1,077 Results Found

This study focuses on the crucial aspect of lossless compression for FAST pulsar search data. The deep generative model PixelCNN, stacking multiple masked convolutional layers, achieves neural network autoregressive modeling, making it one of the mos...

Deep learning-based malware classification using image representations has emerged as a highly effective paradigm for threat detection. However, training robust neural networks is frequently bottlenecked by data scarcity and severe class imbalances i...

Personal credit assessment plays a crucial role in the financial system, which not only relates to the financial activities of individuals but also affects the overall credit system and economic health of society. However, the current problem of data...

Analysis of retinal fundus images is essential for eye-care physicians in the diagnosis, care and treatment of patients. Accurate fundus and/or retinal vessel maps give rise to longitudinal studies able to utilize multimedia image registration and di...

The majority of digital images are stored in compressed form. Generally, image classification using convolution neural network (CNN) is done in uncompressed form rather than compressed one. Training the CNN in the compressed domain eliminates the req...

Compared with ordinary image classification tasks, fine-grained image classification is closer to real-life scenes. Its key point is how to find the local areas with sufficient discrimination and perform effective feature learning. Based on a bilinea...

Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have gained improved results in remote sensing image data classification. Multispectral image classification can benefit from the rich spectral information extracted by these m...

A convolutional neural network (CNN) surrogate model (SM)-assisted proximal policy optimization (PPO) inverse design methodology is proposed for pixelated differential bandpass filters (DBPFs). A dumbbell-shaped defected ground structure (DGS) is enc...

The astounding development of optical sensing imaging technology, coupled with the impressive improvements in machine learning algorithms, has increased our ability to understand and extract information from scenic events. In most cases, Convolution...

Color cognizant capability has a significant impact in service robots for object detection based on color, traffic signal interpretation for autonomous vehicles, etc. Conventional clustering algorithms such as K-means and mean shift can be used for p...

Mapping landslides using automated methods is a challenging task, which is still largely done using human efforts. Today, the availability of high-resolution EO data products is increasing exponentially, and one of the targets is to exploit this data...

This paper presents a piano-playing robotic system that integrates numbered musical notation recognition with automated manipulator control. The system captures the notation using a camera, applies four-point detection for perspective correction, and...

Cylindrical panorama stitching is able to generate high resolution images of a scene with a wide field-of-view (FOV), making it a useful scene representation for applications like environmental sensing and robot localization. Traditional image stitch...

Remote sensing image denoising is of great significance for the subsequent use and research of images. Gaussian noise and salt-and-pepper noise are prevalent noises in images. Contemporary denoising algorithms often exhibit limitations when addressin...

To effectively solve the problems that most convolutional neural networks cannot be applied to the pixelwise input in remote sensing (RS) classification and cannot adequately represent the spectral sequence information, we propose a new multispectral...

Airborne hyperspectral data has high spectral-spatial information. However, how to mine and use this information effectively is still a great challenge. Recently, a three-dimensional convolutional neural network (3D-CNN) provides a new effective way...

Convolutional neural networks and the per-pixel loss function have shown their potential to be the best combination for super-resolving severely degraded images. However, there are still challenges, such as the massive number of parameters requiring...

The purpose of the current study is to propose a novel meta-heuristic image analysis approach using multi-objective optimization, named ‘Pixel-wise k-Immediate Neighbors’ to identify pores and fractures (both natural and induced, even in...

To achieve effective deep fusion features for improving the classification accuracy of hyperspectral remote sensing images (HRSIs), a pixel frequency spectrum feature is presented and introduced to convolutional neural networks (CNNs). Firstly, the f...

Convolutional neural networks (CNN) have achieved great success in the optical image processing field. Because of the excellent performance of CNN, more and more methods based on CNN are applied to polarimetric synthetic aperture radar (PolSAR) image...

The smart-pixel-based optical convolutional neural network was proposed to improve kernel refresh rates in scalable optical convolutional neural networks (CNNs) by replacing the spatial light modulator with a smart pixel light modulator while preserv...

When the spatial distribution of winter wheat is extracted from high-resolution remote sensing imagery using convolutional neural networks (CNN), field edge results are usually rough, resulting in lowered overall accuracy. This study proposed a new p...

Convolutional neural networks (CNNs) can adapt to more complex data, extract deeper characteristics from images, and achieve higher classification accuracy in remote sensing image scene classification and object detection compared to traditional shal...

Improving the accuracy of edge pixel classification is an important aspect of using convolutional neural networks (CNNs) to extract winter wheat spatial distribution information from remote sensing imagery. In this study, we established a method usin...

Despite the success of hand-crafted features in computer visioning for many years, nowadays, this has been replaced by end-to-end learnable features that are extracted from deep convolutional neural networks (CNNs). Whilst CNNs can learn robust featu...

Soybean variety is connected to stress resistance ability, as well as nutritional and commercial value. Near-infrared hyperspectral imaging was applied to classify three varieties of soybeans (Zhonghuang37, Zhonghuang41, and Zhonghuang55). Pixel-wise...

Satellite imagery is the only feasible approach to annual monitoring and reporting on land cover change. Unfortunately, conventional pixel-based classification methods based on spectral response only (e.g., using random forests algorithms) have shown...

Achieving balance between efficiency and performance is a key problem for convolution neural network (CNN)-based single-image super-resolution (SISR) algorithms. Existing methods tend to directly output high-resolution (HR) pixels or residuals to rec...

The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural netwo...

Classification of healthy and diseased wheat heads in a rapid and non-destructive manner for the early diagnosis of Fusarium head blight disease research is difficult. Our work applies a deep neural network classification algorithm to the pixels of h...

We propose a practical Convolution Neural Network (CNN) model termed the CNN for Semantic Segmentation for driver Assistance system (CSSA). It is a novel semantic segmentation model for probabilistic pixel-wise segmentation, which is able to predict...

Information about clouds is important for observing and predicting weather and climate as well as for generating and distributing solar power. Most existing approaches extract cloud information from satellite data by classifying individual pixels ins...

Convolutional neural networks (CNNs) are widely used in medical image classification, yet their robustness to localized perturbations remains limited. This study evaluates one-pixel attacks on VGG16, MobileNetV2, and EfficientNetV2-B0 using brain tum...

Automated guided vehicles (AGVs) are important in modern factories. The main functions of an AGV are its own localization and object detection, for which both sensor and localization methods are crucial. For localization, we used a small imaging sens...

With the rapid development of information technology, the security of images has emerged as a significant area of research. This study presents an algorithm that integrates chaotic image encryption and a convolutional neural network (CNN) to enhance...

In this paper, we propose an intra-picture prediction method for depth video by a block clustering through a neural network. The proposed method solves a problem that the block that has two or more clusters drops the prediction performance of the int...

Normalized Difference Vegetation Index (NDVI) is an important remote sensing indicator for characterizing vegetation growth status and ecosystem changes. Improving NDVI prediction accuracy is of great significance for regional ecological monitoring a...

To effectively utilize the rich spectral information of hyperspectral remote sensing images (HRSIs), the fractional Fourier transform (FRFT) feature of HRSIs is proposed to reflect the time-domain and frequency-domain characteristics of a spectral pi...

Convolutional Neural Networks (CNNs), due to their recent successes, have gained lots of attention in various vision-based applications. They have proven to produce incredible results, especially on big data, that require high processing demands. How...

Single-pixel hyperspectral imaging (HSI) has received a lot of attention in recent years due to its advantages of high sensitivity, wide spectral ranges, low cost, and small sizes. In this article, we perform a single-pixel HSI experiment based on an...

During recent years, convolutional neural network (CNN)-based methods have been widely applied to hyperspectral image (HSI) classification by mostly mining the spectral variabilities. However, the spatial consistency in HSI is rarely discussed except...

Deep convolutional neural networks (CNNs) can achieve good performance in image denoising due to their superiority in the extraction of structural information. However, they may ignore the relationships between pixels to limit effects for image denoi...

Convolutional neural networks (CNNs) have been extended to hyperspectral imagery (HSI) classification due to its better feature representation and high performance, whereas multiple feature learning has shown its effectiveness in computer vision area...

Inspired by the interpretability of Kolmogorov–Arnold Networks (KANs), a novel Pixel-level Feature Selection (PFS) model based on KANs (PFSKANs) is proposed as a fundamentally distinct alternative from trainable Convolutional Neural Networks (C...

Reversible data hiding (RDH) enables the reversible embedding of additional data into cover media, allowing the cover media to be perfectly recovered after extracting the embedded data. RDH relies on accurate prediction of pixels to generate sharply...

In this paper, we present an evaluation of four encoder–decoder CNNs in the segmentation of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected CNNs are FCN, SegNet, U-Net, and DeepLabV3+, which was originally pr...

This paper introduces a novel inverse design framework that combines pixelated microstrip antenna modeling, convolutional neural network (CNN), and binary particle swarm optimization (BPSO) to automate the process of generating antenna structures fro...

Improving the accuracy of edge pixel classification is crucial for extracting the winter wheat spatial distribution from remote sensing imagery using convolutional neural networks (CNNs). In this study, we proposed an approach using a partly connecte...

This paper deals with a soil moisture retrieval model design with airborne measurements for remote monitoring of soil moisture level in large crop fields. A small quadrotor unmanned aerial vehicle (UAV) is considered as a remote sensing platform for...

The explanation of the decisions provided by a model are crucial in a domain such as medical diagnosis. With the advent of deep learning, it is very important to explain why a classification is reached by a model. This work tackles the transparency p...