Evaluating the impact of task demands and block resolution on the effectiveness of pixel-based visualization

Pixel-based visualization is a popular method of conveying large amounts of numerical data graphically. Application scenarios include business and finance, bioinformatics and remote sensing. In this work, we examined how the usability of such visual representations varied across different tasks and block resolutions. The main stimuli consisted of temporal pixel-based visualization with a white-red color map, simulating monthly temperature variation over a six-year period. In the first study, we included 5 separate tasks to exert different perceptual loads. We found that performance varied considerably as a function of task, ranging from 75% correct in low-load tasks to below 40% in high-load tasks. There was a small but consistent effect of resolution, with the uniform patch improving performance by around 6% relative to higher block resolution. In the second user study, we focused on a high-load task for evaluating month-to-month changes across different regions of the temperature range. We tested both CIE L*u*v* and RGB color spaces. We found that the nature of the change-evaluation errors related directly to the distance between the compared regions in the mapped color space. We were able to reduce such errors by using multiple color bands for the same data range. In a final study, we examined more fully the influence of block resolution on performance, and found block resolution had a limited impact on the effectiveness of pixel-based visualization.peer-reviewe

Complexity plots

In this paper, we present a novel visualization technique for assisting in observation and analysis of algorithmic\ud complexity. In comparison with conventional line graphs, this new technique is not sensitive to the units of\ud measurement, allowing multivariate data series of different physical qualities (e.g., time, space and energy) to be juxtaposed together conveniently and consistently. It supports multivariate visualization as well as uncertainty visualization. It enables users to focus on algorithm categorization by complexity classes, while reducing visual impact caused by constants and algorithmic components that are insignificant to complexity analysis. It provides an effective means for observing the algorithmic complexity of programs with a mixture of algorithms and blackbox software through visualization. Through two case studies, we demonstrate the effectiveness of complexity plots in complexity analysis in research, education and application

Zero-Shot Digital Rock Image Segmentation with a Fine-Tuned Segment Anything Model

Accurate image segmentation is crucial in reservoir modelling and material characterization, enhancing oil and gas extraction efficiency through detailed reservoir models. This precision offers insights into rock properties, advancing digital rock physics understanding. However, creating pixel-level annotations for complex CT and SEM rock images is challenging due to their size and low contrast, lengthening analysis time. This has spurred interest in advanced semi-supervised and unsupervised segmentation techniques in digital rock image analysis, promising more efficient, accurate, and less labour-intensive methods. Meta AI's Segment Anything Model (SAM) revolutionized image segmentation in 2023, offering interactive and automated segmentation with zero-shot capabilities, essential for digital rock physics with limited training data and complex image features. Despite its advanced features, SAM struggles with rock CT/SEM images due to their absence in its training set and the low-contrast nature of grayscale images. Our research fine-tunes SAM for rock CT/SEM image segmentation, optimizing parameters and handling large-scale images to improve accuracy. Experiments on rock CT and SEM images show that fine-tuning significantly enhances SAM's performance, enabling high-quality mask generation in digital rock image analysis. Our results demonstrate the feasibility and effectiveness of the fine-tuned SAM model (RockSAM) for rock images, offering segmentation without extensive training or complex labelling

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Comparative Evaluation and Implementation of State-of-the-Art Techniques for Anomaly Detection and Localization in the Continual Learning Framework

openThe capability of anomaly detection (AD) to detect defects in industrial environments using only normal samples has attracted significant attention. However, traditional AD methods have primarily concentrated on the current set of examples, leading to a significant drawback of catastrophic forgetting when faced with new tasks. Due to the constraints in flexibility and the challenges posed by real-world industrial scenarios, there is an urgent need to strengthen the adaptive capabilities of AD models. Hence, this thesis introduces a unified framework that integrates continual learning (CL) and anomaly detection (AD) to accomplish the goal of anomaly detection in the continual learning (ADCL). To evaluate the effectiveness of the framework, a comparative analysis is performed to assess the performance of the three specific feature-based methods for the AD task: Coupled-Hypersphere-Based Feature Adaptation (CFA), Student-Teacher approach, and PatchCore. Furthermore, the framework incorporates the utilization of replay techniques to facilitate continual learning (CL). A comprehensive evaluation is conducted using a range of metrics to analyze the relative performance of each technique and identify the one that exhibits superior results. To validate the effectiveness of the proposed approach, the MVTec AD dataset, consisting of real-world images with pixel-based anomalies, is utilized. This dataset serves as a reliable benchmark for Anomaly Detection in the context of Continual Learning, providing a solid foundation for further advancements in the field.The capability of anomaly detection (AD) to detect defects in industrial environments using only normal samples has attracted significant attention. However, traditional AD methods have primarily concentrated on the current set of examples, leading to a significant drawback of catastrophic forgetting when faced with new tasks. Due to the constraints in flexibility and the challenges posed by real-world industrial scenarios, there is an urgent need to strengthen the adaptive capabilities of AD models. Hence, this thesis introduces a unified framework that integrates continual learning (CL) and anomaly detection (AD) to accomplish the goal of anomaly detection in the continual learning (ADCL). To evaluate the effectiveness of the framework, a comparative analysis is performed to assess the performance of the three specific feature-based methods for the AD task: Coupled-Hypersphere-Based Feature Adaptation (CFA), Student-Teacher approach, and PatchCore. Furthermore, the framework incorporates the utilization of replay techniques to facilitate continual learning (CL). A comprehensive evaluation is conducted using a range of metrics to analyze the relative performance of each technique and identify the one that exhibits superior results. To validate the effectiveness of the proposed approach, the MVTec AD dataset, consisting of real-world images with pixel-based anomalies, is utilized. This dataset serves as a reliable benchmark for Anomaly Detection in the context of Continual Learning, providing a solid foundation for further advancements in the field

IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer

Advanced image tampering techniques are increasingly challenging the trustworthiness of multimedia, leading to the development of Image Manipulation Localization (IML). But what makes a good IML model? The answer lies in the way to capture artifacts. Exploiting artifacts requires the model to extract non-semantic discrepancies between manipulated and authentic regions, necessitating explicit comparisons between the two areas. With the self-attention mechanism, naturally, the Transformer should be a better candidate to capture artifacts. However, due to limited datasets, there is currently no pure ViT-based approach for IML to serve as a benchmark, and CNNs dominate the entire task. Nevertheless, CNNs suffer from weak long-range and non-semantic modeling. To bridge this gap, based on the fact that artifacts are sensitive to image resolution, amplified under multi-scale features, and massive at the manipulation border, we formulate the answer to the former question as building a ViT with high-resolution capacity, multi-scale feature extraction capability, and manipulation edge supervision that could converge with a small amount of data. We term this simple but effective ViT paradigm IML-ViT, which has significant potential to become a new benchmark for IML. Extensive experiments on five benchmark datasets verified our model outperforms the state-of-the-art manipulation localization methods.Code and models are available at \url{https://github.com/SunnyHaze/IML-ViT}

STRUCTURED ILLUMINATION MICROSCOPE IMAGE RECONSTRUCTION USING UNROLLED PHYSICS-INFORMED GENERATIVE ADVERSARIAL NETWORK (UPIGAN)

In three-dimensional structured illumination microscopy (3D-SIM) where the images are taken from the object through the point spread function (PSF) of the imaging system, data acquisition can result in images taken under undesirable aberrations that contribute to a model mismatch. The inverse imaging problem in 3D-SIM has been solved using a variety of conventional model-based techniques that can be computationally intensive. Deep learning (DL) approaches, as opposed to traditional restoration methods, tackle the issue without access to the analytical model. This research aims to provide an unrolled physics-informed generative adversarial network (UPIGAN) for the reconstruction of 3D-SIM images utilizing data samples of mitochondria and lysosomes obtained from a 3D-SIM system. This design makes use of the benefits of physics knowledge in the unrolling step. Moreover, the GAN employs a Residual Channel Attention super-resolution deep neural network (DNN) in its generator architecture. The results indicate that the addition of both physics-informed unrolling and GAN incorporation yield improvements in reconstructed results compared to the regular DL approach