Optical flow

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Optical flow or optical flow is the real motion pattern of objects, surfaces, and sides in the visual scene caused by the relative motion between the observer and the scene. The concept of optical flow was introduced by American psychologist James J. Gibson in the 1940s to describe the visual stimuli given to the animals that move in the world. Gibson emphasized the importance of optical flow for the perception of affordance, the ability to differentiate possible actions in the environment. Gibson's followers and his ecological approach to psychology have further pointed to the role of optical flow stimulus for the perception of movement by observers in the world; perception of shape, distance, and movement of things in the world; and drive control.

The term optical stream is also used by robotics, which includes related techniques of image processing and navigation control including motion detection, object segmentation, time-to-contact information, expansion calculation focus, lighting, motion compensation coding, and stereo disparity measurements.

Video Optical flow

Estimates

Ordered drawing sequences allow motion estimation either as instant image speed or discrete image transfer. Fleet and Weiss provide a tutorial introduction to gradient-based optical flow. John L. Barron, David J. Fleet, and Steven Beauchemin provide performance analysis of a number of optical flow techniques. It emphasizes the accuracy and measurement density.

The optical flow method attempts to calculate the movement between two frame images taken at t and ${\ displaystyle t \ Delta t}$ in each voxel position. This method is called differential because they are based on local Taylor series approximations of the image signal; ie, they use partial derivatives with respect to spatial and temporal coordinates.

Untuk kasus dimensi 2D t (3D atau n -D kasus serupa) voxel di lokasi ${\ displaystyle (x, y, t)}  ÂÂ$ dengan intensitas ${\ displaystyle I (x, y, t)}  ÂÂ$ akan dipindahkan oleh ${\ displaystyle \ Delta x}  ÂÂ$ , ${\ displaystyle \ Delta y}  ÂÂ$ dan ${\ displaystyle \ Delta t}  ÂÂ$ antara dua bingkai gambar, dan berikut ini pembatasan keteguhan kecerahan dapat diberikan:

${\ displaystyle I (x, y, t) = I (x \ Delta x, y \ Delta y, t \ Delta t)}  ÂÂ$

Dengan asumsi pergerakan menjadi kecil, batasan gambar pada ${\ displaystyle I (x, y, t)}  ÂÂ$ dengan deret Taylor dapat dikembangkan untuk mendapatkan:

$            {\displaystyle         \mathrm{Saya}        (        x                \mathrm{?}        x        ,        y                \mathrm{?}        y        ,        t                \mathrm{?}        t        )        =        \mathrm{Saya}        (        x        ,        y        ,        t        )                          \frac{              \mathrm{?}              \mathrm{Saya}            }{              \mathrm{?}              x            }                \mathrm{?}        x                          \frac{              \mathrm{?}              \mathrm{Saya}            }{              \mathrm{?}              y            }                \mathrm{?}        y                          \frac{              \mathrm{?}              \mathrm{Saya}            }{              \mathrm{?}              t            }                \mathrm{?}        t              }        \{\backslash \; displaystyle\; I\; (x\; \backslash \; Delta\; x,\; y\; \backslash \; Delta\; y,\; t\; \backslash \; Delta\; t)\; =\; Saya\; (x,\; y,\; t)\; \{\backslash \; frac\; \{\backslash \; partial\; I\}\; \{\backslash \; partial\; x\}\}\; \backslash \; Delta\; x\; \{\backslash \; frac\; \{\backslash \; parsial\; I\}\; \{\backslash \; parsial\; y\}\}\; \backslash \; Delta\; y\; \{\backslash \; frac\; \{\backslash \; parsial\; I\}\; \{\backslash \; parsial\; t\}\}\; \backslash \; Delta\; t\; \}\;  ÂÂ$ H.O.T.

Dari persamaan ini berarti bahwa:

${\ displaystyle {\ frac {\ parsial I} {\ parsial x}} \ Delta x {\ frac {\ parsial I} {\ parsial y}} \ Delta y { \ frac {\ partial I} {\ partial t}} \ Delta t = 0}  ÂÂ$

atau

${\ displaystyle {\ frac {\ parsial I} {\ parsial x}} {\ frac {\ Delta x} {\ Delta t}} {\ frac {\ partial I} {\ parsial y}} {\ frac {\ Delta y} {\ Delta t}} {\ frac {\ parsial I} {\ parsial t}} {\ frac {\ Delta t} {\ Delta t} } = 0}  ÂÂ$

yang mengakibatkan

${\ displaystyle {\ frac {\ parsial I} {\ parsial x}} V_ {x} {\ frac {\ parsial I} {\ parsial y}} V_ { y} {\ frac {\ partial I} {\ partial t}} = 0}  ÂÂ$

Demikian:

${\ displaystyle I_ {x} V_ {x} I_ {y} V_ {y} = - I_ {t}}  ÂÂ$

atau

${\ displaystyle \ nabla I ^ {T} \ cdot {\ vec {V}} = - I_ {t}}  ÂÂ$

This is an equation in two unknown and unfinished things. This is known as the aperture problem of optical flow algorithms. To find the optical flow required another set of equations, which are given by some additional constraints. All optical flow methods introduce additional conditions to estimate actual flow.

Methods for determination

• The phase correlation - the reverse of the normalized cross-power spectrum
• Block based methods - minimize the number of quadratic differences or the number of absolute differences, or maximize the normal cross-correlation
• A differential method for estimating optical flow, based on partial derivatives of the image signal and/or sought-flow fields and high-partial partial derivatives, such as:
  • Lucas-Kanade Method - related to image patches and affinity models for stream fields
  • Horn-Schunck method - optimizes functional based on residuals from brightness brightness constraints, and certain regularization terms that express the expected subtlety of the flow field
  • Buxton-Buxton Method - based on the edge-motion model in the image sequence
  • Black-Jepson Method - rough optical flow through correlation
  • Common variational methods - various modifications/extensions from Horn-Schunck, using other data terms and other terms of fluency.
• Discrete optimization methods - the search space is quantized, then image matching is resolved through label assignments on each pixel, so that related deformations minimize the distance between the source and the target image. The optimal solution is often recovered through a Max-flow flow-cut algorithm, linear programming or propagation methods of belief.

Much of this, in addition to the state-of-the-art algorithms currently being evaluated on the Middlebury Benchmark Dataset.

Maps Optical flow

Usage

Approximate motion and video compression have been developed as a major aspect of optical flow research. While the field of superficial optical flow is similar to a solid motion field derived from motion estimation techniques, optical flow is the study of not only the determination of the optical flow field itself, but also its use in estimating the three dimensional nature and scene structure, as well as the motion of 3D objects and observers relative to scenery, mostly using Jacobian Drawings.

Optical flow is used by robotics researchers in many areas such as: object detection and tracking, dominant field extraction images, motion detection, robotic navigation and visual odometer. Optical flow information has been recognized as useful for controlling micro air vehicles.

The application of optical flow includes the problem of concluding not only observer motion and objects in the scene, but also the structure of objects and environment. Because movement awareness and the formation of mental maps of our environmental structure are important components of animal (and human) vision, the conversion of these innate abilities to the capabilities of computers is equally important in the field of machine vision.

Consider a five-frame ball clip that moves from the bottom left of the field of vision, to the upper right. The motion estimation technique can determine that in the two-dimensional plane the ball moves up and to the right and the vector representing this movement can be extracted from the frame sequence. For the purposes of video compression (eg, MPEG), the sequence is now described as required. However, in the field of machine vision, the question of whether the ball moves to the right or if the observer moves to the left is unknown critical information. Even if a static and patterned background is present in five frames, can we confidently state that the ball is moving to the right, because the pattern may have infinite distance to the observer.

src: www.mathworks.com

Optical flow sensor

The optical flow sensor is a visual sensor capable of measuring optical flow or visual movement and generating measurements based on optical flow. Various configurations of optical flow sensors exist. One configuration is an image sensor chip connected to a processor that is programmed to run an optical flow algorithm. Another configuration uses a vision chip, which is an integrated circuit that has image sensors and processors on the same die, allowing for concise implementations. An example of this is a generic optical mouse sensor used in optical mouse. In some cases, the processing circuit can be implemented using analog or mixed signal circuits to allow rapid optical flow calculations using minimal current consumption.

One area of ​​contemporary research is the use of neuromorphic engineering techniques to implement circuits that respond to optical flow, and thus may be appropriate for use in optical flow sensors. Such circuits can draw inspiration from biological neural circuits that also respond to optical flow.

Optical flow sensors are widely used in computer optical mice, as the main sensing component for measuring mouse movements on the surface.

Optical flow sensors are also used in robotics applications, especially where there is a need to measure visual movement or relative motion between robots and other objects around the robot. The use of optical flow sensors in unmanned aerial vehicles (UAVs), to avoid stability and obstacles, is also the current research area.

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See also

• An optical ambient array
• Optical mouse
• Range imagery
• Vision processing unit

src: www.frontiersin.org

References

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External links

• Look for Optical Flow
• Optical Flow Art article on fxguide.com (using optical flow in Visual Effects)
• Evaluation of optical flow and ground truth sequence.
• Middlebury Evaluation of optical flow and ground truth sequence.
• mrf-registration.net - Optical flow estimation via MRF
• The French Aerospace LabÃ,: Implementation of optical flow GPUs based on Lucas-Kanade
• CUDA Implementation by CUVI (CUDA Vision & Imaging Library)
• Horn and Schunck Optical Flows: Online demo and source code of Horn and Schunck methods
• TV-L1 Optical Flow: Online demo and source code Zach et al. method
• Strong Optical Flow: Online demos and source code Brox et al. method

Source of the article : Wikipedia