Measuring Task Performance Using Gaze Regions

We present a novel method for measuring task performance using gaze regions, i.e., scene regions fixated by a subject as he or she performs a familiar manual task. The scene regions are learned as a bag of features representation, using library lookup based on the Histogram of Oriented Gradients feature descriptor [1]. By establishing a set of task-specific exemplar models, i.e., models sourced from Pareto optimal sequences, the approach recognizes the local optima within a set of task-specific unlabeled models by estimating the distance (of each unlabeled model) to the exemplar models. During testing, the method is evaluated against a dataset of egocentric sequences, each containing gaze data, belonging to three manual skill-based activities. The results show perfect classification’s accuracy on several proposed schemes

Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)

Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, images of sea turtle carapaces were collected, each belonging to one of sixteen Chelonia mydas juveniles. Then, co-variant and robust image descriptors from these images were learned, enabling indexing and retrieval. In this paper, several classification results of sea turtle carapaces using the learned image descriptors are presented. It was found that a template-based descriptor, i.e. Histogram of Oriented Gradients (HOG) performed much better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must because of the minimal gradient and color information in the carapace images. Using HOG, we obtained an average classification accuracy of 65%.

Iban Plaited Mat Motif Classification with Adaptive Smoothing

Decorative mats plaited by the Iban communities in Borneo contains motifs that reflect their traditional beliefs. Each motif has its own special meaning and taboos. A typical mat motif contains multiple smaller patterns that surround the main motif hence is likely to cause misclassification. We introduce a classification framework with adaptive sampling to remove smaller features whilst retaining larger (and discriminative) image structures. Canny filter and Probabilistic Hough Transform are gradually applied to a clean greyscale image until a threshold value pertaining to the image’s structural information is reached. Morphological dilation is then applied to improve the appearance of the retained edges. The resulting image is described using Binary Robust Invariant Scalable Keypoints (BRISK) features with Random Sample Consensus (RANSAC). We reported the classification accuracy against six common image deformations at incremental degrees: Scale+Rotation, Viewpoint, Image Blur, Joint Photographic Experts Group (JPEG) Compression, Scale and Illumination. From our sensitivity analysis, we found the optimal threshold for adaptive smoothing to be 75.0%. The optimal scheme obtained 100.0% accuracy for JPEG Compression, Illumination, and Viewpoint set. Using adaptive smoothing, we achieved an average increase in accuracy of 11.0% compared to the baseline

Unsupervised Classification of Intrusive Igneous Rock Thin Section Images using Edge Detection and Colour Analysis

Classification of rocks is one of the fundamental tasks in a geological study. The process requires a human expert to examine sampled thin section images under a microscope. In this study, we propose a method that uses microscope automation, digital image acquisition, edge detection and colour analysis (histogram). We collected 60 digital images from 20 standard thin sections using a digital camera mounted on a conventional microscope. Each image is partitioned into a finite number of cells that form a grid structure. Edge and colour profile of pixels inside each cell determine its classification. The individual cells then determine the thin section image classification via a majority voting scheme. Our method yielded successful results as high as 90% to 100% precision

A Gamified Approach for Learning Elementary Arithmetic Operations

It is essential for children to learn in a fun and interesting way so that they can maximally absorb difficult concepts in a stress-less manner. With children nowadays being exposed to various interactive digital media types, offline and online, teachers are finding it hard to compete for their focus and attention when using only conventional teaching aids, such as flashcards and posters. We studied a gamified app-based approach for learning elementary mathematical operations to see the effects of its implementation, if any, to young children. The game mechanic involves the player seeking Non-Playable Characters (NPCs) positioned randomly around town to receive game quest(s). Each quest requires the player to solve a simple arithmetic problem (i.e., addition and subtraction) to earn game points. Based on the pre- and post-test's results, we found a statistically significant difference between the mean scores

User Interface/User Experience (UI/UX) Analysis & Design of Mobile Banking App for Senior Citizens: A Case Study in Sarawak, Malaysia

Smartphones are having such a huge impact to our society and in our daily lives. However, most smartphone applications are not that user-friendly for a senior-aged person. Due to the COVID-19 pandemic, everything now is done online including mobile banking services. There are seniors who refuse to use mobile banking applications in Malaysia because they are not familiar nor comfortable with the app's interface and flow. This study aims to perform a need analysis on user interface and user experience (UI/UX) design for Malaysian seniors when using a mobile banking app. A questionnaire was used in this research as a quantitative research tool, involving 36 respondents aged 55 years old and above, and currently a resident of Sarawak. The questionnaire is split into 5 sections, i.e., demographic, technology background, task, task rating, and preferences. We observed that “Fast loading time” is ranked as the most important feature with the highest mean value of 5.0. The least important feature is “Payment via QR Code” with a mean value of 2.7. Our findings can be used as a basis to prioritize features when designing a mobile banking app to accommodate senior users

Customer’s Spontaneous Facial Expression Recognition

In the field of consumer science, customer facial expression is often categorized either as negative or positive. Customer who portrays negative emotion to a specific product mostly means they reject the product while a customer with positive emotion is more likely to purchase the product. To observe customer emotion, many researchers have studied different perspectives and methodologies to obtain high accuracy results. Conventional neural network (CNN) is used to recognize customer spontaneous facial expressions. This paper aims to recognize customer spontaneous expressions while the customer observed certain products. We have developed a customer service system using a CNN that is trained to detect three types of facial expression, i.e. happy, sad, and neutral. Facial features are extracted together with its histogram of gradient and sliding window. The results are then compared with the existing works and it shows an achievement of 82.9% success rate on average

Classification of Digital Chess Pieces and Board Position using SIFT

Assistive technology has been given more attention in recent years to help people with disabilities to perform common tasks. Rather than designing a specialised tool for the task, it is more cost-effective and less inhibitory to make use of existing hardware integrated with a smart interface. Towards this end goal, we present our work on assisting a visually impaired person playing an online chess game. We evaluated an invariant feature descriptor, i.e., SIFT, for the task of classifying individual chess pieces across multiple visual themes. We compared two strategies for building the visual codebook, i.e., k-means clustering vs. image blending. The proposed pipeline receives live screen feeds from the browser at a fixed interval and produces an output in the form of chess pieces’ label and board position. Our proposed pipeline, paired with a visual codebook built using k-means clustering, managed an average accuracy rate of 6/10