A Color Image Segmentation Method Based on Region Salient Color and Fuzzy C-Means Algorithm

Feng, Lihui; Li, Haibin; Gao, Yakun; Zhang, Yakun

doi:10.1007/s00034-019-01126-w

Cited by 28 publications

(19 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then use a skin color segmentation process to detect the possible faces in the input image. Here also Fuzzy C-Means [15] and K-Means [16] clustering techniques were used to compare the cluster analysis among the three techniques. The experimental result is shown in table 1.…”

Section: Iv! Performance Analysis and Discussionmentioning

confidence: 99%

Human Face Detection: Manual vs. Kohonen Self Organizing Map

Bandyopadhyay¹,

Bose²

2020

Preprint

View full text Add to dashboard Cite

In today's world it is very much important to maintain the security of information and its risks. The biometric-based techniques are very much useful in these problems. Among the several kinds of biometric-based technique, face detection is much complex and much more important. Due to the age and several other problems, a human face structure changes over time, again a human has lots of expressions. Sometimes due to the lighting condition or the variation of the angle of an input device, the pattern of a human face structure also changed. As a result, the face cannot be detected properly. In this paper, a method is proposed that can detect the human faces both automatically and manually very efficiently. In manual mode, a user can select the input faces referred by the system according to their choice. In automated mode, the system detected all possible face areas using the Kohonen Self-Organizing Feature Map technique. This method reduced the complex color image into a vector quantized image with desired colors. Then a color segmentation technique is used to detect the possible face skin areas from the vector quantized image. Then the Histogram Oriented Gradient technique used to detect the feature from the faces and K-Nearest Neighbour Classifier is used to compare both face images detected by the two modes. The automated method prosed better accuracy than the manual method.

show abstract

Section: Iv! Performance Analysis and Discussionmentioning

confidence: 99%

Human Face Detection: Manual vs. Kohonen Self Organizing Map

Bandyopadhyay¹,

Bose²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In the color classification of wooden boards, only the wood color information is useful in the image. Therefore, the HSV color information [22] is used to process wood images before classification. The wood color ranges in the H, S, and V channels can be used for removing the background and stains simultaneously [23].…”

Section: Image Preprocessingmentioning

confidence: 99%

“…Based on the 15,000 obtained wood images, Figure 8 shows the numbers of pixels with each value in the H, S, and V channels, respectively. We calculate the confidence intervals for pixel values at a 95% confidence level, which are [9,22] in the H channel, [59,131] in the S channel, and [134,219] in the V channel, respectively. According to these intervals, any irrelevant color whose pixel values are not in these intervals can be removed in the process of image preprocessing.…”

Section: Wood Color Distributionmentioning

confidence: 99%

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

et al. 2020

View full text Add to dashboard Cite

Color classification of wooden boards is helpful to improve the appearance of wooden furniture that is spliced from multiple wooden boards. Due to the similarity of colors among wooden boards, manual color classification is inaccurate and unstable. Thus, supervised learning algorithms can hardly be used in this scenario. Moreover, wooden boards are long, and their images have a high resolution, which may lead to the growth of computational complexity. To overcome these challenges, in this paper, we propose a new mechanism for color classification of wooden boards based on machine vision. The image of the wooden board is preprocessed to subtract irrelevant colors, and the feature vector is extracted based on 3D color histogram to reduce the computational complexity. In the offline clustering, the feature vector sets are partitioned into different clusters through the K-means algorithm. Then, the clustering result can be used in the online classification to classify the new wood image. Furthermore, to process the abnormal images of wooden boards, we propose an improved algorithm with centroid improvement and image filtering. The experimental results verify the effectiveness of the proposed mechanism.

show abstract

“…The folders and files in the data repository were named using the labels in the second table. While there is a rich literature source for segmentation methods, and some of these articles provide good in-class comparisons with related routines, there is a need for an updated and comprehensive cross-class review for this topic, the last of which was published more than five years ago [ 3 , [6] , [7] , [8] , [9] ].…”

Section: Data Descriptionmentioning

confidence: 99%

Testing dataset for head segmentation accuracy for the algorithms in the ‘BGSLibrary’ v3.0.0 developed by Andrews Sobral

et al. 2020

View full text Add to dashboard Cite

This dataset consists of video files that were created to test the accuracy of background segmentation algorithms contained in the C ++ wrapper ‘BGSLibrary’ v3.0.0 developed by Andrews Sobral. The comparison is based on segmentation accuracy of the algorithms on a series of indoor color-depth video clips of a single person's head and upper body, each highlighting a common factor that can influence the accuracy of foreground-background segmentation. The algorithms are run on the color image data, while the ‘ground truth’ is semi-automatically extracted from the depth data. The camera chosen for capturing the videos features paired color-depth image sensors, with the color sensor having specifications typical of mobile devices and webcams, which cover most of the use cases for these algorithms. The factors chosen for testing are derived from a literature review accompanying the dataset as being able to influence the efficacy of background segmentation. The assessment criteria for the results were set based on the requirements of common use cases such as gamecasting and mobile communications to allow the readers to make their own judgements on the merits of each algorithm for their own purposes.

show abstract

A Color Image Segmentation Method Based on Region Salient Color and Fuzzy C-Means Algorithm

Cited by 28 publications

References 30 publications

Human Face Detection: Manual vs. Kohonen Self Organizing Map

Human Face Detection: Manual vs. Kohonen Self Organizing Map

Color Classification of Wooden Boards Based on Machine Vision and the Clustering Algorithm

Testing dataset for head segmentation accuracy for the algorithms in the ‘BGSLibrary’ v3.0.0 developed by Andrews Sobral

Contact Info

Product

Resources

About