- Manoranjan Paul (M’03, SM'13) received B.Sc.Eng. (hons.) degree in Computer Science and Engineering from Bangladesh U... moreManoranjan Paul (M’03, SM'13) received B.Sc.Eng. (hons.) degree in Computer Science and Engineering from Bangladesh University of Engineering and Technology (BUET), Bangladesh, in 1997 and PhD degree from Monash University, Australia in 2005. He joined to the department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Bangladesh as a Lecturer in 1997 and was promoted to Assistant Professor in 2000. He has worked as a Research Fellow in the University of New South Wales, ADFA, Australia in 2005~2006, Monash University, Australia in 2006~2009, and Nanyang Technological University, Singapore in 2009~2011. He is currently working as a Senior Lecturer in the School of Computing and Mathematics, Charles Sturt University, Australia. His major research interests are in the fields of Image Processing, Video Coding, Data compression, Hyperspectral imagery, Medical Signal Analysis, and Computer Vision.
He has published more than 85 refereed publications. Dr. Paul regularly published journal articles in the IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, and IEEE Transactions on Multimedia, which are considered as the top ranked journals in the image processing, video technology, and multimedia fields respectively. He organized a special session on “New video coding technologies” in IEEE ISCAS 2010. He was a keynote speaker on video coding in IEEE ICCIT 2010 and videoEveryWhere workshop in IEEE WoWMoM 2014. He was also a tutorial speaker in DICTA 2013. Dr Paul is a Senior member of the IEEE (M’03 SM'13) and ACS (’05). Dr. Paul is an Associate Editor of rank Q2 journal EURASIP Journal on Advances in Signal Processing (JASP). Dr. Paul has served as a guest editor of Journal of Multimedia from 2008-2013. Currently Dr. Paul is an Editor of the International Journal of Engineering and Industries (IJEI) and the International Journal of Soft Computing, Mathematics and Control (IJSCMC). He has been on Technical Program Committees of more than 25 international conferences. Dr. Paul obtained Vice Chancellor Research Excellence Award 2013 in Faculty Level, Charles Sturt University. He also obtained Research Excellence Award 2011, School of Computing and Mathematics, Charles Sturt University. Dr. Paul obtained more than $1,000,000 competitive grants including ARC DP.
Dr. Manoranjan Paul obtained Australian Research Council (ARC) Discovery Project Grant $315,000.00 on "DP130103670: Multiview Video Coding using Cuboid Data Compression" with Professor Manzur Murshededit - Emeritus Professor Manzur Murshed, Monash University/Federation University, Professor Michael Frater, University of New South Wales, Associate Professor Weisi Lin, Nanyang Technological Universityedit
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Light detection and ranging (LiDAR) sensors have accrued an ever-increasing presence in the agricultural sector due to their non-destructive mode of capturing data. LiDAR sensors emit pulsed light waves that return to the sensor upon... more
Light detection and ranging (LiDAR) sensors have accrued an ever-increasing presence in the agricultural sector due to their non-destructive mode of capturing data. LiDAR sensors emit pulsed light waves that return to the sensor upon bouncing off surrounding objects. The distances that the pulses travel are calculated by measuring the time for all pulses to return to the source. There are many reported applications of the data obtained from LiDAR in agricultural sectors. LiDAR sensors are widely used to measure agricultural landscaping and topography and the structural characteristics of trees such as leaf area index and canopy volume; they are also used for crop biomass estimation, phenotype characterisation, crop growth, etc. A LiDAR-based system and LiDAR data can also be used to measure spray drift and detect soil properties. It has also been proposed in the literature that crop damage detection and yield prediction can also be obtained with LiDAR data. This review focuses on di...
Research Interests:
Research Interests:
Research Interests:
Lung cancer is the leading cause of cancer death worldwide and a good prognosis depends on early diagnosis. Unfortunately, screening programs for the early diagnosis of lung cancer are uncommon. This is in-part due to the at-risk groups... more
Lung cancer is the leading cause of cancer death worldwide and a good prognosis depends on early diagnosis. Unfortunately, screening programs for the early diagnosis of lung cancer are uncommon. This is in-part due to the at-risk groups being located in rural areas far from medical facilities. Reaching these populations would require a scaled approach that combines mobility, low cost, speed, accuracy, and privacy. We can resolve these issues by combining the chest X-ray imaging mode with a federated deep-learning approach, provided that the federated model is trained on homogenous data to ensure that no single data source can adversely bias the model at any point in time. In this study we show that an image pre-processing pipeline that homogenizes and debiases chest X-ray images can improve both internal classification and external generalization, paving the way for a low-cost and accessible deep learning-based clinical system for lung cancer screening. An evolutionary pruning mecha...
Research Interests:
Research Interests:
Research Interests:
The application of computer vision for COVID-19 diagnosis is complex and challenging, given the risks associated with patient misclassifications. Arguably, the primary value of medical imaging for COVID-19 lies rather on patient... more
The application of computer vision for COVID-19 diagnosis is complex and challenging, given the risks associated with patient misclassifications. Arguably, the primary value of medical imaging for COVID-19 lies rather on patient prognosis. Radiological images can guide physicians assessing the severity of the disease, and a series of images from the same patient at different stages can help to gauge disease progression. Based on these premises, a simple method based on lung-pathology features for scoring disease severity from Chest X-rays is proposed here. As the primary contribution, this method shows to be correlated to patient severity in different stages of disease progression comparatively well when contrasted with other existing methods. An original approach for data selection is also proposed, allowing the simple model to learn the severity-related features. It is hypothesized that the resulting competitive performance presented here is related to the method being feature-bas...
Research Interests:
Research Interests:
Research Interests:
The application of computer vision for COVID-19 diagnosis is complex and challenging, given the risks associated with patient misclassifications. Arguably, the primary value of medical imaging for COVID-19 lies rather on patient... more
The application of computer vision for COVID-19 diagnosis is complex and challenging, given the risks associated with patient misclassifications. Arguably, the primary value of medical imaging for COVID-19 lies rather on patient prognosis. Radiological images can guide physicians assessing the severity of the disease, and a series of images from the same patient at different stages can help to gauge disease progression. Based on these premises, a simple method based on lung-pathology features for scoring disease severity from Chest X-rays is proposed here. As the primary contribution, this method shows to be correlated to patient severity in different stages of disease progression comparatively well when contrasted with other existing methods. An original approach for data selection is also proposed, allowing the simple model to learn the severity-related features. It is hypothesized that the resulting competitive performance presented here is related to the method being feature-bas...
Research Interests:
Research Interests:
Research Interests:
Augmented reality-based surgeries have not been successfully implemented in oral and maxillofacial areas due to limitations in geometric accuracy and image registration. This paper aims to improve the accuracy and depth perception of the... more
Augmented reality-based surgeries have not been successfully implemented in oral and maxillofacial areas due to limitations in geometric accuracy and image registration. This paper aims to improve the accuracy and depth perception of the augmented video. The proposed system consists of a rotational matrix and translation vector algorithm to reduce the geometric error and improve the depth perception by including 2 stereo cameras and a translucent mirror in the operating room. The results on the mandible/maxilla area show that the new algorithm improves the video accuracy by 0.30-0.40 mm (in terms of overlay error) and the processing rate to 10-13 frames/s compared to 7-10 frames/s in existing systems. The depth perception increased by 90-100 mm. The proposed system concentrates on reducing the geometric error. Thus, this study provides an acceptable range of accuracy with a shorter operating time, which provides surgeons with a smooth surgical flow.
Research Interests:
High Efficiency Video Coding (HEVC) could not provide real time facilities to the limited processing and battery powered electronic devices as its encoding time complexity increases multiple times compared to its predecessor. Numerous... more
High Efficiency Video Coding (HEVC) could not provide real time facilities to the limited processing and battery powered electronic devices as its encoding time complexity increases multiple times compared to its predecessor. Numerous researchers contribute to address this limitation by reducing a number of motion estimation (ME) modes where they analyze homogeneity, residual and statistical correlation among different modes. Although their approaches save some encoding time, however, could not reach the similar rate-distortion (RD) performance with HEVC encoder as they merely depend on existing Lagrangian cost function (LCF) within HEVC framework. To overcome this limitation, in this paper, we capture visual attentive Foreground motion and salient region (FMSR) which are sensitive to human visual system for quality assessment. The FMSR features captured by visual attentive and dynamic background modeling are adaptively synthesized to determine a subset of candidate modes. This preprocessing phase is independent from LCF. Since the proposed technique can avoid exhaustive exploration of all modes with simple criteria, it can reduce 27% encoding time on average. With efficient selection of FMSR-based appropriate block partitioning modes, it can also improve up to 1.0dB peak signal-to-noise ratio (PSNR).
Research Interests:
Research Interests:
Research Interests:
Research Interests:
In recent years, quantum image processing got a lot of attention in the field of image processing due to the opportunity to place huge image data in quantum Hilbert space. Hilbert space or Euclidean space has infinite dimensions to locate... more
In recent years, quantum image processing got a lot of attention in the field of image processing due to the opportunity to place huge image data in quantum Hilbert space. Hilbert space or Euclidean space has infinite dimensions to locate and process the image data faster. Moreover, several types of research show that the computational time of the quantum process is faster than classical computers. Encoding and compressing the image in the quantum domain is still a challenging issue. From the literature survey, we have proposed a DCT-EFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) algorithm to represent and compress gray image efficiently which save computational time and minimize the complexity of preparation. This work aims to represent and compress various gray image sizes in quantum computers using the DCT (Discrete Cosine Transform) and EFRQI (Efficient Flexible Representation of Quantum Image) approaches together. The Quirk simulation tool is...
Research Interests:
The application of computer vision for COVID-19 diagnosis is complex and challenging, given the risks associated with patient misclassifications. Arguably, the primary value of medical imaging for COVID-19 lies rather on patient... more
The application of computer vision for COVID-19 diagnosis is complex and challenging, given the risks associated with patient misclassifications. Arguably, the primary value of medical imaging for COVID-19 lies rather on patient prognosis. Radiological images can guide physicians assessing the severity of the disease, and a series of images from the same patient at different stages can help to gauge disease progression. Based on these premises, a simple method based on lung-pathology features for scoring disease severity from Chest X-rays is proposed here. As the primary contribution, this method shows to be correlated to patient severity in different stages of disease progression comparatively well when contrasted with other existing methods. An original approach for data selection is also proposed, allowing the simple model to learn the severity-related features. It is hypothesized that the resulting competitive performance presented here is related to the method being feature-bas...
Research Interests:
Research Interests:
Video Summarization is a computer-based technique to generate a shorter version of the original long video for memory management and information retrieval. The existing method for video summarization applies affective (the state of... more
Video Summarization is a computer-based technique to generate a shorter version of the original long video for memory management and information retrieval. The existing method for video summarization applies affective (the state of excitement, interestingness, and panic) level of a viewer captured by Electroencephalography (EEG) while watching a video. The traditional methods for extracting features from EEG signals employ Discrete Fourier Transform (DFT). However, DFT is mainly suitable for stationary signals. As EEG signals are non-linear and non-stationary in nature, DFT is not appropriate for EEG signals. In addition to this, the high-frequency components of EEG signals usually contain the distinct properties than the other frequency components. Empirical Mode Decomposition (EMD) technique extracts the different frequency components from high to low from non-linear and non-stationary signals, such EEG. Therefore, we propose a new video summarization method applying EMD Decomposed EEG Signals (EDES) to extract the high-frequency components. The proposed approach calculates Power Spectral Density (PSD) from the high-frequency components and generates a neuronal attention curve for a video. Finally, a video summary is produced by selecting the affective video events from the neuronal attention curve. The experimental results reveal that the proposed approach performs better than the existing state- of-the-art method.
Research Interests:
Existing methods for video summarization fails to achieve a satisfactory result for a video with camera movement, low contrast, and significant illumination changes. To solve these problems, we propose a novel framework for video... more
Existing methods for video summarization fails to achieve a satisfactory result for a video with camera movement, low contrast, and significant illumination changes. To solve these problems, we propose a novel framework for video summarization based on the smooth pursuit which is the state of eye movement when a user follows a moving object in a video. First, we propose a new method to distinguish smooth pursuit from another type of gaze points, such as fixation and saccade. Later, we assign a probability score to each frame based on the smooth pursuit information. Finally, we select a set of key frames based on the probability score. To evaluate the proposed method, we implement it on Office video dataset that contains videos with camera movement/shaking and illumination changes. Experimental results show the superior performance compared to the single view results of the state-of-the-art GMM based method.
Research Interests:
Research Interests:
Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed... more
Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cro...
Research Interests:
The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical... more
The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of betw...
Research Interests:
Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are... more
Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. A...
Research Interests:
With the development of displaying techniques, free viewpoint video (FVV) system shows its potential to provide immersive perceptual feeling by changing viewpoints. To provide this luxury, a large number of high quality views have to be... more
With the development of displaying techniques, free viewpoint video (FVV) system shows its potential to provide immersive perceptual feeling by changing viewpoints. To provide this luxury, a large number of high quality views have to be synthesised from limited number of viewpoints. However, in this process, a portion of the background is occluded by the foreground object in the generated synthesised videos. Recent techniques, i.e. view synthesized prediction using Gaussian model (VSPGM) and adaptive weighting between warped and learned foregrounds indicate that learning techniques may fill occluded areas almost correctly. However, these techniques use temporal correlation by assuming that original texture of the target viewpoint are already available to fill up occluded areas which is not a practical solution. Moreover, if a pixel position experiences foreground once during learning, the existing techniques considered it as foreground throughout the process. However, the actual fact is that after experiencing a foreground a pixel position can be background again. To address the aforementioned issues, in the proposed view synthesise technique, we apply Gaussian mixture modelling (GMM) on the output images of inverse mapping (IM) technique for further improving the quality of the synthesised videos. In this technique, the foreground and background pixel intensities are refined from adaptive weights of the output of inverse mapping and the pixel intensities from the corresponding model(s) of the GMM. This technique provides a better pixel correspondence, which improves 0.10~0.46dB PSNR compared to the IM technique.
Research Interests:
Research Interests:
<p>Entropy analysis using original pixel reflectance and residual between adjacent bands for HS images from NASA dataset [<a... more
<p>Entropy analysis using original pixel reflectance and residual between adjacent bands for HS images from NASA dataset [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161212#pone.0161212.ref010" target="_blank">10</a>].</p
Research Interests:
Research Interests:
Retinal fundus examination is necessary for the early diagnosis of eye disease, especially diabetic retinopathy. Population screening often results in poor quality retinal images that complicate the automated diagnosis of retinal... more
Retinal fundus examination is necessary for the early diagnosis of eye disease, especially diabetic retinopathy. Population screening often results in poor quality retinal images that complicate the automated diagnosis of retinal features, such as precise segmentation of blood vessels, microaneurysms, cotton stains, and hard exudates. Fluorescein fundus angiogram (FFA) has solved some problems, but it is invasive and has side effects. In this research work, we proposed a method of image enhancement based on contrast-sensitive steps as a valuable aid for the automatic segmentation of pathological (unhealthy) images. Experimental results based on the Digital retinal images for vessel extraction (DRIVE) and STructured analysis of the retina (STARE) databases showed that the proposed image enhancement method improved the performance over other existing methods, from 92% to 95% in accuracy and from 71% to 75% in sensitivity. This significant improvement in the contrast of retinal background images of retinal color has the potential to provide better vessel images for observing ocular diseases.
Research Interests:
Research Interests:
High Efficiency Video Coding (HEVC) is the latest video encoding standard and has approximately 50% bit-rate saving compared to its predecessor. However, the motion estimation (ME) is considerably complicated by the incorporation of... more
High Efficiency Video Coding (HEVC) is the latest video encoding standard and has approximately 50% bit-rate saving compared to its predecessor. However, the motion estimation (ME) is considerably complicated by the incorporation of varieties of partitioning modes and a quad-tree based coding structure, and also by increasing the basic coding unit size by a factor of 16. Motion estimation is the most complex task in the video encoding process, consuming 60–80% of overall encoding time. This paper proposes a new algorithm, angle-restricted test zone (ARTZ) for motion estimation which is based on a test zone (TZ) search, exploiting directional probabilities of motion vector search. In our experiments, this proposal achieves a time saving in motion estimation of about 20% to 50% compared to a TZ search in the HEVC test model (HM) implementation for UHD videos without significant degradation of PSNR.
Research Interests:
Research Interests:
Research Interests:
Research Interests:
—Successful, timely diagnosis of neuropsychiatric diseases is key to management. Research efforts in the area of diagnosis of Alzheimer's disease have used various aspects of computer-aided multi-class diagnosis approaches with varied... more
—Successful, timely diagnosis of neuropsychiatric diseases is key to management. Research efforts in the area of diagnosis of Alzheimer's disease have used various aspects of computer-aided multi-class diagnosis approaches with varied degrees of success. However, there is still need for more efficient and reliable approaches to successful diagnosis of the disease. This research used deep learning framework with modified k-sparse autoencoder (mKSA)classification to locate neutrally degenerated areas of the brain MRI, low amyloid beta 1-42 imaging in cerebrospinal fluid (CSF) and positron emission tomography (PET) imaging of amyloid; each with a sample of 150 images. Results show a correlation between computational demarcation of infected regions and the images. Degeneration in the studied areas was evidenced by high phosphorylated t-/p-tau levels in CSF, regional hypometabolism fluorodeoxyglucose PET, and the presence of atrophy patterns. The use of mKSA algorithm in boosting classification helped to improve the classifier performance. The KSA method with deep learning framework is used for the first time to produce accurate results in diagnosis of Alzheimer's disease.