Oluwakorede M. Oluyide

Bio: Oluwakorede M. Oluyide is an academic researcher from University of KwaZulu-Natal. The author has contributed to research in topics: Pedestrian detection & Computer science. The author has an hindex of 1, co-authored 2 publications receiving 11 citations.

Automatic lung segmentation based on Graph Cut using a distance-constrained energy

Abstract: Lung segmentation serves to ensure that all the parts of the lungs are considered during pulmonary image analysis by isolating the lung from the surrounding anatomy in the image. Research has shown that computed tomography (CT) images greatly improves the accuracy of the diagnosis obtained by a physician for lung cancer detection. Therefore, inspired by the success of Graph Cut in image segmentation and given that manual methods of analysing CT images are tedious and time-consuming, an automatic segmentation method based on Graph Cut is proposed which makes use of a distance-constrained energy (DCE). Graph Cut produces globally optimal solutions by modelling the image data and spatial relationship among the pixels. However, several anatomical regions in the thoracic CT image have pixel intensity values similar to the lungs, leading to results where the lung tissue and all these regions are included in the segmentation result. The global energy function is, therefore, further constrained by using the distance of pixels from a coarsely segmented region of the CT image containing the lungs. The proposed method, utilising the DCE function, shows significant improvement over using the unconstrained energy function in segmenting the lungs from the CT images using Graph Cut.

Fast Background Subtraction and Graph Cut for Thermal Pedestrian Detection

Abstract: Most studies in pedestrian detection from surveillance videos focus on analysing footage from visible image cameras which require external light and are sensitive to illumination changes. The presence or absence of external light determines the possibility of monitoring a scene while variations in illumination determines the degree of detection accuracy. In this paper, pedestrian detection is performed on thermal (infrared) images using a Graph-based background-subtraction technique. First, to address the limitation of thermal images such as polarity changes and halo around objects of extreme temperatures, motion is used as leverage in generating a reliable background which allows for candidate region extraction for further processing. Second, to address the limitations of automatic detection methods in the presence of multiple objects and absence of sharp edges, interactive Graph Cut is used to perform the final labelling of the valid candidate regions. Experiments on the all-inclusive benchmark dataset of thermal imagery from the Ohio State University (OSU) shows the effectiveness of the proposed method.

Automatic Dynamic Range Adjustment for Pedestrian Detection in Thermal (Infrared) Surveillance Videos

Abstract: This paper presents a novel candidate generation algorithm for pedestrian detection in infrared surveillance videos. The proposed method uses a combination of histogram specification and iterative histogram partitioning to progressively adjust the dynamic range and efficiently suppress the background of each video frame. This pairing eliminates the general-purpose nature associated with histogram partitioning where chosen thresholds, although reasonable, are usually not suitable for specific purposes. Moreover, as the initial threshold value chosen by histogram partitioning is sensitive to the shape of the histogram, specifying a uniformly distributed histogram before initial partitioning provides a stable histogram shape. This ensures that pedestrians are present in the image at the convergence point of the algorithm. The performance of the method is tested using four publicly available thermal datasets. Experiments were performed with images from four publicly available databases. The results show the improvement of the proposed method over thresholding with minimum-cross entropy, the robustness across images acquired under different conditions, and the comparable results with other methods in the literature.

A semi-automatic motion-constrained Graph Cut algorithm for Pedestrian Detection in thermal surveillance videos

Abstract: This article presents a semi-automatic algorithm that can detect pedestrians from the background in thermal infrared images. The proposed method is based on the powerful Graph Cut optimisation algorithm which produces exact solutions for binary labelling problems. An additional term is incorporated into the energy formulation to bias the detection framework towards pedestrians. Therefore, the proposed method obtains reliable and robust results through user-selected seeds and the inclusion of motion constraints. An additional advantage is that it enables the algorithm to generalise well across different databases. The effectiveness of our method is demonstrated on four public databases and compared with several methods proposed in the literature and the state-of-the-art. The method obtained an average precision of 98.92% and an average recall of 99.25% across the four databases considered and outperformed methods which made use of the same databases.

A Fast 2-D Otsu lung tissue image segmentation algorithm based on improved PSO

Abstract: In order to reduce the time of lung tissue image segmentation, we proposed a fast 2-D Otsu lung tissue image segmentation algorithm based on improved PSO. Firstly, in the 2-D Otsu algorithm, the search scope of 2-D gray threshold is limited in a long and narrow region, which is the neighbourhood of the diagonal from the third region to the first region of the 2-D gray histogram, and the search scope and the computation are reduced, operation speed is improved. Secondly, In the PSO algorithm, the position of the particles is adjusted during iterating based on the principle of symmetric disposition, so as to avoid PSO falling into local optimal solution and improves the accuracy of threshold searching. Finally, a lung CT image with 1280×960 resolutions is segmented by our algorithm and other traditional algorithms, and a comparison is given. The segmentation threshold of our method is 85, the difference is less than 5 comparing with that of other traditional algorithms, and it shows that our method has almost the same searching precision as the traditional algorithms. The time cost is only 162ms, which is far less than the traditional algorithms, and it shows that our method improve the segmentation speed. It can be concluded that our method can not only satisfy the requirement of segmentation precision, but also meet the requirement of operation speed.

COVID-19 detection on IBM quantum computer with classical-quantum transfer learning

Abstract: Diagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT) In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) of IBM, as well as in different simulators (Pennylane, Qiskit-Aer and Cirq) By using a small number of data sets such as 126 COVID-19 and 100 normal CT images, we obtained a positive or negative classification of COVID-19 with 90% success in classical computers, while we achieved a high success rate of 94%–100% in quantum computers Also, according to the results obtained, machine learning process in classical computers requiring more processors and time than quantum computers can be realized in a very short time with a very small quantum processor such as 4 qubits in quantum computers If the size of the data set is small;due to the superior properties of quantum, it is seen that according to the classification of COVID-19 and normal, in terms of machine learning, quantum computers seem to outperform traditional computers © TUBITAK

Human Detection in Aerial Thermal Images Using Faster R-CNN and SSD Algorithms

Abstract: The automatic detection of humans in aerial thermal imagery plays a significant role in various real-time applications, such as surveillance, search and rescue and border monitoring. Small target size, low resolution, occlusion, pose, and scale variations are the significant challenges in aerial thermal images that cause poor performance for various state-of-the-art object detection algorithms. Though many deep-learning-based object detection algorithms have shown impressive performance for generic object detection tasks, their ability to detect smaller objects in the aerial thermal images is analyzed through this study. This work carried out the performance evaluation of Faster R-CNN and single-shot multi-box detector (SSD) algorithms with different backbone networks to detect human targets in aerial view thermal images. For this purpose, two standard aerial thermal datasets having human objects of varying scale are considered with different backbone networks, such as ResNet50, Inception-v2, and MobileNet-v1. The evaluation results demonstrate that the Faster R-CNN model trained with the ResNet50 network architecture out-performed in terms of detection accuracy, with a mean average precision (mAP at 0.5 IoU) of 100% and 55.7% for the test data of the OSU thermal dataset and AAU PD T datasets, respectively. SSD with MobileNet-v1 achieved the highest detection speed of 44 frames per second (FPS) on the NVIDIA GeForce GTX 1080 GPU. Fine-tuning the anchor parameters of the Faster R-CNN ResNet50 and SSD Inception-v2 algorithms caused remarkable improvement in mAP by 10% and 3.5%, respectively, for the challenging AAU PD T dataset. The experimental results demonstrated the application of Faster R-CNN and SSD algorithms for human detection in aerial view thermal images, and the impact of varying backbone network and anchor parameters on the performance improvement of these algorithms.

COVID-19 detection on IBM quantum computer with classical-quantum transfer learning

Abstract: Diagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important. Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT). In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers. Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) of IBM, as well as in different simulators (Pennylane, Qiskit-Aer and Cirq). By using a small number of data sets such as 126 COVID-19 and 100 Normal CT images, we obtained a positive or negative classification of COVID-19 with 90% success in classical computers, while we achieved a high success rate of 94-100% in quantum computers. Also, according to the results obtained, machine learning process in classical computers requiring more processors and time than quantum computers can be realized in a very short time with a very small quantum processor such as 4 qubits in quantum computers. If the size of the data set is small; Due to the superior properties of quantum, it is seen that according to the classification of COVID-19 and Normal, in terms of machine learning, quantum computers seem to outperform traditional computers.

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

Abstract: Summary Objectives : Automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods. Methods : We used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation. Results : We organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field. Conclusions : We noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general.