Rotates the fractal before determining the slice that will be used asĪ base for the symmetry effect. So the symmetry center is always centered on the screen. If checked, the center of the screen is used instead of the Center parameter, TryĬhanging this to see the various effects that are possible.Īnd click Eyedropper) to select the center by clicking inside the fractal Together with the RotationĪngle parameter, this selects the slice of the fractal that is used. Point A is also rotated around an octagon or rotated. In the following construction, we start with one single point, Point A, which is reflected about the x- and y-axes, and the two lines that bisect the quadrants (namely, yx and y-x). Specifies the coordinates of the symmetry center. Using GeoGebra, we can simulate a kaleidoscope, starting with one point or any small picture you like. To view the slice of the fractal that is used as a base for the symmetry This is the number of times the slice of theįractal is copied and rotated to obtain the final image. The following parameters are available: Symmetry Order Some sections of the fractal lend themselves much better Try experimenting with the Center and Rotation angle parameters There are also other options that produce sharp transitions. It is available as a transformation in Standard.uxf and as a transformation plug-in in Standard.ulb.īy tweaking the parameters, you can simulate many different kinds of symmetry.īy default, the slices are aligned and mirrored to make the edges match, but Radial slice of the fractal, creating a kaleidoscope effect. (1.The Kaleidoscope transformation fills the screen with rotated copies of a small Let us take a sample image with four pixels which is arranged in 2D as follows:. In general, the number of qubits $(n)$ for a $N$-pixel image is calculated as:. That means, for storing a 4-pixel image, we need just 2-qubits for 8-pixel image we need 3-qubits, and so on. In QPIE we take advantage of this fact to design an efficient and robust encoding scheme for Black-and-White (B&W) or RGB images and exponentially reduce the memory required to store the data. If we have $n$ -qubits, we have access to up to $2^n$ -states in superposition. When looking through a kaleidoscope, it is often possible to rotate the portion containing the mirrors so that the image and its reflections shift. The QPIE representation uses the probability amplitudes of a quantum state to store the pixel values of a classical image. Kaleidoscopes work by reflecting light in at least two angled mirrors to form symmetrical patterns. This section discusses about the Quantum Probability Image Encoding (QPIE) representation and also talks about extending the usage of these QImRs to perform edge detection using the Quantum Hadamard Edge Detection (QHED) algorithm. On the other hand, the previous application shows how one can convert classical images to quantum images using the Quantum Image Representations (QImRs) like Flexible Representation of Quantum Images (FRQI) and Novel Enhanced Quantum Representation (NEQR) techniques. Although, edge detection is fairly efficient in classical image processing, it becomes very slow for larger images due to the huge resolution of these images and the pixel-wise computation that is necessary for most of the classical edge detection algorithms. ![]() ), in certain cases over classical image processing. Quantum Image processing being an emerging field, is very intriguing and enables one to have exponential speedup (as mentioned in their paper by Ruan et al. The process of edge detection is used extensively in modern classical image processing algorithms for extracting the structure of the objects/features depicted in an image. Quantum Simulation as a Search AlgorithmĮstimating Pi Using Quantum Phase Estimation AlgorithmĪn essential part of any image feature extraction procedure is Edge Detection. Grover's search with an unknown number of solutions Investigating Quantum Hardware Using Microwave PulsesĮxploring the Jaynes-Cummings Hamiltonian with Qiskit Pulse Introduction to Quantum Error Correction using Repetition Codes Investigating Quantum Hardware Using Quantum Circuits Solving the Travelling Salesman Problem using Phase Estimation Quantum Edge Detection - QHED Algorithm on Small and Large Images Quantum Image Processing - FRQI and NEQR Image Representations Implementations of Recent Quantum Algorithms Hybrid quantum-classical Neural Networks with PyTorch and Qiskit Solving Satisfiability Problems using Grover's Algorithm Solving combinatorial optimization problems using QAOA Solving Linear Systems of Equations using HHL Classical Computation on a Quantum Computer
0 Comments
Leave a Reply. |