Abstract: Techniques are described for automatically selecting between multiple image capture subsystems with overlapping fields of view but different optical properties. A selection may be made by estimating a plurality of operational characteristics of an image capture event, and, based on those estimates, selecting a primary image capture subsystem for the image capture event. For example, in a device such as a cellphone comprising two capture subsystems, each subsystem including a lens system and sensor system where each subsystem has a different fixed optical zoom parameter, a subsystem can be chosen based on a combination of desired zoom value, estimated focus distance, and estimated scene brightness.

Abstract: Techniques are disclosed for editing captured media to overcome operational difficulties that may arise during capture operations. According to these techniques, content may be captured with a pair of cameras, a first camera having a wider field of view than a second camera. Object(s) may be detected from captured content from the wider field of view camera. The captured content may be processed from the wider field of view camera in a location of at least one detected object. Typically, operators may attempt to frame content using content from the narrower field of view camera. As a result, operators may be unaware that desired content is captured using a second, wider field of view camera. Results from the processed wider field of view data may be proposed to operators for review and, if desired, retention.

Abstract: Techniques are disclosed for editing captured media to overcome operational difficulties that may arise during capture operations. According to these techniques, content may be captured with a pair of cameras, a first camera having a wider field of view than a second camera. Object(s) may be detected from captured content from the wider field of view camera. The captured content may be processed from the wider field of view camera in a location of at least one detected object. Typically, operators may attempt to frame content using content from the narrower field of view camera. As a result, operators may be unaware that desired content is captured using a second, wider field of view camera. Results from the processed wider field of view data may be proposed to operators for review and, if desired, retention.

Abstract: Techniques are described for automatically selecting between multiple image capture subsystems with overlapping fields of view but different optical properties. A selection may be made by estimating a plurality of operational characteristics of an image capture event, and, based on those estimates, selecting a primary image capture subsystem for the image capture event. For example, in a device such as a cellphone comprising two capture subsystems, each subsystem including a lens system and sensor system where each subsystem has a different fixed optical zoom parameter, a subsystem can be chosen based on a combination of desired zoom value, estimated focus distance, and estimated scene brightness.

Abstract: Techniques are described for automatically selecting between multiple image capture subsystems with overlapping fields of view but different optical properties. A selection may be made by estimating a plurality of operational characteristics of an image capture event, and, based on those estimates, selecting a primary image capture subsystem for the image capture event. For example, in a device such as a cellphone comprising two capture subsystems, each subsystem including a lens system and sensor system where each subsystem has a different fixed optical zoom parameter, a subsystem can be chosen based on a combination of desired zoom value, estimated focus distance, and estimated scene brightness.

Abstract: Techniques are described for automatically selecting between multiple image capture subsystems with overlapping fields of view but different optical properties. A selection may be made by estimating a plurality of operational characteristics of an image capture event, and, based on those estimates, selecting a primary image capture subsystem for the image capture event. For example, in a device such as a cellphone comprising two capture subsystems, each subsystem including a lens system and sensor system where each subsystem has a different fixed optical zoom parameter, a subsystem can be chosen based on a combination of desired zoom value, estimated focus distance, and estimated scene brightness.

Abstract: A method to improve the efficiency of the detection and tracking of machine-readable objects is disclosed. The properties of image frames may be pre-evaluated to determine whether a machine-readable object, even if present in the image frames, would be likely to be detected. After it is determined that one or more image frames have properties that may enable the detection of a machine-readable object, image data may be evaluated to detect the machine-readable object. When a machine-readable object is detected, the location of the machine-readable object in a subsequent frame may be determined based on a translation metric between the image frame in which the object was identified and the subsequent frame rather than a detection of the object in the subsequent frame. The translation metric may be identified based on an evaluation of image data and/or motion sensor data associated with the image frames.

Abstract: A method to improve the efficiency of the detection and tracking of machine-readable objects is disclosed. The properties of image frames may be pre-evaluated to determine whether a machine-readable object, even if present in the image frames, would be likely to be detected. After it is determined that one or more image frames have properties that may enable the detection of a machine-readable object, image data may be evaluated to detect the machine-readable object. When a machine-readable object is detected, the location of the machine-readable object in a subsequent frame may be determined based on a translation metric between the image frame in which the object was identified and the subsequent frame rather than a detection of the object in the subsequent frame. The translation metric may be identified based on an evaluation of image data and/or motion sensor data associated with the image frames.