SUSIG: an on-line signature database, associated protocols and benchmark results

We present a new online signature database (SUSIG). The database consists of two parts that are collected using different pressure-sensitive tablets ( one with and the other without an LCD display). A total of 100 people contributed to each part, resulting in a database of more than 3,000 genuine signatures and 2,000 skilled forgeries. The genuine signatures in the database are real signatures of the contributors. In collecting skilled forgeries, forgers were shown the signing process on the monitor and were given a chance to practice. Furthermore, for a subset of the forgeries ( highly skilled forgeries), this animation was mapped onto the LCD screen of the tablet so that the forgers could trace over the mapped signature. Forgers in this group were also informed of how close they were to the reference signature, so that they could improve their forgery quality. We describe the signature acquisition process and several verification protocols for this database. We also report the performance of a state-of-the-art signature verification system using the associated protocols. The results show that the highly skilled forgery set is significantly more difficult compared to the skilled forgery set, providing researchers with challenging forgeries. The database is available through http://icproxy.sabanciuniv.edu:215

Anveshak - A Groundtruth Generation Tool for Foreground Regions of Document Images

We propose a graphical user interface based groundtruth generation tool in this paper. Here, annotation of an input document image is done based on the foreground pixels. Foreground pixels are grouped together with user interaction to form labeling units. These units are then labeled by the user with the user defined labels. The output produced by the tool is an image with an XML file containing its metadata information. This annotated data can be further used in different applications of document image analysis.Comment: Accepted in DAR 201

Leaf segmentation in plant phenotyping: a collation study

Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy (>>90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://​www.​plant-phenotyping.​org/​datasets) to support future challenges beyond segmentation within this application domain

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Bias-variance analysis of ECOC and bagging using neural nets

One of the methods used to evaluate the performance of ensemble classifiers is bias and variance analysis. In this chapter, we analyse bootstrap aggregating (bagging) and Error Correcting Output Coding (ECOC) ensembles using a biasvariance framework; and make comparisons with single classifiers, while having Neural Networks (NNs) as base classifiers. As the performance of the ensembles depends on the individual base classifiers, it is important to understand the overall trends when the parameters of the base classifiers -nodes and epochs for NNs-, are changed.We show experimentally on 5 artificial and 4 UCI MLR datasets that there are some clear trends in the analysis that should be taken into consideration while designing NN classifier systems

Word mining in a sparsely-labeled handwritten collection

Word-spotting techniques are usually based on detailed modeling of target words, followed by search for the locations of such a target word in images of handwriting. In this study, the focus is on deciding for the presence of target words in lines of text, regardless and disregarding their horizontal position. Line strips are modeled using a Bag-of-Glyphs approach using a self-organized map. This approach uses the presence of fragmented-connected component shapes (glyphs) in a line strip to characterize this text passage, similar to the Bag-of-Words approach for 'ASCII'-encoded documents in regular Information Retrieval. Subsequently, the presence of a word or word category is trained to a support-vector machine in an iterative setup which involves an active group of users. Results are promising for a large proportion of words and are dependent both on the amount of labeled lines as well as shape uniqueness. Particularly useful is the ability to train on abstract content classes such as proper names, municipalities or word-bigram presence in the line-strip images

Evaluation of renal function in non-hypertensive patients with obstructive sleep apnea [Obstrüktif uyku apne sendromu olan normotansif hastalarda böbrek fonksiyonlari{dotless}ni{dotless}n degerlendirilmesi]

Objective: Obstructive sleep apnea syndrome (OSAS) is one of the most common sleep disorders in society. The presence of hypertension is shown in 30-60% of OSAS patients. Creatinine clearance (CC) in hypertensive OSAS patients was found to be lower than non-hypertensive OSAS patients. In our study, we aimed to determine CC in non- hypertensive OSAS patients comparing with the control group and determine that CC is affected by the severity of OSAS. Material and Methods: Ninety-three patients with complaints of snoring who were diagnosed as obstructive sleep apnea syndrome with polysomnography were examined in the study between March 2009-November 2010. Renal function tests were performed in these patients and creatinine clearance was calculated. Results: According to the OSAS severity, 30 patients were in the mild, 32 in moderate and 31 in severe OSAS group.There were no statistically significant differences in the demograpic data, systolic and diastolic blood pressure and CC between OSAS and control groups There was no corelation between severity of disease and CC. Conclusion: Due to the pathophysiologic features of OSAS, chronic kidney disease can develop in these patients, although hypertension has not yet developed. Therefore it must be considered that, hypertension may develop in patients with OSAS; blood pressure monitoring, evaluation and monitoring of renal function should not be neglected. © 2012 by Erciyes University School of Medicine

Relation of neutrophil-to-lymphocyte ratio with GRACE risk score to in-hospital cardiac events in patients with ST-segment elevated myocardial infarction

PubMed ID: 24078555In this study, we aimed to investigate the association of the neutrophil-to-lymphocyte ratio (NLR) with Global Registry of Acute Coronary Events (GRACE) risk score in patients with ST-segment elevated myocardial infarction (STEMI). We analyzed 101 consecutive patients with STEMI. Patients were divided into 3 groups by use of GRACE risk score. The association between NLR and GRACE risk score was assessed. The NLR showed a proportional increase correlated with GRACE risk score (P <.001). The occurrence of in-hospital cardiac death, reinfarction, or new-onset heart failure was significantly related to NLR at admission (P <.001). Likewise, NLR and GRACE risk score showed a significant positive correlation (r =.803, P <.001). In multivariate analysis, NLR resulted as a predictor of worse in-hospital outcomes independent of GRACE risk score. Our study suggests that the NLR is significantly associated with adverse in-hospital outcomes, independent of GRACE risk score in patients with STEMI. © 2014 The Author(s)