Explicit Visual Prompting for Universal Foreground Segmentations

Foreground segmentation is a fundamental problem in computer vision, which includes salient object detection, forgery detection, defocus blur detection, shadow detection, and camouflage object detection. Previous works have typically relied on domain-specific solutions to address accuracy and robustness issues in those applications. In this paper, we present a unified framework for a number of foreground segmentation tasks without any task-specific designs. We take inspiration from the widely-used pre-training and then prompt tuning protocols in NLP and propose a new visual prompting model, named Explicit Visual Prompting (EVP). Different from the previous visual prompting which is typically a dataset-level implicit embedding, our key insight is to enforce the tunable parameters focusing on the explicit visual content from each individual image, i.e., the features from frozen patch embeddings and high-frequency components. Our method freezes a pre-trained model and then learns task-specific knowledge using a few extra parameters. Despite introducing only a small number of tunable parameters, EVP achieves superior performance than full fine-tuning and other parameter-efficient fine-tuning methods. Experiments in fourteen datasets across five tasks show the proposed method outperforms other task-specific methods while being considerably simple. The proposed method demonstrates the scalability in different architectures, pre-trained weights, and tasks. The code is available at: https://github.com/NiFangBaAGe/Explicit-Visual-Prompt.Comment: arXiv admin note: substantial text overlap with arXiv:2303.1088

Plant Seed Identification

Plant seed identification is routinely performed for seed certification in seed trade, phytosanitary certification for the import and export of agricultural commodities, and regulatory monitoring, surveillance, and enforcement. Current identification is performed manually by seed analysts with limited aiding tools. Extensive expertise and time is required, especially for small, morphologically similar seeds. Computers are, however, especially good at recognizing subtle differences that humans find difficult to perceive. In this thesis, a 2D, image-based computer-assisted approach is proposed. The size of plant seeds is extremely small compared with daily objects. The microscopic images of plant seeds are usually degraded by defocus blur due to the high magnification of the imaging equipment. It is necessary and beneficial to differentiate the in-focus and blurred regions given that only sharp regions carry distinctive information usually for identification. If the object of interest, the plant seed in this case, is in- focus under a single image frame, the amount of defocus blur can be employed as a cue to separate the object and the cluttered background. If the defocus blur is too strong to obscure the object itself, sharp regions of multiple image frames acquired at different focal distance can be merged together to make an all-in-focus image. This thesis describes a novel non-reference sharpness metric which exploits the distribution difference of uniform LBP patterns in blurred and non-blurred image regions. It runs in realtime on a single core cpu and responses much better on low contrast sharp regions than the competitor metrics. Its benefits are shown both in defocus segmentation and focal stacking. With the obtained all-in-focus seed image, a scale-wise pooling method is proposed to construct its feature representation. Since the imaging settings in lab testing are well constrained, the seed objects in the acquired image can be assumed to have measureable scale and controllable scale variance. The proposed method utilizes real pixel scale information and allows for accurate comparison of seeds across scales. By cross-validation on our high quality seed image dataset, better identification rate (95%) was achieved compared with pre- trained convolutional-neural-network-based models (93.6%). It offers an alternative method for image based identification with all-in-focus object images of limited scale variance. The very first digital seed identification tool of its kind was built and deployed for test in the seed laboratory of Canadian food inspection agency (CFIA). The proposed focal stacking algorithm was employed to create all-in-focus images, whereas scale-wise pooling feature representation was used as the image signature. Throughput, workload, and identification rate were evaluated and seed analysts reported significantly lower mental demand (p = 0.00245) when using the provided tool compared with manual identification. Although the identification rate in practical test is only around 50%, I have demonstrated common mistakes that have been made in the imaging process and possible ways to deploy the tool to improve the recognition rate

Recurrent Segmentation for Variable Computational Budgets

State-of-the-art systems for semantic image segmentation use feed-forward pipelines with fixed computational costs. Building an image segmentation system that works across a range of computational budgets is challenging and time-intensive as new architectures must be designed and trained for every computational setting. To address this problem we develop a recurrent neural network that successively improves prediction quality with each iteration. Importantly, the RNN may be deployed across a range of computational budgets by merely running the model for a variable number of iterations. We find that this architecture is uniquely suited for efficiently segmenting videos. By exploiting the segmentation of past frames, the RNN can perform video segmentation at similar quality but reduced computational cost compared to state-of-the-art image segmentation methods. When applied to static images in the PASCAL VOC 2012 and Cityscapes segmentation datasets, the RNN traces out a speed-accuracy curve that saturates near the performance of state-of-the-art segmentation methods

Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision

Signal capture stands in the forefront to perceive and understand the environment and thus imaging plays the pivotal role in mobile vision. Recent explosive progresses in Artificial Intelligence (AI) have shown great potential to develop advanced mobile platforms with new imaging devices. Traditional imaging systems based on the "capturing images first and processing afterwards" mechanism cannot meet this unprecedented demand. Differently, Computational Imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems.Thanks to AI, CI can now be used in real systems by integrating deep learning algorithms into the mobile vision platform to achieve the closed loop of intelligent acquisition, processing and decision making, thus leading to the next revolution of mobile vision.Starting from the history of mobile vision using digital cameras, this work first introduces the advances of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Motivated by the fact that most existing studies only loosely connect CI and AI (usually using AI to improve the performance of CI and only limited works have deeply connected them), in this work, we propose a framework to deeply integrate CI and AI by using the example of self-driving vehicles with high-speed communication, edge computing and traffic planning. Finally, we outlook the future of CI plus AI by investigating new materials, brain science and new computing techniques to shed light on new directions of mobile vision systems

A Survey of Deep Learning-Based Object Detection

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline, thoroughly and deeply, in this survey, we first analyze the methods of existing typical detection models and describe the benchmark datasets. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.Comment: 30 pages,12 figure

Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision

To appear in CVIUStudies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision

Efficient and Accurate Disparity Estimation from MLA-Based Plenoptic Cameras

This manuscript focuses on the processing images from microlens-array based plenoptic cameras. These cameras enable the capturing of the light field in a single shot, recording a greater amount of information with respect to conventional cameras, allowing to develop a whole new set of applications. However, the enhanced information introduces additional challenges and results in higher computational effort. For one, the image is composed of thousand of micro-lens images, making it an unusual case for standard image processing algorithms. Secondly, the disparity information has to be estimated from those micro-images to create a conventional image and a three-dimensional representation. Therefore, the work in thesis is devoted to analyse and propose methodologies to deal with plenoptic images. A full framework for plenoptic cameras has been built, including the contributions described in this thesis. A blur-aware calibration method to model a plenoptic camera, an optimization method to accurately select the best microlenses combination, an overview of the different types of plenoptic cameras and their representation. Datasets consisting of both real and synthetic images have been used to create a benchmark for different disparity estimation algorithm and to inspect the behaviour of disparity under different compression rates. A robust depth estimation approach has been developed for light field microscopy and image of biological samples

Low-cost portable microscopy systems for biomedical imaging and healthcare applications

In recent years, the development of low-cost portable microscopes (LPMs) has opened new possibilities for disease detection and biomedical research, especially in resource-limited areas. Despite these advancements, the majority of existing LPMs are hampered by sophisticated optical and mechanical designs, require extensive post-data analysis, and are often tailored for specific biomedical applications, limiting their broader utility. Furthermore, creating an optical-sectioning microscope that is both compact and cost effective presents a significant challenge. Addressing these critical gaps, this PhD study aims to: (1) develop a universally applicable LPM featuring a simplified mechanical and optical design for real-time biomedical imaging analysis, and (2) design a novel, smartphone-based optical sectioning microscope that is both compact and affordable. These objectives are driven by the need to enhance accessibility to quality diagnostic tools in varied settings, promising a significant leap forward in the democratization of biomedical imaging technologies. With 3D printing, optimised optical design, and AI techniques, we can develop LPM’s real time analysis functionality. I conducted a literature review on LPMs and related applications in my study and implemented two low-cost prototype microscopes and one theoretical study. 1) The first project is a portable AI fluorescence microscope based on a webcam and the NVIDIA Jetson Nano (NJN) with real-time analysis functionality. The system was 3D printed, weighing ~250 grams with a size of 145mm × 172 mm × 144 mm (L×W×H) and costing ~$400. It achieves a physical magnification of ×5 and can resolve 228.1 lp/mm USAF features. The system can recognise and count fluorescent beads and human red blood cells (RBCs). 2) I developed a smartphone-based optical sectioning microscope using the HiLo technique. To our knowledge, it is the first smartphone-based HiLo microscope that offers low-cost optical-sectioned widefield imaging. It has a 571.5 μm telecentric scanning range and an 11.7 μm axial resolution. I successfully used it to realize optical sectioning imaging of fluorescent beads. For this system, I developed a new low-cost HiLo microscopy technique using microlens arrays (MLAs) with incoherent light-emitting diode (LED) light sources. I conducted a numerical simulation study assessing the integration of uncoherent LEDs and MLAs for a low-cost HiLo system. The MLA can generate structured illumination in HiLo. How the MLA’s geometry structure and physical parameters affect the image performance were discussed in detail. This PhD thesis explores the advancement of low-cost portable microscopes (LPMs) through the integration of 3D printing, optimized optical design, and artificial intelligence (AI) techniques to enhance their real-time analysis capabilities. The research involved a comprehensive literature review on LPMs and their applications, leading to the development of two innovative prototype LPMs, alongside a theoretical study. These works contribute significantly to the field by not only addressing the technical and financial barriers associated with advanced microscopy but also by laying the groundwork for future innovations in portable and accessible biomedical imaging. Through its focus on simplification, affordability, and practicality, the research holds promise for substantially expanding the reach and impact of diagnostic imaging technologies, especially in those resource-limited areas.In recent years, the development of low-cost portable microscopes (LPMs) has opened new possibilities for disease detection and biomedical research, especially in resource-limited areas. Despite these advancements, the majority of existing LPMs are hampered by sophisticated optical and mechanical designs, require extensive post-data analysis, and are often tailored for specific biomedical applications, limiting their broader utility. Furthermore, creating an optical-sectioning microscope that is both compact and cost effective presents a significant challenge. Addressing these critical gaps, this PhD study aims to: (1) develop a universally applicable LPM featuring a simplified mechanical and optical design for real-time biomedical imaging analysis, and (2) design a novel, smartphone-based optical sectioning microscope that is both compact and affordable. These objectives are driven by the need to enhance accessibility to quality diagnostic tools in varied settings, promising a significant leap forward in the democratization of biomedical imaging technologies. With 3D printing, optimised optical design, and AI techniques, we can develop LPM’s real time analysis functionality. I conducted a literature review on LPMs and related applications in my study and implemented two low-cost prototype microscopes and one theoretical study. 1) The first project is a portable AI fluorescence microscope based on a webcam and the NVIDIA Jetson Nano (NJN) with real-time analysis functionality. The system was 3D printed, weighing ~250 grams with a size of 145mm × 172 mm × 144 mm (L×W×H) and costing ~$400. It achieves a physical magnification of ×5 and can resolve 228.1 lp/mm USAF features. The system can recognise and count fluorescent beads and human red blood cells (RBCs). 2) I developed a smartphone-based optical sectioning microscope using the HiLo technique. To our knowledge, it is the first smartphone-based HiLo microscope that offers low-cost optical-sectioned widefield imaging. It has a 571.5 μm telecentric scanning range and an 11.7 μm axial resolution. I successfully used it to realize optical sectioning imaging of fluorescent beads. For this system, I developed a new low-cost HiLo microscopy technique using microlens arrays (MLAs) with incoherent light-emitting diode (LED) light sources. I conducted a numerical simulation study assessing the integration of uncoherent LEDs and MLAs for a low-cost HiLo system. The MLA can generate structured illumination in HiLo. How the MLA’s geometry structure and physical parameters affect the image performance were discussed in detail. This PhD thesis explores the advancement of low-cost portable microscopes (LPMs) through the integration of 3D printing, optimized optical design, and artificial intelligence (AI) techniques to enhance their real-time analysis capabilities. The research involved a comprehensive literature review on LPMs and their applications, leading to the development of two innovative prototype LPMs, alongside a theoretical study. These works contribute significantly to the field by not only addressing the technical and financial barriers associated with advanced microscopy but also by laying the groundwork for future innovations in portable and accessible biomedical imaging. Through its focus on simplification, affordability, and practicality, the research holds promise for substantially expanding the reach and impact of diagnostic imaging technologies, especially in those resource-limited areas