Mathematical Representation of Color Spaces and Its Role in Communication Systems

Al-Saleem, Riyadh M.; Al-Hilali, Baraa Mohammed Ibrahim; Abboud, Izz Kadhum

doi:10.1155/2020/4640175

Cited by 9 publications

(8 citation statements)

References 5 publications

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“…It is well-known that the z-transform has been applied as an indispensable tool for the analysis and design of discretetime signals and systems. Generally speaking, the z-transform converts a real-or complex-valued discrete-time sequence to a complex-valued z-function [9][10][11][12][13][14][15][16][17][18]. The existing z-transform is actually a scalar function.…”

Section: Introductionmentioning

confidence: 99%

Tensor z‐Transform

Chang,

2024

Journal of Applied Mathematics

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The multi-input multioutput (MIMO) systems involving multirelational signals generated from distributed sources have been emerging as the most generalized model in practice. The existing work for characterizing such a MIMO system is to build a corresponding transform tensor, each of whose entries turns out to be the individual z-transform of a discrete-time impulse response sequence. However, when a MIMO system has a global feedback mechanism, which also involves multirelational signals, the aforementioned individual z-transforms of the overall transfer tensor are quite difficult to formulate. Therefore, a new mathematical framework to govern both feedforward and feedback MIMO systems is in crucial demand. In this work, we define the tensor z-transform to characterize a MIMO system involving multirelational signals as a whole rather than the individual entries separately, which is a novel concept for system analysis. To do so, we extend Cauchy’s integral formula and Cauchy’s residue theorem from scalars to arbitrary-dimensional tensors, and then, to apply these new mathematical tools, we establish to undertake the inverse tensor z-transform and approximate the corresponding discrete-time tensor sequences. Our proposed new tensor z-transform in this work can be applied to design digital tensor filters including infinite-impulse-response (IIR) tensor filters (involving global feedback mechanisms) and finite-impulse-response (FIR) tensor filters. Finally, numerical evaluations are presented to demonstrate certain interesting phenomena of the new tensor z-transform.

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Section: Introductionmentioning

confidence: 99%

Tensor z‐Transform

Chang,

2024

Journal of Applied Mathematics

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“…Quantifying color with metrics and indices is important for turfgrass research, but metrics and indices alone may not convey color character adequately without visual connection to published standards. Color is a visual sensation processed to be perceived (Al‐Saleem et al., 2020; Neitz et al., 2001). Perception refers to the way sensory information is organized, interpreted, and consciously experienced (What is Perception?…”

Section: Introductionmentioning

confidence: 99%

Predicting Munsell color for turfgrass leaves

Berndt¹,

Gaussoin

2023

Crop Science

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Linking turfgrass color to hue, value, chroma (H V/C) in the Munsell Plant Tissue Color Book is a visual comparison process for specifying and communicating plant color. If subjectivity of visual comparison can be mitigated, then the accuracy of color matching may be improved. Research was conducted to develop an algorithm predicting H V/C from CIE-xyY color (xyY) in digital images of leaves of four turfgrasses. First, value-chroma (V/C) arrays for Munsell hue groups 5Y, 2.5GY, 5GY, 7.5GY, 10GY, and 2.5G were converted to xyY. Next, chromaticity (xy) plots from each array were fitted with 95% prediction bands. Then, xy points for leaves (N = 60) for each turfgrass were plotted versus prediction bands. The prediction interval containing the most leaf xy points specified leaf H. Pairing each V from each hue group (N = 98) with its corresponding Y regressing log 10 V on log 10 Y predicted leaf V from leaf Y (log 10 V = 0.0715 + 0.5303logY -0.0332logY 2 ). Pairing each C (N = 98) with its corresponding xy predicted leaf C from leaf xy (C = −11.6021 − 11.2908x + 48.0880y). Predicted H V/C for "Champion" and "TifEagle" hybrid bermudagrass [Cynodon dactylon (L.) Pers. var. dactylon × Cynodon transvaalensis Burtt-Davy] was 10GY 8/4 and 7.5GY 8/4 mirroring H V/C predicted from xyY using open-source algorithms in R and Python. The experimental algorithm predicted H V/C accurately, and objectively advancing the way turfgrass color is specified and communicated. Specifying turfgrass color in situ could be possible by interfacing the algorithm with smartphone technology.

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“…It is planned to be viable with “CIELAB” similar measure of progress in these mathematical qualities with practically a similar measure of progress seen visually ( Al-saleem, Al-Hilali & Abboud, 2020 ). The overall numerical equations is as follows:…”

Section: Introductionmentioning

confidence: 99%

“…Basic qualities/values demonstrate the reaction/response of the cone to white below (w) and reference radiation (wr). The change degree (D) is set to zero for non-change (self-lighting incentive) and the module for a full change (colorfastness) ( Al-saleem, Al-Hilali & Abboud, 2020 ). Practically, the values ranging from 0.65 to 1.0, is determined by the Eq.…”

Section: Introductionmentioning

confidence: 99%

GaborNet: investigating the importance of color space, scale and orientation for image classification

Rimiru¹,

Gateri²,

Kimwele³

2022

PeerJ Computer Science

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Content-Based Image Retrieval (CBIR) is the cornerstone of today’s image retrieval systems. The most distinctive retrieval approach used, involves the submission of an image-based query whereby the system is used in the extraction of visual characteristics like the shape, color, and texture from the images. Examination of the characteristics is done for ensuring the searching and retrieval of proportional images from the image database. Majority of the datasets utilized for retrieval lean towards to comprise colored images. The colored images are regarded as in RGB (Red, Green, Blue) form. Most colored images use the RGB image for classifying the images. The research presents the transformation of RGB to other color spaces, extraction of features using different color spaces techniques, Gabor filter and use Convolutional Neural Networks for retrieval to find the most efficient combination. The model is also known as Gabor Convolution Network. Even though the notion of the Gabor filter being induced in CNN has been suggested earlier, this work introduces an entirely different and very simple Gabor-based CNN which produces high recognition efficiency. In this paper, Gabor Convolutional Networks (GCNs or GaborNet), with different color spaces are used to examine which combination is efficient to retrieve natural images. An extensive experiment using Cifar 10 dataset was made and comparison of simple CNN, ResNet 50 and GCN model was also made. The models were evaluated through a several statistical analysis based on accuracy, precision, recall, F-Score, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results shows GaborNet model effectively retrieve images with 99.68% of AUC and 99.09% of Recall. The results also shows different images are effectively retrieved using different color space. Therefore research concluded it is very significance to transform images to different color space and use GaborNet for effective retrieval.

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Mathematical Representation of Color Spaces and Its Role in Communication Systems

Cited by 9 publications

References 5 publications

Tensor z‐Transform

Tensor z‐Transform

Predicting Munsell color for turfgrass leaves

GaborNet: investigating the importance of color space, scale and orientation for image classification

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