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Finger vein is a new and promising trait in biometric recognition and some related progress have been achieved in recent years. Considering that there are many different sensors in a biometric system, sensor interoperability is a very important issue and still neglected in the state-of-the-art finger vein recognition. Based on the analysis of the shortcomings in the current finger vein ROI extraction...
The appearance of masses in in X-ray mammograms is one of the early signs of women breast cancer. Currently, mammography is the single most effective and reliable technique in the investigation of breast abnormalities detection such as masses. However, their detection is still a challenging problem due, to the diversity in shape, size, ambiguous margins and to the poor contrast between the cancerous...
Mammography is currently the most efficient imaging technique employed in radiology for examining breast cancer. Searching for a suitable, flexible and efficient breast profile segmentation method has been shown to be a herculean task in digital mammography. The extraction of the breast profile region is a fundamental pre-processing step in computer- aided detection of breast cancer. Principally,...
The detection and analysis of retinal vessels in ophthalmology is of great use in the diagnosis and progression monitoring of diabetic retinopathy. Automatic Detection of the vessel network has however been challenging due to noise from uneven contrast and illumination during the retinal image acquisition process. This paper presents a robust segmentation technique that combines phase congruence and...
This paper proposes a novel no-reference Perception-based Image Quality Evaluator (PIQUE) for real-world imagery. A majority of the existing methods for blind image quality assessment rely on opinion-based supervised learning for quality score prediction. Unlike these methods, we propose an opinion unaware methodology that attempts to quantify distortion without the need for any training data. Our...
Segmentation is considered as a core step for any recognition or classification method and for the text within any document to be effectively recognized it must be segmented accurately. In this paper a text and writer independent algorithm for the segmentation of sub-words in Arabic words has been presented. The concept is based around the global binarization of an image at various thresholding levels...
Iris recognition becomes an important technology in our society. Visual patterns of human iris provide rich texture information for personal identification. However, it is greatly challenging to match intra-class iris images with large variations in unconstrained environments because of noises, illumination variation, heterogeneity and so on. To track current state-of-the-art algorithms in iris recognition,...
This paper presents the results of the HDSRC 2014 competition on handwritten digit string recognition in challenging datasets organized in conjunction with ICFHR 2014. The general objective of this competition is to identify, evaluate and compare recent developments in Western Arabic digit string recognition with varying length. In addition, this competition introduces two new challenging datasets...
In this paper, we present a non-invasive method of counting fish in their natural habitat using automated analysis of video data. Our approach uses three modular components to preprocess, detect, and track the fish. The preprocessing reduces noise present in the image while enhancing the fish using several different techniques. The fish detection is based on two background subtraction algorithms which...
Blur metrics have been used in broad range of applications to quantify the amount of blur especially in images. The spatially varying blur due to defocus or camera shake is hard to estimate. It is observed that the existing blur metrics does not perform well for images having very few or many features. In this work, we present contrast based blur invariant features named as CBIF, which utilizes useful...
Accurate iris recognition from the distantly acquired face or eye images requires development of effective strategies which can account for significant variations in the segmented iris image quality. Unlike conventional stop-and-stare mode iris recognition, iris images acquired under less constrained imaging environment specially those under visible illumination, are degraded by multiple sources of...
Text line segmentation is one of the main parts of document image analysis, it provides crucial information for automated reading, word spotting, alignment between image and transcription, or indexing of documents. Yet it remains an open problem for handwritten historical documents because of complex layouts on the one hand, such as curved and touching text lines, and binarization problems on the...
Most of the algorithms proposed for text line detection are designed to process binary images as input. For severely degraded documents, binarization often introduces significant noise and other artifacts. In this work we present a novel method designed to detect text lines directly in gray scale images. The method consists of two stages. Potential characters are detected in the first stage. This...
Text line detection is a pre-processing step for automated document analysis such as word spotting or OCR. It is additionally used for document structure analysis or layout analysis. Considering mixed layouts, degraded documents and handwritten documents, text line detection is still challenging. We present a novel approach that targets torn documents having varying layouts and writing. The proposed...
As a famous cultural wealth, the sketch of fresco is one of the most important art expression forms in the World Heritage. To avoid the damage of natural and human factors, painters can only use photos and videos to depict sketch in most world culture heritage sites, otherwise real frescos. Therefore, a computational method for extracting sketch is helpful and meaningful in sketch copying and researching...
This paper introduces a novel procedure to segment retinal vessels using new technique namely Morphological Angular Scale-Space (MASS). Line structuring element is rotated about the seed point to determine the curvature of the vessels thereby ensuring that the components remains connected along vessels segmented. Scale-Space is created by varying the length of the structuring element which gradually...
A new robust to noise algorithm for mammographic image segmentation is presented in this paper. Its main objective is the separation of breast and background, establishing correctly the boundaries of the breast. The algorithm is a combination of several image processing operations such as histogram specification, re-sampling, histogram adjustment, arithmetic and morphological operations. It was tested...
Logo spotting is of a great interest because it enables to categorize the document images of a digital library of scanned documents according to their sources, without any costly semantic analysis of their textual transcript. In this paper, we present an approach for logo spotting, based on the matching of keypoints extracted both from the query document images and a given set of logos (gallery) using...
The cursive and ligature nature of the Arabic script make the segmentation of words into individual characters a difficult task. Despite attempts to apply methods for cursive Latin and other scripts to Arabic script, it is generally insufficient to segment the Arabic text. This paper proposes a new segmentation algorithm for the handwritten Arabic text and the main idea consists of segmenting the...
Segmentation is a fundamental step in the process of handwritten digits recognition. However, it is common to have images with connected digits after the segmentation task and this affects the classifier accuracy. This paper presents an approach for handwritten connected digits classification based on instance selection. The new technique uses information from all data of the training set to build...
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