Abstract: The embodiments herein provide a system and method for integrating objects in monocular simultaneous localization and mapping (SLAM). State of art object SLAM approach use two popular threads. In first, instance specific models are assumed to be known a priori. In second, a general model for an object such as ellipsoids and cuboids is used. However, these generic models just give the label of the object category and do not give much information about the object pose in the map. The method and system disclosed provide a SLAM framework on a real monocular sequence wherein joint optimization is performed on object localization and edges using category level shape priors and bundle adjustment. The method provides a better visualization incorporating object representations in the scene along with the 3D structure of the base SLAM system, which makes it useful for augmented reality (AR) applications.

Abstract: An interpolation method may include scheduling spatially adjacent image blocks for interpolation, and calculating ranges of values of an attribute of the image blocks, wherein at least one value for a first one of the image blocks may be used for a second one of the image blocks. Calculating the ranges of values may include calculating a root value of the attribute at a root location of an array of the spatially adjacent image blocks, and adding incremental values of the attribute to the root value at points of the image blocks that are offset from the root location. The root location may be centrally located in an array of the spatially adjacent image blocks. The interpolation may be calculated in a diagonal hierarchical manner based on a plane equation.

Abstract: A system for estimating a pose of one or more persons in a scene includes a camera configured to capture one or more images of the scene; and a data processor configured to execute computer executable instructions for: (i) receiving the one or more images of the scene from the camera; (ii) extracting features from the one or more images of the scene for providing inputs to a keypoint subnet and a person detection subnet; (iii) generating one or more keypoints using the keypoint subnet; (iv) generating one or more person instances using the person detection subnet; (v) assigning the one or more keypoints to the one or more person instances by learning pose structures from image data; and (vi) determining one or more poses of the one or more persons in the scene using the assignment of the one or more keypoints to the one or more person instances.

Abstract: A method for a plausibility check of the output of an artificial neural network (ANN) utilized as a classifier. The method includes: a plurality of images for which the ANN has ascertained an association with one or multiple classes of a predefined classification, and the association that is ascertained in each case by the ANN, are provided; for each image at least one feature parameter is determined which characterizes the type, the degree of specificity, and/or the position of at least one feature contained in the image; for each combination of an image and an association, a spatially resolved relevance assessment of the image is ascertained by applying a relevance assessment function; a setpoint relevance assessment is ascertained for each combination, using the feature parameter; a quality criterion for the relevance assessment function is ascertained based on the agreement between the relevance assessments and the setpoint relevance assessments.

Abstract: An object of the present invention is to provide a dispensing audit support apparatus and a dispensing audit support method with a high collation accuracy robustness. According to a dispensing audit support apparatus according to one aspect of the present invention, since a position, shape and size of a region of interest are set according to a position of a collation-target medicine in a captured image, and a position, shape and size of a master image are set according to the set region of interest, it is possible to avoid or reduce distortion of the medicine shape, blur, inclusion of an end part into the image, and the like due to the position and orientation of the collation-target medicine. Therefore, influence on collation accuracy is small, and it is possible to enhance the robustness of the collation accuracy.

Abstract: An object of the present invention is to provide a dispensing audit support apparatus and a dispensing audit support method with a high collation accuracy robustness. According to a dispensing audit support apparatus according to one aspect of the present invention, since a position, shape and size of a region of interest are set according to a position of a collation-target medicine in a captured image, and a position, shape and size of a master image are set according to the set region of interest, it is possible to avoid or reduce distortion of the medicine shape, blur, inclusion of an end part into the image, and the like due to the position and orientation of the collation-target medicine. Therefore, influence on collation accuracy is small, and it is possible to enhance the robustness of the collation accuracy.

Abstract: A method for detecting adverse conditions associated with a device includes receiving, at one or more processing devices at one or more locations, one or more image frames; receiving a set of signals representing outputs of one or more sensors of a device; estimating, based on the one or more image frames, a first set of one or more motion values; estimating, based on the set of signals, a second set of one or more motion values; determining that a degree of correlation between (i) a first motion represented by the first set of one or more motion values and (ii) a second motion represented by the second set of one or more motion values fails to satisfy a threshold condition; and in response to determining that the degree of correlation fails to satisfy the threshold condition, determining presence of an adverse condition associated with the device.

Abstract: A system that provides tracking during growth stages, harvesting stages, processing stages, packaging stages and shipping stages of a grown product lifecycle. Various sets of sensors are associated with grown product and/or purchaser order. Sensors enable a purchaser to access live or real time video displays of a group of product associated with the purchaser.

Abstract: The present application provides an object tracking method. The object tracking method includes obtaining an image of an area in front of a vehicle; dividing the image of the area in the front of the vehicle into a plurality of sub-images; determining a plurality of first sub-images that satisfy a plurality of threshold conditions out of the plurality of sub-images; selecting a plurality of target sub-images out of the plurality of first sub-images, at least one of the plurality of first sub-images is not selected as one of the plurality of target sub-images; and recognizing a target object in one of the plurality of target sub-images.

Abstract: An apparatus and method for three-dimensional (3D) geometric data compression, includes storage of a first 3D geometric mesh of a first data size, which includes a 3D representation of a plurality of objects in a 3D space. The apparatus includes circuitry that receives motion tracking data of the plurality of objects from a plurality of position trackers. The motion tracking data includes motion information of each of the plurality of objects from a first position to a second position in the 3D space. The 3D geometric mesh is segmented into a plurality of 3D geometric meshes corresponding to the plurality of objects, based on the motion tracking data. As a result of the segmentation of the 3D geometric mesh before encoding and the use of motion tracking data, the plurality of 3D geometric meshes are efficiently encoded.

Abstract: Various embodiments of systems and methods for image matching and retrieval process are disclosed.

Abstract: An image processing apparatus and an image processing method for processing far-infrared ray images are provided. Specific temperature ranges constituted by pixels having values falling within a temperature range characteristic of a specific target are extracted from a far-infrared ray image captured of the same target. Of the specific temperature ranges, those having motion vectors close to each other are integrated so as to generate integrated ranges. The integrated ranges having motion vectors close to a global motion vector indicative of the motion of the image as a whole are excluded to obtain excluded integrated ranges. Visible light ranges corresponding to the specific temperature ranges in the excluded integrated ranges are extracted from the visible light image to generate visible light motion ranges. The positions of the visible light motion ranges are corrected on the basis of the motion vector of the excluded integrated ranges as a whole.

Abstract: In an embodiment, an intelligent system includes an electronic circuit configured to execute a neural network, to detect at least one feature in an image of a body portion while executing the neural network, and to determine a respective position and a respective class of each of the detected at least one feature while executing the neural network. For example, such a system can execute a neural network to detect at least one feature in an image of a lung, to determine a respective position within the image of each detected feature, and to classify each of the detected features as one of the following: A-line, B-line, pleural line, consolidation, and pleural effusion.

Abstract: This disclosure provides a system and a method. The method may include: determining a processing instruction; acquiring image data based on the processing instruction; determining a configuration file based on the image data, in which the configuration file may be configured to guide implementation of the processing instruction; constructing a data processing pipeline based on the configuration file; executing the data processing process based on the data processing pipeline, in which the data processing process may be generated based on the data processing pipeline; generating a processing result of the image data based on the executed data processing process; and storing the processing result of the image data in a first storage space.

Abstract: Apparatuses, systems and methods are provided for detecting vehicle occupant actions. More particularly, apparatuses, systems and methods are provided for detecting vehicle occupant actions based on digital image data.

Abstract: Described is a system and method for accurate image and/or video scene classification. More specifically, described is a system that makes use of a specialized convolutional-neural network (hereafter CNN) based technique for the fusion of bottom-up whole-image features and top-down entity classification. When the two parallel and independent processing paths are fused, the system provides an accurate classification of the scene as depicted in the image or video.

Abstract: The present invention is a two-wheel vehicle riding person number determination system including an imaging means configured to image a two-wheel vehicle that is installed in a predetermined position and travels on a road, and a two-wheel vehicle riding person number determining means configured to process an image of the imaging means, extract a contour shape of an upper position of the two-wheel vehicle that travels on the road, detect humped shapes corresponding to heads of persons who ride on the two-wheel vehicle from the contour shape of the upper position of the two-wheel vehicle, and determine, on the basis of the humped shapes, whether or not the number of the persons who ride on the two-wheel vehicle is at least two persons or more.

Abstract: An image recognition method is provided. The method includes obtaining predicted locations of joints of a target person in a to-be-recognized image based on a joint prediction model, where the joint prediction model is pre-constructed by: obtaining a plurality of sample images; inputting training features of the sample images and a body model feature to a neural network and obtaining predicted locations of joints in the sample images outputted by the neural network; updating a body extraction parameter and an alignment parameter; and inputting the training features of the sample images and the body model feature to the neural network to obtain the joint prediction model.

Abstract: Provided are a device and method for analyzing spatiotemporal data of a geographical space including one or more regions. The method includes imaging spatiotemporally varying data, selecting a representative image according to each transition state based on a spatiotemporal change in the imaged data, grouping pixels in the selected image by clustering the pixels, and performing group-specific data analyses.

Abstract: Automated stain finding. In an embodiment, an image of a sample comprising one or more stains is received. For each of a plurality of pixels in the image, an optical density vector for the pixel is determined. The optical density vector comprises a value for each of the one or more stains, and represents a point in an optical density space that has a number of dimensions equal to a number of the one or more stains. The optical density vectors are transformed from the optical density space into a representation in a lower dimensional space. The lower dimensional space has a number of dimensions equal to one less than the number of dimensions of the optical density space. An optical density vector corresponding to each of the one or more stains is identified based on the representation.