Color Image Segmentation

Ivanovici, Mihai; Richard, Noël; Paulus, Dietrich

doi:10.1007/978-1-4419-6190-7_8

Cited by 10 publications

(5 citation statements)

References 101 publications

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“…Busin et al (2004) had proved the weakening effect on the correlation between every two bands of the RGB color space with linear or nonlinear transformation methods. Therefore, some representative color spaces such as HSI, YCbCr, YIQ, I 1 I 2 I 3 , CIE (L*a*b*) and Nrgb were collected for comparative study (Andreadis et al 1990;Skarbek et al 1994;Golland and Bruckstein 1996;Tao and Xu 2001;Wang et al 2003;Lv and WANG 2006;Kampel et al 2007;Sun et al 2009;Ivanovici et al 2013). …”

Section: Methodsmentioning

confidence: 99%

Investigation of Optimal Segmentation Color Space of Bayer True Color Images with Multi-Objective Optimization Methods

Duan

et al. 2015

J Indian Soc Remote Sens

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For the selection of the optimal segmentation space of Bayer true color unmanned aerial vehicle image, this paper introduces multi-objectives constraints optimization to solve the inconsistency of multiple indicators. First, the Bayer color images were converted to YIQ(Luninance, Inphase, Quadrature), YCbCr(Luninance, Blue-difference, Red-difference), I 1 I 2 I 3 (Three linear transformed color-opponent dimensions), HSI(Hue, Saturation, Intensity), Nrgb(Normalized Red, Green, Blue) and CIE(L*a*b*) (Comission Internationale de l′Eclairage, L*a*b* for Lightness and two color-opponent dimensions)color space, then the transformed images were segmented with multi-resolution segmentation method. By introducing the multi-objective constraint function, three parameters such as the topology index, geometric index and spectral area matching index were synthetically considered to determine the optimal segmentation scale. Based on that, the multi-objective constraint function was built to comprehensively analyze the result of segmentation, so as to find out the optimal color space for a certain type of building. And then the global optimum color space appropriate for all kinds of buildings can be gained through the comprehensive analysis of the F value of different types of buildings. Finally a series of images of different acquisition conditions and ground features were selected to conduct the test. The result shows that the optimal segmentation color spaces of different types of buildings vary a little. For cottage the I 1 I 2 I 3 space can get the excellent object areas that reflect the real edge of the ground features, while the YCbCr space has some advantages on the segmentation of tile-building. Overall, only I 1 I 2 I 3 color space has better integrated segmentation result for all buildings, and it is considered to be the best color space suitable for segmentation.

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Section: Methodsmentioning

confidence: 99%

Investigation of Optimal Segmentation Color Space of Bayer True Color Images with Multi-Objective Optimization Methods

Duan

et al. 2015

J Indian Soc Remote Sens

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“…In practice, the fractal dimension has been used for the discrimination between various signals or patterns exhibiting fractal properties, such as textures [ 38 ]. Often, the application of texture classification is image segmentation [ 1 ].…”

Section: Methodsmentioning

confidence: 99%

“…Segmentation is considered to be at the border between image processing and image analysis, as the segmentation map is often used for consequent object detection or recognition tasks. It is said that segmentation is one of the most difficult or complex operations performed on images [ 1 ]. What makes it difficult or complex?…”

Section: Introductionmentioning

confidence: 99%

Color Image Complexity versus Over-Segmentation: A Preliminary Study on the Correlation between Complexity Measures and Number of Segments

et al. 2020

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It is said that image segmentation is a very difficult or complex task. First of all, we emphasize the subtle difference between the notions of difficulty and complexity. Then, in this article, we focus on the question of how two widely used color image complexity measures correlate with the number of segments resulting in over-segmentation. We study the evolution of both the image complexity measures and number of segments as the image complexity is gradually decreased by means of low-pass filtering. In this way, we tackle the possibility of predicting the difficulty of color image segmentation based on image complexity measures. We analyze the complexity of images from the point of view of color entropy and color fractal dimension and for color fractal images and the Berkeley data set we correlate these two metrics with the segmentation results, more specifically the number of quasi-flat zones and the number of JSEG regions in the resulting segmentation map. We report on our experimental results and draw conclusions.

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“…Consequently, image pre-processing should bethe primary step. Pre-processing can include threeprocedures: gray image conversion, noise reduction, and image reconstruction of images [19,20]. The most popular pre-processing method is the conversion to a grayscale image.…”

Section: Preprocessing Phasementioning

confidence: 99%

Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor

Zaman

Din

et al. 2022

JERR

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The segmentation, detection and extraction of malignant tumor regions from magnetic resonance (MR) images are challenging tasks in medical image analysis. Approaches based on machine and deep learning have been introduced, which performed better than traditional image processing methods. However, many approaches still show limited ability due to the complex dataset and image modalities. This study evaluated the deep learning approach's performance and traditional image processing algorithms for medical imaging segmentation, detection, and classification. The proposed system comprises multiple stages. The Median filters are used in the pre-processing step, and morphological operation and Otsu thresholding are used to segment MR images. Discrete Wavelet Transform (DWT) algorithm is considered in the extraction features, and their classification is executed by a convolutional neural network (CNN) and support vector machine (SVM) algorithms. The Mat lab has been used for simulation and experimental findings to evaluate the suggested method's performance on the brain's complex and highly 2D structures. The results show that the methodology is reliable and efficient, with 93.5% accuracy.

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Color Image Segmentation

Cited by 10 publications

References 101 publications

Investigation of Optimal Segmentation Color Space of Bayer True Color Images with Multi-Objective Optimization Methods

Investigation of Optimal Segmentation Color Space of Bayer True Color Images with Multi-Objective Optimization Methods

Color Image Complexity versus Over-Segmentation: A Preliminary Study on the Correlation between Complexity Measures and Number of Segments

Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor

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