Abstract: Computer vision systems and methods for property scene understanding from digital images, videos, media and/or sensor information are provided. The system obtains media content indicative of an asset, performs feature segmentation and material recognition, performs object detection on the features, performs hazard detection to detect one or more safety hazards, and performs damage detection to detect any visible damage, to develop a better understanding of the property using one or more features in the media content. The system can output the feature segmentation and material detection, the hazard detection, the content feature detection, and the damage detection, and all other available models to an adjuster or other user on a user interface.

Abstract: Systems and methods for fine adjustment of computerized roof models are provided. The system generates a 3D roof structure model based on at least one image obtained from an aerial imagery database. Alternatively, the system could retrieve at least one stored 3D roof structure model from a 3D roof structure model database. The system weighs (e.g., scores) each 3D roof structure model candidate and determines an optimal 3D roof structure model by applying a variable neighborhood search to a 3D roof structure model candidate having a highest confidence score among the weighed 3D roof structure model candidates.

Abstract: Computer vision systems and methods for determining roof shapes from imagery using segmentation networks are provided. The system obtains an image of a structure from an image database, and determines a flat roof structure ratio and a sloped roof structure ratio of the roof structure using a neural network. Based on segmentation processing by the neural network, the system determines a flat roof structure ratio and a sloped roof structure ratio based on a portion of the roof structure classified as being flat and a portion of the roof structure classified as being sloped. Then, the system determines a ratio of each shape type of the roof structure using a neural network. The system generates a roof structure shape report indicative of a predominant shape of the roof structure and ratios of each shape type of the roof structure.

Abstract: Systems and methods for detection and localization of image and document forgery. The method can include the step of receiving a dataset having a plurality of authentic images and a plurality of manipulated images. The method can also include the step of benchmarking a plurality of image forgery algorithms using the dataset. The method can further include the step of generating a plurality of receiver operating characteristic (ROC) curves for each of the plurality of image forgery algorithms. The method also includes the step of calculating a plurality of area under curve metrics for each of the plurality of ROC curves. The method further includes the step of training a neural network for image forgery based on the plurality of area under curve metrics.

Abstract: Systems and methods for generating augmented reality environments from 2D drawings are provided. The system performs a camera calibration process to determine how a camera transforms images from the real world into a 2D image plane. The system calculates a camera pose and determines an object position and an object orientation relative to a known coordinate system. The system detects and processes a 2D drawing/illustration and generates a 3D model from the 2D drawing/illustration. The system performs a rendering process, wherein the system generates an augmented reality environment which includes the 3D model superimposed on an image of the 2D drawing/illustration. The system can generate the augmented reality environment in real time, allowing the system to provide immediate feedback to the user. The images processed by the system can be from a video, from multiple image photography, etc.

Abstract: Computer vision systems and methods for generating building models using three-dimensional sensing and augmented reality (AR) techniques are provided. Image frames including images of a structure to be modeled are captured by a camera of a mobile device such as a smart phone, as well as three-dimensional data corresponding to the image frames. An object of interest, such as a structural feature of the building, is detected using both the image frames and the three-dimensional data. An AR icon is determined based upon the type of object detected, and is displayed on the mobile device superimposed on the image frames. The user can manipulate the AR icon to better fit or match the object of interest in the image frames, and can capture the object of interest using a capture icon displayed on the display of the mobile device.

Abstract: Systems and methods for rapidly developing annotated computer models of structures and properties is provided. The system generates three-dimensional (3D) models of structures and property using a wide variety of digital imagery, and/or can process existing 3D models created by other systems. The system processes the 3D models to automatically identify candidate objects within the 3D models that may be suitable for annotation, such as roof faces, chimneys, windows, gutters, etc., using computer vision techniques to automatically identify such objects. Once the candidate objects have been identified, the system automatically generates user interface screens which gather relevant information related to the candidate objects, so as to rapidly obtain, associate, and store annotation information related to the candidate objects.

Abstract: Systems and methods for property feature detection and extraction using digital images. The image sources could include aerial imagery, satellite imagery, ground-based imagery, imagery taken from unmanned aerial vehicles (UAVs), mobile device imagery, etc. The detected geometric property features could include tree canopy, pools and other bodies of water, concrete flatwork, landscaping classifications (gravel, grass, concrete, asphalt, etc.), trampolines, property structural features (structures, buildings, pergolas, gazebos, terraces, retaining walls, and fences), and sports courts. The system can automatically extract these features from images and can then project them into world coordinates relative to a known surface in world coordinates (e.g., from a digital terrain model).

Abstract: Computer vision systems and methods for segmenting and classifying building components, contents, materials or attributes are provided. The system obtains media content indicative of an asset. The system identifies and segments items of the asset based on one or more segmentation models. The system determines, based on one or more classification models, a value associated with material or other attribute classification for each of the segmented items. The value indicates how likely the segmented item belongs to a particular material or attribute type. The system determines a material or attribute type for each of the segmented items based on a comparison of the confidence value of the material or the attribute to pre-calculated threshold values. The threshold values define a cut-off indicative of a segmented item most likely to be a particular type of material or attribute.

Abstract: Systems and methods for automatically detecting, classifying, and processing objects captured in an images or videos are provided. In one embodiment, the system receives an image from an image source and detects one or more objects in the image. The system performs a high-level classification of the one or more objects in the image. The system performs a specific classification of the one or more objects, determines a price of the one or more objects, and generates a pricing report comprising a price of the one or more objects. In another embodiment, the system captures at least one image or video frame and classifies an object present in the image or video frame using a neural network. The system adds the classified object and an assigned object code to an inventory and processes the inventory to assign the classified object a price.

Abstract: A system for improved localization of image forgery. The system generates a variational information bottleneck objective function and works with input image patches to implement an encoder-decoder architecture. The encoder-decoder architecture controls an information flow between the input image patches and a representation layer. The system utilizes information bottleneck to learn useful residual noise patterns and ignore semantic content present in each input image patch. The system trains a neural network to learn a representation indicative of a statistical fingerprint of a source camera model from each input image patch while excluding semantic content thereof. The system can determine a splicing manipulation localization by the trained neural network.

Abstract: A system for detecting and extracting a ground surface condition from an image comprising a memory and a processor in communication with the memory. The processor performs a high resolution scan of at least one input image and generates an orthomosaic model and a digital surface model based on the performed high resolution scan. The processor generates an image tile based on the generated models and determines a label indicative of a probability of a presence of a ground surface condition for each pixel of the generated image tile via a computer vision model. The processor generates a label tensor for the at least one input image based on the determined labels and extracts a two-dimensional geospatial representation of a detected ground surface condition based on the generated label tensor. The processor generates a report indicative of damage associated with the detected ground surface condition based on the extracted two-dimensional geospatial representation.

Abstract: Computer vision systems and methods for determining roof shapes from imagery using segmentation networks are provided. The system obtains an image of a structure from an image database, and determines a flat roof structure ratio and a sloped roof structure ratio of the roof structure using a neural network. Based on segmentation processing by the neural network, the system determines a flat roof structure ratio and a sloped roof structure ratio based on a portion of the roof structure classified as being flat and a portion of the roof structure classified as being sloped. Then, the system determines a ratio of each shape type of the roof structure using a neural network. The system generates a roof structure shape report indicative of a predominant shape of the roof structure and ratios of each shape type of the roof structure.

Abstract: Systems and methods for fine adjustment of computerized roof models are provided. The system generates a 3D roof structure model based on at least one image obtained from an aerial imagery database. Alternatively, the system could retrieve at least one stored 3D roof structure model from a 3D roof structure model database. The system weighs (e.g., scores) each 3D roof structure model candidate and determines an optimal 3D roof structure model by applying a variable neighborhood search to a 3D roof structure model candidate having a highest confidence score among the weighed 3D roof structure model candidates.

Abstract: Systems and methods for rapidly developing annotated computer models of structures and properties is provided. The system generates three-dimensional (3D) models of structures and property using a wide variety of digital imagery, and/or can process existing 3D models created by other systems. The system processes the 3D models to automatically identify candidate objects within the 3D models that may be suitable for annotation, such as roof faces, chimneys, windows, gutters, etc., using computer vision techniques to automatically identify such objects. Once the candidate objects have been identified, the system automatically generates user interface screens which gather relevant information related to the candidate objects, so as to rapidly obtain, associate, and store annotation information related to the candidate objects.

Abstract: Machine learning systems and methods for price list determination from free-form text data are provided. The system obtains a text description of an item, such as an item that is the subject of an insurance loss claim and processes the text description using a first machine learning model to identify an item being described by the text description. The system then processes the text description using a second machine learning model to identify one or more candidate matching items from a database. The system then automatically populates one or more user interface screens of a claims processing software application using the output of the first machine learning model and the output of the second machine learning model.

Abstract: Computer vision systems and methods for optimizing correlation clustering for image segmentation are provided. The system receives input data and generates a correlation clustering formulation for Benders Decomposition for optimized correlation clustering of the input data. The system optimizes the Benders Decomposition for the generated correlation clustering formulation and performs image segmentation using the optimized Benders Decomposition.

Abstract: Computer vision systems and methods for noisy contour alignment are provided. The system generates a loss function and trains a convolutional neural network with the loss function and a plurality of images of a dataset to learn to align contours with progressively increasing complex forward and backward transforms over increasing scales. The system can align an attribute of an image of the dataset by the trained neural network.

Abstract: A system for modeling a three-dimensional structure utilizing two-dimensional segments comprising a memory and a processor in communication with the memory. The processor extracts a plurality of two-dimensional segments corresponding to the three-dimensional structure from a plurality of images indicative of different views of the three-dimensional structure. The processor determines a plurality of three-dimensional candidate segments based on the extracted plurality of two-dimensional segments and adds the plurality of three-dimensional candidate segments to a three-dimensional segment cloud. The processor transforms the three-dimensional segment cloud into a wireframe indicative of the three-dimensional structure by performing a wireframe extraction process on the three-dimensional segment cloud.

Abstract: Computer vision systems and methods for text classification are provided. The system detects a plurality of text regions in an image and generates a bounding box for each detected text region. The system utilizes a neural network to recognize text present within each bounding box and classifies the recognized text, based on at least one extracted feature of each bounding box and the recognized text present within each bounding box, according to a plurality of predefined tags. The system can associate a key with a value and return a key-value pair for each predefined tag.