Image Thresholding Using the Complement Feature

Ameer, Salah

doi:10.3844/ajeassp.2020.311.317

Cited by 6 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The component added to obtain a unit vector, Equation (1), can be generalized to any fuzzy complement. However, more work is needed to find the best formula and whether improvements can be attained, (Ameer, 2020) for some suggestions.…”

Section: Discussionmentioning

confidence: 99%

“…The Eigen vectors of AG represent the axes of inertia for the data set. The largest vector Vmax (corresponding to the maximum Eigen value λmax) points toward the direction of maximum inertia (Ameer, 2020). The first components of each resulting Eigen vector, corresponding to the intensity of the central pixel, is the most suitable for performing the segmentation.…”

Section: Methodsmentioning

confidence: 99%

“…A simple but effective Eigen structure is proposed as a solution. The schemes are based on a recent work (Ameer, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Local Features Supported by the Complement Feature for Image Segmentation

Ameer¹

2020

American Journal of Engineering and Applied Sciences

Self Cite

View full text Add to dashboard Cite

An Eigen formulation is proposed for image thresholding/segmentation. A vector composed of local features, normalized intensity of each pixel and that of the neighboring pixels, is used to represent each pixel. A "complement" component is appended to this vector to produce a "unit" vector. The auto-correlation matrix is computed for each pixel in the image using this unit vector. The first component (corresponding to the intensity of the current pixel) from all Eigen vectors, obtained from the autocorrelation matrix, are used as multi-level thresholds. Similar procedure can be adopted using powers of the current pixel intensity value. In general, more than one threshold can be obtained. Results on a wide range of images are demonstrated to show the effectiveness of the proposed schemes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Local Features Supported by the Complement Feature for Image Segmentation

Ameer¹

2020

American Journal of Engineering and Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, IQA is based on the complement feature recently proposed [18]. A correlation/covariance matrix is composed using the data in the block, can also be used for the whole image or any regularly shaped portion.…”

Section: Introductionmentioning

confidence: 99%

“…The main goal of this work is to explore the auto correlation matrix [18] and the wealth of information embedded in it, EQ (as an FR-IQA scheme) is one of these products.…”

Section: Introductionmentioning

confidence: 99%

EQ: An Eigen Image Quality Assessment based on the Complement Feature

Ameer¹,

Colleges²

2020

IJIGSP

Self Cite

View full text Add to dashboard Cite

An Eigen formulation is proposed for image quality assessment IQA. Each block is represented by an array composed of feature vectors (intensity/color at this stage). After attaching the complement feature(s), the autocorrelation matrix is computed for each block. The proposed full reference FR-IQA is simply the deviation of the Eigen values of the degraded image from that of the original image. Interestingly, the second largest Eigen value was sufficient to perform this comparison. Results and comparisons with SSIM and GMSD schemes on different types of degradation are demonstrated to show the effectiveness of the proposed schemes. Using TID2013 database, the proposed scheme outperforms SSIM. In addition, the proposed schemes is closer to the MOS score compared to GMSD; however, the correlation with MOS is inferior as illustrated in the tables. These results are concluded from the average behaviour on all the images using all degradations (with 5 levels) on the database.

show abstract

Brain Image Decomposition on Image Alteration

Rahmawati

Daengs

Putra

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Image transformation is essential to explore and find out specific information that does not exist and has not been previously known from an image, such as pixels, geometry, size or colour. Therefore, this paper aims to analyze the image transformation by generating the value of the thresholding method from low to high in image segmentation. The segmentation process works based on two-colour models, namely HSV and RGB colours. Image segmentation problems occur when an object has more than two colours. There is lighting that affects the condition of the thing, so it is proposed to change the threshold value from low to high to compare the segmentation results of each given value to determine the optimal threshold value. The image data used is a brain MRI image from the website www.hipwee.com. The Matlab 2019a application is used to assist the analysis process. Based on the analysis results, in this case, the smaller the HSV and RGB threshold values, the segmentation of the detected image results is not detailed and the less precise, and vice versa. The best Brain MRI Image Segmentation results when using a threshold value of HSV=0.5 and RGB=5.

show abstract

Image Thresholding Using the Complement Feature

Cited by 6 publications

References 14 publications

Local Features Supported by the Complement Feature for Image Segmentation

Local Features Supported by the Complement Feature for Image Segmentation

EQ: An Eigen Image Quality Assessment based on the Complement Feature

Brain Image Decomposition on Image Alteration

Contact Info

Product

Resources

About