8,002 Results Found

Visual computing in medicine involves handling the generation, acquisition, processing, analysis, exploration, visualization, and interpretation of medical visual information. Machine learning has become a prominent tool for data analytics and proble...

Human eye movement is one of the most important functions for understanding our surroundings. When a human eye processes a scene, it quickly focuses on dominant parts of the scene, commonly known as a visual saliency detection or visual attention pre...

Visual reinforcement learning is important in various practical applications, such as video games, robotic manipulation, and autonomous navigation. However, a major challenge in visual reinforcement learning is the generalization to unseen environmen...

Visual encoding models are important computational models for understanding how information is processed along the visual stream. Many improved visual encoding models have been developed from the perspective of the model architecture and the learning...

Effective communication crucially depends on the ability to produce and recognize structured signals, as apparent in language and birdsong. Although it is not clear to what extent similar syntactic-like abilities can be identified in other animals, r...

Multiple-instance learning (MIL) is a paradigm of machine learning that aims to classify a set (bag) of objects (instances), assigning labels only to the bags. This problem is often addressed by selecting an instance to represent each bag, transformi...

Higher education institutions must enable students to acquire skills and capacities that prepare them for working life and enhance their employability. This will lead to an applied learning- and teaching-enhancement-oriented sustainable Higher Educat...

In this paper, we focus on the challenges of training efficiency, the designation of reward functions, and generalization in reinforcement learning for visual navigation and propose a regularized extreme learning machine-based inverse reinforcement l...

Localization and mapping remain critical challenges for Unmanned Ground Vehicles (UGVs) operating in unstructured natural environments, such as forests and agricultural fields. While Visual SLAM (VSLAM) and Visual–Inertial SLAM (VI-SLAM) have m...

Visual behaviour in zebrafish, often measured by the optokinetic reflex (OKR), serves as a valuable model for studying aspects of human neurological and ocular diseases and for conducting therapeutic or toxicology assays. Traditional methods for OKR...

Due to the limitations of LiDAR, such as its high cost, short service life and massive volume, visual sensors with their lightweight and low cost are attracting more and more attention and becoming a research hotspot. As the hardware computation powe...

In real-world visual recognition tasks, long-tailed distribution is a pervasive challenge, where the extreme class imbalance severely limits the representation learning capability of deep models. Although supervised learning has demonstrated certain...

Problem solving applications require users to exercise caution in their data usage practices. Prior to installing these applications, users are encouraged to read and comprehend the terms of service, which address important aspects such as data priva...

This paper presents the construction of a new objective method for estimation of visual perceiving quality. The proposal provides an assessment of image quality without the need for a reference image or a specific distortion assumption. Two main proc...

Audio-visual emotion recognition aims to distinguish human emotional states by integrating the audio and visual data acquired in the expression of emotions. It is crucial for facilitating the affect-related human-machine interaction system by enablin...

Recently, deep learning frameworks have been deployed in visual odometry systems and achieved comparable results to traditional feature matching based systems. However, most deep learning-based frameworks inevitably need labeled data as ground truth...

The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emergi...

The trackers based on discriminative correlation filter (DCF) have achieved remarkable performance in visual target tracking in recent years. Since the targets are usually affected by various factors such as deformation, rotation, motion blur and so...

Visual Simultaneous Localization and Mapping (VSLAM) has been a hot topic of research since the 1990s, first based on traditional computer vision and recognition techniques and later on deep learning models. Although the implementation of VSLAM metho...

Labeling of samples is a recurrent and time-consuming task in data analysis and machine learning and yet generally overlooked in terms of visual analytics approaches to improve the process. As the number of tailored applications of learning models in...

Visual map-based robot navigation is a strategy that only uses the robot vision system, involving four fundamental stages: learning or mapping, localization, planning, and navigation. Therefore, it is paramount to model the environment optimally to p...

This article provides a detailed review of recent advances in audio-visual speech recognition (AVSR) methods that have been developed over the last decade (2013–2023). Despite the recent success of audio speech recognition systems, the problem...

An inherent requirement of teaching using online learning platforms is that the teacher must analyze student activity and performance in relation to course learning objectives. Therefore, all e-learning environments implement a module to collect such...

Cloud providers such as Amazon Web Services (AWS) stand out as useful platforms to teach distributed computing concepts as well as the development of Cloud-native scalable application architectures on real-world infrastructures. Instructors can benef...

In this paper, we study deep learning approaches for monocular visual odometry (VO). Deep learning solutions have shown to be effective in VO applications, replacing the need for highly engineered steps, such as feature extraction and outlier rejecti...

The visual sensor network (VSN), a new type of wireless sensor network composed of low-cost wireless camera nodes, is being applied for numerous complex visual analyses in wild environments, such as visual surveillance, object recognition, etc. Howev...

In response to the urgent need for rapid and precise post-disaster damage evaluation, this study introduces the Visual Prompt Damage Evaluation (ViPDE) framework, a novel contrastive learning-based approach that leverages the embedded knowledge withi...

Deep learning has emerged as a powerful tool in computational neuroscience, enabling the modeling of complex neural processes and supporting data-driven insights into brain function. However, the non-transparent nature of many deep learning models li...

Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is...

Maker education that incorporates computational thinking streamlines learning and helps familiarize learners with recent advances in science and technology. Computational thinking (CT) is a vital core capability that anyone can learn. CT can be learn...

When driving, people make decisions based on current traffic as well as their desired route. They have a mental map of known routes and are often able to navigate without needing directions. Current published self-driving models improve their perform...

As a substitute for human arms, underwater vehicle dual-manipulator systems (UVDMSs) have attracted the interest of global researchers. Visual servoing is an important tool for the positioning and tracking control of UVDMSs. In this paper, a reinforc...

Zero-shot learning (ZSL) enables models to recognize categories not encountered during training, which is crucial for categories with limited data. Existing methods overlook efficient temporal modeling in multimodal data. This paper proposes a Tempor...

Recognition of emotions and sentiment (affective states) from human audio–visual information is widely used in healthcare, education, entertainment, and other fields; therefore, it has become a highly active research area. The large variety of...

The performance achievable by using Unmanned Aerial Vehicles (UAVs) for a large variety of civil and military applications, as well as the extent of applicable mission scenarios, can significantly benefit from the exploitation of formations of vehicl...

Large-scale models trained on extensive datasets have become the standard due to their strong generalizability across diverse tasks. In-context learning (ICL), widely used in natural language processing, leverages these models by providing task-speci...

Breast cancer is a serious threat to women. Many machine learning-based computer-aided diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer based on histopathological images. Even though many such classification methods...

In the field of visualization, understanding users’ analytical reasoning is important for evaluating the effectiveness of visualization applications. Several studies have been conducted to capture and analyze user interactions to comprehend thi...

The constant development of digital technologies has allowed living in a digital environment based on connections, also transforming the context of the educational process. Experiences show that digital technologies have influenced the way of learnin...

Traditional supervised learning is dependent on the label of the training data, so there is a limitation that the class label which is not included in the training data cannot be recognized properly. Therefore, zero-shot learning, which can recognize...

Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking...

Visual acuity (VA) is a measure of the ability to distinguish shapes and details of objects at a given distance and is a measure of the spatial resolution of the visual system. Vision is one of the basic health indicators closely related to a person&...

The preparation of infrared reference images is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared data on-site is difficult and time-consuming. Fortunately, the infrared images c...

This work proposes a hybrid deep learning-based framework for visual feedback control in an eye-in-hand robotic system. The framework uses an early fusion approach in which real and synthetic images define the training data. The first layer of a ResN...

Background/Objectives: Verbs are considered to be more abstract than nouns, as they represent actions, states, and events, which are less tangible, more flexible in their meaning and thus less univocally specified. It has been suggested that children...

Recently, decreasing energy consumption under the premise of building comfort has become a popular topic, especially visual comfort. Existing research on visual comfort lacks a standard of how to select indicators. Moreover, studies on individual vis...

Risky driving behavior seriously affects the driver’s ability to react, execute and judge, which is one of the major causes of traffic accidents. The timely and accurate identification of the driving status of drivers is particularly important,...

Visual servoing is a control method that utilizes image feedback to control robot motion, and it has been widely applied in unmanned aerial vehicle (UAV) motion control. However, due to field-of-view (FOV) constraints, visual servoing still faces cha...

With the development of multimedia systems in wireless environments, the rising need for artificial intelligence is to design a system that can properly communicate with humans with a comprehensive understanding of various types of information in a h...

Visual odometry is the task of estimating the trajectory of the moving agents from consecutive images. It is a hot research topic both in robotic and computer vision communities and facilitates many applications, such as autonomous driving and virtua...