Image Segmentation Algorithm Research for Sport Graphics Based on Multi-Layer Background Subtraction

2013 ◽  
Vol 339 ◽  
pp. 265-268
Author(s):  
Ming Jing Lu ◽  
Zhan Ping Li
Keyword(s):  
Background Subtraction ◽  
Color Space ◽  
Texture Features ◽  
Bilateral Filter ◽  
Local Binary Patterns ◽  
Stationary Objects ◽  
Long Time ◽  
Rgb Color Space ◽  
Image Segmentation Algorithm ◽  
Similar Intensity

Segmentation of motion in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. In this paper, we propose a robust multi-layer background subtraction technique which takes advantages of local texture features represented by local binary patterns (LBP) and photometric invariant color measurements in RGB color space. Due to the use of a simple layer-based strategy, the approach can model moving background pixels with quasiperiodic flickering as well as background scenes which may vary over time due to the addition and removal of long-time stationary objects. The use of a cross-bilateral filter allows to implicitly smooth detection results over regions of similar intensity and preserve object boundaries.

Multiple Motion Segmentation of Sport Image Based on Multi-Layer Background Subtraction

2013 ◽  
Vol 846-847 ◽  
pp. 1266-1269
Author(s):  
Meng Zhao ◽  
Zhan Ping Li
Keyword(s):  
Background Subtraction ◽  
Color Space ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Growth Conditions ◽  
Subtraction Method ◽  
Subtraction Technique ◽  
Stationary Objects ◽  
Long Time ◽  
Rgb Color Space

Segmentation of motion in an image sequence is one of the most challenging problems in image processing. In image analysis engineering, accurate statistics of sport image is one of the most important subjects, while at the same time one that finds numerous applications. In this paper, we propose a robust multi-layer background subtraction technique which takes advantages of local texture features represented by local binary patterns (LBP) and photometric invariant color measurements in RGB color space. Due to the use of a simple layer-based strategy, the approach can model moving background pixels with quasiperiodic flickering as well as background scenes which may vary over time due to the addition and removal of long-time stationary objects, which plays an important role in optimizing the growth conditions of sport image. Segmentation of sport images is successfully realized by means of multi-layer background subtraction method and then the sport image is computed precisely.

A Novel Approach Based on Multi-Layer Background Subtraction and Seed Region Growing for Sport Graphics Segmentation

2014 ◽  
Vol 513-517 ◽  
pp. 3358-3361
Author(s):  
Li Liang ◽  
Hong Wei Wang
Keyword(s):  
Background Subtraction ◽  
Color Space ◽  
Region Growing ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Seed Region ◽  
Novel Approach ◽  
Segmentation Quality ◽  
Stationary Objects ◽  
Long Time

Segmentation of motion in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. In this paper, we propose a robust multi-layer background subtraction technique and seed region growing approach which takes advantages of local texture features represented by local binary patterns (LBP) and photometric invariant color measurements in RGB color space. Due to the use of hybridization of layer-based strategy and seed region growing approach, the approach can model moving background pixels with quasiperiodic flickering as well as background scenes which may vary over time due to the addition and removal of long-time stationary objects. The experiment results prove that in the view of the sport image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

3-Dimensional Brain MRI Segmentation Based on Multi-Layer Background Subtraction and Seed Region Growing Algorithm

2014 ◽  
Vol 536-537 ◽  
pp. 218-221
Author(s):  
Xiao Hong Wang ◽  
Bin Liu ◽  
Zhi Qiang Song
Keyword(s):  
Background Subtraction ◽  
Brain Mri ◽  
Color Space ◽  
Region Growing ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Seed Region ◽  
Mri Segmentation ◽  
Stationary Objects ◽  
Long Time

Segmentation of brain MRI in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. In this paper, we propose a robust multi-layer background subtraction technique and seed region growing approach which takes advantages of local texture features represented by local binary patterns (LBP) and photometric invariant color measurements in RGB color space. Due to the use of hybridization of layer-based strategy and seed region growing approach, the approach can model moving background pixels with quasiperiodic flickering as well as background scenes which may vary over time due to the addition and removal of long-time stationary objects. The experiment results prove that in the view of the brain MRI segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

scholarly journals Color space influence on ANN skin lesion classification using statistics texture feature

2021 ◽  
Vol 44 (1) ◽  
pp. 53-62
Author(s):  
Felicia Anisoara Damian ◽  
Simona Moldovanu ◽  
Luminita Moraru
Keyword(s):  
Color Space ◽  
Back Propagation ◽  
Texture Feature ◽  
Texture Features ◽  
Skin Lesions ◽  
Color Spaces ◽  
Color Channel ◽  
Feed Forward Back Propagation ◽  
Rgb Color Space ◽  
Hidden Layer

This study aims to investigate the ability of an artificial neural network to differentiate between malign and benign skin lesions based on two statistics terms and for RGB (R red, G green, B blue) and YIQ (Y luminance, and I and Q chromatic differences) color spaces. The targeted statistics texture features are skewness (S) and kurtosis (K) which are extracted from the histograms of each color channel corresponding to the color spaces and for the two classes of lesions: nevi and melanomas. The extracted data is used to train the Feed-Forward Back Propagation Networks (FFBPNs). The number of neurons in the hidden layer varies: it can be 8, 16, 24, or 32. The results indicate skewness features computed for the red channel in the RGB color space as the best choice to reach the goal of our study. The reported result shows the advantages of monochrome channels representation for skin lesions diagnosis.

scholarly journals Image Retrieval with Fusion of Thepade’s Sorted Block Truncation Coding n-ary based Color and Local Binary Pattern based Texture Features with Different Color Places

2020 ◽  
Vol 9 (5) ◽  
pp. 28-34
Keyword(s):  
Image Retrieval ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Color Space ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Block Truncation Coding ◽  
Local Texture ◽  
Color Features ◽  
Different Color

In these years, there has been a gigantic growth in the generation of data. Innovations such as the Internet, social media and smart phones are the facilitators of this information boom. Since ancient times images were treated as an effective mode of communication. Even today most of the data generated is image data. The technology for capturing, storing and transferring images is well developed but efficient image retrieval is still a primitive area of research. Content Based Image Retrieval (CBIR) is one such area where lot of research is still going on. CBIR systems rely on three aspects of the image content namely texture, shape and color. Application specific CBIR systems are effective whereas Generic CBIR systems are being explored. Previously, descriptors are used to extract shape, color or texture content features, but the effect of using more than one descriptor is under research and may yield better results. The paper presents the fusion of TSBTC n-ary (Thepade's Sorted n-ary Block Truncation Coding) Global Color Features and Local Binary Pattern (LBP) Local Texture Features in Content Based Image with Different Color Places TSBTC n-ary devises global color features from an image. It is a faster and better technique compared to Block Truncation Coding. It is also rotation and scale invariant. When applied on an image TSBTC n-ary gives a feature vector based on the color space, if TSBTC n-ary is applied on the obtained LBP (Local Binary Patterns) of the image color planes, the feature vector obtained is be based on local texture content. Along with RGB, the Luminance chromaticity color space like YCbCr and Kekre’s LUV are also used in experimentation of proposed CBIR techniques. Wang dataset has been used for exploration of proposed method. It consists of 1000 images (10 categories having 100 images each). Obtained results have shown performance improvement using fusion of BTC extracted global color features and local texture features extracted with TSBTC n-ary applied on Local Binary Patterns (LBP).

scholarly journals The Application of Machine Learning for Cultivar Discrimination of Sweet Cherry Endocarp

Agriculture ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Ewa Ropelewska
Keyword(s):  
Sweet Cherry ◽  
Color Space ◽  
Geometric Parameters ◽  
Geometric Features ◽  
Color Spaces ◽  
Discriminative Models ◽  
Sweet Cherries ◽  
Rgb Color Space ◽  
Cultivar Discrimination ◽  
Color Channels

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.

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

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.

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

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.

EXTRACTION OF BIBLIOGRAPHY INFORMATION BASED ON IMAGE OF BOOK COVER

2000 ◽  
Vol 14 (07) ◽  
pp. 963-978 ◽  
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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