<p dir="ltr">Computer vision (CV) using deep learning can equip machines with the ability to understand visual information. CV has seen widespread adoption across numerous industries, from autonomous vehicles to facial recognition on smartphones. However, alongside these advancements, there have been increasing concerns about reproducing the results. The difficulty of reproducibility may arise due to multiple reasons, such as differences in execution environments, missing or incompatible software libraries, proprietary data, and the stochastic nature in some software. A study conducted by the Nature journal reveals that more than 70% of researchers failed to reproduce other researcher's experiments; over 50% failed to reproduce their own experiments. Given the critical role that computer vision plays in many applications, for example in edge devices like mobile phones and drones, irreproducibility poses significant challenges for researchers and practitioners. To address these concerns, this thesis presents a systematic approach at analyzing and improving the reproducibility of computer vision models through case studies. This approach combines rigorous documentation standards, standardized software environment, and a comprehensive guide of best practices. By implementing these strategies, we aim to bridge the gap between research and practice, ensuring that innovations in computer vision can be effectively reproduced and deployed. </p>

Aging is a complex process by which a combination of environmental, genetic and stochastic factors generate whole-system changes that modify organ and tissue function and alter physiological processes. Over the last few decades, many genetic and environmental modulators of aging have been found to be highly conserved between humans and a diverse group of model organisms. Yet, an integrative understanding of how these environmental and genetic variables interact over time in a whole organism to modulate the systemic changes involved in aging is lacking. The goal of this thesis project is to advance a systems perspective of aging by providing the experimental tools and conceptual framework for dissecting the regulatory connection between environmental inputs, molecular outputs and long term aging phenotypes in Caenorhabditis elegans, an experimentally tractable multi-cellular model for aging. Specifically, this work advances the quantitative imaging toolsets available to biologists by developing and refining microfluidic, hardware, computer vision, and software integration tools for high-throughput, high-content imaging of C. elegans. As a result of these technological advances, new roles for the TGF-beta and serotonin signaling pathways in encoding environmental food signals to influence longevity were uncovered and quantitatively characterized. Moreover, this work develops and integrates new microfluidic technologies with off-chip support systems to establish a platform for long-term tracking of the health and longevity trajectories of large numbers of individual C. elegans. The capabilities of this platform have the potential to address many important questions in aging including addressing environmental determinants of aging, the sources of inter-individual variability, the time course of aging-related declines and the effects of interventional strategies to improve health outcomes. Together, the toolsets for quantitative imaging and the long-term culture platform permit the large-scale investigation of both the internal state and long-term behavioral and health outputs of an important multicellular model organism for aging.

The main outcomes of this research are the design of a foreground detection algorithm, which is more accurate and less time consuming than existing algorithms. By the term accuracy we mean an exact mask (which satisfies the respective ground truth value) of the foreground object(s). Motion detection being the prior component of foreground detection process can be achieved via pixel based and block based methods, both of which have their own merits and disadvantages. Pixel based methods are efficient in terms of accuracy but a time consuming process, so cannot be recommended for real time applications. On the other hand block based motion estimation has relatively less accuracy but consumes less time and is thus ideal for real-time applications. In the first proposed algorithm, block based motion estimation technique is opted for timely execution. To overcome the issue of accuracy another morphological based technique was adopted called opening-and-closing by reconstruction, which is a pixel based operation so produces higher accuracy and requires lesser time in execution. Morphological operation opening-and-closing by reconstruction finds the maxima and minima inside the foreground object(s). Thus this novel simultaneous process compensates for the lower accuracy of block based motion estimation. To verify the efficiency of this algorithm a complex video consisting of multiple colours, and fast and slow motions at various places was selected. Based on 11 different performance measures the proposed algorithm achieved an average accuracy of more than 24.73% than four of the well-established algorithms. Background subtraction, being the most cited algorithm for foreground detection, encounters the major problem of proper threshold value at run time. For effective value of the threshold at run time in background subtraction algorithm, the primary component of the foreground detection process, motion is used, in this next proposed algorithm. For the said purpose the smooth histogram peaks and valley of the motion were analyzed, which reflects the high and slow motion areas of the moving object(s) in the given frame and generates the threshold value at run time by exploiting the values of peaks and valley. This proposed algorithm was tested using four recommended video sequences including indoor and outdoor shoots, and were compared with five high ranked algorithms. Based on the values of standard performance measures, the proposed algorithm achieved an average of more than 12.30% higher accuracy results.

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Computer Science / Master / Master of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Computer Science / Master / Master of Philosophy

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy