A heterogeneous sensor fusion apparatus includes a point processor configured to detect a first object by processing a detection point input from a first sensor, an image processor configured to detect a second object by processing an image input from a second sensor, a point-matching unit configured to calculate a matching rate by matching the detection point with the image, and to determine whether the first object and the second object are identical based on the calculated matching rate, an association unit configured to generate track information by fusing information from the first sensor and the second sensor when the first object and the second object are identical, and an output unit configured to output the generated track information.

In various examples, a hazard detection system plots hazard indicators from multiple detection sensors to grid cells of an occupancy grid corresponding to a driving environment. For example, as the ego-machine travels along a roadway, one or more sensors of the ego-machine may capture sensor data representing the driving environment. A system of the ego-machine may then analyze the sensor data to determine the existence and/or location of the one or more hazards within an occupancy grid—and thus within the environment. When a hazard is detected using a respective sensor, the system may plot an indicator of the hazard to one or more grid cells that correspond to the detected location of the hazard. Based, at least in part, on a fused or combined confidence of the hazard indicators for each grid cell, the system may predict whether the corresponding grid cell is occupied by a hazard.

According to one embodiment, an analysis apparatus includes processing circuitry. The processing circuitry acquires sensor data from a measurement target, calculates a state value based on the sensor data, sets, based on time-series data of the state value and predetermined criteria, a plurality of noticed sections in the time-series data, performs clustering using the state value regarding each of the noticed sections and generates a clustering result, and generates, based on the clustering result, stress information including characteristic information of each of a plurality of clusters.

An image processing device according to one aspect of the present disclosure includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: receive a visible image of a face; receive a near-infrared image of the face; adjust brightness of the visible image based on a frequency distribution of pixel values of the visible image and a frequency distribution of pixel values of the near-infrared image; specify a relative position at which the visible image is related to the near-infrared image; invert adjusted brightness of the visible image; detect a region of a pupil from a synthetic image obtained by adding up the visible image the brightness of which is inverted and the near-infrared image based on the relative position; and output information on the detected pupil.

An RGB-D fusion information-based obstacle target classification method includes: collecting an original image through a binocular camera within a target range, and acquiring a disparity map of the original image; collecting a color-calibrated RGB image through a reference camera of the binocular camera within the target range; acquiring an obstacle target through disparity clustering in accordance with the disparity map and the color-calibrated RGB image, and acquiring a target disparity map and a target RGB image of the obstacle target; calculating depth information about the obstacle target in accordance with the target disparity map; and acquiring a classification result of the obstacle target through RGB-D channel information fusion in accordance with the depth information and the target RGB image.

Various implementations disclosed herein include devices, systems, and methods that provide measurements of objects based on a location of a surface of the objects. An exemplary process may include obtaining a three-dimensional (3D) representation of a physical environment that was generated based on depth data and light intensity image data, generating a 3D bounding box corresponding to an object in the physical environment based on the 3D representation, determining a class of the object based on the 3D semantic data, determining a location of a surface of the object based on the class of the object, the location determined by identifying a plane within the 3D bounding box having semantics in the 3D semantic data satisfying surface criteria for the object, and providing a measurement of the object, the measurement of the object determined based on the location of the surface of the object.

The invention relates to a method for use in characterizing lesions in radiology images, comprising performing a computer-based analysis of a pathology image of a sample of a lesion of a subject in order to recognize tissue and/or cellular characteristics of the lesion, wherein the analysis produces a derived pathology image that represents the recognized tissue and/or cellular characteristics, computing one or more radiology features for the lesion from a radiology image of the lesion, and determining correlations between the computed one or more radiology features and the recognized tissue and/or cellular characteristics. With this method, biological ground truth information can be used to identify radiology features that are indicative of certain tissue and/or cellular characteristics of lesions and that may therefore be better suited for characterizing the lesions. Such radiology features can then be used together with the corresponding correlations for an improved characterization of lesions in radiology images.

Briefly, embodiments disclosed herein relate to image cropping, such as for digital images, for example.

The present disclosure relates to a communication method and system for converging a 5.sup.th-Generation (5G) communication system for supporting higher data rates beyond a 4.sup.th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. Accordingly, the embodiments herein provide a method for managing operations of an electronic device. The method includes transmitting an input signal corrupted by noise to a trained model with a plurality of output states. Further, the method includes dynamically determining an entropy for the plurality of output states of the trained model. Further, the method includes determining whether the entropy exceeds a pre-defined threshold. Furthermore, the method includes automatically enabling an electronic device module of the electronic device in response to determining that the entropy exceeds the pre-defined threshold.

The disclosure discloses a method for dynamically monitoring the content of a rare earth element (REE) component based on a time-series feature. Using an image information acquisition device to periodically acquire a time-series image of a rare earth (RE) solution to be monitored; extracting a time-series feature of the time-series image in a mixed color space; determining whether a time-series feature value of the time-series image is in an expected interval of the mixed color space; calculating a histogram intersection distance between the time-series image and a sample image in a sample data set in the HSV color space, and determining the content of the REE component corresponding to the time-series image according to a component content corresponding to a sample image with a larger histogram intersection distance, if the determination result indicates no; otherwise, directly waiting for the acquisition of a time-series image at a next sampling time point.