Abstract: Described are systems and methods that use one or more two-dimensional (“2D”) body images of a body to determine body fat measurements of that body. For example, a standard 2D camera of a portable device, such as a cell phone, tablet, laptop, etc., may be used to generate one or more 2D body images of a user. Those 2D body images, or image, may be processed using the disclosed implementations to determine a body fat measurement of the body represented in the image.

Abstract: Described are systems and methods directed to generation and subsequent update of a dimensionally accurate body model of a body, such as a human body, based on two-dimensional (“2D”) images of at least a portion of that body and/or face images of a face of the body. A user may use a 2D camera, such as a digital camera typically included in many of today's portable devices (e.g., cell phones, tablets, laptops, etc.) to produce body images that are used to generate a body model of the body of the user. Subsequently, the body model may be updated based on a face image of the face of the user, without requiring the user to provide another body image.

Abstract: Described are systems and methods to determine one or more body dimensions of a body based on a processing of one or more two-dimensional images that include a representation of the body. Body dimensions include any length, circumference, etc., of any part of a body, such as shoulder circumference, chest circumference, waist circumference, hip circumference, inseam length, bicep circumference, leg circumference, etc.

Abstract: Described are systems and methods directed to generation of a dimensionally accurate three-dimensional (“3D”) model of a body, such as a human body, based on two-dimensional (“2D”) images of at least a portion of that body. A user may use a 2D camera, such as a digital camera typically included in many of today's portable devices (e.g., cell phones, tablets, laptops, etc.) and obtain a series of 2D body images of at least a portion of their body from different views with respect to the camera. The 2D body images may then be used to generate a plurality of predicted body parameters corresponding to the body represented in the 2D body images. Those predicted body parameters may then be further processed to generate a dimensionally accurate 3D model or avatar of the body of the user.

Abstract: Described are systems and methods directed to generation of a personalized three-dimensional (“3D”) body model of a body, such as a human body, based on two-dimensional (“2D”) images of that body and the generation and presentation of predicted personalized 3D body models of the body when one or more body measurements (e.g., body fat, body weight, muscle mass) are changed. For example, a user may provide a target body measurement value and the implementations will generate one or more predicted personalized 3D body models representative of a predicted appearance of the body with the target body measurement value.

Abstract: Described are systems and methods to determine one or more body dimensions of a body based on a processing of one or more two-dimensional images that include a representation of the body. Body dimensions include any length, circumference, etc., of any part of a body, such as shoulder circumference, chest circumference, waist circumference, hip circumference, inseam length, bicep circumference, leg circumference, etc.

Abstract: Described are systems and method directed to generation of a dimensionally accurate three-dimensional (“3D”) body model of a body, such as a human body, based on two-dimensional (“2D”) images of that body. A user may use a 2D camera, such as a digital camera typically included in many of today's portable devices (e.g., cell phones, tablets, laptops, etc.) and obtain a series of 2D body images of their body from different directions with respect to the camera. The 2D body images may then be used to generate a plurality of predicted body parameters corresponding to the body represented in the 2D body images. Those predicted body parameters may then be further processed to generate a dimensionally accurate 3D model of the body of the user.

Abstract: Described are systems and method directed to generation of a dimensionally accurate three-dimensional (“3D”) model of a body, such as a human body, based on two-dimensional (“2D”) images of that body. A user may use a 2D camera, such as a digital camera typically included in many of today's portable devices (e.g., cell phones, tablets, laptops, etc.) and obtain a series of 2D body images of their body from different views with respect to the camera. The 2D body images may then be used to generate a plurality of predicted body parameters corresponding to the body represented in the 2D body images. Those predicted body parameters may then be further processed to generate a dimensionally accurate 3D model or avatar of the body of the user.

Abstract: Described are systems and method directed to generation of a dimensionally accurate three-dimensional (“3D”) body model of a body, such as a human body, based on two-dimensional (“2D”) images of that body. A user may use a 2D camera, such as a digital camera typically included in many of today's portable devices (e.g., cell phones, tablets, laptops, etc.) and obtain a series of 2D body images of their body from different directions with respect to the camera. The 2D body images may then be used to generate a plurality of predicted body parameters corresponding to the body represented in the 2D body images. Those predicted body parameters may then be further processed to generate a dimensionally accurate 3D model of the body of the user.

Abstract: Described are systems and methods directed to generation of a personalized three-dimensional (“3D”) body model of a body, such as a human body, based on two-dimensional (“2D”) images of that body and the generation and presentation of predicted personalized 3D body models of the body when one or more body measurements (e.g., body fat, body weight, muscle mass) are changed. For example, a user may provide a target body measurement value and the implementations will generate one or more predicted personalized 3D body models representative of a predicted appearance of the body with the target body measurement value.

Abstract: Described are systems and methods directed to generation of a personalized three-dimensional (“3D”) body model of a body, such as a human body, based on two-dimensional (“2D”) images of that body and the generation and presentation of predicted personalized 3D body models of the body when one or more body measurements (e.g., body fat, body weight, muscle mass) are changed. For example, a user may provide a target body measurement value and the implementations will generate one or more predicted personalized 3D body models representative of a predicted appearance of the body with the target body measurement value.

Abstract: Described are systems and methods directed to generation of a personalized three-dimensional (“3D”) body model of a body, such as a human body, based on two-dimensional (“2D”) images of that body and the generation and presentation of predicted personalized 3D body models of the body when one or more body measurements (e.g., body fat, body weight, muscle mass) are changed. For example, a user may provide a target body measurement value and the implementations will generate one or more predicted personalized 3D body models representative of a predicted appearance of the body with the target body measurement value.

Abstract: Techniques for providing an accurate auto-crop feature for images captured by an image capture device may be described herein. For example, one or more image masks for a color image captured by an image capture device may be received by a computer system. Metadata about the color image that identifies portions of the image as foreground and the color image itself may also be received by the computer system. Further, a representation of a user and a floor region associated with a user may be extracted from the color image using the one or more image masks and the metadata. A first area of the color image may be cropped with respect to the extracted representation of the user and the floor region associated with the user to generate a second area of the color image. In embodiments, a third area of the color image may be obscured based on the received metadata.

Abstract: Devices and techniques are generally described for image editing. In various examples, color image data and segmentation image data may be identified. In some examples, the segmentation image data may identify a first portion of the color image data as a foreground region and a second portion of the color image data as a background region. A segmentation boundary may be identified between the foreground region and the background region. A first area of the second portion may be identified, where the first area extends from the segmentation boundary to a first area outer boundary. A second area of the second portion may be identified, where the second area extends from the first area outer boundary to a second area outer boundary. A blended and blurred representation of the first area and the second area may be generated.

Abstract: Techniques for providing an accurate auto-crop feature for images captured by an image capture device may be described herein. For example, one or more image masks for a color image captured by an image capture device may be received by a computer system. Metadata about the color image that identifies portions of the image as foreground and the color image itself may also be received by the computer system. Further, a representation of a user and a floor region associated with a user may be extracted from the color image using the one or more image masks and the metadata. A first area of the color image may be cropped with respect to the extracted representation of the user and the floor region associated with the user to generate a second area of the color image. In embodiments, a third area of the color image may be obscured based on the received metadata.

Abstract: Devices and techniques are generally described for segmentation of image data using depth data. In various examples, color image data may be received from a digital camera. In some examples, depth image data may be received from a depth sensor. In various examples, the depth image data may be separated into a plurality of clusters of depth image data, wherein each cluster is associated with a respective range of depth values. In some further examples, a determination may be made that a first cluster of image data corresponds to an object of interest, such as a human subject, in the image data. In various examples, pixels of the first cluster may be encoded with foreground indicator data. In some further examples, segmented image data may be generated. The segmented image data may comprise pixels encoded with the foreground indicator data.

Abstract: A computerized aircraft system, such as an unmanned aircraft system (UAS) is provided with a cloud detection system. The UAS includes a monocular electro-optic or infra-red camera which acquires consecutively, in real time, a plurality of images within a field of view of the camera. The system identifies feature points in each of the consecutive images, and generates macro representations of cloud formations (3D representations of the clouds) based on tracking of the feature points across the plurality of images. A cloud avoidance system takes in nominal own-ship waypoints, compares those waypoints to the 3D cloud representations and outputs modified waypoints to avoid the detected clouds.

Abstract: A method and system for detecting floating objects in maritime video is disclosed. The horizon is detected within the video. Modeling of the sky and water is performed on the video. Objects are detected that are not water and sky within the video.

Abstract: A method and system for detecting floating objects in maritime video is disclosed. The horizon is detected within the video. Modeling of the sky and water is performed on the video. Objects are detected that are not water and sky within the video.