Fuzzy contextual inference system for medical image enhancement

Subramani, Bharath; Veluchamy, Magudeeswaran

doi:10.1016/j.measurement.2019.106967

Cited by 22 publications

(13 citation statements)

References 20 publications

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“…The detection image of the iris part and the detection image of the non-iris part are shown in Figure 14. (3) The minimum pooling method is used to minimize the detection image in 2 × 2 dimensions [27] to form a 32 × 32 dimensional pooling detection image. In order to better reflect the irregular and black-and-white form of the iris, the Hamming distance [28] is used for representation.…”

Section: Preliminary Determination Of the Pupil Areamentioning

confidence: 99%

“…Second, the correction of images (blurring, displacement, etc.) using algorithms [27] improves the feasibility of multi-type iris recognition and further improves the environmental independence. In addition, starting from the acquisition sensor, improving the acquisition status during image acquisition and improving the iris quality from the beginning is also one of the current research directions of iris generality [28].…”

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confidence: 99%

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Heterogeneous Iris One-to-One Certification with Universal Sensors Based On Quality Fuzzy Inference and Multi-Feature Fusion Lightweight Neural Network

Liu

Zhu

et al. 2020

Sensors

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Due to the unsteady morphology of heterogeneous irises generated by a variety of different devices and environments, the traditional processing methods of statistical learning or cognitive learning for a single iris source are not effective. Traditional iris recognition divides the whole process into several statistically guided steps, which cannot solve the problem of correlation between various links. The existing iris data set size and situational classification constraints make it difficult to meet the requirements of learning methods under a single deep learning framework. Therefore, aiming at a one-to-one iris certification scenario, this paper proposes a heterogeneous iris one-to-one certification method with universal sensors based on quality fuzzy inference and a multi-feature entropy fusion lightweight neural network. The method is divided into an evaluation module and a certification module. The evaluation module can be used by different devices to design a quality fuzzy concept inference system and an iris quality knowledge concept construction mechanism, transform human logical cognition concepts into digital concepts, and select appropriate concepts to determine iris quality according to different iris quality requirements and get a recognizable iris. The certification module is a lightweight neural network based on statistical learning ideas and a multi-source feature fusion mechanism. The information entropy of the iris feature label was used to set the iris entropy feature category label and design certification module functions according to the category label to obtain the certification module result. As the requirements for the number and quality of irises changes, the category labels in the certification module function were dynamically adjusted using a feedback learning mechanism. This paper uses iris data collected from three different sensors in the JLU (Jilin University) iris library. The experimental results prove that for the lightweight multi-state irises, the abovementioned problems are ameliorated to a certain extent by this method.

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Section: Preliminary Determination Of the Pupil Areamentioning

confidence: 99%

mentioning

confidence: 99%

Heterogeneous Iris One-to-One Certification with Universal Sensors Based On Quality Fuzzy Inference and Multi-Feature Fusion Lightweight Neural Network

Liu

Zhu

et al. 2020

Sensors

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show abstract

“…Second, correction of images (blurring, displacement, etc.) using algorithms [21] improves the feasibility of multi-type iris recognition and further improves the environmental independence.…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Feature Fusion and Entropy Feature Lightweight Neural Network for Constrained Multi-State Heterogeneous Iris Recognition

Liu

Zhu

et al. 2020

IEEE Access

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Current iris recognition technology faces practical difficulties. For example, due to the unsteady morphology of a heterogeneous iris generated by a variety of different devices and environments, the traditional processing methods of statistical learning or cognitive learning for a single iris source are not effective. The existing iris data set size and situational classification constraints make it difficult to meet the requirements of learning methods under a single deep learning framework. Therefore, this paper proposes a method of heterogeneous iris recognition based on an entropy feature lightweight neural network under multi-source feature fusion. The method is divided into an image-processing module and a recognition module. The image-processing module converts the iris image into a recognition label via a convolutional neural network. The recognition module is based on statistical learning ideas and design of a multi-source feature fusion mechanism. The information entropy of the iris feature label is used to set the iris entropy feature category label and design the recognition function according to the category label to obtain the recognition result. As the requirement for the number and quality of irises changes, the category labels in the recognition function are dynamically adjusted using a feedback learning mechanism. This paper uses iris data collected from three different devices in the JLU iris library. The experimental results prove that for multi-category classification of lightweight constrained multi-state irises, the abovementioned problems are ameliorated to a certain extent by this method.

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“…Since HE shifts the mean brightness of the enhanced image to the middle gray level, researchers have separated the image histogram into multiple sub-histograms and performed HE on each sub-histogram individually [12]- [16]. For detail preservation, recent indirect methods utilize a two-dimensional (2D) histogram [19]- [21], [51], [52] or fuzzy contextual information [45], [53] to build the image histogram which gives more weight to the pixels in texture regions. Compared with other indirect methods, these methods achieve better enhancement performance and less information loss.…”

Section: Introductionmentioning

confidence: 99%