Image to Image Translation for Domain Adaptation

We propose a general framework for unsupervised domain adaptation, which allows deep neural networks trained on a source domain to be tested on a different target domain without requiring any training annotations in the target domain. This is achieved by adding extra networks and losses that help regularize the features extracted by the backbone encoder network. To this end we propose the novel use of the recently proposed unpaired image-toimage translation framework to constrain the features extracted by the encoder network. Specifically, we require that the features extracted are able to reconstruct the images in both domains. In addition we require that the distribution of features extracted from images in the two domains are indistinguishable. Many recent works can be seen as specific cases of our general framework. We apply our method for domain adaptation between MNIST, USPS, and SVHN datasets, and Amazon, Webcam and DSLR Office datasets in classification tasks, and also between GTA5 and Cityscapes datasets for a segmentation task. We demonstrate state of the art performance on each of these datasets

Algorithms and evaluation for object detection and tracking in computer vision

Vision-based object detection and tracking, especially for video surveillance applications, is studied from algorithms to performance evaluation. This dissertation is composed of four topics: (1) Background Modeling and Detection, (2) Performance Evaluation of Sensitive Target Detection, (3) Multi-view Multi-target Multi-Hypothesis Segmentation and Tracking of People, and (4) A Fine-Structure Image/Video Quality Measure. First, we present a real-time algorithm for foreground-background segmentation. It allows us to capture structural background variation due to periodic-like motion over a long period of time under limited memory. Our codebook-based representation is efficient in memory and speed compared with other background modeling techniques. Our method can handle scenes containing moving backgrounds or illumination variations, and it achieves robust detection for different types of videos. In addition to the basic algorithm, three features improving the algorithm are presented - Automatic Parameter Estimation, Layered Modeling/Detection and Adaptive Codebook Updating. Second, we introduce a performance evaluation methodology called Perturbation Detection Rate (PDR) analysis for measuring performance of foreground-background segmentation. It does not require foreground targets or knowledge of foreground distributions. It measures the sensitivity of a background subtraction algorithm in detecting possible low contrast targets against the background as a function of contrast. We compare four background subtraction algorithms using the methodology. Third, a multi-view multi-hypothesis approach to segmenting and tracking multiple persons on a ground plane is proposed. The tracking state space is the set of ground points of the people being tracked. During tracking, several iterations of segmentation are performed using information from human appearance models and ground plane homography. Two innovations are made in this chapter - (1) To more precisely locate the ground location of a person, all center vertical axes of the person across views are mapped to the top-view plane to find the intersection point. (2) To tackle the explosive state space due to multiple targets and views, iterative segmentation-searching is incorporated into a particle filtering framework. By searching for people's ground point locations from segmentations, a set of a few good particles can be identified, resulting in low computational cost. In addition, even if all the particles are away from the true ground point, some of them move towards the true one through the iterated process as long as they are located nearby. Finally, an objective no-reference measure is presented to assess fine-structure image/video quality. The proposed measure using local statistics reflects image degradation well in terms of noise and blur

Rapid and accurate clinical testing for COVID-19 by nicking and extension chain reaction system-based amplification (NESBA)

We herein describe rapid and accurate clinical testing for COVID-19 by nicking and extension chain reaction system-based amplification (NESBA), an ultrasensitive version of NASBA. The primers to identify SARS-CoV-2 viral RNA were designed to additionally contain the nicking recognition sequence at the 5'-end of conventional NASBA primers, which would enable nicking enzyme-aided exponential amplification of T7 RNA promoter-containing double-stranded DNA (T7DNA). As a consequence of this substantially enhanced amplification power, the NESBA technique was able to ultrasensitively detect SARS-CoV-2 genomic RNA (gRNA) down to 0.5 copies/μL (= 10 copies/reaction) for both envelope (E) and nucleocapsid (N) genes within 30 min under isothermal temperature (41 °C). When the NESBA was applied to test a large cohort of clinical samples (n = 98), the results fully agreed with those from qRT-PCR and showed the excellent accuracy by yielding 100% clinical sensitivity and specificity. By employing multiple molecular beacons with different fluorophore labels, the NESBA was further modulated to achieve multiplex molecular diagnostics, so that the E and N genes of SARS-CoV-2 gRNA were simultaneously assayed in one-pot. By offering the superior analytical performances over the current qRT-PCR, the isothermal NESBA technique could serve as very powerful platform technology to realize the point-of-care (POC) diagnosis for COVID-19

A FINE-STRUCTURE IMAGE/VIDEO QUALITY MEASURE USING LOCAL STATISTICS

An objective no-reference measure is presented to assess fine-structure image/video quality. It was designed to measure image/video quality for video surveillance applications, especially for background modeling and foreground object detection. The proposed measure using local statistics reflects image degradation well in terms of noise and blur. The experimental results on a background subtraction algorithm validate the usefulness of the proposed measure, by showing its correlation with the algorithm’s performance. 1

Sustainable Asphalt Mixtures with Enhanced Water Resistance for Flood-Prone Regions Using Recycled LDPE and Carnauba–Soybean Oil Additive

This manuscript presents a comprehensive study on the sustainable optimization of asphalt mixtures tailored for regions prone to flooding. The research addresses the challenges associated with water damage to asphalt pavements by incorporating innovative additives. The study centers on incorporating recycled Low-Density Polyethylene (LDPE) and a tailored Carnauba–Soybean Oil Additive, advancing asphalt mixtures with a Control mix, LDPE (5%) + Control, and LDPE (5%) + 3% Oil + Control. A critical aspect of the research involves subjecting these mixtures to 30 wetting and drying cycles, simulating the conditions prevalent in tropical flood-prone areas. The incorporation of innovative additives in asphalt mixtures has demonstrated significant improvements across various performance parameters. Tensile Strength Ratio (TSR) tests revealed enhanced tensile strength, with the LDPE (5%) + 3% Oil-modified mixture exhibiting an impressive TSR of 85.7%. Dynamic Modulus tests highlighted improved rutting resistance, showcasing a remarkable increase to 214 MPa in the LDPE (5%) with a 3% Oil-modified mixture. The Semi-Circular Bending (SCB) test demonstrated increased fracture resistance and energy absorption, particularly in the LDPE (5%) with 3% Oil-modified mixture. Hamburg Wheel-Tracking (HWT) tests indicated enhanced moisture resistance and superior rutting resistance at 20,000 cycles for the same mixture. Cantabro tests underscored improved aggregate shatter resistance, with the LDPE (5%) + 3% Oil-modified mixture exhibiting the lowest weight loss rate at 9.820%. Field tests provided real-world insights, with the LDPE (5%) + 3% Oil mixture displaying superior stability, a 61% reduction in deflection, and a 256% improvement in surface modulus over the control mixture. This research lays the groundwork for advancing the development of sustainable, high-performance road pavement materials, marking a significant stride towards resilient infrastructure in flood-prone areas

Background updating for visual surveillance

Abstract. Scene changes such as moved objects, parked vehicles, or opened/closed doors need to be carefully handled so that interesting foreground targets can be detected along with the short-term background layers created by those changes. A simple layered modeling technique is embedded into a codebook-based background subtraction algorithm to update a background model. In addition, important issues related to background updating for visual surveillance are discussed. Experimental results on surveillance examples, such as unloaded packages and unattended objects, are presented by showing those objects as short-term background layers.