A comparative study of interactive segmentation with different number of strokes on complex images

Interactive image segmentation is the way to extract an object of interest with the guidance of the user. The guidance from the user is an iterative process until the required object of interest had been segmented. Therefore, the input from the user as well as the understanding of the algorithms based on the user input has an essential role in the success of interactive segmentation. The most common user input type in interactive segmentation is using strokes. The different number of strokes are utilized in each different interactive segmentation algorithms. There was no evaluation of the effects on the number of strokes on this interactive segmentation. Therefore, this paper intends to fill this shortcoming. In this study, the input strokes had been categorized into single, double, and multiple strokes. The use of the same number of strokes on the object of interest and background on three interactive segmentation algorithms: i) Nonparametric Higher-order Learning (NHL), ii) Maximal Similarity-based Region Merging (MSRM) and iii) Graph-Based Manifold Ranking (GBMR) are evaluated, focusing on the complex images from Berkeley image dataset. This dataset contains a total of 12,000 test color images and ground truth images. Two types of complex images had been selected for the experiment: image with a background color like the object of interest, and image with the object of interest overlapped with other similar objects. This can be concluded that, generally, more strokes used as input could improve image segmentation accuracy

Visual Analytics: Design Study for Exploratory Analytics on Peer Profiles, Activity and Learning Performance for MOOC Forum Activity Assessment

The massively open online course (MOOC) has become an increasingly popular alternative platform for education due to its open concept and free features. Due to its features that allow enrolment on a massive scale and participation across the globe, it presented new analytic challenges. The vast amount and variety of data generated pose challenges for the learning analytics community to analyse especially concerning peer presence and peer learning. Forum activity data offers the opportunity to assess the relationship between forum activities and user backgrounds with the learner’s progression and retention rate. Furthermore, there are several challenges in implementing data visualization in real-world scenarios such as different task characterisation compared to the existing analytics, along with varied factors on the usability of visualization among the domain analysts. Despite many research on learning analytics, most of the approaches were data-driven and there were only a handful of studies that were focused on interactive visualization design to facilitate MOOC forum user activity assessment using real-world scenarios and educational theories-driven. Our design study aims to investigate and formulate a visual analytic design to facilitate enriched visual analysis towards assessing forum activity in Malaysian MOOC, particularly in pattern and relationship exploration on the user diverse background and activities with the learning performance. This paper presents our review on visual learning analytics and current MOOC practice in Malaysia, our design study methodology and proposed conceptual visual analytics design on visualizing forum activity data

An empirical analysis of test input generation tools for android apps through a sequence of events

Graphical User Interface (GUI) testing of Android apps has gained considerable interest from the industries and research community due to its excellent capability to verify the operational requirements of GUI components. To date, most of the existing GUI testing tools for Android apps are capable of generating test inputs by using different approaches and improve the Android apps’ code coverage and fault detection performance. Many previous studies have evaluated the code coverage and crash detection performances of GUI testing tools in the literature. However, very few studies have investigated the effectiveness of the test input generation tools, especially in the events sequence length of the overall test coverage and crash detection. The event sequence length generally shows the number of steps required by the test input generation tools to detect a crash. It is critical to highlight its effectiveness due to its significant effects on time, testing effort, and computational cost. Thus, this study evaluated the effectiveness of six test input generation tools for Android apps that support the system events generation on 50 Android apps. The generation tools were evaluated and compared based on the activity coverage, method coverage, and capability in detecting crashes. Through a critical analysis of the results, this study identifies the diversity and similarity of test input generation tools for Android apps to provide a clear picture of the current state of the art. The results revealed that a long events sequence performed better than a shorter events sequence. However, a long events sequence led to a minor positive effect on the coverage and crash detection. Moreover, the study showed that the tools achieved less than 40% of the method coverage and 67% of the activity coverage. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Design and development of multimedia and multi-marker detection techniques in interactive augmented reality colouring book

The aim of this paper is to the design and develop multimedia and multi-markers detection techniques in interactive Augmented Reality (AR) colouring book application for aquarium museum. This study is conducted to create entertaining AR colouring mobile application on Android Operating System which allows users to express, create and interact with their creativity through colouring activities. It allows users to engage and relish the stimulating colouring book content by switching between a reality and augmented world. Conversely, users may tend to lose interest in the colouring activities, but with AR technology it keeps colouring relaxing and inspiring. The design and development of this project was carried out using Unity3D integrates with Vuforia Engine. The multimedia and multi-markers scripting was written in C# programming language

Sizes of Superpixels and their Effect on Interactive Segmentation

Semi-automated segmentation, also known as interactive image segmentation, is an algorithm that extracts a region of interest (ROI) from an image based on user input. The said algorithm will be fed the user input information repeatedly until the required region of interest is successfully segmented. Pre-processing steps can be used to speed up the segmentation process while improving the end result. The use of superpixels is one example of such pre-processing step. A superpixel is a group of pixels that share similar characteristics such as texture and colour. Despite the fact that it is used as a pre-processing step in many interactive segmentation algorithms, less studies had been conducted to assess the effects of the size of superpixels required by interactive segmentation algorithms to achieve an optimal result. Therefore, the purpose of this research is to address this issue in order to bridge this research gap. This study will be performed using the Maximum Similarity based region merging (MSRM) with input strokes on selected images from the Berkeleys and Grabcut image data sets, generated by superpixels extractions via energy-driven samples (SEEDS We infer from this research that an image with a minimum of 500 superpixels will aid the interactive segmentation algorithm in producing a decent segmentation result with pixel accuracy of 0.963, F-score of 0.844, and Jaccard index of 0.756. When the superpixels for an image are raised to 10,000, the segmentation results degrade. In conclusion, the size of the superpixels would have an impact on the final segmentation results

Problem characterization for visual analytics in MOOC learner's support monitoring: A case of Malaysian MOOC

Malaysia and many other developing countries progressively adopting massively open online course (MOOC) in their national higher education approach. We have observed an increasing need for facilitating MOOC monitoring that is associated with the rising adoption of MOOCs. Our observation suggests that recent adoption cases led analyst and instructors to focus on monitoring enrollment and learning activities. Visual analytics in MOOC support education analysts in analyzing MOOC data via interactive visualization. Existing literature on MOOC visualization focuses on enabling visual analysis on MOOC data from forum and course material. We found limited studies that investigate and characterize domain problems or design requirements of visual analytics for MOOC. This paper aims to present the empirical problem characterization and abstraction for visual analytics in MOOC learner’s support monitoring. Detailed characterization and abstraction of the domain problem help visualization designer to derive design requirements in generating appropriate visualization solution. We examined the literature and conducted a case study to elicit a problem abstraction based on data, users, and tasks. We interviewed five Malaysian MOOC experts from three higher education institutes using semi-structured questions. Our case study reveals the priority of enabling MOOC analysis on learner’s progression and course completion. There is an association between design and analysis priority with the pedagogical type of implemented MOOC and users. The characterized domain problems and requirements offer a design foundation for visual analytics in MOOC monitoring analysis

Effects of Different Superpixel Algorithms on Interactive Segmentations

Semi-automated segmentation or more commonly known as interactive image segmentation is an algorithm that extracts a region of interest (ROI) from an image based on the input information from the user. The said algorithm will be repetitively fed with such input information until required region of interest is successfully segmented. To accelerate this segmentation procedure as well as enhancing the result, pre-processing steps can be applied. The application of superpixel is an example of such pre-processing step. Superpixel can be defined as a collection of pixels that share common features such as texture and colours. Though employed as pre-processing step in many interactive segmentation algorithms, to date, no study has been conducted to assess the effects of such incorporations on the segmentation algorithms. Thus, this study aims to address this issue. In this study, five different types of superpixels ranging from watershed, density, graph, clustering and energy optimization categories are evaluated. The superpixels generated by these five algorithms will be used on two interactive image segmentation algorithms: i) Maximal Similarity based Region Merging (MSRM) and ii) Graph-Based Manifold Ranking (GBMR) with single and multiple strokes on various images from the Berkeley image dataset. The result of testing had shown that MSRM achieved better result compared to GBMR in both single and multiple input strokes using SEEDS superpixel algorithm. This study summary concluded that at different superpixel algorithms produced different results and that it is not possible to single out one particular superpixel algorithm that can work well for all the interactive segmentation algorithms. As such, the key to achieving a decent segmentation result lies in choosing the right superpixel algorithms for a given interactive segmentation algorithm

Superpixel Sizes using Topology Preserved Regular Superpixel Algorithm and their Impacts on Interactive Segmentation

Interactive Image Segmentation is a type of semiautomated segmentation that uses user input to extract the object of interest. It is possible to speed up and improve the end result of segmentation by using pre-processing steps. The use of superpixels is an example of a pre-processing step. A superpixel is a collection of pixels with similar properties such as texture and colour. Previous research was conducted to assess the impact of the number of superpixels (based on SEEDS superpixel aglorithms) required to achieve the best segmentation results. The study, however, only examined one type of input (strokes) and a small number of images. As a result, the goal of this study is to extend previous work by performing interactive segmentation with input strokes and a combination of bounding box and strokes on images from Grabcut image data sets generated by Topology preserved regular superpixel (TPRS). Based on our findings, an image with 1000 to 2500 superpixels and a combination of bounding box and strokes will help the interactive segmentation algorithm produce a good segmentation result. Finally, the size of the superpixels would influence the final segmentation results as well as the input type

Sizes of Superpixels and their Effect on Interactive Segmentation

Semi-automated segmentation, also known as interactive image segmentation, is an algorithm that extracts a region of interest (ROI) from an image based on user input. The said algorithm will be fed the user input information repeatedly until the required region of interest is successfully segmented. Pre-processing steps can be used to speed up the segmentation process while improving the end result. The use of superpixels is one example of such pre-processing step. A superpixel is a group of pixels that share similar characteristics such as texture and colour. Despite the fact that it is used as a pre-processing step in many interactive segmentation algorithms, less studies had been conducted to assess the effects of the size of superpixels required by interactive segmentation algorithms to achieve an optimal result. Therefore, the purpose of this research is to address this issue in order to bridge this research gap. This study will be performed using the Maximum Similarity based region merging (MSRM) with input strokes on selected images from the Berkeleys and Grabcut image data sets, generated by superpixels extractions via energy-driven samples (SEEDS We infer from this research that an image with a minimum of 500 superpixels will aid the interactive segmentation algorithm in producing a decent segmentation result with pixel accuracy of 0.963, F-score of 0.844, and Jaccard index of 0.756. When the superpixels for an image are raised to 10,000, the segmentation results degrade. In conclusion, the size of the superpixels would have an impact on the final segmentation results