A method for directionally warning as to a target object behind a vehicle acquires images of environment surrounding the vehicle. Objects in the environment images are identified and high-risk target object or objects from all the objects is labeled. A relative direction and a relative location of the target object with the transportation are confirmed. A warning device is adjusted based on the relative direction and the relative location. The warning device gives directional warning as to the target object. A collision risk warning system and a transportation applying the method are also disclosed.

A method for re-recognizing an object image is provided based on multi-feature information capture and correlation analysis weights of an input feature map by using a convolutional layer with a spatial attention mechanism and a channel attention mechanism, causing channel and spatial information to effectively combined, which not only focus on an important feature and suppress an unnecessary feature, but also improve a representation of a feature. A multi-head attention mechanism is used to process a feature after an image is divided into blocks to capture abundant feature information and determine a correlation between features to improve performance and efficiency of object image retrieval. The convolutional layer with the channel attention mechanism and the spatial attention mechanism is combined with a transformer having the multi-head attention mechanism to focus on globally important features and capture fine-grained features, thereby improving performance of re-recognition.

A method, computer system, and a computer program product for image evaluation is provided. The present invention may include extracting one or more individual objects from an image. The present invention may include determining a general sentiment for each of the one or more individual objects. The present invention may include determining a personal sentiment score each of the one or more individual objects. The present invention may include generating an overall sentiment score for the image based on at least the general sentiment score for each of the one or more individual objects and the personal sentiment score for each of the one or more individual objects. The present invention may include determining the overall sentiment score for the image exceeds a personal threshold of a user. The present invention may include providing one or more improvement mechanisms to the user.

A device configured to capture a first image of an item on a platform using a camera and to determine a first number of pixels in the first image that corresponds with the item. The device is further configured to capture a first depth image of an item on the platform using a three-dimensional (3D) sensor and to determine a second number of pixels within the first depth image that corresponds with the item. The device is further configured to determine that the difference between the first number of pixels in the first image and the second number of pixels in the first depth image is less than the difference threshold value, to extract the plurality of pixels corresponding with the item in the first image from the first image to generate a second image, and to output the second image.

A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations to accept ecosystem data and detect, analyze, and notify a user about a species in an environment. The system analysis and notification may include impact determination of the species on the environment and shall learn from the received and analyzed data, bringing intelligence to the system.

Systems, devices, and methods for training a neural network and performing image interest point detection and description using the neural network. The neural network may include an interest point detector subnetwork and a descriptor subnetwork. An optical device may include at least one camera for capturing a first image and a second image. A first set of interest points and a first descriptor may be calculated using the neural network based on the first image, and a second set of interest points and a second descriptor may be calculated using the neural network based on the second image. A homography between the first image and the second image may be determined based on the first and second sets of interest points and the first and second descriptors. The optical device may adjust virtual image light being projected onto an eyepiece based on the homography.

Various techniques for facilitating communication with and across a network platform and one or more user computing devices are described. For example, these techniques may include (i) generating preference data based on capturing and analyzing user interactions with respect to a set of training photographs, (ii) generating, verifying, and processing search requests to allow users to identify other users and/or target objects on the network platform, and (iii) generating, verifying, and processing link requests to allow users to create links to or associations with other users and/or target objects on the network platform, among others.

Systems and methods of the present disclosure include receiving an aerial image that includes a view of property (e.g., real property, personal property, or other types of property), automatically identifying the property included in the aerial image, automatically determining one or more characteristics of the property based at least in part on the aerial image, and automatically adjusting an insurance policy term for an insurance policy relating to the property based at least in part on the one or more characteristics of the property. Certain embodiments include automatically determining the one or more characteristics of the property also based at least in part on data received from one or more smart home devices associated with the property, one or more public records relating to the property, a supplemental image accessed from a camera located in or around the property, and so forth.

A method for intelligently measuring vehicle speed based on a binocular stereo vision system includes: training a Single Shot Multibox Detector neural network to obtain a license plate recognition model; calibrating the binocular stereo vision system to acquire parameters of two cameras; detecting the license plates in the captured video frames with the license plate recognition model, locating the license plate position; performing feature point extraction and stereo matching by a feature-based matching algorithm; screening and eliminating the matching point pairs, and reserving the coordinates of the matching point pair closest to the license plate center; performing stereo measurement on the screened matching point pair to get the spatial coordinates of the position; calculating and obtaining the speed of the target vehicle. The present invention is easy to install and adjust, could simultaneously recognize multiple trained features automatically, and better suit the intelligent transportation networks and IoT (Internet of Things).

Examples provide a self-supervised language model for document-to-document similarity scoring and ranking long documents of arbitrary length in an absence of similarity labels. In a first stage of a two-staged hierarchical scoring, a sentence similarity matrix is created for each paragraph in the candidate document. A sentence similarity score is calculated based on the sentence similarity matrix. In the second stage, a paragraph similarity matrix is constructed based on aggregated sentence similarity scores associated with the first candidate document. A total similarity score for the document is calculated based on the normalize the paragraph similarity matrix for each candidate document in a collection of documents. The model is trained using a masked language model and intra-and-inter document sampling. The documents are ranked based on the similarity scores for the documents.