Abstract: An apparatus includes a first acquisition unit that acquires a plurality of likelihood maps by setting a plurality of different weight parameters in a trained model that outputs, with an image feature extracted from the input image as an input, a likelihood map including, in association with a position in the input image, a likelihood indicating a possibility that the object is present, and a detection unit that detects, based on the acquired plurality of the likelihood maps, the position of the object included in the input image, wherein the trained model is a model that has learned the weight parameters based on loss values at least acquired using a first loss function for reducing a likelihood around a position of interest in the likelihood map, and a second loss function for increasing a likelihood acquired at the position of the object in the input image.

Abstract: A technology is described for spatially estimating thermal emissivity. A method can include obtaining spectral emissivity data and satellite imaging data for a geographic area. A weighted emissivity model of emissivity values may be generated for surfaces included in the geographic area from the spectral emissivity data and the satellite imaging data, wherein the spectral emissivity data is mapped to the satellite imaging data to generate the weighted emissivity model. Thermal imaging data for the geographic area may be received from an airborne thermal imaging sensor and a thermal emissivity map can be generated for the geographic area using the thermal imaging data and the weighted emissivity model. The emissivity values from the weighted emissivity model can be used to estimate thermal emissivity values.

Abstract: Image processing methods, devices, and storage media are provided. One of the image processing methods includes: obtaining an image comprising a target object; inputting the image into a mask prediction model to obtain a first mask image corresponding to the target object in the image; inputting the first mask image and the image comprising the target object into a mask quality unification model to obtain a second mask image, wherein the mask quality unification model is configured to adjust a fineness associated with the first mask image to a target fineness to obtain the second mask image, and wherein the second mask image has image semantic information consistent with image semantic information of the image comprising the target object; and inputting the image comprising the target object and the second mask image into a target image obtaining model to obtain a target image corresponding to the target object.

Abstract: A method and apparatus for determining a location of a signal light, a storage medium, a program and a roadside device provided by the present application, which relate to intelligent traffic technology and automatic driving technology, including: for a plurality of frames of images captured including the signal light, determining horizontal edges and vertical edges in each of the images; combining, according to the obtained horizontal edges and vertical edges, to obtain a plurality of rectangular frames; and determining, according to the plurality of rectangular frames and preset size information of a frame of the signal light, the location of the signal light in the image.

Abstract: The present disclosure is directed to systems and methods for estimating machine rotation state. The rotation state can include the rotation speed and/or angle as the machine rotates. In some implementations, an estimating machine rotation state system can receive image data, timestamped consecutively, from camera devices of a machine. The estimating machine rotation state system can generate optical flow vectors for the pixels of the image data and determine the hue and intensity of the pixels based on the optical flow vectors. Using the hues, intensities, and timestamps of the image data, the estimating machine rotation state system can generate an intensity plot over time. The estimating rotation states system can then determine a rotation speed of the machine based on the intensity plot. The estimating rotation states system can also determine a rotation angle of the machine by integrating the intensity plot over time.

Abstract: Systems and methods to perform noise-adaptive non-blind deblurring on an input image that includes blur and noise involve implementing a first neural network on the input image to obtain one or more parameters and performing regularized deconvolution to obtain a deblurred image from the input image. The regularized deconvolution uses the one or more parameters to control noise in the deblurred image. A method includes implementing a second neural network to remove artifacts from the deblurred image and provide an output image.

Abstract: In an embodiment, a volumetry method is provided. The method can include receiving one or more 2D images of a site including one or more containers configured to hold a liquid, the 2D images being acquired at a first time. The method can also include identifying a container within the 2D images. The method can further include selecting an identified container within the 2D images for volumetric analysis based upon a degree of occlusion of its front facing surface. The method can additionally include classifying a portion of the selected container containing the liquid. The method can further include determining the volume of liquid held within the container based upon the classified portion.

Abstract: A method of processing an image for improving image quality is provided. The method includes inputting an image into a first artificial neural network as an input variable to create a feature map and inputting an image into a second artificial neural network as an input variable to create an estimated class probability distribution map for each of frequency channels. Further, the method may comprise determining a class for each of the frequency channels on the basis of a probability distribution included in the estimated class probability distribution map, creating an estimated class map that includes a determined class, converting the estimated class map into an estimated frequency map, combining the estimated frequency map with the feature map to create a combined feature map, and inputting the combined feature map into a third artificial neural network as an input variable to create an output image.

Abstract: An information processing method includes processing of: acquiring, from a plurality of time-series images in which an object is captured, first information including at least a plurality of positions or a plurality of sizes of the object; executing prediction processing of predicting second information including at least one of a position or a size of the object at a next time point in a time-series based on the first information and recursively executing the prediction processing based on the first information and the second information to predict the second information of the object at a time point further next to the next time point; executing recognition processing of recognizing motion of the object based on the second information; and determining a total number of times of recursion of the prediction processing based on a result of the recognition processing.

Abstract: Systems and methods are configured to generate a frequency map representing a density of objects found in regions of a sample that may be used in setting parameters for imaging the regions. Various embodiments involve binarizing the pixels for a raw image of the sample to transform the image into binary data. Run-length encoded components are identified from the data for dimensions of the raw image. Each component is a length of a sequence of adjacent pixels found in a dimension with the same value in the binary data. A projection of the image is then generated from projection values for the dimensions. Each projection value provides a measure of the density of objects present in a dimension with respect to the components identified for the dimension. This projection is used to identify a level of density for each region of the sample from which the frequency map is generated.

Abstract: A system mitigates transitions among pixels a mosaic image. The system uses two balancing adjustments and an easing transition adjustment. The first balancing adjustment uses a histogram to remap the pixels in the mosaic to a normal distribution. The second balancing adjustment minimizes transitions between adjacent individual images in the mosaic based on pixels positions of edge pixels and intensity differences between edge pixels in the adjacent images. The easing transitions adjustment modifies the intensity of a target pixel as a function of a radius from the target pixel, the distance between the target pixel and a nearest neighbor pixel, and an intensity difference between the nearest neighbor pixel and a pixel adjacent to the nearest neighbor pixel. A revised mosaic image is created as a function of the first balancing adjustment, the second balancing adjustment, and the easing transition adjustment.

Abstract: A foveated down sampling and correction (FDS-C) circuit for combined down sampling and correction of chromatic aberrations in images. The FDS-C circuit performs down sampling and interpolation of pixel values of a first subset of pixels of a color in a raw image using down sampling scale factors and first interpolation coefficients to generate first corrected pixel values for pixels of the color in a first corrected version of the raw image. The FDS-C circuit further performs interpolation of pixel values of a second subset of the pixels in the first corrected version using second interpolation coefficients to generate second corrected pixel values for pixels of the color in a second corrected version of the raw image. Pixels in the first subset are arranged in a first direction, pixels in the second subset are arranged in a second direction, and the down sampling scale factors vary along the first direction.

Abstract: Methods and systems for image deblurring are described. The weights of a deblurring network are first trained by meta-training the deblurring network on both a primary deblurring task and an auxiliary reconstruction task. Application-time training of the deblurring network is then performed, using an application-time blurry input image, to obtain values of application-time trained weights. Application-time training includes performing the auxiliary reconstruction task on the application-time blurry input image, and updating the weights of the deblurring network based on an auxiliary loss computed from the auxiliary reconstruction task. A deblurred output image is generated from the application-time blurry input image, using the application-time trained weights in the deblurring network.

Abstract: A method may include obtaining a set of events, of a set of pixels of a dynamic vision sensor, associated with an object; determining a set of voltages of the set of pixels, based on the set of events; generating a set of images, based on the set of voltages of the set of pixels; inputting the set of images into a first neural network configured to output a visual motion estimation of the object; inputting the set of images into a second neural network configured to output a confidence score of the visual motion estimation output by the first neural network; obtaining the visual motion estimation of the object and the confidence score of the visual motion estimation of the object, based on inputting the set of images into the first neural network and the second neural network; and providing the visual motion estimation of the object and the confidence score.

Abstract: A method of video deblurring by an electronic device is described. The processing circuitry of the electronic device acquires N continuous image frames from a video clip the N being a positive integer, and the N continuous image frames including a blurry image frame to be processed. The processing circuitry of the electronic device performs three-dimensional (3D) convolution processing on the N continuous image frames with a generative adversarial network model, to acquire spatio-temporal information corresponding to the blurry image frame.

Abstract: The disclosure provides a data processing method and apparatus, a storage medium and an electronic device. The method includes: performing first processing on data by using a first sub-model corresponding to an artificial intelligence (AI) processing model, to obtain an intermediate processing result, the AI processing model corresponding to the first sub-model and a second sub-model, the first sub-model being generated according to M neural network layers in the AI processing model, the second sub-model being generated according to K neural network layers in the AI processing model, M and K being positive integers greater than or equal to 1; transmitting the intermediate processing result to a first server; and receiving a target processing result from the first server, the target processing result being based on a result of second processing on the intermediate processing result by using the second sub-model.

Abstract: An image processing apparatus is configured to perform processing of detecting an object by using a predetermined number of sample points referred to as particles, the processing including: executing a detection processing configured to calculate a weight for each of the particles, and detect the object by using the calculated weights; and executing a resampling processing configured to assign a particle number to each of the predetermined number of particles, calculate, for each particle, a comparative value by multiplying an average of the weights of the predetermined number of particles by the particle number, calculate, for each particle, a cumulative weight by adding the particle's own weight to the weights of all the particles assigned with the respective particle numbers smaller than the particle's own particle number, and perform a reselection process that executes reselection on the particles of all the particle numbers.