Automatic Multiple Choice Examination Questions Marking and Grade Generator Software

This paper discusses a feasible software solution that enables automatic marking andgrading of scripts. Technology keeps expanding, and more advanced innovations arebeing implemented with time. The marking and allocation of grades for examina-tion scripts through human efforts are gradually becoming a thing of the past. Hence,machines and software applications are introduced to make the entire marking andgrading of examination scripts more efficient, fast, and less tedious. Computer visionis an artificial intelligence (AI) knowledge domain that ensures devices obtain usefulinformation from digital images, videos, and other visual inputs. Image processingand recognition, a unique part of computer vision alongside the python program-ming language and the OpenCV library was employed for this project. These are themost used in developing most recent applications that utilize, to some extent, arti-ficial intelligence to attain specific desired results. The result of the project seeksto develop a maintainable android software application that uses image processingtechnology to scan patterns or images and grades results of multiple-choice questionscripts based on a set marking scheme. This ensures that desired results are obtainedwhile increasing efficiency and productivity

AUTOMATIC ASSESSMENT MARK ENTRY SYSTEM USING LOCAL BINARY PATTERN (LBP)

Offline handwritten recognition continues to be a fundamental research problem in document analysis and retrieval. The common method used in extracting handwritten mark from assessment forms is to assign a person to manually type in the marks into a spreadsheet. This method is found to be very time consuming, not cost effective and prone to human mistakes. In this project, a number recognition system is developed using local binary pattern (LBP) technique to extract and convert students’ identity numbers and handwritten marks on assessment forms into a spreadsheet. The template of the score sheet is designed as in Appendix 1 to collect sample of handwritten numbers. The training data contain three sets of LBP histograms for each digit. The recognition rate of handwritten digits using LBP is about 50% because LBP could not fully describe the structure of the digits. Instead, LBP is useful in term of arranging the digits ‘0 to 9’ from highest similarity score to the lowest similarity score as compared to sample using chi square distance. The recognition rate is greatly improved to about 95% by verifying the output of chi square distance with the salient structural features of digits

Grading multiple choice exams with low-cost andportable computer-vision techniques

Although technology for automatic grading of multiple choice exams has existed for several decades, it is not yet as widely available or affordable as it should be. The main reasons preventing this adoption are the cost and the complexity of the setup procedures. In this paper, Eyegrade, a system for automatic grading of multiple choice exams is presented. While most current solutions are based on expensive scanners, Eyegrade offers a truly low-cost solution requiring only a regular off-the-shelf webcam. Additionally, Eyegrade performs both mark recognition as well as optical character recognition of handwritten student identification numbers, which avoids the use of bubbles in the answer sheet. When compared with similar webcam-based systems, the user interface in Eyegrade has been designed to provide a more efficient and error-free data collection procedure. The tool has been validated with a set of experiments that show the ease of use (both setup and operation), the reduction in grading time, and an increase in the reliability of the results when compared with conventional, more expensive systems.This work was partially funded by the EEE project, “Plan Nacional de I+D+I TIN2011-28308-C03-01” and the “Emadrid: Investigación y desarrollo de tecnologias para el e-learning en la Comunidad de Madrid” project (S2009/TIC-1650).Publicad

Evaluation of CNN-based Single-Image Depth Estimation Methods

While an increasing interest in deep models for single-image depth estimation methods can be observed, established schemes for their evaluation are still limited. We propose a set of novel quality criteria, allowing for a more detailed analysis by focusing on specific characteristics of depth maps. In particular, we address the preservation of edges and planar regions, depth consistency, and absolute distance accuracy. In order to employ these metrics to evaluate and compare state-of-the-art single-image depth estimation approaches, we provide a new high-quality RGB-D dataset. We used a DSLR camera together with a laser scanner to acquire high-resolution images and highly accurate depth maps. Experimental results show the validity of our proposed evaluation protocol

Rapid assessment using automated marking

Includes abstract.Includes bibliographical references (leaves 96-97).Automated marking is heavily dependent on image acquisition and processing routines. Therefore ways of formatting examination and test papers were sought which allow for automated marking. Current marking machines utilise scanners to digitize answer scripts for marking purposes. These are more often than not, quite expensive. In investigating affordable ways of digitising answer scripts, the imaging device of choice was found to be a web camera. This was so because web cameras are readily available and are more affordable than scanners. This allowed the building of a prototype marking machine which was tested and performed as expected

Transforming youth work

"This report sets out the Learning and Skills Development Agency’s response to the Transforming youth work consultation published by Connexions and the DfEE in March 2001. The original consultation document is available on the Connexions website at www.connexions.gov.uk" -- page 1

Barnes Hospital Bulletin

https://digitalcommons.wustl.edu/bjc_barnes_bulletin/1264/thumbnail.jp

Influence of study design on digital pathology image quality evaluation : the need to define a clinical task

Despite the current rapid advance in technologies for whole slide imaging, there is still no scientific consensus on the recommended methodology for image quality assessment of digital pathology slides. For medical images in general, it has been recommended to assess image quality in terms of doctors’ success rates in performing a specific clinical task while using the images (clinical image quality, cIQ). However, digital pathology is a new modality, and already identifying the appropriate task is difficult. In an alternative common approach, humans are asked to do a simpler task such as rating overall image quality (perceived image quality, pIQ), but that involves the risk of nonclinically relevant findings due to an unknown relationship between the pIQ and cIQ. In this study, we explored three different experimental protocols: (1) conducting a clinical task (detecting inclusion bodies), (2) rating image similarity and preference, and (3) rating the overall image quality. Additionally, within protocol 1, overall quality ratings were also collected (task-aware pIQ). The experiments were done by diagnostic veterinary pathologists in the context of evaluating the quality of hematoxylin and eosin-stained digital pathology slides of animal tissue samples under several common image alterations: additive noise, blurring, change in gamma, change in color saturation, and JPG compression. While the size of our experiments was small and prevents drawing strong conclusions, the results suggest the need to define a clinical task. Importantly, the pIQ data collected under protocols 2 and 3 did not always rank the image alterations the same as their cIQ from protocol 1, warning against using conventional pIQ to predict cIQ. At the same time, there was a correlation between the cIQ and task-aware pIQ ratings from protocol 1, suggesting that the clinical experiment context (set by specifying the clinical task) may affect human visual attention and bring focus to their criteria of image quality. Further research is needed to assess whether and for which purposes (e.g., preclinical testing) task-aware pIQ ratings could substitute cIQ for a given clinical task