Preparation of palm oil methyl esters for alkenyl succinic anhydride production

The fractions of fatty acid methyl esters (FAME) i.e. crude palm oil methyl esters (CPOME), RBD palm olein methyl esters (RBD Palm Olein ME) and used frying oil methyl esters (UFOME) rich in unsaturated fatty esters were used to prepare alkenyl succinic anhydrides (ASA). The fractions were obtained via fractional distillation that separated the unsaturated fatty esters from the saturated fatty esters. The fractions with the highest content of unsaturated fatty esters were reacted with maleic anhydride (MA) for 8 hours at 240oC with the MA/FAME ratio of 1.5. The reaction was conducted without catalyst and solvent. The crude alkenyl succinic anhydride (ASA) obtained was purified by column chromatography. The purified compound was characterised by FTIR

Business Category Classification via Indistinctive Satellite Image Analysis Using Deep Learning

Satellite image analysis has numerous useful applications in various domains. Extracting their visual information has been made easier using remote sensing and deep learning technologies that intelligently interpret clear visual cues. However, satellite information has the potential for more complex tasks, such as recommending business locations and categories based on the implicit patterns and structures of the regions of interest. Nonetheless, this task is significantly more challenging due to the absence of obvious visual cues and the highly similar appearance of each location. This study aims to analyze satellite image similarity between business class categories and investigate the capabilities of state-of-the-art deep learning models for learning non-obvious visual cues. Specifically, a satellite image dataset is constructed using business locations and annotated with the business categories for image structural similarity analysis, followed by business category classification via fine-tuning of deep learning classifiers. The models are then analyzed by visualizing the features learned to determine if they could capture hidden information for such a task. Experiments show that business locations have significantly high SSIM regardless of categories, and deep learning models only recorded a top accuracy of 60%. However, feature visualization using Grad-CAM shows that the models learn biased features and disregard highly informative details such as roads. It is concluded that typical learning models and strategies are insufficient to effectively solve this complex visual problem; thus, further research should be done to formulate solutions for such non-obvious classifications with the potential to support business recommendation applications

A New Framework for Efficient Low-light Image Enhancement using Approximated Gaussian Process

Gaussian Process (GP) is a robust distribution modeling technique that is very promising for computer vision systems. In particular, its multivariate distribution modeling is especially effective for low-light image enhancement where localized enhancement is required to address the over- and under-enhancement problem, and also retrieval of features which has been lost due to low illumination. However, GP lacks practicality due to its computation complexity that increases cubically following data increment. This paper proposes a sparse GP regression based solution whereby clustering is exploited to reduce the training cost of a GP model. Instead of utilizing all values from an image, clustering groups similar training pixels or image patches pairs into clusters and the cluster centers are used to train an approximate GP enhancement model. Experiments conducted showed the proposed framework can achieve training time reduction of as much as 75% from the baseline. In line with this, the proposed approach also improved the enhancement performance in both PSNR and features retrieval metric, and is competitive with the current state-of-the-art

Exploring the Contributions of Low-Light Image Enhancement to Network-Based Object Detection

Low-light is a challenging environment for both human and computer vision to perform tasks such as object classification and detection. Recent works have shown potential in employing enhancements algorithms to support and improve such tasks in low-light, however there has not been any focused analysis to understand the direct effects that low-light enhancement have on an object detector. This work aims to quantify and visualize such effects on the multi-level abstractions involved in network-based object detection. First, low-light image enhancement algorithms are employed to enhance real low-light images, and then followed by deploying an object detection network on the low-light as well as the enhanced counterparts. A comparison of the activations in different layers, representing the detection features, are used to generate statistics in order to quantify the enhancements’ contribution to detection. Finally, this framework was used to analyze several low-light image enhancement algorithms and identify their impact on the detection model and task. This framework can also be easily generalized to any convolutional neural network-based models for the analysis of different enhancements algorithms and tasks

Dashboard Camera View Vehicle License Plate Compliance Verification

Dashboard cameras have become a popular device installed in vehicles around the world. The visual information captured in the form of images and videos has the potential for many practical applications, for instance the verification of license plate compliance can be applied using such cameras, which allow more efficient enforcement as compared to static surveillance cameras or manual verification by authorities. From existing literature, it is found that despite a rise in research and deployment of license plate detection and recognition systems as well as optical character verification, there has yet to be any notable progress of either fields in such a dynamic application. Hence, this project proposes a license plate detection and compliance verification framework for Malaysian standard vehicle license plates. Specifically, the YOLOv4 detector is adapted as the license plate detection model with an image processing pipeline for verification, named the Malaysian License Plate Verification (MLPV) system. Experiments were carried out to evaluate the classification of compliance on license plate only images, dashcam view images supported by license plate detection, and dashcam videos via frames processing. The results show great potential for license plate verification to be performed based on dashcam videos in practical scenarios

Getting to know low-light images with the Exclusively Dark dataset

Low-light is an inescapable element of our daily surroundings that greatly affects the efficiency of our vision. Research works on low-light imagery have seen a steady growth, particularly in the field of image enhancement, but there is still a lack of a go-to database as a benchmark. Besides, research fields that may assist us in low-light environments, such as object detection, has glossed over this aspect even though breakthroughs-after-breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark datasets such as PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark dataset to elevate this data drought. It consists exclusively of low-light images captured in visible light only, with image and object level annotations. Moreover, we share insightful findings in regards to the effects of low-light on the object detection task by analyzing the visualizations of both hand-crafted and learned features. We found that the effects of low-light reach far deeper into the features than can be solved by simple “illumination invariance”. It is our hope that this analysis and the Exclusively Dark dataset can encourage the growth in low-light domain researches on different fields. The dataset can be downloaded at https://github.com/cs-chan/Exclusively-Dark-Image-Dataset

Low-light is More Than Darkness: An Empirical Study on Illumination Types and Enhancement Methods

Low-light images challenge both human perception and computer vision algorithms. Despite notable progress in this field, there are still various gaps that are yet to be investigated, such as the significance of low-light illumination characteristics towards image enhancement and object classification. Therefore, this paper details various analyses to study this phenomenon and provide insights for future developments of algorithms and solutions. Specifically, comparative analysis was done to investigate human and machine perception towards ”low-light types”, followed by empirical studies on the effect of illumination types towards state-of-the-art image enhancement quality and also their pre-processing capability for downstream task, namely object classification. It is found that illumination types significantly influences the performance of enhancement algorithms that tend to cater for a “general” type of low-light illumination. This lack of illumination type awareness therefore leads models to perform well in certain conditions, but severely underperforms in others. Thus, it is imperative for upcoming works to incorporate such illumination information for potential breakthroughs in this area

Automated Tactical Analysis of Broadcast Badminton Match Videos

In elite badminton, tactics analysis studies playing patterns to help players identify tactical strengths and weaknesses and gain competitive advantage. Tactical analysis involves many laborious tasks (e.g. record match videos, annotate data from videos, match analysis, etc.). Abundance of online broadcast badminton match videos spurs interest in automated tactical analysis