Intelligent Debris Mass Estimation Model for Autonomous Underwater Vehicle

Marine debris poses a significant threat to the survival of marine wildlife, often leading to entanglement and starvation, ultimately resulting in death. Therefore, removing debris from the ocean is crucial to restore the natural balance and allow marine life to thrive. Instance segmentation is an advanced form of object detection that identifies objects and precisely locates and separates them, making it an essential tool for autonomous underwater vehicles (AUVs) to navigate and interact with their underwater environment effectively. AUVs use image segmentation to analyze images captured by their cameras to navigate underwater environments. In this paper, we use instance segmentation to calculate the area of individual objects within an image, we use YOLOV7 in Roboflow to generate a set of bounding boxes for each object in the image with a class label and a confidence score for every detection. A segmentation mask is then created for each object by applying a binary mask to the object's bounding box. The masks are generated by applying a binary threshold to the output of a convolutional neural network trained to segment objects from the background. Finally, refining the segmentation mask for each object is done by applying post-processing techniques such as morphological operations and contour detection, to improve the accuracy and quality of the mask. The process of estimating the area of instance segmentation involves calculating the area of each segmented instance separately and then summing up the areas of all instances to obtain the total area. The calculation is carried out using standard formulas based on the shape of the object, such as rectangles and circles. In cases where the object is complex, the Monte Carlo method is used to estimate the area. This method provides a higher degree of accuracy than traditional methods, especially when using a large number of samples

Enhancement and stylization of photographs

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 89-95).A photograph captured by a digital camera may be the final product for many casual photographers. However, for professional photographers, this photograph is only the beginning: experts often spend hours on enhancing and stylizing their photographs. These enhancements range from basic exposure and contrast adjustments to dramatic alterations. It is these enhancements - along with composition and timing - that distinguish the work of professionals and casual photographers. The goal of this thesis is to narrow the gap between casual and professional photographers. We aim to empower casual users with methods for making their photographs look better. Professional photographers could also benefit from our findings: our enhancement methods produce a better starting point for professional processing. We propose and evaluate three different methods for image enhancement and stylization. First method is based on photographic intuition and is fully automatic. The second method relies on expert's input for training; after the training this method can be used to automatically predict expert adjustments for previously unseen photographs. The third method uses a grammar-based representation to sample the space of image filter and relies on user input to select novel and interesting filters.by Vladimir Leonid Bychkovsky.Ph.D

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform

Entropy in Image Analysis III

Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future

Implementation exploration of imaging algorithms on FPGAs

This portfolio thesis documents the work carried out as part of the Engineering Doctorate (EngD) programme undertaken at the Institute for System Level Integration. This work was sponsored and aided by Thales Optronics Ltd, a company well versed in developing specialised electro-optical devices. Field programmable gate arrays (FPGAs) are the devices of choice for custom image processing algorithms due to their reconfigurable nature. This also makes them more economical for low volume production runs where non-recoverable engineering costs are a large factor. Asynchronous circuits have had a remarkable surge in development over the last 20 years, to such an extent that they are beginning to displace conventional designs for niche applications. Their unique ability to adapt to environmental and data dependent processing needs have lead them to out-perform synchronous designs in ASIC platforms for certain applications. Abstract The main body of research was separated into three areas of work presented as three technical documents. The first area of research addresses an FPGA implementation of contrast limited adaptive histogram equalisation (CLAHE), an algorithm which provides increased visual performance over conventional methods. From this, a novel implementation strategy was provided along with the key design factors for future use in a commercial context. The second area of research investigates the ability to create asynchronous circuits on FPGA devices. The main motivation for this work was to establish if any of the benefits which had been demonstrated for ASIC devices can be applied to FPGA devices. The investigation surmised the most suitable asynchronous design style for FPGA devices, a design flow to allow asynchronous circuits to function correctly on FPGAs and novel design strategies to implement consistent and repeatable asynchronous components. The result of this work established a route to implement circuits asynchronously in an FPGA. The final area of research focused on a unique conversion tool that allows synchronous circuits to run asynchronously on FPGAs whilst maintaining the same data flow patterns. This research produced an automated tool capable of implementing circuits on an FPGA asynchronously from their synchronous descriptions. This approach allowed the primary motivators of this work to be addressed. The results of this work show timing, resource utilisation and noise spectrum benefits by implementing circuits asynchronously on FPGA devices

Energy-efficient circuits and systems for computational imaging and vision on mobile devices

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 125-127).Eighty five percent of images today are taken by cell phones. These images are not merely projections of light from the scene onto the camera sensor but result from a deep calculation. This calculation involves a number of computational imaging algorithms such as high dynamic range (HDR) imaging, panorama stitching, image deblurring and low-light imaging that compensate for camera limitations, and a number of deep learning based vision algorithms such as face recognition, object recognition and scene understanding that make inference on these images for a variety of emerging applications. However, because of their high computational complexity, mobile CPU or GPU based implementations of these algorithms do not achieve real-time performance. Moreover, offloading these algorithms to the cloud is not a viable solution because wirelessly transmitting large amounts of image data results in long latency and high energy consumption, making them unsuitable for mobile devices. This work solves these problems by designing energy-efficient hardware accelerators targeted at these applications. It presents the architecture of two complete computational imaging systems for energy-constrained mobile environments: (1) an energy-scalable accelerator for blind image deblurring, with an on-chip implementation and (2) a low-power processor for real-time motion magnification in videos, with an FPGA implementation. It also presents a 3D imaging platform and image processing workflow for 3D surface area assessment of dermatologic lesions. It demonstrates that such accelerator-based systems can enable energy-efficient integration of computational imaging and vision algorithms into mobile and wearable devices.by Priyanka Raina.Ph. D

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Visible Light Communication (VLC)

Visible light communication (VLC) using light-emitting diodes (LEDs) or laser diodes (LDs) has been envisioned as one of the key enabling technologies for 6G and Internet of Things (IoT) systems, owing to its appealing advantages, including abundant and unregulated spectrum resources, no electromagnetic interference (EMI) radiation and high security. However, despite its many advantages, VLC faces several technical challenges, such as the limited bandwidth and severe nonlinearity of opto-electronic devices, link blockage and user mobility. Therefore, significant efforts are needed from the global VLC community to develop VLC technology further. This Special Issue, “Visible Light Communication (VLC)”, provides an opportunity for global researchers to share their new ideas and cutting-edge techniques to address the above-mentioned challenges. The 16 papers published in this Special Issue represent the fascinating progress of VLC in various contexts, including general indoor and underwater scenarios, and the emerging application of machine learning/artificial intelligence (ML/AI) techniques in VLC