A RECONSTRUCTION OF GRAY SCALE IMAGE INTO RGB COLOR SPACE IMAGE USING YCBCR COLOR SPACING AND LUMINANCE MAPPING IN MATLAB

The aim of this study was to evaluate the usefulness of the texture and geometric parameters of endocarp (pit) for distinguishing different cultivars of sweet cherries using image analysis. The textures from images converted to color channels and the geometric parameters of the endocarp (pits) of sweet cherry ‘Kordia’, ‘Lapins’, and ‘Büttner’s Red’ were calculated. For the set combining the selected textures from all color channels, the accuracy reached 100% when comparing ‘Kordia’ vs. ‘Lapins’ and ‘Kordia’ vs. ‘Büttner’s Red’ for all classifiers. The pits of ‘Kordia’ and ‘Lapins’, as well as ‘Kordia’ and ‘Büttner’s Red’ were also 100% correctly discriminated for discriminative models built separately for RGB, Lab and XYZ color spaces, G, L and Y color channels and for models combining selected textural and geometric features. For discrimination ‘Lapins’ and ‘Büttner’s Red’ pits, slightly lower accuracies were determined—up to 93% for models built based on textures selected from all color channels, 91% for the RGB color space, 92% for the Lab and XYZ color spaces, 84% for the G and L color channels, 83% for the Y channel, 94% for geometric features, and 96% for combined textural and geometric features.

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Gray Scale Image Coloring Method Based on GAN

Proceedings of the 3rd International Conference on Data Science and Information Technology ◽

10.1145/3414274.3414485 ◽

2020 ◽

Author(s):

Long Guo ◽

Lu Liang ◽

Shi Zeng ◽

Ji He ◽

Gaoshang Dai

Keyword(s):

Gray Scale ◽

Gray Scale Image

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A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

Remote Sensing ◽

10.3390/rs13061211 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1211

Author(s):

Pan Fan ◽

Guodong Lang ◽

Bin Yan ◽

Xiaoyan Lei ◽

Pengju Guo ◽

...

Keyword(s):

Vision System ◽

False Positive Rate ◽

Clustering Algorithms ◽

False Negative ◽

Color Space ◽

False Negative Rate ◽

Feature Selection Method ◽

Image Features ◽

Accurate Identification ◽

Rgb Color Space

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.

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A Watershed-Segmentation-Based Improved Algorithm for Extracting Cultivated Land Boundaries

Remote Sensing ◽

10.3390/rs13050939 ◽

2021 ◽

Vol 13 (5) ◽

pp. 939

Author(s):

Yongan Xue ◽

Jinling Zhao ◽

Mingmei Zhang

Keyword(s):

Color Space ◽

Cultivated Land ◽

Color Spaces ◽

Region Merging ◽

Watershed Segmentation ◽

Coverage Area ◽

Time Efficiency ◽

Image Contrast Enhancement ◽

Segmentation Accuracy ◽

Rgb Color Space

To accurately extract cultivated land boundaries based on high-resolution remote sensing imagery, an improved watershed segmentation algorithm was proposed herein based on a combination of pre- and post-improvement procedures. Image contrast enhancement was used as the pre-improvement, while the color distance of the Commission Internationale de l´Eclairage (CIE) color space, including the Lab and Luv, was used as the regional similarity measure for region merging as the post-improvement. Furthermore, the area relative error criterion (δA), the pixel quantity error criterion (δP), and the consistency criterion (Khat) were used for evaluating the image segmentation accuracy. The region merging in Red–Green–Blue (RGB) color space was selected to compare the proposed algorithm by extracting cultivated land boundaries. The validation experiments were performed using a subset of Chinese Gaofen-2 (GF-2) remote sensing image with a coverage area of 0.12 km2. The results showed the following: (1) The contrast-enhanced image exhibited an obvious gain in terms of improving the image segmentation effect and time efficiency using the improved algorithm. The time efficiency increased by 10.31%, 60.00%, and 40.28%, respectively, in the RGB, Lab, and Luv color spaces. (2) The optimal segmentation and merging scale parameters in the RGB, Lab, and Luv color spaces were C for minimum areas of 2000, 1900, and 2000, and D for a color difference of 1000, 40, and 40. (3) The algorithm improved the time efficiency of cultivated land boundary extraction in the Lab and Luv color spaces by 35.16% and 29.58%, respectively, compared to the RGB color space. The extraction accuracy was compared to the RGB color space using the δA, δP, and Khat, that were improved by 76.92%, 62.01%, and 16.83%, respectively, in the Lab color space, while they were 55.79%, 49.67%, and 13.42% in the Luv color space. (4) Through the visual comparison, time efficiency, and segmentation accuracy, the comprehensive extraction effect using the proposed algorithm was obviously better than that of RGB color-based space algorithm. The established accuracy evaluation indicators were also proven to be consistent with the visual evaluation. (5) The proposed method has a satisfying transferability by a wider test area with a coverage area of 1 km2. In addition, the proposed method, based on the image contrast enhancement, was to perform the region merging in the CIE color space according to the simulated immersion watershed segmentation results. It is a useful attempt for the watershed segmentation algorithm to extract cultivated land boundaries, which provides a reference for enhancing the watershed algorithm.

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EXTRACTION OF BIBLIOGRAPHY INFORMATION BASED ON IMAGE OF BOOK COVER

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001400000611 ◽

2000 ◽

Vol 14 (07) ◽

pp. 963-978 ◽

Cited By ~ 2

Author(s):

HUA YANG ◽

MASAAKI KASHIMURA ◽

NORIKADU ONDA ◽

SHINJI OZAWA

Keyword(s):

Color Image ◽

Color Space ◽

Input Image ◽

Color Image Processing ◽

Front Cover ◽

Stroke Width ◽

Other Information ◽

Region Extraction ◽

Hsi Color Space ◽

Rgb Color Space

This paper describes a new system for extracting and classifying bibliography regions from the color image of a book cover. The system consists of three major components: preprocessing, color space segmentation and text region extraction and classification. Preprocessing extracts the edge lines of the book and geometrically corrects and segments the input image, into the parts of front cover, spine and back cover. The same as all color image processing researches, the segmentation of color space is an essential and important step here. Instead of RGB color space, HSI color space is used in this system. The color space is segmented into achromatic and chromatic regions first; and both the achromatic and chromatic regions are segmented further to complete the color space segmentation. Then text region extraction and classification follow. After detecting fundamental features (stroke width and local label width) text regions are determined. By comparing the text regions on front cover with those on spine, all extracted text regions are classified into suitable bibliography categories: author, title, publisher and other information, without applying OCR.

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