Abstract: Segmentation first breaks the images into segments or regions, with the segments of the region having text or symbols. The segmented image is separately applied to two different CNN-based models. Each model produces text boxes where potential text might exist. Then, a selective NMS algorithm is applied to the output of each model to produce a final group of text regions. These text regions are analyzed and actions are taken.

Abstract: Segmentation first breaks the images into segments or regions, with the segments of the region having text or symbols. The segmented image is separately applied to two different CNN-based models. Each model produces text boxes where potential text might exist. Then, a selective NMS algorithm is applied to the output of each model to produce a final group of text regions. These text regions are analyzed and actions are taken.

Abstract: Segmentation first breaks the images into segments or regions, with the segments of the region having text or symbols. The segmented image is separately applied to two different CNN-based models. Each model produces text boxes where potential text might exist. Then, a selective NMS algorithm is applied to the output of each model to produce a final group of text regions. These text regions are analyzed and actions taken.

Abstract: In various examples, a system receives image data characterizing an image of an item. Additionally, the system implements a first set of operations and a second set of operations. In some examples, the first set of operations includes performing a structural similarity analysis of the item, based on the image data, and determining a structural similarity score based on the structural similarity analysis of the item. In other examples, the second set of operations includes generating a plurality of derivative images by applying a plurality of distortions to the image of the item, extracting one or more features based at least on the plurality of derivative images, and determining the quality of the image based at least on the extracted one or more features and the structural similarity score.

Abstract: In various examples, a system receives image data characterizing an image of an item. Additionally, the system implements a first set of operations and a second set of operations. In some examples, the first set of operations includes performing a structural similarity analysis of the item, based on the image data, and determining a structural similarity score based on the structural similarity analysis of the item. In other examples, the second set of operations includes generating a plurality of derivative images by applying a plurality of distortions to the image of the item, extracting one or more features based at least on the plurality of derivative images, and determining the quality of the image based at least on the extracted one or more features and the structural similarity score.

Abstract: A systems including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, perform: receiving a plurality of images from one or more devices, the images corresponding to a store shelf of a store; combining the plurality of images to generate a shelf image corresponding to the store shelf; encoding the shelf image into a first processing format; processing the shelf image in the first processing format with a neural network using pre-trained weights; determining positions in the shelf image that correspond to an out-of-stock detection based on outputs from the neural network; and generating a report for the out-of-stock detection, the report including an indication of coordinates of the out-of-stock detection and an item of the store that corresponds to the coordinates. Other embodiments are described.

Abstract: In various examples, a system receives image data characterizing an image of an item. Additionally, the system implements a first set of operations and a second set of operations. In some examples, the first set of operations includes performing a structural similarity analysis of the item, based on the image data, and determining a structural similarity score based on the structural similarity analysis of the item. In other examples, the second set of operations includes generating a plurality of derivative images by applying a plurality of distortions to the image of the item, extracting one or more features based at least on the plurality of derivative images, and determining the quality of the image based at least on the extracted one or more features and the structural similarity score.

Abstract: Systems, methods, and computer-readable storage media for cataloguing and assessing images. This is performed by a system which receives images of an item, and identifying, within each image, the item. The system performs a structural similarity analysis of the item and for each image applies a plurality of distortions, such that for each image in the images multiple distorted images are generated. The system identifies within the distorted images at least one feature and applies a regression model to the images using the at least one feature and the structural similarity score.

Abstract: The system and method described herein provide for a machine-learning model to automate determination of product attributes for a product based on images associated with the product. The product attributes can be used in online commerce to facilitate product selection by a customer. In accordance with this disclosure, the product attributes may be determined using machine-learning technology by processing images associated with the product (including product packaging). The machine-learning technology is trained using product-related vocabulary and potential attributes that can be discovered by analyzing the images associated with the product.

Abstract: Segmentation first breaks the images into segments or regions, with the segments of the region having text or symbols. The segmented image is separately applied to two different CNN-based models. Each model produces text boxes where potential text might exist. Then, a selective NMS algorithm is applied to the output of each model to produce a final group of text regions. These text regions are analyzed and actions taken.

Abstract: Systems and methods are disclosed that enable more accurately generating customized alerts with computer vision (CV) and machine learning (ML), based on features extracted from collected imagery, when the features have contextual or situational significance. For example, a CV platform monitoring container content levels may generate an alert that may include placing a shopping cart entry or providing a purchase recommendation. Accompanying ML capability can leverage the CV historical data to improve container content and level determinations, and also adjust a consumption pattern threshold used as a trigger.

Abstract: Systems, methods, and computer-readable storage media for cataloguing and assessing images. This is performed by a system which receives images of an item, and identifying, within each image, the item. The system performs a structural similarity analysis of the item and for each image applies a plurality of distortions, such that for each image in the images multiple distorted images are generated. The system identifies within the distorted images at least one feature and applies a regression model to the images using the at least one feature and the structural similarity score.

Abstract: Systems, methods, and machine readable media are provided for targeted advertising. One or more images of individuals in a location in a facility are captured with an imaging device. Face detection is performed on the one or more images. Based on a face detection result, Convolutional Neural Network models are used to determine a gender and estimate an age of each detected face. A targeted audience is determined, based on pre-determined criteria using the age estimation and gender identification of each detected face. A relevant advertisement is selected for the targeted audience for display on a display device. The relevant advertisement may be selected based at least in part on a footfall score (fscore) indicative of customer traffic in the facility.

Abstract: The system and method described herein provide for a machine-learning model to automate determination of product attributes for a product based on images associated with the product. The product attributes can be used in online commerce to facilitate product selection by a customer. In accordance with this disclosure, the product attributes may be determined using machine-learning technology by processing images associated with the product (including product packaging). The machine-learning technology is trained using product-related vocabulary and potential attributes that can be discovered by analyzing the images associated with the product.