Computer Vision Metrics: Textbook Edition by Scott Krig

By Scott Krig

Based at the profitable 2014 publication released through Apress, this textbook version is multiplied to supply a complete historical past and state of the art survey for basic machine imaginative and prescient tools. With over 800 crucial references, in addition to chapter-by-chapter studying assignments, either scholars and researchers can dig deeper into middle laptop imaginative and prescient themes. The survey covers every thing from function descriptors, local and worldwide characteristic metrics, characteristic studying architectures, deep studying, neuroscience of imaginative and prescient, neural networks, and distinct instance architectures to demonstrate desktop imaginative and prescient and software program optimization tools.
To supplement the survey, the textbook comprises valuable analyses which supply perception into the ambitions of assorted equipment, why they paintings, and the way they're optimized.
The textual content promises an important survey and a necessary taxonomy, hence supplying a key studying instrument for college kids, researchers and engineers, to complement the numerous potent hands-on assets and open resource tasks, akin to OpenCVand different imaging and deep studying instruments.

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By Scott Krig

Based at the profitable 2014 publication released through Apress, this textbook version is multiplied to supply a complete historical past and state of the art survey for basic machine imaginative and prescient tools. With over 800 crucial references, in addition to chapter-by-chapter studying assignments, either scholars and researchers can dig deeper into middle laptop imaginative and prescient themes. The survey covers every thing from function descriptors, local and worldwide characteristic metrics, characteristic studying architectures, deep studying, neuroscience of imaginative and prescient, neural networks, and distinct instance architectures to demonstrate desktop imaginative and prescient and software program optimization tools.
To supplement the survey, the textbook comprises valuable analyses which supply perception into the ambitions of assorted equipment, why they paintings, and the way they're optimized.
The textual content promises an important survey and a necessary taxonomy, hence supplying a key studying instrument for college kids, researchers and engineers, to complement the numerous potent hands-on assets and open resource tasks, akin to OpenCVand different imaging and deep studying instruments.

Show description

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Applications of surface reconstruction to localization and mapping are used in simultaneous localization and mapping (SLAM) and in structure from motion (SFM) methods—for example, in robotics navigation. One goal of SLAM is to localize, or find the current position and the 6DOF camera pose. Another goal is to create a local region map, which includes depth. To dig deeper into SLAM and SFM methods, see the historical survey by Bailey and Durrant-Whyte [458, 459]. 3D Representations: Voxels, Depth Maps, Meshes, and Point Clouds Depth information is represented and stored in a variety of convertible formats, depending on the intended use.

Camera Calibration Parameters. Camera calibration is outside the scope of this work, however the parameters are defined as 11 free parameters [414, 417]—three for rotation, three for translation, and five intrinsic—plus one or more lens distortion parameters to reconstruct 3D points in world coordinates from the pixels in 2D camera space. The camera calibration may be performed using several methods, including a known calibration image pattern or one of many selfcalibration methods [418]. Extrinsic parameters define the location of the camera in world coordinates, and intrinsic parameters define the relationships between pixel coordinates in camera image coordinates.

Also, the strength of the infrared emitters that can be used is limited by practicality and safety. The distance for effectively using structured light indoors is restricted by the amount of power that can be used for the IR emitters; perhaps 5 m is a realistic limit for indoor infrared light. Kinect claims a range of about 4 m for the current TOF (time of flight) method using uniform constant infrared illumination, while the first-generation Kinect sensor had similar depth range using structured light.

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