Neural network classifiers (NNCs) are known to be vulnerable to malicious adversarial perturbations of inputs including those modifying a small fraction of the input features named sparse or
In recent years, deep neural networks have shown superior performance in various fields, but interpretability has always been the Achilles’ heel of deep neural networks. The existing visual interpretation methods for deep neural networks still...
Understanding how learners process data visualizations with seductive details is essential for improving comprehension and engagement. This study examined the influence of task-relevant and task-irrelevant seductive details on attentional distributio...
Saliency map generation in synthetic aperture radar (SAR) imagery has become a promising research area, since it has a close relationship with quick potential target identification, rescue services, etc. Due to the multiplicative speckle noise and co...
This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient textural saliency map under the contextual cues of apro...
This work presents a Bayesian statistical approach to the saliency map estimation problem. More specifically, we formalize the saliency map estimation issue in the fully automatic Markovian framework. The major and original contribution of the propos...
As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for model decisions. This is necessary for building trust and transparency in...
Accurately counting the number of insect pests from digital images captured on yellow sticky traps remains a challenge in the field of insect pest monitoring. In this study, we develop a new approach to counting the number of insect pests using a sal...
This study proposes a model for transient stability assessment, which is a convolutional neural network model combined with a saliency map (S–CNN model). The convolutional neural network model is trained on dynamic data acquired through the dat...
To solve problems of brightness and detail information loss in infrared and visible image fusion, an effective infrared and visible image fusion method using rolling guidance filtering and gradient saliency map is proposed in this paper. The rolling...
Land surface water mapping is one of the most basic classification tasks to distinguish water bodies from dry land surfaces. In this paper, a water mapping method was proposed based on multi-scale level sets and a visual saliency model (MLSVS), to ov...
With the development of artificial intelligence technology, machine learning models are becoming more complex and accurate. However, the explainability of the models is decreasing, and much of the decision process is still unclear and difficult to ex...
One of the significant qualities of the human vision, which differentiates it from computer vision, is so called attentional control, which is the innate ability of our human eyes to select what visual stimuli to pay attention to at any moment in tim...
Egocentric video analysis is an important tool in healthcare that serves a variety of purposes, such as memory aid systems and physical rehabilitation, and feature extraction is an indispensable process for such analysis. Local feature descriptors ha...
A target detection method based on an improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. We propose two strategies to improve the SSD: model structure o...
In this article, a hierarchical method for action recognition based on temporal and spatial features is proposed. In current HAR methods, camera movement, sensor movement, sudden scene changes, and scene movement can increase motion feature errors an...
The purpose of infrared and visible image fusion is to generate images with prominent targets and rich information which provides the basis for target detection and recognition. Among the existing image fusion methods, the traditional method is easy...
Glaucoma, a leading cause of blindness, damages the optic nerve, making early diagnosis challenging due to no initial symptoms. Fundus eye images taken with a non-mydriatic retinograph help diagnose glaucoma by revealing structural changes, including...
Bottom-up saliency models identify the salient regions of an image based on features such as color, intensity and orientation. These models are typically used as predictors of human visual behavior and for computer vision tasks. In this paper, we con...
Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand...
Automatic extraction of salient regions is beneficial for various computer vision applications, such as image segmentation and object recognition. The salient visual information across images is very useful and plays a significant role for the visual...
In order to detect both bright and dark small moving targets effectively in infrared (IR) video sequences, a saliency histogram and geometrical invariability based method is presented in this paper. First, a saliency map that roughly highlights the s...
Throughout the past decade, vehicular networks have attracted a great deal of interest in various fields. The increasing number of vehicles has led to challenges in traffic regulation. Vehicle-type detection is an important research topic that has fo...
Prediction of visual attention is a new and challenging subject, and to the best of our knowledge, there are not many pieces of research devoted to the anticipation of students’ cognition when solving tests. The aim of this paper is to propose,...
Deep learning has achieved remarkable performance in skin lesion classification, but its lack of interpretability often remains a critical barrier to clinical adoption. In this study, we investigate the spatial properties of saliency-based model expl...
Visual saliency maps have been developed to estimate the bottom-up visual attention of humans. A conventional saliency map represents a bottom-up visual attention using image features such as the intensity, orientation, and color. However, it is diff...
The detection of ships in optical remote sensing images with clouds, waves, and other complex interferences is a challenging task with broad applications. Two main obstacles for ship target detection are how to extract candidates in a complex backgr...
Infrared and visible image fusion technology provides many benefits for human vision and computer image processing tasks, including enriched useful information and enhanced surveillance capabilities. However, existing fusion algorithms have faced a g...
Convolutional neural networks utilize a hierarchy of neural network layers. The statistical aspects of information concentration in successive layers can bring an insight into the feature abstraction process. We analyze the saliency maps of these lay...
Frequency-hopping (FH) communication adversarial research is a key area in modern electronic countermeasures. To address the challenge posed by interfering parties that use deep neural networks (DNNs) to classify and identify multiple intercepted FH...
To visually detect sea-surface targets, the objects of interest must be effectively and rapidly isolated from the background of sea-surface images. In contrast to traditional image detection methods, which employ a single visual feature, this paper p...
The rapid development of remote sensing and space technology provides multisource remote sensing image data for earth observation in the same area. Information provided by these images, however, is often complementary and cooperative, and multisource...
This paper examines the transformative potential of generative artificial intelligence (AI) and neuroaesthetic methodologies in archaeology, museum collections and art history. It introduces the concept of the AI multiverse, which allows archaeologis...
In recent years, deep convolutional neural networks (DCNNs) have shown promising performance in medical image analysis, including breast lesion classification in 2D ultrasound (US) images. Despite the outstanding performance of DCNN solutions, explai...
A few-shot personalized saliency prediction based on adaptive image selection considering object and visual attention is presented in this paper. Since general methods predicting personalized saliency maps (PSMs) need a large number of training image...
Deregulated protein kinases are crucial in promoting cancer cell proliferation and driving malignant cell signaling. Although these kinases are essential targets for cancer therapy due to their involvement in cell development and proliferation, only...
Numerous autonomous robots are used not only for factory automation as labor saving devices, but also for interaction and communication with humans in our daily life. Although superior compatibility for semantic recognition of generic objects provide...
Classifying brain tumour transcriptomic data is crucial for precision medicine but remains challenging due to high dimensionality and limited interpretability of conventional models. This study benchmarks three image-based deep learning approaches, D...
As one of the world’s economically valuable fruit crops, citrus has its quality and productivity closely tied to the degree of fruit ripeness. However, accurately and efficiently detecting citrus ripeness in complex orchard environments for sel...
Optically thin layers of tiny ice particles near the summer mesopause, known as noctilucent clouds, are of significant interest within the aeronomy and climate science communities. Ground-based optical cameras mounted at various locations in the arct...
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural network’s prediction. Gradient-based methods are generally faster than other branches of vision interpretability and independent of human guida...
Hand gesture recognition is a vital means of communication to convey information between humans and machines. We propose a novel model for hand gesture recognition based on computer vision methods and compare results based on images with complex scen...
Aerial scene classification is an active and challenging problem in high-resolution remote sensing imagery understanding. Deep learning models, especially convolutional neural networks (CNNs), have achieved prominent performance in this field. The ex...
Microalgae are an evolutionarily ancient and morphologically diverse group of photosynthetic eukaryotes, with taxonomic resolution complicated by environmentally driven phenotypic plasticity. This study merges deep learning and explainable artificial...
This study investigates the neurophysiological effects of transcutaneous electroacupuncture stimulation (TEAS) on brain activity, using advanced machine learning techniques. This work analyzed the electroencephalograms (EEG) of 48 study participants,...
The rising frequency of natural disasters demands efficient and accurate structural damage assessments to ensure public safety and expedite recovery. Human error, inconsistent standards, and safety risks limit traditional visual inspections by engine...
Aluminum profile surface defects can greatly affect the performance, safety, and reliability of products. Traditional human-based visual inspection has low accuracy and is time consuming, and machine vision-based methods depend on hand-crafted featur...
Explainable artificial intelligence (XAI) has emerged as a crucial field for understanding and interpreting the decisions of complex machine learning models, particularly deep neural networks. This review presents a structured overview of XAI methodo...
Pedestrian detection is an important task in many intelligent systems, particularly driver assistance systems. Recent studies on pedestrian detection in infrared (IR) imagery have employed data-driven approaches. However, two problems in deep learnin...
Salient object detection aims to distinguish the most visually conspicuous regions, playing an important role in computer vision tasks. However, complex natural scenarios can challenge salient object detection, hindering accurate extraction of object...
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