New set of multi-channel orthogonal moments for color image representation and recognition

2019 ◽  
Vol 88 ◽  
pp. 153-173 ◽  
Author(s):  
Khalid M. Hosny ◽  
Mohamed M. Darwish
Keyword(s):  
Color Image ◽  
Image Representation ◽  
Orthogonal Moments

New Set of Invariant Quaternion Krawtchouk Moments for Color Image Representation and Recognition

2021 ◽  
pp. 2250037
Author(s):  
Gaber Hassan ◽  
Khalid M. Hosny ◽  
R. M. Farouk ◽  
Ahmed M. Alzohairy
Keyword(s):  
Quaternion Algebra ◽  
Color Image ◽  
Image Representation ◽  
Color Images ◽  
Color Channel ◽  
Krawtchouk Polynomials ◽  
Krawtchouk Moments ◽  
Similarity Transformations ◽  
The Stability ◽  
Discrete Moments

One of the most often used techniques to represent color images is quaternion algebra. This study introduces the quaternion Krawtchouk moments, QKrMs, as a new set of moments to represent color images. Krawtchouk moments (KrMs) represent one type of discrete moments. QKrMs use traditional Krawtchouk moments of each color channel to describe color images. This new set of moments is defined by using orthogonal polynomials called the Krawtchouk polynomials. The stability against the translation, rotation, and scaling transformations for QKrMs is discussed. The performance of the proposed QKrMs is evaluated against other discrete quaternion moments for image reconstruction capability, toughness against various types of noise, invariance to similarity transformations, color face image recognition, and CPU elapsed times.

A novel enhanced quantum image representation based on bit-planes for log-polar coordinates

2022 ◽  
Vol 22 (1&2) ◽  
pp. 17-37
Author(s):  
Xiao Chen ◽  
Zhihao Liu ◽  
Hanwu Chen ◽  
Liang Wang
Keyword(s):  
Color Image ◽  
Image Representation ◽  
Polar Coordinates ◽  
Quantum Cost ◽  
Color Information ◽  
Simulation Experiments ◽  
Quantum Image ◽  
Comparison Results ◽  
Bit Planes ◽  
Quantum Image Representation

Quantum image representation has a significant impact in quantum image processing. In this paper, a bit-plane representation for log-polar quantum images (BRLQI) is proposed, which utilizes $(n+4)$ or $(n+6)$ qubits to store and process a grayscale or RGB color image of $2^n$ pixels. Compared to a quantum log-polar image (QUALPI), the storage capacity of BRLQI improves 16 times. Moreover, several quantum operations based on BRLQI are proposed, including color information complement operation, bit-planes reversing operation, bit-planes translation operation and conditional exchange operations between bit-planes. Combining the above operations, we designed an image scrambling circuit suitable for the BRLQI model. Furthermore, comparison results of the scrambling circuits indicate that those operations based on BRLQI have a lower quantum cost than QUALPI. In addition, simulation experiments illustrate that the proposed scrambling algorithm is effective and efficient.

A Novel Image Representation Model for Color Image Compression

2013 ◽  
Vol 13 (13) ◽  
pp. 2589-2593
Author(s):  
Guangwei Wang ◽  
Xinggang Zhang ◽  
Cenggang Xiong ◽  
Yihua Lan
Keyword(s):  
Image Compression ◽  
Color Image ◽  
Image Representation ◽  
Representation Model ◽  
Color Image Compression

Color image representation model and its application based on an improved FRQI

2018 ◽  
Vol 16 (01) ◽  
pp. 1850005 ◽  
Author(s):  
Panchi Li ◽  
Xiande Liu
Keyword(s):  
Color Change ◽  
Color Image ◽  
Image Representation ◽  
Encryption Algorithm ◽  
Geometric Transformation ◽  
Bloch Sphere ◽  
Two Phase ◽  
Color Transformation ◽  
Simulation Results ◽  
Classical Computer

To address quantum description for color images, an improved FRQI model called FRQCI is proposed in this paper. In our model, the qubit has two-phase parameters [Formula: see text] and [Formula: see text], and the primary color R is stored in [Formula: see text], the primary colors G and B are stored in [Formula: see text]. We provide several simple image processing operators, including color change and geometric transformation. Next, we focus on an encryption algorithm, which includes two parts: position scrambling and color transformation. All operations involved in this paper are implemented by the rotation of the qubit on the Bloch sphere. The simulation results on classical computer show the effectiveness of the proposed method.

scholarly journals 3D Image Representation Using Separable Discrete Orthogonal Moments

2019 ◽  
Vol 148 ◽  
pp. 389-398 ◽  
Author(s):  
Imad Batioua ◽  
Rachid Benouini ◽  
Khalid Zenkouar ◽  
Said Najah ◽  
Hakim El Fadili ◽  
...  
Keyword(s):  
Image Representation ◽  
3D Image ◽  
Orthogonal Moments ◽  
Discrete Orthogonal
Sign in / Sign up

Export Citation Format

Share Document