Research on the finger vein image capture and finger edge extraction

Author(s):  
Qingchang Guo ◽  
Bing Qiao
Author(s):  
Ralph M. Albrecht ◽  
Scott R. Simmons ◽  
Marek Malecki

The development of video-enhanced light microscopy (LM) as well as associated image processing and analysis have significantly broadened the scope of investigations which can be undertaken using (LM). Interference/polarization based microscopies can provide high resolution and higher levels of “detectability” especially in unstained living systems. Confocal light microscopy also holds the promise of further improvements in resolution, fluorescence studies, and 3 dimensional reconstruction. Video technology now provides, among other things, a means to detect differences in contrast difficult to detect with the human eye; furthermore, computerized image capture, processing, and analysis can be used to enhance features of interest, average images, subtract background, and provide a quantitative basis to studies of cells, cell features, cell labelling, and so forth. Improvements in video technology, image capture, and cost-effective computer image analysis/processing have contributed to the utility and potential of the various interference and confocal microscopic instrumentation.Electron microscopic technology has made advances as well. Microprocessor control and improved design have contributed to high resolution SEMs which have imaging capability at the molecular level and can operate at a range of accelerating voltages starting at 1KV. Improvements have also been seen in the HVEM and IVEM transmission instruments. As a whole, these advances in LM and EM microscopic technology provide the biologist with an array of information on structure, composition, and function which can be obtained from a single specimen. Corrrelative light microscopic analysis permits examination of living specimens and is critical where the “history” of a cell, cellular components, or labels needs to be known up to the time of chemical or physical fixation. Features such as cytoskeletal elements or gold label as small as 0.01 μm, well below the 0.2 μm limits of LM resolution, can be “detected” and their movement followed by VDIC-LM. Appropriate identification and preparation can then lead to the examination of surface detail and surface label with stereo LV-HR-SEM. Increasing the KV in the HR-SEM while viewing uncoated or thinly coated specimens can provide information from beneath the surface as well as increasing Z contrast so that positive identification of surface and subsurface colloidal gold or other heavy metal labelled/stained material is possible. Further examination of the same cells using stereo HVEM or IVEM provides information on internal ultrastructure and on the relationship of labelled material to cytoskeletal or organellar distribution, A wide variety of investigations can benefit from this correlative approach and a number of instrumentational configurations and preparative pathways can be tailored for the particular study. For a surprisingly small investment in time and technique, it is often possible to clear ambiguities or questions that arise when a finding is presented in the context of only one modality.


2018 ◽  
Vol 1 (2) ◽  
pp. 34-44
Author(s):  
Faris E Mohammed ◽  
Dr. Eman M ALdaidamony ◽  
Prof. A. M Raid

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems.   © 2018 JASET, International Scholars and Researchers Association    


2018 ◽  
Author(s):  
Jordan Carlson ◽  
J. Aaron Hipp ◽  
Jacqueline Kerr ◽  
Todd Horowitz ◽  
David Berrigan

BACKGROUND Image based data collection for obesity research is in its infancy. OBJECTIVE The present study aimed to document challenges to and benefits from such research by capturing examples of research involving the use of images to assess physical activity- or nutrition-related behaviors and/or environments. METHODS Researchers (i.e., key informants) using image capture in their research were identified through knowledge and networks of the authors of this paper and through literature search. Twenty-nine key informants completed a survey covering the type of research, source of images, and challenges and benefits experienced, developed specifically for this study. RESULTS Most respondents used still images in their research, with only 26.7% using video. Image sources were categorized as participant generated (N = 13; e.g., participants using smartphones for dietary assessment), researcher generated (N = 10; e.g., wearable cameras with automatic image capture), or curated from third parties (N = 7; e.g., Google Street View). Two of the major challenges that emerged included the need for automated processing of large datasets (58.8%) and participant recruitment/compliance (41.2%). Benefit-related themes included greater perspectives on obesity with increased data coverage (34.6%) and improved accuracy of behavior and environment assessment (34.6%). CONCLUSIONS Technological advances will support the increased use of images in the assessment of physical activity, nutrition behaviors, and environments. To advance this area of research, more effective collaborations are needed between health and computer scientists. In particular development of automated data extraction methods for diverse aspects of behavior, environment, and food characteristics are needed. Additionally, progress in standards for addressing ethical issues related to image capture for research purposes are critical. CLINICALTRIAL NA


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