Super-efficient spatially adaptive contrast enhancement algorithm for superficial vein imaging

Bandara, A. M. R. R.; Rajarata, K. A. S. H. Kulathilake; Giragama, P. W. G. R. M. P. B.

doi:10.1109/iciinfs.2017.8300427

Cited by 18 publications

(21 citation statements)

References 10 publications

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“…3 This library is based on their open-source biometric recognition platform, using the base types and techniques provided by the bob.bio.base package. 4 The bob.bio.vein package contains several finger vein preprocessing and feature extraction/comparison schemes as well as performance evaluation tools and supports a few publicly available vein datasets. It is written in Python and uses several Python libraries like NumPi 5 and SciPi.…”

Section: Related Workmentioning

confidence: 99%

An Available Open-Source Vein Recognition Framework

Kauba

Uhl

2019

Advances in Computer Vision and Pattern Recognition

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An available recognition toolkit is one of the basic requirements for conducting research in finger-and hand vein recognition. Currently, there is only one comprehensive, open-source, software package available, which includes a full finger vein recognition toolkit. We present a novel, full fledged vein recognition software framework implemented in MATLAB. Our PLUS OpenVein software package contains various well-established and state-of-the-art vein enhancement, feature extraction and comparison schemes. Moreover, it contains tools to evaluate the recognition performance and provides functions to perform feature-and score-level fusion. It is fully open source and available free of charge for research and non-commercial purposes. This vein recognition framework has already been used in several papers and can be a valuable tool for other researchers working in vein recognition.

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Section: Related Workmentioning

confidence: 99%

An Available Open-Source Vein Recognition Framework

Kauba

Uhl

2019

Advances in Computer Vision and Pattern Recognition

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“…The details about parameters selection have been described in [10]. The enhanced image can be obtained by using…”

Section: A Suace Algorithmmentioning

confidence: 99%

“…CLAHE is able to handle the illumination variation by doing local histogram equalization and also can regulate the amplification of the details. However, it introduces a box-shaped artifact which may cause to suppress some details and also it amplifies some undesirable details [10].…”

Section: Introductionmentioning

confidence: 99%

“…Speeded up adaptive contrast enhancement (SUACE) algorithm is an outcome of one of our previous studies, in which the goal is about enhancing superficial vein images that have been captured with infrared radiation in real-time [10]. Although SUACE was invented for the purpose of real-time execution on resource constrained devices, it also exhibited several other superior properties such as self-illumination balancing, ability to enhance a particular type of detail and the ability to regulate the detail amplification [10]. In this study, we assess the suitability of SUACE in retinal image enhancement for segmenting the blood vessels.…”

Section: Introductionmentioning

confidence: 99%

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A retinal image enhancement technique for blood vessel segmentation algorithm

Bandara

Giragama²

2017

2017 IEEE International Conference on Industrial and Information Systems (ICIIS)

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The morphology of blood vessels in retinal fundus images is an important indicator of diseases like glaucoma, hypertension and diabetic retinopathy. The accuracy of retinal blood vessels segmentation affects the quality of retinal image analysis which is used in diagnosis methods in modern ophthalmology. Contrast enhancement is one of the crucial steps in any of retinal blood vessel segmentation approaches. The reliability of the segmentation depends on the consistency of the contrast over the image. This paper presents an assessment of the suitability of a recently invented spatially adaptive contrast enhancement technique for enhancing retinal fundus images for blood vessel segmentation. The enhancement technique was integrated with a variant of Tyler Coye algorithm, which has been improved with Hough line transformation based vessel reconstruction method. The proposed approach was evaluated on two public datasets STARE and DRIVE. The assessment was done by comparing the segmentation performance with five widely used contrast enhancement techniques based on wavelet transform, contrast limited histogram equalization, local normalization, linear un-sharp masking and contourlet transform. The results revealed that the assessed enhancement technique is well suited for the application and also outperforms all compared techniques.

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“…The recent advancement of machine learning has enabled learning of optimal features for applications by observing a large sample of images from the application domain. However, the feature learning and inference in modern approaches require an enormous amount of computer resources and time [1,2] though visual computing on resourceconstrained devices are trendy [3]. In addition to the computational cost, the state-of-the-art machine learned features are less invariant to dramatic geometrical transformation and illumination.…”

Section: Introductionmentioning

confidence: 99%

Nature Inspired Dimensional Reduction Technique for Fast and Invariant Visual Feature Extraction

Bandara

Ranathunga

Abdullah

2019

IJATCSE

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Fast and invariant feature extraction is crucial in certain computer vision applications where the computation time is constrained in both training and testing phases of the classifier. In this paper, we propose a nature-inspired dimensionality reduction technique for fast and invariant visual feature extraction. The human brain can exchange the spatial and spectral resolution to reconstruct missing colors in visual perception. The phenomenon is widely used in the printing industry to reduce the number of colors used to print, through a technique, called color dithering. In this work, we adopt a fast error-diffusion color dithering algorithm to reduce the spectral resolution and extract salient features by employing novel Hessian matrix analysis technique, which is then described by a spatial-chromatic histogram. The computation time, descriptor dimensionality and classification performance of the proposed feature are assessed under drastic variances in orientation, viewing angle and illumination of objects comparing with several different state-of-the-art handcrafted and deep-learned features. Extensive experiments on two publicly available object datasets, coil-100 and ALOI carried on both a desktop PC and a Raspberry Pi device show multiple advantages of using the proposed approach, such as the lower computation time, high robustness, and comparable classification accuracy under weakly supervised environment. Further, it showed the capability of operating solely inside a conventional SoC device utilizing a small fraction of the available hardware resources.

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Super-efficient spatially adaptive contrast enhancement algorithm for superficial vein imaging

Cited by 18 publications

References 10 publications

An Available Open-Source Vein Recognition Framework

An Available Open-Source Vein Recognition Framework

A retinal image enhancement technique for blood vessel segmentation algorithm

Nature Inspired Dimensional Reduction Technique for Fast and Invariant Visual Feature Extraction

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