A hand segmentation scheme using clustering technique in homogeneous background

Segmentation serves as the first step in any image analysis and it plays a very vital role as the success of the image analysis in the later stage depends very much on a suitable and robust segmentation scheme. Hand segmentation on the other hand is the first step for hand image analysis such as hand gesture recognition. An image subtraction method is implemented on a gray level image, RGB color image and image in normalized RGB color space under homogeneous background to investigate their appropriateness for segmentation. A skin color model based on the clustering property of skin color is then built to improve the segmentation result obtained from the image subtraction on the normalized RGB image. It is found that the proposed skin color modeling technique is able to improve the segmentation and provide a faster and reliable method for hand segmentation

Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study

Color image segmentation is a very emerging research topic in the area of color image analysis and pattern recognition. Many state-of-the-art algorithms have been developed for this purpose. But, often the segmentation results of these algorithms seem to be suffering from miss-classifications and over-segmentation. The reasons behind these are the degradation of image quality during the acquisition, transmission and color space conversion. So, here arises the need of an efficient image enhancement technique which can remove the redundant pixels or noises from the color image before proceeding for final segmentation. In this paper, an effort has been made to study and analyze different image enhancement techniques and thereby finding out the better one for color image segmentation. Also, this comparative study is done on two well-known color spaces HSV and LAB separately to find out which color space supports segmentation task more efficiently with respect to those enhancement techniques.Comment: 27 pages, 17 figures, 2 Tables, 1 flowchar

A New Automatic Watercolour Painting Algorithm Based on Dual Stream Image Segmentation Model with Colour Space Estimation

Image processing plays a crucial role in automatic watercolor painting by manipulating the digital image to achieve the desired watercolor effect. segmentation in automatic watercolor painting algorithms is essential for region-based processing, color mixing and blending, capturing brushwork and texture, and providing artistic control over the final result. It allows for more realistic and expressive watercolor-like paintings by processing different image regions individually and applying appropriate effects to each segment. Hence, this paper proposed an effective Dual Stream Exception Maximization (DSEM) for automatic image segmentation. DSEM combines both color and texture information to segment an image into meaningful regions. This approach begins by converting the image from the RGB color space to a perceptually-based color space, such as CIELAB, to account for variations in lighting conditions and human perception of color.  With the color space conversion, DSEM extracts relevant features from the image. Color features are computed based on the values of the color channels in the chosen color space, capturing the nuances of color distribution within the image. Simultaneously, texture features are derived by computing statistical measures such as local variance or co-occurrence matrices, capturing the textural characteristics of the image. Finally, the model is applied over the deep learning model for the classification of the color space in the painting. Simulation analysis is performed compared with conventional segmentation techniques such a CNN and RNN. The comparative analysis states that the proposed DSEM exhibits superior performance compared to conventional techniques in terms of color space estimation, texture analysis and region merging. The performance of classification with DSEM is ~12% higher than the conventional techniques

Color and Texture Feature Extraction Using Gabor Filter - Local Binary Patterns for Image Segmentation with Fuzzy C-Means

Image segmentation to be basic for image analysis and recognition process. Segmentation divides the image into several regions based on the unique homogeneous image pixel. Image segmentation classify homogeneous pixels basedon several features such as color, texture and others. Color contains a lot of information and human vision can see thousands of color combinations and intensity compared with grayscale or with black and white (binary). The method is easy to implement to segementation is clustering method such as the Fuzzy C-Means (FCM) algorithm. Features to beextracted image is color and texture, to use the color vector L* a* b* color space and to texture using Gabor filters. However, Gabor filters have poor performance when the image is segmented many micro texture, thus affecting the accuracy of image segmentation. As support in improving the accuracy of the extracted micro texture used method of Local Binary Patterns (LBP). Experimental use of color features compared with grayscales increased 16.54% accuracy rate for texture Gabor filters and 14.57% for filter LBP. While the LBP texture features can help improve the accuracy of image segmentation, although small at 2% on a grayscales and 0.05% on the color space L* a* b*

A New Texture Based Segmentation Method to Extract Object from Background

Extraction of object regions from complex background is a hard task and it is an essential part of image segmentation and recognition. Image segmentation denotes a process of dividing an image into different regions. Several segmentation approaches for images have been developed. Image segmentation plays a vital role in image analysis. According to several authors, segmentation terminates when the observer2019;s goal is satisfied. The very first problem of segmentation is that a unique general method still does not exist: depending on the application, algorithm performances vary. This paper studies the insect segmentation in complex background. The segmentation methodology on insect images consists of five steps. Firstly, the original image of RGB space is converted into Lab color space. In the second step 2018;a2019; component of Lab color space is extracted. Then segmentation by two-dimension OTSU of automatic threshold in 2018;a-channel2019; is performed. Based on the color segmentation result, and the texture differences between the background image and the required object, the object is extracted by the gray level co-occurrence matrix for texture segmentation. The algorithm was tested on dreamstime image database and the results prove to be satisfactory

Human Perception Based Color Image Segmentation

Color image segmentation is probably the most important task in image analysis and understanding. A novel Human Perception Based Color Image Segmentation System is presented in this paper. This system uses a neural network architecture. The neurons here uses a multisigmoid activation function. The multisigmoid activation function is the key for segmentation. The number of steps ie. thresholds in the multisigmoid function are dependent on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSI color space. Here the main use of neural network is to detect the number of objects automatically from an image. It labels the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images

Digital image processing techniques for pre-diagnosis of psoriasis skin diseases / Rozita Jailani

This research involves the analysis of psoriasis skin lesions images. The images are captured using digital camera under controlled conditions. These images were collected from psoriasis patients at Hospital Universiti Kebangsaan Malaysia (HUKM) over five months starting from July 2002. The images include three major types of psoriasis skin lesions. Two major works were carried out; one is done using image color and another one on skin lesion border segmentation. Color analysis was employed to distinguish the three major types of psoriasis skin diseases infecting the Malaysian population. Four color analysis techniques were applied; normalization techniques, Gaussian parameters', color spaces and pre-processing techniques. These color analyses produce a color model to distinguish the psoriasis skin disease. Second, border segmentation technique is introduced. Skin lesion border segmentation is a new technique to segment the psoriasis image into lesion, skin and other background. Accurate and reliable outline detection is important in order to segment the image into lesion, skin and other background, thereby ensuring that asymmetry and diameter measurement can be carried out only in the lesion image. Results from psoriasis skin lesion segmentation can be used in skin lesion shape, diameter and asymmetry calculation. From the results, a simple unified approach model for color analysis was constructed integrating significant normalization technique, Gaussian parameters', color space and pre-processing techniques. The mean value of red color component was found to be significant in pre-diagnosing the types of psoriasis skin diseases. In this color model, the plaque confidence interval is between 1.823 to 2.248, guttate confidence interval is between 1.169 to 1.594 and erythroderma confidence interval is between 2.974 to 3.399. Many combinations of the processing techniques had been tried to find robust border segmentation technique. From all the techniques, the proposed segmentation technique gives higher reliability and visually accurate continuous boundaries for a range of images. In this research, it produces visually accurate border segmentation more than 90% of psoriasis skin disease images. The results from border segmentation can be used for diameter calculation, asymmetry of lesion and recognition of the lesion border

Color based image segmentation using different versions of k-means in two spaces

In this paper color based image segmentation is done in two spaces. First in LAB color space and second in RGB space all that done using three versions of K-Means: K-Means, Weighted K-Means and Inverse Weighted K-Means clustering algorithms for different types of images: biological images (tissues and blood cells) and ordinary full colored images. Comparison and analysis are done between these three algorithms in order to differentiate between them