Research Repository :: Browsing by Author "Maiseli, Baraka J."

Browsing by Author "Maiseli, Baraka J."

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Adaptive Charbonnier superresolution method with robust edge preservation capabilities
(Journal of Electronic Imaging, 2013-12-16) Maiseli, Baraka J.; Liu, Qiang; Elisha, Ogada Achieng; Gao, Huijun
Superresolution (SR) is known to be an ill-posed inverse problem, which may be solved using some regularization techniques. We have proposed an adaptive regularization method, based on a Charbonnier nonlinear diffusion model to solve an SR problem. The proposed model is flexible because of its automatic capability to reap the strengths of either linear isotropic diffusion, Charbonnier model, or semi-Charbonnier model, depending on the local features of the image. On the contrary, the models proposed from other research works are fixed and hence less feature dependent. This makes such models insensitive to local structures of the images, thereby producing poor reconstruction results. Empirical results obtained from experiments, and presented here, show that the proposed method produces superresolved images which are more natural and contain well-preserved and clearly distinguishable image structures, such as edges. In comparison with other methods, the proposed method demonstrates higher performance in terms of the quality of images it generates.
Adaptive Charbonnier Superresolution Method with Robust Edge Preservation Capabilities
(International Society for Optics and Photonics, 2013) Maiseli, Baraka J.; Liu, Qiang; Elisha, Ogada Achieng; Gao, Huijun
Superresolution (SR) is known to be an ill-posed inverse problem, which may be solved using some regularization techniques. We have proposed an adaptive regularization method, based on a Charbonnier nonlinear diffusion model to solve an SR problem. The proposed model is flexible because of its automatic capability to reap the strengths of either linear isotropic diffusion, Charbonnier model, or semi-Charbonnier model, depending on the local features of the image. On the contrary, the models proposed from other research works are fixed and hence less feature dependent. This makes such models insensitive to local structures of the images, thereby producing poor reconstruction results. Empirical results obtained from experiments, and presented here, show that the proposed method produces superresolved images which are more natural and contain well-preserved and clearly distinguishable image structures, such as edges. In comparison with other methods, the proposed method demonstrates higher performance in terms of the quality of images it generates
An Automatic and Cost-Effective Parasitemia Identification Framework for Low-End Microscopy Imaging Devices
(IEEE, 2014) Maiseli, Baraka J.; Mei, Jiangyuan; Gao, Huijun; Yin, Shen
In the detection of Malarial parasites from a patient, it is usually necessary to carefully examine the corresponding blood-slide smear and distinguish the infected and healthy Red Blood Cells (RBCs). If this process is done manually, as evidenced in common traditional approaches, the following challenges may be encountered: inaccuracy of the lab results, which originates from normal human errors or lack of experience of a person conducting diagnosis, and large processing times. Consequently, doctors and specialists are likely to provide improper prescriptions to patients. With the improvement of the computational power of computers, however, the whole diagnosis process can be automated. Several methods in literature have been proposed for this purpose. Most of these methods demand the availability of high-end microscopy imaging systems to generate reliable and accurate results. Such costly advanced devices may not be afforded by developing countries with sluggish economic growth. In this paper, therefore, we have developed a cost-effective framework which can address the mentioned challenge. Our approach introduces a Super Resolution (SR) model into the existing framework to enhance the resolution of the input images before letting them subjected to the subsequent detection stages. This provides a possibility for applying the low-end microscopy devices capable of capturing Low Resolution (LR) blood smear images for identifying the degree of Malaria in a patient. In the proposed framework, the SR component uses the nonlinear Charbonnier diffusion model in the regularization part because of its good regularity characteristics. Experimental results demonstrate strong correlation of our method and the manual one.
An automatic and cost-effective parasitemia identification framework for low-end microscopy imaging devices
(IEEE, 2015-09-03) Maiseli, Baraka J.; Mei, Jiangyuan; Gao, Huijun; Yin, Shen
In the detection of Malarial parasites from a patient, it is usually necessary to carefully examine the corresponding blood-slide smear and distinguish the infected and healthy Red Blood Cells (RBCs). If this process is done manually, as evidenced in common traditional approaches, the following challenges may be encountered: inaccuracy of the lab results, which originates from normal human errors or lack of experience of a person conducting diagnosis, and large processing times. Consequently, doctors and specialists are likely to provide improper prescriptions to patients. With the improvement of the computational power of computers, however, the whole diagnosis process can be automated. Several methods in literature have been proposed for this purpose. Most of these methods demand the availability of high-end microscopy imaging systems to generate reliable and accurate results. Such costly advanced devices may not be afforded by developing countries with sluggish economic growth. In this paper, therefore, we have developed a cost-effective framework which can address the mentioned challenge. Our approach introduces a Super Resolution (SR) model into the existing framework to enhance the resolution of the input images before letting them subjected to the subsequent detection stages. This provides a possibility for applying the low-end microscopy devices capable of capturing Low Resolution (LR) blood smear images for identifying the degree of Malaria in a patient. In the proposed framework, the SR component uses the nonlinear Charbonnier diffusion model in the regularization part because of its good regularity characteristics. Experimental results demonstrate strong correlation of our method and the manual one.
Collaborative Development and Utilization of iLabs in East Africa
(IGI Global, 2012) Mwikirize, Cosmas; Tumusiime, Arthur Asiimwe; Musasizi, Paul Isaac; Tickodri-Togboa, Sandy Stevens; Jiwaji, Adnaan; Nombo, Josiah; Maiseli, Baraka J.; Sapula, Teyana; Mwambela, Alfred
Since 2005, Makerere University and the University of Dar es Salaaam have taken definitive steps to-ward the development and utilization of iLabs. This chapter presents the iLabs experiences of the two East African universities. The experiences presented here are characterized by: institutionalization of developer teams, development of ELVIS-based iLabs, staff & student exchanges, and utilization of iLabs to support curricula. The two universities have also undertaken to setup iLabs communities at peer universities and other higher institutions of learning in East Africa.
Consumers' Acceptance Intentions to Use Cashback Systems: A Case of SNAPnSAVE Application in South Africa
(ACM Digital Library, 2018) Matemba, Elizabeth D.; Guoxin, Li; Maiseli, Baraka J.; Tladi, Portia M.
Recently, a cashback application called SNAPnSAVE was established in South Africa to reward customers after their online shopping. Considering several advantages of the application, such as increase in merchants' sales and improvement of customers' satisfaction, we have established a theoretical model that captures psychological factors explaining customers' acceptance levels to use the application. Building on the extended technology acceptance model (TAM), the proposed model shows that trust, perceived risk, and developer's reputation significantly impact customers' behavioral intentions to accept the SNAPnSAVE application. Of these factors, developer's reputation has never been explored by previous studies. Our findings suggest that developers should focus on branding their products, gaining trust from customers, and lowering unnecessary perceived risks encountered by technology users during financial transactions. Furthermore, given the sluggish growth of mobile-based rewarding systems in Africa, the findings may be extended and used by developers to design products that meet specific demands of even a larger market in the continent. The study cautions that TAM, in its original form, cannot be directly deployed in the African context because of some cultural and habitual differences between Africa and other (developed) continents.
Diffusion-steered denoising framework for suppressing multiplicative noise in ultrasonograms
(African Journal of Applied Research, 2017-10-07) Kessy, Suzan; Msuya, Hubert; Kisangiri, Michael; Maiseli, Baraka J.
Ultrasound imaging, a non-invasive and cost-effective imaging modality, is probably the most preferred diagnostic tool in medicine. Despite its merits, ultrasonograms are usually corrupted by multiplicative noise, a consequence that limits doctors to provide more accurate treatments and decisions. Attempts to address the problem have been made, but we have found little works that adopt the diffusion framework, which scholars have reported that it produces promising results in additive noise cases. In the current work, we have modified the classical Perona-Malik (PM) diffusion model to deal with multiplicative noise. Inspired by the ability of PM to restore semantically critical features, we have embedded a log-based regularization term, statistically modeled to mitigate multiplicative effects in the ultrasound images, into the modified PM. Additionally, the diffusivity kernel of PM has been re-designed to ensure that the diffusion process is properly steered. Modification of the PM kernel was achieved through integration of the half-quadratic diffusivity, which has a corresponding energy functional that is strictly convex, a promising mathematical property that encourages unique solutions and guarantees stability of the evolutionary system. Our interest is to emphasize regularization in flat image regions while maintaining plausible edges and contours. Subjective and quantitative evaluations demonstrate that the proposed model produces better results compared with some state-of-the-art methods. Even more importantly, our approach guarantees convergence, stability, and robustness when tested for a range of ultrasonograms. Probably the intriguing property of our framework is its ability to evolve a denoising image over a longer period without smudging or destroying its sensitive features. The proposed approach may further be extended and actualized in practical imaging devices.
Diffusion-Steered Super-Resolution Image
(IntechOpen, 2018) Maiseli, Baraka J.
For decades, super-resolution has been a widely applied technique to improve the spatial resolution of an image without hardware modification. Despite the advantages, super-resolution suffers from ill-posedness, a problem that makes the technique susceptible to multiple solutions. Therefore, scholars have proposed regularization approaches as attempts to address the challenge. The present work introduces a parameterized diffusion-steered regularization framework that integrates total variation (TV) and Perona-Malik (PM) smoothing functionals into the classical super-resolution model. The goal is to establish an automatic interplay between TV and PM regularizers such that only their critical useful properties are extracted to well pose the super-resolution problem, and hence, to generate reliable and appreciable results. Extensive analysis of the proposed resolution-enhancement model shows that it can respond well on different image regions. Experimental results provide further evidence that the proposed model outperforms.
Diffusion-steered super-resolution method based on the Papoulis–Gerchberg algorithm
(IET Image Processing, 2016-05-03) Maiseli, Baraka J.
Papoulis-Gerchberg (PG) algorithm, a technique to extrapolate signals, has attracted many researchers for its lower complexity, higher computational efficiency, and effectiveness. One field that receives these merits is super-resolution, which fuses multiple band-limited scenes to generate a high-resolution image. Most super-resolution methods based on the PG algorithm, however, underperform when input images are seriously degraded by blur, noise, and sampling. The current study addresses the challenges by embedding the PG algorithm into a super-resolution minimization problem. The proposed method is iterative and incorporates a diffusion-driven smoothness prior that updates its regularisation process according to the local image features. This well-crafted prior, which attempts to overcome the super-resolution ill-posedness, provides an automatic interplay between flat and contour regions, and ensures necessary levels of regularisations to generate sharper and detailed images. Results show that the current method outperforms some state-of-the-art super-resolution approaches including those based on total variation. Even more importantly, the authors' method contains a robust noise suppressor that treats comfortably noisy scenes.
Diffusion-Steered Super-Resolution Method Based on the Papoulis-Gerchberg Algorithm
(IET Digital Library, 2016) Maiseli, Baraka J.
Papoulis-Gerchberg (PG) algorithm, a technique to extrapolate signals, has attracted many researchers for its lower complexity, higher computational efficiency, and effectiveness. One field that receives these merits is super-resolution, which fuses multiple band-limited scenes to generate a high-resolution image. Most super-resolution methods based on the PG algorithm, however, underperform when input images are seriously degraded by blur, noise, and sampling. The current study addresses the challenges by embedding the PG algorithm into a super-resolution minimization problem. The proposed method is iterative and incorporates a diffusion-driven smoothness prior that updates its regularisation process according to the local image features. This well-crafted prior, which attempts to overcome the super-resolution ill-posedness, provides an automatic interplay between flat and contour regions, and ensures necessary levels of regularisations to generate sharper and detailed images. Results show that the current method outperforms some state-of-the-art super-resolution approaches including those based on total variation. Even more importantly, the authors' method contains a robust noise suppressor that treats comfortably noisy scenes.
Edge Preservation Image Enlargement and Enhancement Method Based on the Adaptive Perona–Malik Non-Linear Diffusion Model
(IEEE, 2014) Maiseli, Baraka J.; Elisha, Ogada Achieng; Mei, Jiangyuan; Gao, Huijun
In this study, the authors have proposed a new super resolution (SR) model based on the Perona–Malik regularisation scheme. The new model integrates into its regularisation component an adaptive exponential term which automatically adjusts itself depending on the local image features. This lends more sensitivity and adaptability to the proposed model, thereby making the reconstruction process much less punishing against semantically important features. Therefore, regularisation is stronger in homogeneous regions, and weaker in the neighbourhood of boundaries. The proposed method has a promising capability of supressing noise more effectively, while preserving important image features. The approach used differs significantly from the available methods, especially in the manner in which adaptability has been deployed. Noting that SR methods are less sensitive to the local image topography, a factor that causes the super-resolved images to be visually poor, the new method sensitively probes the local features of the image, and determines the necessary level of reconstruction and regularisation. Additionally, the formulation robustly introduces a backward diffusion, a phenomenon proved from literature to have a tendency of sharpening edges. The authors have included empirical reconstruction results to demonstrate that their model produces better images in comparison with other classical methods.
Edge preservation image enlargement and enhancement method based on the adaptive Perona–Malik non-linear diffusion model
(IET Image Processing, 2014-12) Maiseli, Baraka J.; Elisha, Ogada Achieng; Mei, Jiangyuan; Gao, Jiangyuan
In this study, the authors have proposed a new super resolution (SR) model based on the Perona–Malik regularisation scheme. The new model integrates into its regularisation component an adaptive exponential term which automatically adjusts itself depending on the local image features. This lends more sensitivity and adaptability to the proposed model, thereby making the reconstruction process much less punishing against semantically important features. Therefore, regularisation is stronger in homogeneous regions, and weaker in the neighbourhood of boundaries. The proposed method has a promising capability of supressing noise more effectively, while preserving important image features. The approach used differs significantly from the available methods, especially in the manner in which adaptability has been deployed. Noting that SR methods are less sensitive to the local image topography, a factor that causes the super-resolved images to be visually poor, the new method sensitively probes the local features of the image, and determines the necessary level of reconstruction and regularisation. Additionally, the formulation robustly introduces a backward diffusion, a phenomenon proved from literature to have a tendency of sharpening edges. The authors have included empirical reconstruction results to demonstrate that their model produces better images in comparison with other classical methods.
Exploring the Adoption of Virtual and Augmented Reality in Enhancing Interactive Learning in Tanzania
(IEEE, 2019-07-18) Misso, Angelina; Stephen, Josephine; Maiseli, Baraka J.; Kissaka, Mussa
The challenge of inadequate teaching and learning facilities impedes the development of the education system in Tanzania. This instigates the need to combine education and technology for provision of education. This paper assessed the adoption of Virtual and Augmented Reality as a tool to improve interactive learning. The research assessed the awareness and perceived usefulness of virtual and augmented realities in education. The research utilized a quantitative methodology whereby an online survey was used to collect data. Results indicate 69.7% of the respondents were aware of virtual and augmented reality. Moreover, 87.4% of the respondents agreed that virtual and augmented reality can be adopted to improve interactive learning. Interestingly, Science, Engineering, and Medical fields were proposed for adoption. These technologies influence students’ interest and motivation to learn through visualizations and interactivity. However, further research is recommended on cost-effective gadgets for virtual reality technology and suitable content for augmented reality technology.
Lp-TV model for structure extraction with end-to-end contour learning
(IEEE, 2017) Song, Chunwei; Maiseli, Baraka J.; Zuo, Wangmeng; Gao, Huijun
Structure extraction is important for human perception. However, for various textured images, computers can hardly achieve this goal. Despite a plethora of studies to address the challenge, results from most previous methods contain unwanted artifacts and over-smoothed structures. Therefore, to address the weaknesses, we have proposed a variational model with end-to-end contour learning capability. Our formulation dwells in two observations: likelihood for representation of residual textures may be well abstracted using super Gaussian distribution, and edge metrics with semantic meaning may benefit structure preservation. The augmented Lagrangian method is adopted for optimal computation. Compared with classical approaches, our method offers a higher performance in structure extraction, including situations where the images have significant nonuniformity of the scale features.
A multi-frame super-resolution method based on the variable-exponent nonlinear diffusion regularizer
(EURASIP Journal on Image and Video Processing, 2015-07-28) Maiseli, Baraka J.; Elisha, Ogada Achieng; Gao, Huijun
In this work, the authors have proposed a multi-frame super-resolution method that is based on the diffusion-driven regularization functional. The new regularizer contains a variable exponent that adaptively regulates its diffusion mechanism depending upon the local image features. In smooth regions, the method favors linear isotropic diffusion, which removes noise more effectively and avoids unwanted artifacts (blocking and staircasing). Near edges and contours, diffusion adaptively and significantly diminishes, and since noise is hardly visible in these regions, an image becomes sharper and resolute—with noise being largely reduced in flat regions. Empirical results from both simulated and real experiments demonstrate that our method outperforms some of the state-of-the-art classical methods based on the total variation framework.
A noise-suppressing and edge-preserving multiframe super-resolution image reconstruction method
(Signal Processing: Image Communication, 2015-03-12) Maiseli, Baraka J.; Ally, Nassor; Gao, Huijun
Super-resolution technology is an approach that helps to restore high quality images and videos from degraded ones. The method stems from an ill-posed minimization problem, which is usually solved using the L2 norm and some regularization techniques. Most of the classical regularizing functionals are based on the Total Variation and the Perona–Malik frameworks, which suffer from undesirable artifacts (blocking and staircasing). To address these problems, we have developed a super-resolution framework that integrates an adaptive diffusion-based regularizer. The model is feature-dependent: linear isotropic in flat regions, a condition that regularizes an image uniformly and removes noise more effectively; and nonlinear anisotropic near boundaries, which helps to preserve important image features, such as edges and contours. Additionally, the new regularizing kernel incorporates a shape-defining parameter that can be automatically updated to ensure convexity and stability of the corresponding energy functional. Comparisons with other methods show that our method is superior and, more importantly, can achieve higher reconstruction factors.
A Noise-Suppressing and Edge-Preserving Multiframe Super-Resolution Image Reconstruction Method
(Elsevier, 2015) Maiseli, Baraka J.; Ally, Nassor; Gao, Huijun
Super-resolution technology is an approach that helps to restore high quality images and videos from degraded ones. The method stems from an ill-posed minimization problem, which is usually solved using the L2 norm and some regularization techniques. Most of the classical regularizing functionals are based on the Total Variation and the Perona–Malik frameworks, which suffer from undesirable artifacts (blocking and staircasing). To address these problems, we have developed a super-resolution framework that integrates an adaptive diffusion-based regularizer. The model is feature-dependent: linear isotropic in flat regions, a condition that regularizes an image uniformly and removes noise more effectively; and nonlinear anisotropic near boundaries, which helps to preserve important image features, such as edges and contours. Additionally, the new regularizing kernel incorporates a shape-defining parameter that can be automatically updated to ensure convexity and stability of the corresponding energy functional. Comparisons with other methods show that our method is superior and, more importantly, can achieve higher reconstruction factors.
Optimum design of chamfer masks using symmetric mean absolute percentage error
(EURASIP Journal on Image and Video Processing, 2019-07-29) Maiseli, Baraka J.
Distance transform, a central operation in image and video analysis, involves finding the shortest path between feature and non-feature entries of a binary image. The process may be implemented using chamfer-based sequential algorithms that apply small-neighborhood masks to estimate the Euclidean metric. Success of these algorithms depends on the cost function used to optimize chamfer weights. And, for years, mean absolute error and mean squared error have been used for optimization. However, studies have revealed weaknesses of these cost functions—sensitivity against outliers, lack of symmetry, and biasedness—which limit their application. In this work, we have proposed a robust and a more accurate cost function, symmetric mean absolute percentage error, which attempts to address some weaknesses. The proposed function averages the absolute percentage errors in a set of measurements and offers interesting mathematical properties (smoothness, differentiability, boundedness, and robustness) that allow easy interpretation and analysis of the results. Numerical results show that chamfer masks designed under our optimization criterion generate lower errors. The present work has also proposed an automatic algorithm that converts coefficients of the designed real-valued masks into integers, which are preferable in most practical computing devices. Lastly, we have modified the chamfer algorithm to improve its speed and then embedded the proposed weights into the algorithm to compute distance maps of real images. Results show that the proposed algorithm is faster and uses fewer number of operations compared with those consumed by the classical chamfer algorithm. Our results may be useful in robotics to address the matching problem.
Recent developments and trends in point set registration methods
(Journal of Visual Communication and Image Representation, 2017) Maiseli, Baraka J.; Gu, Yanfeng; Gao, Huijun
Point set registration (PSR) is the process of computing a spatial transformation that optimally aligns pairs of point sets. The method helps to amalgamate multiple datasets into a common coordinate system. Because of their immense practical applications, several studies have attempted to address challenges inherent in the PSR problem. However, limited works exist to discuss recent developments, failures, and trends of the PSR methods. To date, a classical work of Tam et al., published in 2013, can be regarded as a comprehensive review paper for registration methods. Nevertheless, this work has inadequately revealed a range of possible knowledge gaps of the previous studies. Additionally, since the publication year of their work, more superior and state-of-the-art methods have been proposed. The present study surveys PSR approaches until 2017, and our primary focus is to expose central ideas and limitations of the methods to facilitate experts and practitioners advance the field.
Robust cost function for optimizing chamfer masks
(The Visual Computer, 2018) Maiseli, Baraka J.; Bai, LiFei; Yang, Xianqiang; Gu, Yanfeng; Gao, Huijun
Chamfering, a mask-driven technique, refers to a process of propagating local distances over an image to estimate a reference metric. Performance of the technique depends on the design of chamfer masks using cost functions. To date, most scholars have been using a mean absolute error and a mean squared error to formulate optimization problems for estimating weights in the chamfer masks. However, studies have shown that these optimization functions endure some potential weaknesses, including biasedness and sensitivity to outliers. Motivated by the weaknesses, the present work proposes an alternative difference function, RLog, that is unbiased, symmetrical, and robust. RLog takes the absolute logarithm of the relative accuracy of the estimated distance to compute optimal chamfer weights. Also, we have proposed an algorithm to map entries of the designed real-valued chamfer masks into integers. Analytical and experimental results demonstrate that chamfering based on our weights generate polygons and distance maps with lower errors. Methods and results of our work may be useful in robotics to address the matching problem.

Browsing by Author "Maiseli, Baraka J."

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