The present invention relates to a method for acquiring an object information, the method comprising: obtaining an input image acquired by capturing a sea; obtaining a noise level of the input image; when the noise level indicates a noise lower than a predetermined level, acquiring an object information related to an obstacle included in the input image from the input image by using a first artificial neural network, and when the noise level indicates a noise higher than the predetermined level, obtaining a noise-reduced image of which the environmental noise is reduced from the input image by using a second artificial neural network, and acquiring an object information related to an obstacle included in the sea from the noise-reduced image by using the first artificial neural network.

The present invention relates to a method for acquiring an object information, the method comprising: obtaining an input image acquired by capturing a sea; obtaining a noise level of the input image; when the noise level indicates a noise lower than a predetermined level, acquiring an object information related to an obstacle included in the input image from the input image by using a first artificial neural network, and when the noise level indicates a noise higher than the predetermined level, obtaining a noise-reduced image of which the environmental noise is reduced from the input image by using a second artificial neural network, and acquiring an object information related to an obstacle included in the sea from the noise-reduced image by using the first artificial neural network.

A method of fabricating a semiconductor device includes generating a mask based on second layout data obtained by applying an OPC model to first layout data and performing a semiconductor process using the mask on a substrate, obtaining a plurality of pattern images by selecting a plurality of sample patterns from the substrate, selecting sample images corresponding to the sample patterns from each of the first layout data, the second layout data, and simulation data obtained by performing a simulation based on the second layout data, generating a plurality of input images corresponding to the sample patterns by blending the sample images corresponding to the sample patterns, respectively, and generating an error prediction model for the OPC model by training a machine learning model using a data set including the input images and the pattern images.

In various examples, high beam control for vehicles may be automated using a deep neural network (DNN) that processes sensor data received from vehicle sensors. The DNN may process the sensor data to output pixel-level semantic segmentation masks in order to differentiate actionable objects (e.g., vehicles with front or back lights lit, bicyclists, or pedestrians) from other objects (e.g., parked vehicles). Resulting segmentation masks output by the DNN(s), when combined with one or more post processing steps, may be used to generate masks for automated high beam on/off activation and/or dimming or shading—thereby providing additional illumination of an environment for the driver while controlling downstream effects of high beam glare for active vehicles.

The present disclosure provides a method, apparatus and computer program product for suppressing noise in 3D road surface reconstruction. The method includes: acquiring an image related to a road surface; extracting horizontal line information and ROI (region of interest) information from the image, wherein the horizontal line information comprises a horizontal line pixel value, and the ROI information is used to perform 3D road surface reconstruction for the road surface; judging whether the difference between the horizontal line pixel value and a preset horizontal line pixel value is greater than a pixel threshold; judging whether the horizontal line pixel value is greater than a compensation threshold when the difference is greater than the pixel threshold; determining a compensation value based on the difference when the horizontal line pixel value is not greater than the compensation threshold; and adjusting the ROI information based on the compensation value.

The invention discloses a deep network lung texture recognition method combined with multi-scale attention, which belongs to the field of image processing and computer vision. In order to accurately recognize the typical texture of diffuse lung disease in computed tomography (CT) images of the lung, a unique attention mechanism module and multi-scale feature fusion module were designed to construct a deep convolutional neural network combing multi-scale and attention, which achieves high-precision automatic recognition of typical textures of diffuse lung diseases. In addition, the proposed network structure is clear, easy to construct, and easy to implement.

Embodiments of the disclosure are drawn to apparatuses and methods for anomalous gas concentration detection. A spectroscopic system, such as a wavelength modulated spectroscopy (WMS) system may measure gas concentrations in a target area. However, noise, such as speckle noise, may interfere with measuring relatively low concentrations of gas, and may lead to false positives. A noise model, which includes a contribution from a speckle noise model, may be used to process data from the spectroscopic system. An adaptive threshold may be applied based on an expected amount of noise. A speckle filter may remove measurements which are outliers based on a measurement of their noise. Plume detection may be used to determine a presence of gas plumes. Each of these processing steps may be associated with a confidence, which may be used to determine an overall confidence in the processed measurements/gas plumes.

Embodiments of the disclosure are drawn to apparatuses and methods for anomalous gas concentration detection. A spectroscopic system, such as a wavelength modulated spectroscopy (WMS) system may measure gas concentrations in a target area. However, noise, such as speckle noise, may interfere with measuring relatively low concentrations of gas, and may lead to false positives. A noise model, which includes a contribution from a speckle noise model, may be used to process data from the spectroscopic system. An adaptive threshold may be applied based on an expected amount of noise. A speckle filter may remove measurements which are outliers based on a measurement of their noise. Plume detection may be used to determine a presence of gas plumes. Each of these processing steps may be associated with a confidence, which may be used to determine an overall confidence in the processed measurements/gas plumes.

A gated truncated readout system for position sensitive or imaging detectors that improves resolution over traditional readout systems. The readout system includes two or more amplifiers that receive a multichannel output analog data from the detector. Analog gates control circuitry, included in the readout circuit, receives the signals from the amplifiers, determines a fractional value of the sum-integral of the signals, and enables analog gates operation around an area of interest, disabling all other channels where noise dominates the signal value and thereby improving interpolation accuracy of the signals centroid position and the detector resolution. Filtered signals are transmitted to a centroid interpolation signal processing device for computation of the centroid position. As a result disabling all channels where noise dominates the signal value, the gated truncated readout system provides better accuracy improved detector resolution.

A gated truncated readout system for position sensitive or imaging detectors that improves resolution over traditional readout systems. The readout system includes two or more amplifiers that receive a multichannel output analog data from the detector. Analog gates control circuitry, included in the readout circuit, receives the signals from the amplifiers, determines a fractional value of the sum-integral of the signals, and enables analog gates operation around an area of interest, disabling all other channels where noise dominates the signal value and thereby improving interpolation accuracy of the signals centroid position and the detector resolution. Filtered signals are transmitted to a centroid interpolation signal processing device for computation of the centroid position. As a result disabling all channels where noise dominates the signal value, the gated truncated readout system provides better accuracy improved detector resolution.