
Reading an Image From a Movie File
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We store movies in files, typically in an MPEG format. Let’s look at what’s inside one of those movie files, and how we can grab a frame from a movie as an image and put it into the MATLAB workspace.
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We store movies in files, typically in an MPEG format. Let’s look at what’s inside one of those movie files, and how we can grab a frame from a movie as an image and put it into the MATLAB workspace.
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Much of what we know about robots comes from fiction. Let’s look at fictional robots and the underlying reality.
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A problem arises when using three-angle sequences and particular values of the middle angle leads to a condition called a singularity. This mathematical phenomena is related to a problem that occurs in the physical world with mechanical gimbal systems. Note that in Robotics, Vision & Control (second edition) and RTB10.x the default definition of roll-pitch-yaw […]
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We consider a robot with four joints that moves its end-effector in 3D space.
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When a camera moves in the world, points in the image move in a very specific way. The image plane or pixel velocity is a function of the camera’s motion and the position of the points in the world. This is known as optical flow. Let’s explore the link between camera and image motion.
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For real robots such as those with 6 joints that move in 3D space the inverse kinematics is quite complex, but for many of these robots the solutions have been helpfully derived by others and published. Let’s explore the inverse kinematics of the classical Puma 560 robot.
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We consider the most general type of serial-link robot manipulator which has six joints and can position and orient its end-effector in 3D space.
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An important problem in robotic vision is moving a camera so that the view it sees matches the view we want it to have. To achieve this we exploit knowledge about how an image changes as a camera moves. Then we invert that and compute how the camera should move so the image changes in […]
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We run into problems when we take all of the pixels in a box around an input pixel and that pixel is close to one of the edges of the image. Let’s look at some strategies to deal with edge pixels.
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We introduce spatial operators by a simple example of taking the average value of all pixels in a box surrounding each input pixel. The result is a blurring or smoothing of the input image.