A method for predicting conditions associated with a coal stock pile is described. The method includes collecting aerial data for a site including one or more coal stock piles. Using the aerial data, the method includes performing localization of the site to identify boundaries of the coal stock piles and extracting multi-spectral features. The method also includes obtaining additional data associated with the coal stock piles from at least one data source and merging the aerial data with the additional data. Using the merged data and the extracted multi-spectral features, the method includes analyzing a status of the coal stock piles by a prediction module to predict at least one of an impending combustion event or a severe condition associated with the coal stock piles. In response to the predicted at least one impending combustion event or severe condition, the method includes implementing a response.

Methods and systems classify content using scoring for identifying psychological factors employed by consumers to take action. An interface module obtains content including one or more elements. One or more feature vectors are extracted from the content. The one or more feature vectors include a feature vector that identifies an element of the one or more elements of the content. A classification scoring module generates one or more classification vectors from the one or more feature vectors. The one or more classification vectors include a classification vector that identifies one or more characteristics of the element from the content. The one or more classification vectors are combined and one or more characteristics of the content are identified to form an aggregated vector. A goal of the content is detected by generating a string that describes the content from the aggregated vector. The goal is presented with the content.

A three-dimensional bounding box is determined from a two-dimensional image and a point cloud. A feature vector associated with the image and a feature vector associated with the point cloud may be passed through a neural network to determine parameters of the three-dimensional bounding box. Feature vectors associated with each of the points in the point cloud may also be determined and considered to produce estimates of the three-dimensional bounding box on a per-point basis.

Disclosed is a method for detecting densely occluded fish based on a YOLOv5 network, belonging to the technical field of fish images. The method includes a data set establishment and processing part, a model training part and a model testing part; the data set establishment and processing part includes data collection of fish pictures, data labelling and data division of the fish pictures; and the data division is to divide data into a training set, a verification set and a test set.

An image processing method includes: obtaining an image feature of each of a plurality of images for a same object; determining, according to the image feature of each of the plurality of images, a weight coefficient having a one-to-one correspondence with each image feature; and performing feature fusion processing on the image features of the plurality of images based on the weight coefficient of each image feature to obtain a fusion feature of the plurality of images.

A synthesizing apparatus comprises: an input part that inputs a plurality of feature point sets that are respectively extracted by a plurality of methods from an input image having a curved stripes pattern formed by ridges; and a synthesizing part that synthesizes the plurality of feature point sets by executing a logical operation on the plurality of feature point sets. The synthesizing part can execute a logical OR operation on the plurality of feature point sets. The synthesizing part can also execute a logical AND operation on the plurality of feature point sets.

An image processing method, apparatus, and device, and a storage medium are provided. The method is performed by a computing device, and includes: determining a first image feature of a first size of an input image, the first image feature having at least two channels; performing weight adjustment on each channel in the first image feature by using a first weight adjustment parameter, to obtain an adjusted first image feature, the first weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component; downsampling the adjusted first image feature to obtain a second image feature having a second size; combining the first image feature and the second image feature to obtain a combined image feature; and determining an image processing result according to the combined image feature.

The present invention discloses a method for segmenting pedestrians in roadside images using a variable-scale multi-feature fusion convolutional network. It addresses the challenge of significant changes in pedestrian scale by using two parallel convolutional neural networks to extract the local and global features at different scales, and then fusing them to obtain a variable-scale multi-feature fusion convolutional neural network, and this network is trained using roadside pedestrian images to realize accurate pedestrian segmentation, avoiding issues with boundary fuzziness and missing segments commonly found in single-network methods.

Embodiments of this application disclose an image processing method and apparatus, and a computer-readable storage medium. The method includes acquiring an image pair to be processed, and performing image feature extraction on an image to be processed in the image pair to be processed to obtain an image; extracting an association feature from the image feature, the association feature characterizing mutual information between images in the image pair to be processed; identifying a common view image of a common view region in the image to be processed according to the association feature, and calculating a scale difference between common view images; adjusting a size of the common view image based on the scale difference to obtain an adjusted common view image; and extracting at least one common view feature point from each of the adjusted common view images, and processing the image based on the common view feature point.

Disclosed is a surface sensing apparatus, one embodiment having a source of coherent radiation capable of outputting wavelength emissions to create a first illumination state to illuminate a surface and create a first speckle pattern, an emission deviation facility capable of influencing the emission to illuminate the surface and create a second illumination state and a second speckle pattern, and a sensor capable of sensing a representation of the first and a second speckle intensity from the first and second speckle pattern. Also disclosed are methods of sensing properties of the surface, one embodiment comprising the steps of illuminating the surface having a first surface state with the source of coherent radiation emission, sensing a first speckle intensity from the surface, influencing a relationship of the surface to the emission to create a second surface state and sensing a second speckle intensity from the surface at the second surface state.