Scene Images Research Articles

RCSFN: A remote sensing image scene classification and recognition network based on rectangle convolutional self attention fusion

RCSFN: A remote sensing image scene classification and recognition network based on rectangle convolutional self attention fusion

Consolidation Enhances Sequential Multistep Anticipation but Diminishes Access to Perceptual Features

Many experiences unfold predictably over time. Memory for these temporal regularities enables anticipation of events multiple steps into the future. Because temporally predictable events repeat over days, weeks, and years, we must maintain—and potentially transform—memories of temporal structure to support adaptive behavior. We explored how individuals build durable models of temporal regularities to guide multistep anticipation. Healthy young adults (Experiment 1: N = 99, age range = 18–40 years; Experiment 2: N = 204, age range = 19–40 years) learned sequences of scene images that were predictable at the category level and contained incidental perceptual details. Individuals then anticipated upcoming scene categories multiple steps into the future, immediately and at a delay. Consolidation increased the efficiency of anticipation, particularly for events further in the future, but diminished access to perceptual features. Further, maintaining a link-based model of the sequence after consolidation improved anticipation accuracy. Consolidation may therefore promote efficient and durable models of temporal structure, thus facilitating anticipation of future events.

HRDLNet: a semantic segmentation network with high resolution representation for urban street view images

Semantic segmentation of urban street scenes has attracted much attention in the field of autonomous driving, which not only helps vehicles perceive the environment in real time, but also significantly improves the decision-making ability of autonomous driving systems. However, most of the current methods based on Convolutional Neural Network (CNN) mainly use coding the input image to a low resolution and then try to recover the high resolution, which leads to problems such as loss of spatial information, accumulation of errors, and difficulty in dealing with large-scale changes. To address these problems, in this paper, we propose a new semantic segmentation network (HRDLNet) for urban street scene images with high-resolution representation, which improves the accuracy of segmentation by always maintaining a high-resolution representation of the image. Specifically, we propose a feature extraction module (FHR) with high-resolution representation, which efficiently handles multi-scale targets and high-resolution image information by efficiently fusing high-resolution information and multi-scale features. Secondly, we design a multi-scale feature extraction enhancement (MFE) module, which significantly expands the sensory field of the network, thus enhancing the ability to capture correlations between image details and global contextual information. In addition, we introduce a dual-attention mechanism module (CSD), which dynamically adjusts the network to more accurately capture subtle features and rich semantic information in images. We trained and evaluated HRDLNet on the Cityscapes Dataset and the PASCAL VOC 2012 Augmented Dataset, and verified the model’s excellent performance in the field of urban streetscape image segmentation. The unique advantages of our proposed HRDLNet in the field of semantic segmentation of urban streetscapes are also verified by comparing it with the state-of-the-art methods.

The minimal computational substrate of fluid intelligence

The quantification of cognitive powers rests on identifying a behavioural task that depends on them. Such dependence cannot be assured, for the powers a task invokes cannot be experimentally controlled or constrained a priori, resulting in unknown vulnerability to failure of specificity and generalisability. Evaluating a compact version of Raven's Advanced Progressive Matrices (RAPM), a widely used clinical test of fluid intelligence, we show that LaMa, a self-supervised artificial neural network trained solely on the completion of partially masked images of natural environmental scenes, achieves representative human-level test scores a prima vista, without any task-specific inductive bias or training. Compared with cohorts of healthy and focally lesioned participants, LaMa exhibits human-like variation with item difficulty, and produces errors characteristic of right frontal lobe damage under degradation of its ability to integrate global spatial patterns. LaMa's narrow training and limited capacity suggest matrix-style tests may be open to computationally simple solutions that need not necessarily invoke the substrates of reasoning.

YOSMR: A Ship Detection Method for Marine Radar Based on Customized Lightweight Convolutional Networks

In scenarios such as nearshore and inland waterways, the ship spots in a marine radar are easily confused with reefs and shorelines, leading to difficulties in ship identification. In such settings, the conventional ARPA method based on fractal detection and filter tracking performs relatively poorly. To accurately identify radar targets in such scenarios, a novel algorithm, namely YOSMR, based on the deep convolutional network, is proposed. The YOSMR uses the MobileNetV3(Large) network to extract ship imaging data of diverse depths and acquire feature data of various ships. Meanwhile, taking into account the issue of feature suppression for small-scale targets in algorithms composed of deep convolutional networks, the feature fusion module known as PANet has been subject to a lightweight reconstruction leveraging depthwise separable convolutions to enhance the extraction of salient features for small-scale ships while reducing model parameters and computational complexity to mitigate overfitting problems. To enhance the scale invariance of convolutional features, the feature extraction backbone is followed by an SPP module, which employs a design of four max-pooling constructs to preserve the prominent ship features within the feature representations. In the prediction head, the Cluster-NMS method and α-DIoU function are used to optimize non-maximum suppression (NMS) and positioning loss of prediction boxes, improving the accuracy and convergence speed of the algorithm. The experiments showed that the recall, accuracy, and precision of YOSMR reached 0.9308, 0.9204, and 0.9215, respectively. The identification efficacy of this algorithm exceeds that of various YOLO algorithms and other lightweight algorithms. In addition, the parameter size and calculational consumption were controlled to only 12.4 M and 8.63 G, respectively, exhibiting an 80.18% and 86.9% decrease compared to the standard YOLO model. As a result, the YOSMR displays a substantial advantage in terms of convolutional computation. Hence, the algorithm achieves an accurate identification of ships with different trail features and various scenes in marine radar images, especially in different interference and extreme scenarios, showing good robustness and applicability.

Ground-to-air aircraft infrared image deblurring based on imaging degradation simulation

The issue of infrared image deblurring has been a significant concern. However, in some specific scenes, the current mainstream deblurring algorithms based on optimization or deep learning fail to provide satisfactory results. Aiming to address the ineffectiveness of deep learning methods due to the low-cost datasets' unavailability for specific scenes, we innovatively propose a relatively simple full-chain imaging degradation simulation method using ground-to-air aircraft infrared imaging scene as an example, which considers the effects of blur and noise caused by the atmosphere, imaging system, target motion and detector. Through this method, we could generate abundant blur-clear image pairs by altering various parameters. To enhance the neural network’s generalization ability and the deblurring performance in the specific scenes, we employ a two-step approach: pretraining on the public GoPro dataset and subsequent finetuning on the simulation dataset. After testing on the simulation dataset and some real-world images, we have discovered the importance of selecting a pretraining dataset that closely matches the scene degradation mode. Additionally, regardless of whether the model is pre-trained on the UIRD or GoPro dataset, there are significant enhancements in the deblurring effect following finetuning with our constructed simulation dataset. In summary, compared to the traditional deconvolution methods and the methods trained on a general dataset, our approach not only exhibits superior deblurring capabilities but also effectively mitigates noise and prevents the occurrence of artifactual textures such as ringing artifact.

Unsupervised learning as a computational principle works in visual learning of natural scenes, but not of artificial stimuli.

The question of whether we learn exposed visual features remains a subject of controversy. A prevalent computational model suggests that visual features frequently exposed to observers in natural environments are likely to be learned. However, this unsupervised learning model appears to be contradicted by the significant body of experimental results with human participants that indicates visual perceptual learning (VPL) of visible task-irrelevant features does not occur with frequent exposure. Here, we demonstrate a resolution to this controversy with a new finding: Exposure to a dominant global orientation as task-irrelevant leads to VPL of the orientation, particularly when the orientation is derived from natural scene images, whereas VPL did not occur with artificial images even with matched distributions of local orientations and spatial frequencies to natural scene images. Further investigation revealed that this disparity arises from the presence of higher-order statistics derived from natural scene images-global structures such as correlations between different local orientation and spatial frequency channels. Moreover, behavioral and neuroimaging results indicate that the dominant orientation from these higher-order statistics undergoes less attentional suppression than that from artificial images, which may facilitate VPL. Our results contribute to resolving the controversy by affirming the validity of unsupervised learning models for natural scenes but not for artificial stimuli. They challenge the assumption that VPL occurring in everyday life can be predicted by laws governing VPL for conventionally used artificial stimuli.

Infrared Image Human Target Detection Method Based on Deep Learning

Introduction: In this study, we investigate human target detection techniques for infrared target recognition in complicated backdrops using deep learning approaches. This study uses the human target detection of infrared images in various scenes as its research object. Method: A test was conducted using MatlabR2010a and the real-time C language development platform. The findings indicate that, in the unoptimized case, the computing speed of the method was only 0.34 seconds. However, following optimization, its performance significantly increased to meet the real-time performance requirements. Result: The research findings presented in this work will be crucial to the relief and rescue efforts following engineering mishaps. Conclusion: This study is innovative in that it develops a deep learning-based model for human target detection in infrared photos and thoroughly examines and improves its functionality.

Research on the Factors Influencing the Spatial Quality of High-Density Urban Streets: A Framework Using Deep Learning, Street Scene Images, and Principal Component Analysis

Research on the Factors Influencing the Spatial Quality of High-Density Urban Streets: A Framework Using Deep Learning, Street Scene Images, and Principal Component Analysis

Pseudo-label meta-learner in semi-supervised few-shot learning for remote sensing image scene classification

Pseudo-label meta-learner in semi-supervised few-shot learning for remote sensing image scene classification

Multi-factor quality assessment of digital speckle pattern for speckle projection profilometry

Abstract The digital speckle pattern (DSP) is an essential component in the speckle projection profilometry (SPP) task, its quality directly affects the results of three-dimensional (3D) shape reconstruction. However, the SPP field lacks specialized numerical metrics for evaluating speckle quality. To address this issue, this study introduces a multi-factor metric (MFM) for comprehensive DSP assessment. Through comparing the metric, optimal parameter ranges for DSP design and the advisable matching subset size can be determined for SPP algorithm. A global indicator named valid feature distribution (VFD) based on scale-invariant feature transform (SIFT) and Delaunay triangulation, is defined to analyze the overall information distribution in DSPs. In addition, MFM incorporates a local metric called mean subset intensity gradient (MSIG), which aids in selecting the suitable radius for different DSPs to balance the accuracy and efficiency. The quality assessment targets the speckle scene images, allowing for the reverse adjustment of the most suitable DSP according to different scenes. The performance of DSPs can be evaluated based on the accuracy and completeness of 3D reconstruction results. By conducting simulation experiments on the 3ds Max platform, the recommended parameter range for DSP can be inferred, including speckle density ratio, speckle diameter, and random variation rate. Appropriate subset sizes for different scenes are also investigated. Furthermore, the MFM is verified on a real binocular speckle device, demonstrating that the measurement standard deviation of a complex workpiece can be reduced to 0.078 mm using the recommended DSP.

Attentive multi-granularity perception network for person search

Person search is an extremely challenging task that seeks to identify individuals through joint person detection and person re-identification from uncropped real scene images. Previous studies primarily focus on learning rich features to enhance identification. However, arbitrary feature enhancement strategies may introduce unwanted background noise. Moreover, different scenarios usually exhibit varying pedestrian appearances or even intricate occlusions, leading to inconsistent/incomplete pedestrian features in different images. In this paper, we introduce a novel Attentive Multi-granularity Perception (AMP) module seamlessly integrated into our AMPN network. This module specifically addresses appearance variations and occlusions within a person's Region of Interest (RoI). The AMP module harnesses discriminative relationship features from various local regions, significantly enhancing identification accuracy. It comprises two principal components: the Pedestrian Perception Enhancement (PPE) block and the Background Interference Suppressor (BIS). The PPE block introduces a Spatial-wise Feature Mixer and a Channel-wise Feature Mixer, which effectively capture and refine discriminative relation features. Simultaneously, the BIS operates in parallel with the PPE block, enriching the discriminative relation features and enhancing the distinctiveness between the foreground and background. Our AMP module is plug-and-play and can integrate with other person search models. Extensive experiments validate our model's merits, achieving state-of-the-art performance on CUHK-SYSU and a 4.8% mAP gain over SeqNet on PRW at a desirable speed. Our code is accessible at https://github.com/zqx951102/AMPN.

Application of Gaussian Model and Deep Learning Encoder-Decoder Algorithm for Single-Image Reflection Removal

Images of target scenes shot through clear, reflective materials like glass are frequently interfered by unwanted reflection scenes which often overlaid on top of the targeted scenes. This, has constantly degrades the quality of the captured images and affects their subsequent analyses. While cognitively, distinguishing a recognizable object from its reflection in a picture is not difficult for humans, it is highly difficult and more complex in computer vision due to the ill-posed nature of the problem. In this research an enhanced single-image reflection removal model was developed by combining Gaussian filter and deep learning encoder-decoder for effective performance. While the Gaussian filter denoises the reflection-contaminated image, the encoder-decoder network learns the features of the image to produce reflection-free image. The proposed network is an end-to-end trained network with three losses. The experimental findings showed that the proposed model out-performed several state-of-the-art methods both qualitatively and quantitatively on five different datasets.

The artistic image processing for visual healing in smart city

This study investigates the processing methods of artistic images within the context of Smart city (SC) initiatives, focusing on the visual healing effects of artistic image processing to enhance urban residents’ mental health and quality of life. Firstly, it examines the role of artistic image processing techniques in visual healing. Secondly, deep learning technology is introduced and improved, proposing the overlapping segmentation vision transformer (OSViT) for image blocks, and further integrating the bidirectional long short-term memory (BiLSTM) algorithm. An innovative artistic image processing and classification recognition model based on OSViT-BiLSTM is then constructed. Finally, the visual healing effect of the processed art images in different scenes is analyzed. The results demonstrate that the proposed model achieves a classification recognition accuracy of 92.9% for art images, which is at least 6.9% higher than that of other existing model algorithms. Additionally, over 90% of users report satisfaction with the visual healing effects of the artistic images. Therefore, it is found that the proposed model can accurately identify artistic images, enhance their beauty and artistry, and improve the visual healing effect. This study provides an experimental reference for incorporating visual healing into SC initiatives.

Recognition And Classification of Indian Scripts in Natural Scene Images

In the field of computer vision and document analysis, the identification and categorization of Indian scripts in natural scene images pose a difficult yet crucial challenge. The variety of characters and intricate writing styles in Indian scripts require reliable solutions for precise identification under different environmental conditions. This study presents a novel CNN model designed for identifying scripts in Indian multilingual document images captured by cameras. Experimental evaluations of the model's performance were conducted with two regional languages (Odia and Telugu) and one national language (Hindi). The average accuracy in script recognition for the three language combinations is 95.66%, with Odia achieving 99.00%, Hindi 90.33%, and Telugu 98.12%. The model achieved the highest accuracy in recognition. The model achieved the highest accuracy in recognition Keywords: Text Recognition, Image Augmentation, CNN, LSTM, VGG, ResNet, DenseNet, Datasets, Natural Images

Representasi Pelecehan Seksual Terhadap Laki – Laki Dalam Film Dear David

This study aims to determine the representation of sexual harassment against men in the film Dear David which has a lot of teenage problems in it to find out the researchers used descriptive qualitative research methods with a semiotic analysis approach John Fiske model with three-level social codes, namely the level of reality, level of representation, level of ideology. Data collection by watching and observing several scenes to understand the content of the film, documentation and cutting several scenes of film images that represent representations of sexual harassment of men through literature studies, namely, collecting previous reference journals, websites, the internet, theses, and books related to this research. The results of this study indicate that the representation of sexual harassment against men is seen in the film Dear David with a general form of verbal and nonverbal and sexual fantasies as a trigger for sexual harassment, about sexual harassment against men who become a normal thing and masculinity in men with normal can be a material for sexual harassment as a joke that leads to coercive nonverbal harassment. This representation is intended to be informative regarding sexual harassment against men that is still considered normal and the patriarchal influence of society on men who still think that men cannot be victims of sexual harassment.

Low-contrast X-ray image defect segmentation via a novel core-profile decomposition network

Accurate X-ray image defect segmentation is of paramount importance in industrial contexts, as it is the foundation for product quality control and production safety. Deep learning (DL) has demonstrated powerful image scene understanding capabilities and has achieved unprecedented performance in defect segmentation tasks. However, existing DL methods suffer from significant performance degradation when facing low-contrast X-ray images, as the core information of defects is often obscured and the profile details are ambiguous. To address this issue, this paper explicitly decomposes the X-ray defect segmentation task into two subtasks: core feature learning and elasticity profile refinement, allowing task “serial” decomposition and performance “parallel” improvement. On this basis, a novel core-profile decomposition network (CPDNet) is developed to achieve accurate defect segmentation of X-ray images. Specifically, the core feature learning module is designed to construct the effective feature space from two views, discriminative and structural, to extract defect-related core features from X-ray images. Subsequently, the elasticity profile refinement module is developed to further improve the defect segmentation performance, which makes the first attempt to define the profile refinement as an out-of-distribution detection and leverage the elasticity score to refine the profile details at the pixel level. To fully evaluate the presented method, we conduct a series of experiments using two real-world X-ray defect datasets, and the results demonstrate that the CPDNet outperforms state-of-the-art methods.

Super-Resolution Image Reconstruction of Wavefront Coding Imaging System Based on Deep Learning Network

Wavefront Coding (WFC) is an innovative technique aimed at extending the depth of focus (DOF) of optics imaging systems. In digital imaging systems, super-resolution digital reconstruction close to the diffraction limit of optical systems has always been a hot research topic. With the design of a point spread function (PSF) generated by a suitably phase mask, WFC could also be used in super-resolution image reconstruction. In this paper, we use a deep learning network combined with WFC as a general framework for images reconstruction, and verify its possibility and effectiveness. Considering the blur and additive noise simultaneously, we proposed three super-resolution image reconstruction procedures utilizing convolutional neural networks (CNN) based on mean square error (MSE) loss, conditional Generative Adversarial Networks (CGAN), and Swin Transformer Networks (SwinIR) based on mean absolute error (MAE) loss. We verified their effectiveness by simulation experiments. A comparison of experimental results shows that the SwinIR deep residual network structure based on MAE loss optimization criteria can generate more realistic super-resolution images with more details. In addition, we used a WFC camera to obtain a resolution test target and real scene images for experiments. Using the resolution test target, we demonstrated that the spatial resolution could be improved from 55.6 lp/mm to 124 lp/mm by the proposed super-resolution reconstruction procedure. The reconstruction results show that the proposed deep learning network model is superior to the traditional method in reconstructing high-frequency details and effectively suppressing noise, with the resolution approaching the diffraction limit.

Artifact feature purification for cross-domain detection of AI-generated images

In the era of AIGC, the fast development of visual content generation technologies, such as diffusion models, brings potential security risks to our society. Existing generated image detection methods suffer from performance drops when faced with out-of-domain generators and image scenes. To relieve this problem, we propose Artifact Purification Network (APN) to facilitate the artifact extraction from generated images through the explicit and implicit purification processes. For the explicit one, a suspicious frequency-band proposal method and a spatial feature decomposition method are proposed to extract artifact-related features. For the implicit one, a training strategy based on mutual information estimation is proposed to further purify the artifact-related features. The experiments are conducted in two settings. Firstly, we perform a cross-generator evaluation, wherein detectors trained using data from one generator are evaluated on data generated by other generators. Secondly, we conduct a cross-scene evaluation, wherein detectors trained for a specific domain of content (e.g., ImageNet) are assessed on data collected from another domain (e.g., LSUN-Bedroom). Results show that for cross-generator detection, the average accuracy of APN is 5.6%∼16.4% higher than the previous 11 methods on the GenImage dataset and 1.7%∼50.1% on the DiffusionForensics dataset. For cross-scene detection, APN maintains its high performance. Via visualization analysis, we find that the proposed method can extract diverse forgery patterns and condense the forgery information diluted in irrelated features. We also find that the artifact features APN focuses on across generators and scenes are global and diverse. The code will be available at https://github.com/RichardSunnyMeng/APN-official-codes.

SAR Image Registration: The Combination of Nonlinear Diffusion Filtering, Hessian Features and Edge Points.

Synthetic aperture radar (SAR) image registration is an important process in many applications, such as image stitching and remote sensing surveillance. The registration accuracy is commonly affected by the presence of speckle noise in SAR images. When speckle noise is intense, the number of image features acquired by single-feature-based methods is insufficient. An SAR image registration method that combines nonlinear diffusion filtering, Hessian features and edge points is proposed in this paper to reduce speckle noise and obtain more image features. The proposed method uses the infinite symmetric exponential filter (ISEF) for image pre-processing and nonlinear diffusion filtering for scale-space construction. These measures can remove speckle noise from SAR images while preserving image edges. Hessian features and edge points are also employed as image features to optimize the utilization of feature information. Experiments with different noise levels, geometric transformations and image scenes demonstrate that the proposed method effectively improves the accuracy of SAR image registration compared with the SIFT-OCT, SAR-SIFT, Harris-SIFT, NF-Hessian and KAZE-SAR algorithms.