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Abd Rasid Mamat

Bio: Abd Rasid Mamat is an academic researcher from Universiti Sultan Zainal Abidin. The author has contributed to research in topics: Usability & Dissemination. The author has an hindex of 5, co-authored 17 publications receiving 40 citations.

Papers
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Journal ArticleDOI
TL;DR: In this study, the k-means algorithm was used on three colors model: CIE Lab, RGB and HSV and the clustering process made up to k clusters and generally the best cluster separation is found within HSV, followed by the RGB and CIE lab color models.
Abstract: Clustering process is an essential part of the image processing. Its aim to group the data according to having the same attributes or similarities of the images. Consequently, determining the number of the optimum clusters or the best (well-clustered) for the image in different color models is very crucial. This is because the cluster validation is fundamental in the process of clustering and it reflects the split between clusters. In this study, the k-means algorithm was used on three colors model: CIE Lab, RGB and HSV and the clustering process made up to k clusters. Next, the Silhouette Index (SI) is used to the cluster validation process, and this value is range between 0 to 1 and the greater value of SI illustrates the best of cluster separation. The results from several experiments show that the best cluster separation occurs when k=2 and the value of average SI is inversely proportional to the number of k cluster for all color model. The result shows in HSV color model the average SI decreased 14.11% from k = 2 to k = 8, 11.1% in HSV color model and 16.7% in CIE Lab color model. Comparisons are also made for the three color models and generally the best cluster separation is found within HSV, followed by the RGB and CIE Lab color models.

25 citations

Journal ArticleDOI
TL;DR: This research uses image that is obtained from a digital camera and uses ellipse fitting by applying Randomized Hough Transform to search the potential area of the mango fruit and detects overlapping mango fruits from the complex background image.
Abstract: This paper presents a method of detecting overlapping mango fruits from the complex background image. This research uses image that is obtained from a digital camera. This method is based on pre-processing the input image using the texture analysis to determine the boundary of each overlapping fruits. The image is processed to determine the actual boundary, converted to binary images, and utilise dilation and erosion to determine the object. We use ellipse fitting by applying Randomized Hough Transform to search the potential area of the mango fruit. Ellipse fitting are chosen because the shape of the mango fruit is similar to ellipse shape. Using these techniques, the fruit is successfully detected including the fruits that are overlapping with each other.

12 citations

Journal ArticleDOI
30 Nov 2015
TL;DR: In this article, a framework consisting of learning theories, modules, multimedia elements and usability and acceptance has been developed and applied in an interactive multimedia prototype on road safety education called FIQIR Road Safety.
Abstract: The interactive multimedia is considered as a very promising potential to aid primary school pupils in learning and teaching method in introducing road safety education. Although web based applications for road safety education are available, they are based on overseas countries where the rules and environment settings are different from Malaysia’s environment. An effort to help pupils in interactively learning on road safety education in Malaysia has motivated this study. A framework encompass of learning theories, modules, multimedia elements and, usability and acceptance, has been developed and applied in an interactive multimedia prototype on road safety education called “FIQIR Road Safety”. The prototype has been developed based on a primary school textbook “Cermat Tiba Selamat” by Malaysian Ministry of Education (MOE). FIQIR Road Safety has been designed and developed by utilizing multimedia elements to give an immersive experience to the user. It employs Watch, Learn and Play as the modules where the animations and activities represent actual traffic environment in Malaysia. The proposed framework hopefully can be a guide in developing interactive multimedia application such as FIQIR Road safety.

7 citations

Proceedings ArticleDOI
08 Dec 2015
TL;DR: A method of detecting mango fruit from RGB input image using the Randomized Hough Transform method to find the best ellipse fits to each binary region and the rate of detection was up to 95% for fruit that is partially overlapped and partially covered by leaves.
Abstract: A method of detecting mango fruit from RGB input image is proposed in this research. From the input image, the image is processed to obtain the binary image using the texture analysis and morphological operations (dilation and erosion). Later, the Randomized Hough Transform (RHT) method is used to find the best ellipse fits to each binary region. By using the texture analysis, the system can detect the mango fruit that is partially overlapped with each other and mango fruit that is partially occluded by the leaves. The combination of texture analysis and morphological operator can isolate the partially overlapped fruit and fruit that are partially occluded by leaves. The parameters derived from RHT method was used to calculate the center of the ellipse. The center of the ellipse acts as the gripping point for the fruit picking robot. As the results, the rate of detection was up to 95% for fruit that is partially overlapped and partially covered by leaves.

7 citations

Journal ArticleDOI
TL;DR: The detection of attacks by distinguishing it from legal traffic is of main concern and different concepts and techniques from information theory and image processing domain are examined that takes the aforementioned parameters as input and in turn decides whether an attack has occurred.
Abstract: In a perfect condition, there are only normal network traffic and sometimes flash event traffics due to some eye-catching or heartbreaking events. Nevertheless, both events carry legitimate requests and contents to the server. Flash event traffic can be massive and damaging to the availability of the server. However, it can easily be remedied by hardware solutions such as adding extra processing power and memory devices and software solution such as load balancing. In contrast, a collection of illegal traffic requests produced during distributed denial of service (DDoS) attack tries to cause damage to the server and thus is considered as dangerous where prevention, detection and reaction are imminent in case of occurrence. In this paper, the detection of attacks by distinguishing it from legal traffic is of our main concern. Initially, we categorize the parameters involved in the attacks in relation to their entities. Further, we examine different concepts and techniques from information theory and image processing domain that takes the aforementioned parameters as input and in turn decides whether an attack has occurred. In addition to that, we also pointed out the advantages for each technique, as well as any possible weakness for possible future works.

5 citations


Cited by
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09 Mar 2012
TL;DR: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems as mentioned in this paper, and they have been widely used in computer vision applications.
Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

2,069 citations

Journal ArticleDOI
TL;DR: Compared with four traditional methods, the method proposed demonstrates improved universality and robustness in a non-structural environment, particularly for overlapping and hidden fruits, and those under varying illumination.

320 citations

Journal ArticleDOI
TL;DR: A review of developments in the rapidly developing field of deep learning is presented, with emphasis on practical aspects for application of deeplearning models for the task of fruit detection and localisation, in support of tree crop load estimation.

277 citations

Journal ArticleDOI
TL;DR: The performance of six existing deep learning architectures were compared for the task of detection of mango fruit in images of tree canopies and a new architecture was developed, termed ‘MangoYOLO’, which outperformed other models in processing of full images, requiring just 70 ms per image.
Abstract: The performance of six existing deep learning architectures were compared for the task of detection of mango fruit in images of tree canopies. Images of trees (n = 1 515) from across five orchards were acquired at night using a 5 Mega-pixel RGB digital camera and 720 W of LED flood lighting in a rig mounted on a farm utility vehicle operating at 6 km/h. The two stage deep learning architectures of Faster R-CNN(VGG) and Faster R-CNN(ZF), and the single stage techniques YOLOv3, YOLOv2, YOLOv2(tiny) and SSD were trained both with original resolution and 512 × 512 pixel versions of 1 300 training tiles, while YOLOv3 was run only with 512 × 512 pixel images, giving a total of eleven models. A new architecture was also developed, based on features of YOLOv3 and YOLOv2(tiny), on the design criteria of accuracy and speed for the current application. This architecture, termed ‘MangoYOLO’, was trained using: (i) the 1 300 tile training set, (ii) the COCO dataset before training on the mango training set, and (iii) a daytime image training set of a previous publication, to create the MangoYOLO models ‘s’, ‘pt’ and ‘bu’, respectively. Average Precision plateaued with use of around 400 training tiles. MangoYOLO(pt) achieved a F1 score of 0.968 and Average Precision of 0.983 on a test set independent of the training set, outperforming other algorithms, with a detection speed of 8 ms per 512 × 512 pixel image tile while using just 833 Mb GPU memory per image (on a NVIDIA GeForce GTX 1070 Ti GPU) used for in-field application. The MangoYOLO model also outperformed other models in processing of full images, requiring just 70 ms per image (2 048 × 2 048 pixels) (i.e., capable of processing ~ 14 fps) with use of 4 417 Mb of GPU memory. The model was robust in use with images of other orchards, cultivars and lighting conditions. MangoYOLO(bu) achieved a F1 score of 0.89 on a day-time mango image dataset. With use of a correction factor estimated from the ratio of human count of fruit in images of the two sides of sample trees per orchard and a hand harvest count of all fruit on those trees, MangoYOLO(pt) achieved orchard fruit load estimates of between 4.6 and 15.2% of packhouse fruit counts for the five orchards considered. The labelled images (1 300 training, 130 validation and 300 test) of this study are available for comparative studies.

244 citations

Journal ArticleDOI
Yang Yu1, Zhang Kailiang1, Hui Liu1, Li Yang1, Dongxing Zhang1 
TL;DR: This study proposes a novel harvesting robot for the ridge-planted strawberries as well as a fruit pose estimator called rotated YOLO (R-YOLO), which significantly improves the localization precision of the picking points.
Abstract: At present, the primary technical deterrent to the use of strawberry harvesting robots is the low harvest rate, and there is a need to improve the accuracy and real-time performance of the localization algorithms to detect the picking point on the strawberry stem. The pose estimation of the fruit target (the direction of the fruit axis) can improve the accuracy of the localization algorithm. This study proposes a novel harvesting robot for the ridge-planted strawberries as well as a fruit pose estimator called rotated YOLO (R-YOLO), which significantly improves the localization precision of the picking points. First, the lightweight network Mobilenet-V1 was used to replace the convolution neural network as the backbone network for feature extraction. The simplified network structure substantially increased the operating speed. Second, the rotation angle parameter $\alpha $ was used to label the training set and set the anchors; the rotation of the bounding boxes of the target fruits was predicted using logistic regression with the rotated anchors. The test results of a set of 100 strawberry images showed that the proposed model's average recognition rate to be 94.43% and the recall rate to be 93.46%. Eighteen frames per second (FPS) were processed on the embedded controller of the robot, demonstrating good real-time performance. Compared with several other target detection methods used for the fruit harvesting robots, the proposed model exhibited better performance in terms of real-time detection and localization accuracy of the picking points. Field test results showed that the harvesting success rate reached 84.35% in modified situations. The results of this study provide technical support for improving the target detection of the embedded controller of harvesting robots.

70 citations