Accurate quaternion radial harmonic Fourier moments for color image reconstruction and object recognition

Liu, Yunan; Zhang, Shanshan; Li, Guangyu; Wang, Houjun; Yang, Jian

doi:10.1007/s10044-020-00877-6

Cited by 12 publications

(13 citation statements)

References 32 publications

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“…We define id as the index of a thread in a thread grid. id can be calculated by following formula, id = blockDim.x × blockId.x + threadId.x (19) For an image with N × N resolution, each pixel is represented as (x, y). The pixel in x-th column and y-th row can be mapped to GPU thread id by following equation:…”

Section: B Direct Computation Methods On Gpumentioning

confidence: 99%

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GPU Accelerated Polar Harmonic Transforms for Feature Extraction in ITS Applications

Yang

Tang

et al. 2020

IEEE Access

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Polar Harmonic Transform (PHT) is termed to represent a set of transforms those kernels are basic waves and harmonic in nature, which can improve the effect in Intelligent Transportation System (ITS) applications. PHTs consist of Polar Complex Exponential Transform (PCET), Polar Cosine Transform (PCT) and Polar Sine Transform (PST). PHTs can extract orthogonal and rotation invariant features and demonstrated superior performance in various image processing and computer vision applications. For real time systems and large multimedia databases, execution efficiency is always a significant challenge. With widespread use of Graphics Processing Unit (GPU), this study presents GPU based PHTs. Proposed methods are based on mathematical properties of PHTs and optimization techniques of GPU. Optimal parameter selections for GPU execution are also discussed. In our experiments, proposed methods are over 1800 times faster. INDEX TERMS GPU, Polar harmonic transform, feature extraction, intelligent transportation system.

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Section: B Direct Computation Methods On Gpumentioning

confidence: 99%

“…Al-asady and Al-amery [17] obtain accurate features for human action detection. Lin et al [18] and Liu et al [19] propose region duplication detection scheme for feature point mapping.…”

Section: Introductionmentioning

confidence: 99%

GPU Accelerated Polar Harmonic Transforms for Feature Extraction in ITS Applications

Yang

Tang

et al. 2020

IEEE Access

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“…Most of the current research on color image moments depends on the intensity or a single channel within the color space of color images, where the information and relationship between color components in a specific color space are discarded. Several traditional orthogonal and nonorthogonal moments have been developed into quaternion moments, e.g., quaternion Zernike moments (QZMs) [2], quaternion radial harmonic Fourier moments (QRHFM) [11], quaternion polar harmonic Fourier moments (QPHFMs) [12], and quaternion polar harmonic transforms (QPHTs) [13,14], Although various quaternion image orthogonal moments have been proposed by researchers, most of them give unsatisfied image reconstruction performance. In the present work, accurate quaternion orthogonal transforms, i.e., accurate QPHTs (AQPHTs), are proposed.…”

Section: Introductionmentioning

confidence: 99%

Accurate Quaternion Polar Harmonic Transform for Color Image Analysis

Zhang¹,

Sang

Dai³

2021

Scientific Programming

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Polar harmonic transforms (PHTs) have been applied in pattern recognition and image analysis. But the current computational framework of PHTs has two main demerits. First, some significant color information may be lost during color image processing in conventional methods because they are based on RGB decomposition or graying. Second, PHTs are influenced by geometric errors and numerical integration errors, which can be seen from image reconstruction errors. This paper presents a novel computational framework of quaternion polar harmonic transforms (QPHTs), namely, accurate QPHTs (AQPHTs). First, to holistically handle color images, quaternion-based PHTs are introduced by using the algebra of quaternions. Second, the Gaussian numerical integration is adopted for geometric and numerical error reduction. When compared with CNNs (convolutional neural networks)-based methods (i.e., VGG16) on the Oxford5K dataset, our AQPHT achieves better performance of scaling invariant representation. Moreover, when evaluated on standard image retrieval benchmarks, our AQPHT using smaller dimension of feature vector achieves comparable results with CNNs-based methods and outperforms the hand craft-based methods by 9.6% w.r.t mAP on the Holidays dataset.

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“…In the last decade, image moments and geometric invariance of moments have emerged as effective methods of feature extraction from images [1,2]. Both methods have made great progress in imagerelated fields.…”

Section: Introductionmentioning

confidence: 99%

“…(1) Select a single channel or component from the color space of a color image, such a channel from a red-green-blue (RGB) image, as a grayscale image and calculate its corresponding image moments; (2) directly gray a color image, and then calculate its image moments; (3) calculate the image moments of each monochromatic channel (R, G and B) in a RGB image, and average them to obtain the final result.…”

Section: Introductionmentioning

confidence: 99%

Novel Color Orthogonal Moments-Based Image Representation and Recognition

He¹,

Li²,

Yang³

et al. 2020

Preprint

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Inspired by quaternion algebra and the idea of fractional-order transformation, we propose a new set of quaternion fractional-order generalized Laguerre orthogonal moments (QFr-GLMs) based on fractional-order generalized Laguerre polynomials. Firstly, the proposed QFr-GLMs are directly constructed in Cartesian coordinate space, avoiding the need for conversion between Cartesian and polar coordinates; therefore, they are better image descriptors than circularly orthogonal moments constructed in polar coordinates. Moreover, unlike the latest Zernike moments based on quaternion and fractional-order transformations, which extract only the global features from color images, our proposed QFr-GLMs can extract both the global and local color features. This paper also derives a new set of invariant color-image descriptors by QFr-GLMs, enabling geometric-invariant pattern recognition in color images. Finally, the performances of our proposed QFr-GLMs and moment invariants were evaluated in simulation experiments of correlated color images. Both theoretical analysis and experimental results demonstrate the value of the proposed QFr-GLMs and their geometric invariants in the representation and recognition of color images.

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Accurate quaternion radial harmonic Fourier moments for color image reconstruction and object recognition

Cited by 12 publications

References 32 publications

GPU Accelerated Polar Harmonic Transforms for Feature Extraction in ITS Applications

GPU Accelerated Polar Harmonic Transforms for Feature Extraction in ITS Applications

Accurate Quaternion Polar Harmonic Transform for Color Image Analysis

Novel Color Orthogonal Moments-Based Image Representation and Recognition

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