item identifiers, such as barcodes. For instance, system(s) may receive image data representing images, where the images depict at least a portion of an identifier located on an item. The system(s) may then identify a first portion of the image data representing an image that that is associated a low confidence level. Next, the system(s) may identify a second portion of the image data representing additional images that are associated high confidence levels. Using results for this the second portion of the image data, the system(s) may determine a ground truth result for the first portion of the image data. The system(s) may then store, in one or more databases, data representing the ground truth result in association with the first portion of the image data.