Pupil Segmentation of Abnormal Eye using Image Enhancement in Spatial Domain

Ramlee, Ridza Azri; Ramli, Abdul Rahman; Noh, Zarina Mohd

doi:10.1088/1757-899x/210/1/012031

Cited by 14 publications

(3 citation statements)

References 4 publications

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“…It improves the effect of crack segmentation and extraction by highlighting the features and details of the intercrack region while weakening the background of uninteresting regions. At present, there are two types of common image enhancement methods: the enhancement methods based on the spatial domain (space domain) [13][14][15], which primarily include point pixel processing and area processing; the enhancement methods based on the frequency domain (frequency domain) [16][17][18], which mainly include low-pass filtering, high-pass filtering, homomorphic filtering, etc. This paper focuses on the image enhancement method based on the spatial domain.…”

Section: Crack Image Datasetmentioning

confidence: 99%

Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing

Li,

Yin,

Zhang

2023

Sensors

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Crack disease is one of the most serious and common diseases in road detection. Traditional manual methods for measuring crack detection can no longer meet the needs of road crack detection. In previous work, the authors proposed a crack detection method for asphalt pavements based on an improved YOLOv5s model, which is a better model for detecting various types of cracks in asphalt pavements. However, most of the current research on automatic pavement crack detection is still focused on crack identification and location stages, which contributes little to practical engineering applications. Based on the shortcomings of the above work, and in order to improve its contribution to practical engineering applications, this paper proposes a method for segmenting and analyzing asphalt pavement cracks and identifying parameters based on image processing. The first step is to extract the crack profile through image grayscale, histogram equalization, segmented linear transformation, median filtering, Sauvola binarization, and the connected domain threshold method. Then, the magnification between the pixel area and the actual area of the calibration object is calculated. The second step is to extract the skeleton from the crack profile images of asphalt pavement using the Zhang–Suen thinning algorithm, followed by removing the burrs of the crack skeleton image using the connected domain threshold method. The final step is to calculate physical parameters, such as the actual area, width, segments, and length of the crack with images obtained from the crack profile and skeleton. The results show that (1) the method of local thresholding and connected domain thresholding can completely filter noise regions under the premise of retaining detailed crack region information. (2) The Zhang–Suen iterative refinement algorithm is faster in extracting the crack skeleton of asphalt pavement, retaining the foreground features of the image better, while the connected-domain thresholding method is able to eliminate the missed isolated noise. (3) In comparison to the manual calibration method, the crack parameter calculation method proposed in this paper can better complete the calculation of crack length, width, and area within an allowable margin of error. On the basis of this research, a windowing system for asphalt pavement crack detection, WSPCD1.0, was developed. It integrates the research results from this paper, facilitating automated detection and parameter output for asphalt pavement cracks.

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Section: Crack Image Datasetmentioning

confidence: 99%

Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing

Li,

Yin,

Zhang

2023

Sensors

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“…the problem of separating the iris region from the pupil and the sclera. As a consequence, the problem of eye segmentation is a very hot topic in the computer vision community, as can be observed from the state of the art [24][25][26][27][28][29][30][31]. Human iris boundaries are approximately defined by two circles, i.e.…”

Section: State Of the Artmentioning

confidence: 99%

An image processing pipeline to segment iris for unconstrained cow identification system

Larregui

Cazzato

Castro

2019

Open Computer Science

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One of the most evident costs in cow farming is the identification of the animals. Classic identification processes are labour-intensive, prone to human errors and invasive for the animal. An automated alternative is an animal identification based on unique biometric patterns like iris recognition; in this context, correct segmentation of the region of interest becomes of critical importance. This work introduces a bovine iris segmentation pipeline that processes images taken in the wild, extracting the iris region. The solution deals with images taken with a regular visible-light camera in real scenarios, where reflections in the iris and camera flash introduce a high level of noise that makes the segmentation procedure challenging. Traditional segmentation techniques for the human iris are not applicable given the nature of the bovine eye; at this aim, a dataset composed of catalogued images and manually labelled ground truth data of Aberdeen-Angus has been used for the experiments and made publicly available. The unique ID number for each different animal in the dataset is provided, making it suitable for recognition tasks. Segmentation results have been validated with our dataset showing high reliability: with the most pessimistic metric (i.e. intersection over union), a mean score of 0.8957 has been obtained.

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“…It is an important step in the field of digital image processing and also in the recognition systems because this helps in reducing the other interferences which might become the cause for degraded performance. In this proposed system different step are followed for enhancement and segmentation [24] as shown in fig. 1 and described below:…”

Section: Step 2: Image Enhancement and Segmentationmentioning

confidence: 99%

EAgeBioS: Enhanced Biometric System to handle the Effects of Template Aging

Kumar*,

Lamba,

Jangra

2019

IJITEE

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Biometric Systems are well-known security systems that can be used anywhere for authentication, authorization or any kind of security verifications. In biometric systems, the samples are trained first and then it can be used for testing in long runs. Many recent researches have shown that a biometric system may fail or get compromised because of the aging of the biometric templates. The fact that temporal duration affects the performance of the biometric system has shattered the belief that iris does not change over lifetime. This is also possible in the case of iris. So, the main focus of this work is to analyze the effect of aging and also to propose a new system that can deal with template aging. We have proposed a new iris recognition system with an image enhancement mechanism and different feature extraction mechanisms. In this work, three different features are extracted, which are then fused to be used as one. The full system is trained on a dataset of 2500 samples for the year 2008 and testing is done in three different phases (i) No-Lapse, (ii) 1-Year Lapse and (iii) 2-Year Lapse. A portion of the ND-Iris-Template-Aging dataset [11] is used with a period of three years lapse. Results show that the performance of the hybrid classifier AHyBrK [17] is improved as compared to KNN and ANN and the effect of aging in terms of degraded performance is clear. The performance of this system is measured in terms of False Rejection Rate, Error Rate, and Accuracy. The overall performance of AHyBrK is 51.04% and 52.98% better than KNN and ANN respectively in terms of False Rejection Rate and Error Rate whereas the accuracy of thisproposed system is also improved by 5.52% and 6.04% as compared to KNN and ANN respectively. This proposed system also achieved high accuracy for all the test phases.

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Pupil Segmentation of Abnormal Eye using Image Enhancement in Spatial Domain

Cited by 14 publications

References 4 publications

Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing

Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing

An image processing pipeline to segment iris for unconstrained cow identification system

EAgeBioS: Enhanced Biometric System to handle the Effects of Template Aging

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