Abstract: A computing node in a P2P computer network obtaining a media item to be verified, determining a hash value for the media item based on a digital cryptographic hash function, retrieving a plurality of data records associated with the media item from a blockchain based at least in part on the hash value, wherein a data record is generated by a validation node in the decentralized P2P computer network, evaluating the plurality of data records to determine whether the media item has satisfied pre-defined validation criteria, and providing information describing the media item based at least in part on the plurality of data records, wherein the information provides an indication as to whether the media item satisfied the pre-defined validation criteria.

Abstract: A method comprising receiving, from a infrared image capture device, an image including a fluid storage tank storing a fluid, the image including an image intensity associated with a temperature of the fluid storage tank with an internal surface that is in contact with the fluid and an image intensity associated with a temperature of the first fluid storage tank with an internal surface that is not in contact with the fluid, generating feature maps, sliding a window to obtain a plurality of anchor shapes using a region proposal network, determining if each anchor shape contains an object to generate a plurality of regions of interest, extracting feature maps from each region of interest, classifying objects in each region of interest, identifying stored fluid using the objects, determining volume of stored fluid based on the stored fluid, and providing the volume to a digital device for display.

Abstract: An example system includes a first and second digital device. The first digital device may be configured to provide an interface displaying an image including a depiction of an object, place a bounding shape around the object, and crop contents of the bounding shape to create a portion. The second digital device may be configured to receive the portion, retrieve high-level features and low-level features, apply first Atrous Spatial Pyramid Pooling (ASPP) to the high-level features to aggregate the high-level features as aggregate features, concatenate results to create the aggregate features, up-sample, apply a convolution to the low-level features, concatenate the aggregate features with the low-level features after convolution to form combined features, segment the combined features to generate a polygonal shape outline along outer boundaries of the first object, and provide the first polygonal shape outline to the first digital device for display.

Abstract: Generating and running computer vision pipelines for processing images and/or video. A user may select blocks for the computer vision pipeline, including image transformation blocks that transform images and/or video, and prediction blocks that detect objects or classes of objects in images and/or video. The user may specify an input source for the images and/or video and an output destination for the computer vision pipeline. Upon activation, the computer vision pipeline receives input images and/or video. The input images and/or video are transformed using the image transformation block to produce transformed images and/or video. Objects or classes of objects in the transformed images and/or video are detected using the prediction block to produce detected objects or classes of objects. The detected objects or classes of objects are transmitted to the output destination.

Abstract: An example system includes a first and second digital device. The first digital device may be configured to provide an interface displaying an image including a depiction of an object, place a bounding shape around the object, and crop contents of the bounding shape to create a portion. The second digital device may be configured to receive the portion, retrieve high-level features and low-level features, apply first Atrous Spatial Pyramid Pooling (ASPP) to the high-level features to aggregate the high-level features as aggregate features, concatenate results to create the aggregate features, up-sample, apply a convolution to the low-level features, concatenate the aggregate features with the low-level features after convolution to form combined features, segment the combined features to generate a polygonal shape outline along outer boundaries of the first object, and provide the first polygonal shape outline to the first digital device for display.

Abstract: A system configured to receive video and/or images from an image capture device over a livestock path, generate feature maps from an image of the video by applying at least a first convolutional neural network, slide a window across the feature maps to obtain a plurality of anchor shapes, determine if each anchor shape contains an object to generate a plurality of regions of interest, each of the plurality of regions of interest being a non-rectangular, polygonal shape, extract feature maps from each region of interest, classify objects in each region of interest, in parallel with classification, predict segmentation masks on at least a subset of the regions of interest in a pixel-to-pixel manner, identify individual animals within the objects based on classifications and the segmentation masks, and count individual animals based on identification, and provide the count to a digital device for display, processing, and/or reporting.

Abstract: A method comprising receiving, from a infrared image capture device, an image including a fluid storage tank storing a fluid, the image including an image intensity associated with a temperature of the fluid storage tank with an internal surface that is in contact with the fluid and an image intensity associated with a temperature of the first fluid storage tank with an internal surface that is not in contact with the fluid, generating feature maps, sliding a window to obtain a plurality of anchor shapes using a region proposal network, determining if each anchor shape contains an object to generate a plurality of regions of interest, extracting feature maps from each region of interest, classifying objects in each region of interest, identifying stored fluid using the objects, determining volume of stored fluid based on the stored fluid, and providing the volume to a digital device for display.

Abstract: A method comprising receiving, from a first infrared image capture device, three images including gas equipment including a first image captured at a first time period, a second image captured at a second time period, and a third image captured at a third time period, the three images capturing an infrared spectrum, interpreting one of the three images in a red color channel of an RGB image where pixels are red-tonal in coloring, interpreting an other of the three images in a green color channel of the RGB image where pixels are green-tonal in coloring, interpreting a remaining of the three images in a blue color channel of the RGB image where pixels are blue-tonal in coloring, and providing the RGB image for display, the RGB image indicating movement as at least one color that is different from color of at least some of the gas equipment.

Abstract: A system configured to receive video and/or images from an image capture device over a livestock path, generate feature maps from an image of the video by applying at least a first convolutional neural network, slide a window across the feature maps to obtain a plurality of anchor shapes, determine if each anchor shape contains an object to generate a plurality of regions of interest, each of the plurality of regions of interest being a non-rectangular, polygonal shape, extract feature maps from each region of interest, classify objects in each region of interest, in parallel with classification, predict segmentation masks on at least a subset of the regions of interest in a pixel-to-pixel manner, identify individual animals within the objects based on classifications and the segmentation masks, and count individual animals based on identification, and provide the count to a digital device for display, processing, and/or reporting.

Abstract: An example system includes a first and second digital device. The first digital device may be configured to provide an interface displaying an image including a depiction of an object, place a bounding shape around the object, and crop contents of the bounding shape to create a portion. The second digital device may be configured to receive the portion, retrieve high-level features and low-level features, apply first Atrous Spatial Pyramid Pooling (ASPP) to the high-level features to aggregate the high-level features as aggregate features, concatenate results to create the aggregate features, up-sample, apply a convolution to the low-level features, concatenate the aggregate features with the low-level features after convolution to form combined features, segment the combined features to generate a polygonal shape outline along outer boundaries of the first object, and provide the first polygonal shape outline to the first digital device for display.

Abstract: A system configured to receive video and/or images from an image capture device over a livestock path, generate feature maps from an image of the video by applying at least a first convolutional neural network, slide a window across the feature maps to obtain a plurality of anchor shapes, determine if each anchor shape contains an object to generate a plurality of regions of interest, each of the plurality of regions of interest being a non-rectangular, polygonal shape, extract feature maps from each region of interest, classify objects in each region of interest, in parallel with classification, predict segmentation masks on at least a subset of the regions of interest in a pixel-to-pixel manner, identify individual animals within the objects based on classifications and the segmentation masks, and count individual animals based on identification, and provide the count to a digital device for display, processing, and/or reporting.