Browsing by Subject "Lighting"

Now showing 1 - 5 of 5
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    Automated Microaneurysms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy
    (IEEE, 2021) Tavakoli, Meysam; Mehdizadeh, Alireza; Aghayan, Afshin; Shahri, Reza Pourreza; Ellis, Tim; Dehmeshki, Jamshid; Physics, School of Science
    Detection of red lesions in color retinal images is a critical step to prevent the development of vision loss and blindness associated with diabetic retinopathy (DR). Microaneurysms (MAs) are the most frequently observed and are usually the first lesions to appear as a consequence of DR. Therefore, their detection is necessary for mass screening of DR. However, detecting these lesions is a challenging task because of the low image contrast, and the wide variation of imaging conditions. Recently, the emergence of computer-aided diagnosis systems offers promising approaches to detect these lesions for diagnostic purposes. In this paper we focus on developing unsupervised and supervised techniques to cope intelligently with the MAs detection problem. In the first step, the retinal images are preprocessed to remove background variation in order to achieve a high level of accuracy in the detection. In the main processing step, important landmarks such as the optic nerve head and retinal vessels are detected and masked using the Radon transform (RT) and multi-overlapping windows. Finally, the MAs are detected and numbered by using a combination of RT and a supervised support vector machine classifier. The method was tested on three publicly available datasets and a local database comprising a total of 749 images. Detection performance was evaluated using sensitivity, specificity, and FROC analysis. From the image analysis viewpoint, DR was detected with a sensitivity of 100% and a specificity of 93% on average across all of these databases. Moreover, from lesion-based analysis the proposed approach detected the MAs with sensitivity of 95.7% with an average of 7 false positives per image. These results compare favourably with the best of the published results to date.
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    Impact of Ambient Bright Light on Agitation in Dementia
    (2010) Barrick, Ann Louise; Sloane, Philip D.; Williams, Christianna S.; Mitchell, C. Madeline; Connell, Bettye Rose; Wood, Wendy; Hickman, Susan E.; Preisser, John S.; Zimmerman, Sheryl
    Objective To evaluate the effect of ambient bright light therapy on agitation among institutionalized persons with dementia. Methods High intensity, low glare ambient lighting was installed in activity and dining areas of a state psychiatric hospital unit in North Carolina and a dementia-specific residential care facility in Oregon. The study employed a cluster-unit crossover design involving four ambient lighting conditions: AM bright light, PM bright light, All Day bright light, and Standard light. Sixty-six older persons with dementia participated. Outcome measures included direct observation by research personnel and completion by staff caregivers of the 14-item, short form of the Cohen-Mansfield Agitation Inventory (CMAI). Results Analyses of observational data revealed that for participants with mild/moderate dementia, agitation was higher under AM light (p=0.003), PM light (p<0.001), and All Day light (p=0.001) than Standard light. There also was a trend toward severely demented participants being more agitated during AM light than Standard light (p=0.053). Analysis of CMAI data identified differing responses by site: the North Carolina site significantly increased agitation under AM light (p=0.002) and PM light (p=0.013) compared with All Day light while in Oregon, agitation was higher for All Day light compared to AM light (p=0.030). In no comparison was agitation significantly lower under any therapeutic condition, in comparison to Standard lighting. Conclusions Ambient bright light is not effective in reducing agitation in dementia and may exacerbate this behavioral symptom.
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    Modeling of low illuminance road lighting condition using road temporal profile
    (2015-10-05) Dong, Libo; Chien, Stanley; Christopher, Lauren; Li, Lingxi
    Pedestrian Automatic Emergency Braking (PAEB) system for avoiding/mitigating pedestrian crashes have been equipped on some passenger vehicles. At present, there are many e orts for the development of common standard for the performance evaluation of PAEB. The Transportation Active Safety Institute (TASI) at Indiana University-Purdue University-Indianapolis has been studying the problems and ad- dressing the concerns related to the establishment of such a standard with support from Toyota Collaborative Safety Research Center (CSRC). One of the important components in the PAEB evaluation is the development of standard testing facili- ties at night, in which 70% pedestrian crash social costs occurs [1]. The test facility should include representative low-illuminance environment to enable the examination of sensing and control functions of di erent PAEB systems. This thesis work focuses on modeling low-illuminance driving environment and describes an approach to recon- struct the lighting conditions. The goal of this research is to characterize and model light sources at a potential collision case at low-illuminance environment and deter- mine possible recreation of such environment for PAEB evaluation. This research is conducted in ve steps. The rst step is to identify lighting components that ap- pear frequently on a low-illuminance environment that a ect the performance of the PAEB. The identi ed lighting components include ambient light, same side/opposite side light poles, opposite side car headlight. Next step is to collect all potential pedes- trian collision cases at night with GPS coordinate information from TASI 110 CAR naturalistic driving study video database. Thirdly, since ambient lighting is relatively random and lack of a certain pattern, ambient light intensity for each potential col- lision case is de ned and processed as the average value of a region of interest on all video frames in this case. Fourth step is to classify interested light sources from the selected videos. The temporal pro le method, which compressing region of interest in video data (x,y,t) to image data (x,y), is introduced to scan certain prede ned region on the video. Due to the fact that light sources (except ambient light) impose distinct light patterns on the road, image patterns corresponding to speci c light sources can be recognized and classi ed. All light sources obtained are stamped with GPS coordinates and time information which are provided in corresponding data les along with the video. Lastly, by grouping all light source information of each repre- sentative street category, representative light description of each street category can be generated. Such light description can be used for lighting construction of PAEB test facility.