Browsing by Author "Oluyide, Oluwakorede Monica."

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Automatic lung segmentation using graph cut optimization.
(2015) Oluyide, Oluwakorede Monica.; Viriri, Serestina.; Tapamo, Jules-Raymond.
Medical Imaging revolutionized the practice of diagnostic medicine by providing a means of visualizing the internal organs and structure of the body. Computer technologies have played an increasing role in the acquisition and handling, storage and transmission of these images. Due to further advances in computer technology, research efforts have turned towards adopting computers as assistants in detecting and diagnosing diseases, resulting in the incorporation of Computer-aided Detection (CAD) systems in medical practice. Computed Tomography (CT) images have been shown to improve accuracy of diagnosis in pulmonary imaging. Segmentation is an important preprocessing necessary for high performance of the CAD. Lung segmentation is used to isolate the lungs for further analysis and has the advantage of reducing the search space and computation time involved in disease detection. This dissertation presents an automatic lung segmentation method using Graph Cut optimization. Graph Cut produces globally optimal solutions by modeling the image data and spatial relationship among the pixels. Several objects in the thoracic CT image have similar pixel values to the lungs, and the global solutions of Graph Cut produce segmentation results where the lungs, and all other objects similar in intensity value to the lungs, are included. A distance prior encoding the euclidean distance of pixels from the set of pixels belonging to the object of interest is proposed to constrain the solution space of the Graph Cut algorithm. A segmentation method using the distance-constrained Graph Cut energy is also proposed to isolate the lungs in the image. The results indicate the suitability of the distance prior as a constraint for Graph Cut and shows the effectiveness of the proposed segmentation method in accurately segmenting the lungs from a CT image.
Candidate generation and validation techniques for pedestrian detection in thermal (infrared) surveillance videos.
(2022) Oluyide, Oluwakorede Monica.; Walingo, Tom Mmbasu.; Tapamo, Jules-Raymond.
Video surveillance systems have become prevalent. Factors responsible for this prevalence include, but are not limited to, rapid advancements in technology, reduction in the cost of surveillance systems and changes in user demand. Research in video surveillance is majorly driven by rising global security needs which in turn increase the demand for proactive systems which monitor persistently. Persistent monitoring is a challenge for most video surveillance systems because they depend on visible light cameras. Visible light cameras depend on the presence of external light and can easily be undermined by over-, under, or non-uniform illumination. Thermal infrared cameras have been considered as alternatives to visible light cameras because they measure the intensity of infrared energy emitted from objects and so can function persistently. Many methods put forward make use of methods developed for visible footage, but these tend to underperform in infrared images due to different characteristics of thermal footage compared to visible footage. This thesis aims to increase the accuracy of pedestrian detection in thermal infrared surveillance footage by incorporating strategies into existing frameworks used in visible image processing techniques for IR pedestrian detection without the need to initially assume a model for the image distribution. Therefore, two novel techniques for candidate generation were formulated. The first is an Entropy-based histogram modication algorithm that incorporates a strategy for energy loss to iteratively modify the histogram of an image for background elimination and pedestrian retention. The second is a Background Subtraction method featuring a strategy for building a reliable background image without needing to use the whole video frame. Furthermore, pedestrian detection involves simultaneously solving several sub-tasks while adapting each task with IR-speci_c adaptations. Therefore, a novel semi-supervised single model for pedestrian detection was formulated that eliminates the need for separate modules of candidate generation and validation by integrating region and boundary properties of the image with motion patterns such that all the _ne-tuning and adjustment happens during energy minimization. Performance evaluations have been performed on four publicly available benchmark surveillance datasets consisting of footage taken under a wide variety of weather conditions and taken from different perspectives.