Khairul Muzzammil Saipullah

Technical University of Malaysia Malacca

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Wira Hidayat Mohd Saad

Technical University of Malaysia Malacca

Deok‐Hwan Kim

Inha University

Yewguan Soo

Technical University of Malaysia Malacca

Nurul Atiqah Ismail

Technical University of Malaysia Malacca

Muhammad Idzdihar Idris

Technical University of Malaysia Malacca

Ammar Anuar

Ministry of Health

Q.L. Wong

Technical University of Malaysia Malacca

Syafeeza Ahmad Radzi

Technical University of Malaysia Malacca

M.S.M. Husni

Technical University of Malaysia Malacca

Azura Ramlan

Cooperative Institutions

Universiti Teknologi MARA

Universiti Sains Malaysia

Technical University of Malaysia Malacca

University of Technology Malaysia

Universiti Teknologi MARA System

Universiti Malaysia Perlis

Hospital Universiti Sains Malaysia

Universiti Putra Malaysia

Universiti of Malaysia Sabah

Hospital Pulau Pinang

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Enhancing spectral classification using Adaboost

Khairul Muzzammil Saipullah Nurul Atiqah Ismail

Spectral classification for hyperspectral image is a challenging job because of the number of spectral in a hyperspectral image and high dimensional spectral. In this paper, we proposed a method to enhance the spectral classification using the Adaboost for hyperspectral image analysis. By applying the Adaboost algorithm to the classifier, the classification can be executed iteratively by giving weight to the spectral data, thus will reduce the classification error rate. The Adaboost is implemented to spectral angle mapper (SAM), Euclidean distance (ED), and city block distance (CD). From the experimental results, the Adaboost increases the average classification accuracy of 2000 spectral up to 99.63% using the CD. Overall, Adaboost increases the average classification accuracy of ED, CD, and SAM by 2.54%, 1.95%, and 1.67%.

AdaBoost

Contextual image classification

Statistical classification

10.1109/apace.2012.6457623

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Rotation invariant texture feature extraction based on Sorted Neighborhood Differences

Khairul Muzzammil Saipullah Deok‐Hwan Kim Seok‐Lyong Lee

Rotation invariant texture descriptor plays an important role in texture-based object classification. However the classification accuracy may decrease due to the inconsistent performance of texture descriptor with respect to various rotated angles. In this paper we propose a consistent rotation invariant texture descriptor named Sorted Neighborhood Differences (SND). SND is derived from the integration of sorted neigh- borhood and binary patterns. Experimental results show that overall texture classification accuracy of SND with respect to different rotations using OUTEX TC 0010 texture database is 91.81% whereas those of LBPriu and LBP-HF are 86.42% and 88.28%, respectively. The texture and coin classification accuracies of SND are also consistent in various rotation angles and illumination levels.

Local Binary Patterns

Texture (cosmology)

Texture filtering

Texture compression

10.1109/icme.2011.6011907

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Citations (3)

Dataset of bird's eye chilies farm for stereo image semantic segmentation

Data in Brief (2023)

Khairul Muzzammil Saipullah Wira Hidayat Mohd Saad Q.L. Wong M.S.M. Husni Muhammad Idzdihar Idris

This paper presents a dataset of bird's eye chilies in a single farm for semantic segmentation. The dataset is generated using two cameras that are aligned left and right forming a stereo-vision video capture. By analyzing the disparity between corresponding points in the left and right images, algorithms can calculate the relative distance of objects in the scene. This depth information is useful in various applications, including 3D reconstruction, object tracking, and autonomous navigation. The dataset consists of 1150 left and right compressed images extracted from ten sets of stereo videos taken at ten different locations within the chili farm from the same ages of the bird's eye chilies. Since the dataset is used for semantic segmentation, the ground truth images of manually semantic segmented images are also provided in the dataset. The dataset can be used for 2D and 3D semantic segmentation of the bird's eye view chili farm. Some of the object classes in this dataset are the sky, living things, plantation, flat, construction, nature, and misc.

Ground truth

10.1016/j.dib.2023.109714

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Citations (3)

A robust texture feature extraction using the localized angular phase

Multimedia Tools and Applications (2011)

Khairul Muzzammil Saipullah Deok‐Hwan Kim

Local Binary Patterns

Gray level

10.1007/s11042-011-0766-5

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Citations (22)

Continuous local histogram descriptor for diagnosis of Bronchiolitis Obliterans

Khairul Muzzammil Saipullah Mai Mariam Mohamed Aminuddin I. Mustaffa Nuraishah Sarimin Ammar Anuar

Texture feature is an important feature analysis method in computer-aided diagnosis systems for disease diagnosis. However, texture feature itself could not provide an overall description of the diseases. In this paper, we propose Continuous Local Feature (CLH) to diagnose the Bronchiolitis Obliterans (BO) lung diseases in the chest computer tomography images. CLH is based on the continuous combination of histograms of local texture feature, local shape feature, and the brightness feature. Because CLH extracts more information, it has high discriminating power and is able to classify between the BO lung disease and normal lung region effectively. The experimental results in classifying between BO and normal lung region show that CLH achieves 98.15% of average sensitivity whereas Local Binary Patterns and Gray Level Run Length Matrix achieve 73% and 75.8% of average sensitivities, respectively. In the receiver operating curve analysis, CLH archives 0.9 of area under curve (AUC) whereas LBP and GLRLM achieve 0.78 and 0.86 of AUCs.

Local Binary Patterns

Feature (linguistics)

10.1109/his.2011.6122138

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Fast Video Motion Estimation Algorithm Based on Motion Speed and Multiple Initial Center Points Prediction

Jeongbo gwahaghoe nonmunji. keompyuting ui silje (2010)

Sha-Hu Peng Khairul Muzzammil Saipullah Byung-Choon Yun Deok‐Hwan Kim

This paper proposes a fast motion estimation algorithm based on motion speed and multiple initial center points. The proposed method predicts initial search points by means of the spatio-temporal neighboring motion vectors. A dynamic search pattern based on the motion speed and the predicted initial center points is proposed to quickly obtain the motion vector. Due to the usage of the spatio-temporal information and the dynamic search pattern, the proposed method greatly accelerates the search speed while keeping a good predicted image quality. Experimental results show that the proposed method has a good predicted image quality in terms of PSNR with less searching time comparing with the Full Search, New Three-Step Search, and Four-Step Search.

Motion vector

Quarter-pixel motion

Motion field

Center (category theory)

Source

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Table of contents

IEEE International Conference on Control System, Computing and Engineering (2012)

Yasir Hashim Othman Sidek Ayman Ahmed Salwa Elramly Mohamed El

Table (database)

10.1109/iccsce.2012.6487104

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Real-time video processing using native programming on Android platform

Khairul Muzzammil Saipullah Ammar Anuar Nurul Atiqah Ismail Yewguan Soo

As the smartphone industry grows rapidly, smartphone applications need to be faster and real-time. For this purpose, most of the smartphone platforms run the program on the native language or compiler that can produce native code for hardware. However for the Android platform that based on the JAVA language, most of the software algorithm is running on JAVA that consumes more time to be compiled. In this paper the performance of native programming and high level programming using JAVA are compared with respect to video processing speed. Eight image processing methods are applied to each frame of the video captured from a smartphone that is running on an Android platform. The efficiencies of the two applications with difference programming language are compared by observing their frame processing rate. The experimental results show that out of the eight images processing methods, six methods that are executed using the native programming are faster than that of the JAVA programming with a total average ratio of 0.41. An application of the native programming for real-time object detection is also presented in this paper. The result shows that with native programming on Android platform, even a complicated object detection algorithm can be done in real-time.

Frame rate

Java Programming Language

Machine code

10.1109/cspa.2012.6194733

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Citations (16)

ROS 2 Configuration for Delta Robot Arm Kinematic Motion and Stereo Camera Visualization

Journal of Robotics and Control (JRC) (2022)

Khairul Muzzammil Saipullah Wira Hidayat Mohd Saad Sook Hui Chong Muhammad Idzdihar Idris Syafeeza Ahmad Radzi

The Delta robot is one of the robot types that is used in agriculture and industrial application. However, before the complex physical development of the robot, a simulation needs to be developed to ensure the perfect functionality of the design. Therefore, this paper presented a development of simulation for a parallel delta robot using a Robot Operating System 2 (ROS 2) environment and stereo camera visualization. The contribution of this research is to present the development details and the proposed solution to solve issues encountered during the development. The development of script in the format of eXtensible Markup Language (XML), Unified Robot Description Format (URDF), and Simulation Description Format (SDF) are presented for describing a robot's physical structure, allowing a robotic system to be depicted in a tree structure, and defining the delta robot arm, which is made up of closed-loop kinematic chain linkage that will be simulated in Gazebo. For the results, several Gazebo plugin libraries are compared and tested for the wheels motion control, stereo camera visualization, and delta robot arm kinematic motion. From the experiment, the best method is inverse kinematic motion the method is selected and used in the simulation. The selected method resulted in an average percentage error of 3.92%, 3.72%, and 2.92%, respectively for each joint.

Robot calibration

10.18196/jrc.v3i3.14436

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Citations (3)

Development of real-time monitoring BLE-LoRa positioning system based on RSSI for non-line-of-sight condition

Indonesian Journal of Electrical Engineering and Computer Science (2023)

Abd Shukur Ja’afar Kavetha Suseenthiran Khairul Muzzammil Saipullah Mohamad Zoinol Abidin Abd Aziz Adam Wong Yoon Khang

Indoor positioning has become popular in this decade and is used to locate users or objects in indoor environments. This is because global positioning system (GPS) is not efficient for indoor use due to the multipath fading effect. This research is about development bluetooth low energy (BLE) indoor positioning system with the aid of long range (LoRa) network and guideline on selection of the BLE beacons. Next, positioning systems are developed consisting of BLE beacons, a transceiver of hybrid BLE-LoRa module, a LoRa receiver and Raspberry Pi as real-time monitoring. The received signal strength indicator (RSSI) and BLE Mac address from BLE beacons received via LoRa network are analyzed using the positioning algorithm designed in MATLAB. The positioning algorithm incorporates distance estimation, filter implementation and trilateration technique. The estimated location is analyzed with the root mean square error (RMSE) and cumulative distribution function (CDF). According to the results, implementing the filter reduces the positioning accuracy error, achieving 90% accuracy of positioning error less than 1.20 meters for the whole testbed. Finally, the algorithm is embedded into Raspberry Pi to view the location via desktop.

Trilateration

Beacon

Indoor positioning system

Non-line-of-sight propagation

Testbed

10.11591/ijeecs.v30.i2.pp972-981

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Citations (5)