Edge-preserving image smoothing plays a vital role in the field of computational imaging. It is a valuable technique that has applications in various tasks. However, different tasks have specific requirements for edge preservation. Existing filters do not take into account the task-dependent smoothing behavior, resulting in visually distracting artifacts. We propose a flexible edge-preserving image filter based on a nonconvex Welsch penalty. Compared with the convex models, our model can better handle complex data and capture nonlinear relationships, thus providing better results. We combine deep unsupervised learning and graduated nonconvexity to solve our nonconvex objective function, where the main network structure is designed as a Swin transformer complemented with the locally enhanced feed-forward network. Experimental results show that the proposed method achieves excellent performance in various applications, including image smoothing, high dynamic range tone mapping, detail enhancement, and edge extraction.

Crowd counting aims to derive information about crowd density by quantifying the number of individuals in an image or video. It offers crucial insights applicable to various domains, e.g., secure, efficient decision-making, and management. However, scale variation and irregular shapes of heads pose intricate challenges. To address these challenges, we propose a scale-deformation awareness network (SDANet). Specifically, a scale awareness module is introduced to address the scale variation. It can capture long-distance dependencies and preserve precise spatial information by readjusting weights in height and width directions. Concurrently, a deformation awareness module is introduced to solve the challenge of head deformation. It adjusts the sampling position of the convolution kernel through deformable convolution and learning offset. Experimental results on four crowd-counting datasets prove the superiority of SDANet in accuracy, efficiency, and robustness.

Real-world image denoising is a critical task in image processing, aiming to restore clean images from their noisy counterparts captured in natural environments. While diffusion models have demonstrated remarkable success in image generation, surpassing traditional generative models, their application to image denoising has been limited due to challenges in controlling noise generation effectively. We present a general denoising method inspired by diffusion models. Specifically, our approach employs a diffusion process with linear interpolation, enabling control of noise generation. By interpolating the intermediate noisy image between the original clean image and the corresponding real-world noisy one, our model is able to achieve controllable noise generation. Moreover, we introduce two sampling algorithms for this diffusion model: a straightforward procedure aligned with the diffusion process and an enhanced version that addresses the shortcomings of the former. Experimental results demonstrate that our proposed method, utilizing simple convolutional neural networks such as UNet, achieves denoising performance comparable to that of the transformer architecture.

The most common form of image forgery is copy-move, which arises when an image region is duplicated and pasted onto another region of the same image. An effective algorithm for copy-move forgery detection based on binarized statistical image features (BSIF) and principal component analysis (PCA) is presented. Initially, the suspicious image is converted to grayscale and is subsequently partitioned into overlapping blocks. Feature vectors are extracted from these blocks using BSIF, followed by dimensionality reduction using PCA. Next, as a precursor to the matching step, the feature vectors are sorted lexicographically. Additionally, a morphological opening operation is applied to eliminate outliers. This algorithm offers not just forgery detection but also the ability to localize and identify duplicated regions. The proposed algorithm was assessed using three datasets: CoMoFoD, GRIP, and UNIPA. The experimental results show that this algorithm is fast and has high accuracy for forgery detection and localization. Moreover, it has high robustness under various postprocessing operations, such as brightness, contrast adjustments, and blurring. Furthermore, the proposed algorithm outperforms some recent approaches in overall performance.

Recognizing three-dimensional (3D) objects based on local feature descriptors is a highly challenging task. Existing 3D local feature descriptors rely on single-scale surface normals, which are susceptible to noise and outliers, significantly compromising their effectiveness and robustness. A multi-scale point pair normal encoding (M-POE) method for 3D object recognition is proposed. First, we introduce the M-POE descriptor, which encodes voxelized features with multi-scale normals to describe local surfaces, exhibiting strong distinctiveness and robustness against various interferences. Second, we present guided sample consensus in second-order graphs (GSAC-SOG), an extension of RANSAC that incorporates geometric constraints and reduces sampling randomness, enabling accurate estimation of the object’s six-degree-of-freedom (6-DOF) pose. Finally, a 3D object recognition method based on the M-POE descriptor is proposed. The proposed method is evaluated on five standard datasets with state-of-the-art comparisons. The results demonstrate that (1) M-POE is robust, discriminative, and efficient; (2) GSAC-SOG is robust to outliers; (3) the proposed 3D object recognition method achieves high accuracy and robustness against clutter and occlusion, with recognition rates of 99.45%, 94.21%, and 97.88% on the U3OR, Queen, and CFV datasets, respectively.

The utilization of chest X-ray (CXR) image data analysis for assisting in disease diagnosis is an important application of artificial intelligence. Supervised learning faces challenges due to a lack of large-scale labeled datasets and inaccuracies. Self-supervised learning offers a potential solution, but current research in this area is limited, and the diagnostic accuracy remains unsatisfactory. We propose an approach that integrates the self-supervised Bidirectional Encoder Representations from Image Transformers version 2 (BEiTv2) method with the vector quantization-based knowledge distillation (VQ-KD) strategy into CXR image data to enhance disease diagnosis accuracy. Our methodology demonstrates superior performance compared with existing self-supervised methods, showcasing its efficacy in improving diagnostic outcomes. Through transfer and ablation studies, we elucidate the benefits of the VQ-KD strategy in enhancing model performance and transferability to downstream tasks.

Video hashing is an efficient technique for tasks like copy detection and retrieval. This paper utilizes canonical polyadic (CP) decomposition and Hahn moments to design a robust video hashing. The first significant contribution is the secondary frame construction. It uses three weighted techniques to generate three secondary frames for each video group, which can effectively capture features of video frames from different aspects and thus improves discrimination. Another contribution is the deep feature extraction via the ResNet50 and CP decomposition. The use of the ResNet50 can provide rich features and the CP decomposition can learn a compact and discriminative representation from the rich features. In addition, the Hahn moments of secondary frames are taken to construct hash elements. Extensive experiments on the open video dataset demonstrate that the proposed algorithm surpasses several state-of-the-art algorithms in balancing discrimination and robustness.

In the current distributed environment, reversible data hiding in encrypted images has the disadvantages of low security and nonprogressivity. To address this problem, a homomorphic embedding algorithm is proposed based on polynomial secret sharing (PSS) and Chinese remainder theorem. First, the image owner encrypts the carrier image in streaming encryption and sends it to the data hider. Then, the data hider utilizes PSS to split the carrier image into n shares. At the same time, extra secrets after SS are embedded into the carrier shares using homomorphism. After splitting by Chinese remainder theorem, every share of the embedded data is divided into some sub-shares and then distributed to the participants. The participants that satisfy the threshold condition provide part or all of the sub-shares according to the authority of the data extractor. If each participant provides all sub-shares, the secrets and carrier image can be reconstructed completely. If each participant provides part of the sub-shares, the secrets and carrier image can be reconstructed partly. The experimental results show that the proposed scheme has progressivity, high security, and a large embedding rate (ER). Meanwhile, the ER is not affected by the carrier image.

The smooth regions in depth videos contain a significant proportion of homogeneous content, resulting in many spatial redundancies. To improve the coding efficiency of depth videos, this paper proposes a deep degradation-aware up-sampling-based depth video coding method. For reducing spatial redundancies effectively, the proposed method compresses the depth video at a low resolution, and restores the resolution by utilizing the learning-based up-sampling technology. To recover high-quality depth videos, a degradation-aware up-sampling network is proposed, which explores the degradation information of compression artifacts and sampling artifacts to restore the resolution. Specifically, the compression artifact removal module is used to obtain refined low-resolution depth frames by learning the representation of compression artifacts. Meanwhile, a jointly optimized learning strategy is designed to enhance the capability of recovering high-frequency details, which is beneficial for up-sampling. According to the experimental results, the proposed method achieves considerable performance in depth video coding compared with 3D-HEVC.

Radar imaging techniques, such as synthetic aperture radar, are widely explored in automatic vehicle recognition algorithms for remote sensing tasks. A large basis of literature covering several machine learning methodologies using visual information transformers, self-attention, convolutional neural networks (CNN), long short-term memory (LSTM), CNN-LSTM, CNN-attention-LSTM, and CNN Bi-LSTM models for detection of military vehicles have been attributed with high performance using a combination of these approaches. Tradeoffs between differing number of poses, single/multiple feature extraction streams, use of signals and/or images, as well as the specific mechanisms used to combine them, have widely been debated. We propose the adaptation of several models towards a unique biologically inspired architecture that utilizes both multi-pose and multi-contextual image and signal radar sensor information to make vehicle assessments over time. We implement a compact multi-pose 3D CNN single stream to process and fuse multi-temporal images while a dual sister 2D CNN stream processes the same information over a lower-dimensional power-spectral domain to mimic the way multi-sequence visual imagery is combined with auditory feedback for enhanced situational awareness. These data are then fused across data domains using transformer-modified encoding blocks to Bi-LSTM segments. Classification results on a fundamentally controlled simulated dataset yielded accuracies of up to 98% and 99% in line with literature. This enhanced performance was then evaluated for robustness not previously explored for three simultaneous parameterizations of incidence angle, object orientation, and lowered signal-to-noise ratio values and found to increase recognition on all three cases for low to moderate noised environments.

In recent years, underwater target detection algorithms based on deep learning have greatly promoted the development of the field of marine science and underwater robotics. However, due to the complexity of the underwater environment, there are problems, such as target occlusion, overlap, background confusion, and small object, that lead to detection difficulties. To address this issue, this paper proposes an improved underwater target detection method based on YOLOv8s. First, a lightweight backbone network with efficient transformers is used to replace the original backbone network, which enhances the contextual feature extraction capability. Second, an improved bidirectional feature pyramid network is used in the later multi-scale fusion part by increasing the input of bottom-level information while reducing the model size and number of parameters. Finally, a dynamic head with an attention mechanism is introduced into the detection head to enhance the classification and localization of small and fuzzy targets. Experimental results show that the proposed method improves the mAP0.5:0.95 of 65.7%, 63.7%, and 51.2% with YOLOv8s to that of 69.2%, 66.8%, and 54.8%, on three public underwater datasets, DUO, RUOD, and URPC2020, respectively. Additionally, compared with the YOLOv8s model, the model size decreased from 21.46 to 15.56 MB, and the number of parameters decreased from 11.1 to 7.9 M.

The detection of multiple faces in unconstrained environment in deep learning suffers from insufficient detection accuracy and inefficiency; at the same time, the detection of blurred, occluded, and very small faces is even more unsatisfactory. The detection of blurred, occluded, and very small faces in multiple face detection in unconstrained environment is a hard problem in face detection nowadays. It is difficult to balance the detection accuracy and real-time efficiency in face detection with the improved RetinaFace chosen in this study. Therefore, in order to improve the efficiency of detecting blurred, occluded, and very small faces among multiple faces in unconstrained environments, we introduce deformable convolution, feature pyramid networks (FPN), and coordinate attention (CA) attention mechanism based on RetinaFace algorithm. Deformable convolution can be dynamically adjusted according to the shape and deformation of the recognized object and is no longer limited to a fixed-size square receptive field to improve the image feature extraction capability of the convolutional layer. FPN enhances the feature semantic information of the lower layers with a small increase in computational effort and improves the robustness of the detection algorithm to detect targets of different sizes. CA is a novel, lightweight, and efficient attention mechanism module for improving model performance, which can be easily integrated into mobile networks to improve accuracy with little additional computational overhead. The improved ResRetinaFace algorithm does not increase the computational overhead too much while improving the recognition accuracy, and it can better combine the characteristics of multiple postures and deformations of faces in complex scenes, adapt to the deformation state of faces’ postures, and provide more effective features for face detection, so as to pay better attention to the detection target and enhance the network characterization ability. Meanwhile, the improved algorithm combines the feature pyramid with the context module, which improves the detection effect in the case of blurred, occluded, and very small faces. The experimental outcomes demonstrate that, in contrast to the method before enhancement, the accuracy rates for easy, medium, and hard classification scenarios on the WIDER FACE dataset, utilizing the ResNet50 backbone network, are 94.83%, 93.28%, and 84.99%, respectively. Accompanied by a frames-per-second rate of 7.704, this meets the precision and real-time criteria for face measurement tasks. Validation on the WIDER FACE dataset further affirms that ResRetinaFace consistently achieves reliable face detection while maintaining high detection efficiency.

When facing motion and complex environmental conditions, infrared videos captured by thermal imaging devices often suffer from blurring, leading to unclear or missing details and positional information about the targets. To improve this problem, this work proposes an improved deblurring method suitable for infrared videos based on a deep learning-based deblurring network originally designed for visible light images. This method is built upon the D²Net network by introducing a spatial and channel reconstruction convolution for feature redundancy, enhancing the network’s capability for image feature learning. In terms of the encoder-decoder module, a triple attention mechanism and fast Fourier transform are introduced to further improve the network’s deblurring performance. Through ablative experiments on infrared datasets, the results demonstrate a significant improvement in deblurring performance compared to the original D²Net. Specifically, the improved network achieved a 1.42 dB increase in peak signal-to-noise ratio and a 0.02 dB increase in structural similarity compared to the original network. In summary, this paper achieves promising results in infrared video deblurring tasks, demonstrating the effectiveness of the proposed method.

Aiming at the frequent misdetection and omission in the detection process of airport runway foreign object debris (FOD) and the difficulty of deploying the detection algorithm to embedded devices, we propose a lightweight FOD detection method called PGDIG-YOLO based on the improvement of YOLOv8n. First, a detection layer for detecting small-size objects is added and a large target detection layer is deleted to enhance the network’s ability to sense small-sized objects. Second, a dilation-wise residual module is introduced in the segmentation domain, and the C2FD module is proposed, which effectively solves the problem of misdetection and missed detection of FOD on airport runways. Third, the inner-WMPDIoUv3 is designed to replace the CIoU as a loss function to improve the regression accuracy of the detection frame. Finally, the model is pruned using the Group_sl method, which reduces the amount of computation, compresses the model size, and improves the model inference speed. The experimental results on the homemade dataset FOD-Z show that, compared with the benchmark model YOLOv8n, the model volume and computation of the PGDIG-YOLO network are only 6.6% and 44.4% of the original network, and the accuracy and recall are improved by 1.1% and 3.8%, respectively. Meanwhile, the mAP@0.5, mAP@0.75, and mAP@0.5:0.95 are increased to 99.1%, 93.7%, and 85.6%, respectively. Deploying PGDIG-YOLO to the NVIDIA Jetson Xavier NX 16 GB embedded device, the detection speed reaches 42 FPS, which can realize real-time FOD detection.

Compared with ordinary images, hyperspectral images (HSIs) consist of many bands that can provide rich spatial and spectral information and are widely used in remote sensing. However, HSIs are subject to various types of noise due to limited sensor sensitivity; low light intensity in the bands; and corruption during acquisition, transmission, and storage. Therefore, the problem of HSI denoising has attracted extensive attention from society. Although recent HSI denoising methods provide effective solutions in various optimization directions, their performance under real complex noise is still not optimal. To address these issues, this article proposes a self-modulated cross-attention network that fully utilizes spatial and spectral information. The core of the model is the use of deformable convolution to cross-fuse spatial and spectral features to improve the network denoising capability. At the same time, a self-modulating residual block allows the network to transform features in an adaptive manner based on neighboring bands, improving the network’s ability to deal with complex noise, which we call a feature enhancement block. Finally, we propose a three-segment network architecture that improves the stability of the model. The method proposed in this work outperforms other state-of-the-art methods through comparative analysis of experiments in synthetic and real data.

Most crowd counting methods rely on integrating density maps for prediction, but they encounter performance degradation in the face of density variations. Existing methods primarily employ a multi-scale architecture to mitigate this issue. However, few approaches concurrently consider both scale and timing information. We propose a scale-divided architecture for video crowd counting. Initially, density maps of different Gaussian scales are employed to retain information at various scales, accommodating scale changes in images. Subsequently, we observe that the spatiotemporal network places greater emphasis on individual locations, prompting us to aggregate temporal information at a specific scale. This design enables the temporal model to acquire more spatial information and alleviate occlusion issues. Experimental results on various public datasets demonstrate the superior performance of our proposed method.

Prefabricated steel pipes play a crucial role in prefabricated buildings, and maintaining their surface integrity is crucial to ensuring the safety of these buildings. We propose a surface defect detection algorithm for prefabricated steel pipes, D-YOLOv7-tiny, based on YOLOv7-tiny, to address the challenges of high parameter count and large computational requirements associated with traditional algorithms, making it difficult to deploy at resource-constrained terminals. By incorporating the squeeze-and-excitation attention mechanism into the backbone network, D-YOLOv7-tiny effectively minimizes the impact of redundant information and improves the network’s ability to extract features. In addition, distribution shifting convolution is implemented as a replacement for a portion of traditional convolution in the original effective layer aggregation network module network. This exchange reduces the computational workload of the model without affecting its expressive power. Subsequently, the lightweight and ubiquitous content-aware reassembly of features upsampling operator improved the feature merging of the network. Finally, an attention-based dynamic head was adopted to enhance the model’s robustness while minimizing parameter counts. Compared with YOLOv7-tiny, the mAP of D-YOLOv7-tiny was enhanced by 1.9% through experiments conducted on self collected datasets. During this process, the number of parameters and computational complexity decreased by 7.6% and 39.4%, respectively. The results show that this method achieves lightweight and meets the requirements of practical engineering accuracy and real-time performance.

In the field of computer vision and graphics, high-quality reconstruction of the human body in static scenes has been achieved in recent years by a single multilayer perceptron (MLP) in a number of approaches. However, MLPs have capacity limitations, requiring substantial training time and computational resources for dynamic scene reconstruction, and the quality of reconstruction is significantly constrained. We propose a method for effectively processing complex spatiotemporal signals in dynamic scene human three-dimensional (3D) modeling. The proposed method uses temporal residual neural radiance fields to achieve novel view rendering and new pose synthesis of human bodies. To address the problem of representing temporal signals in video sequences, we construct a temporal residual field that is not related to the MLP architecture. Second, to improve the reconstruction efficiency, we propose an integrated approach that reduces the trainable parameters and accelerates rendering, thereby enhancing the network’s feature representation capability. Finally, we design a multi-dimensional loss function to accurately measure the loss between predicted and actual spatial pixel values. The experimental results show that our proposed approach improves the peak signal-to-noise ratio and structural similarity index accuracy metrics compared with the latest representative methods. It maintains a similar accuracy to Anim-NeRF and Neural Body while achieving a nearly 780-fold increase in time efficiency.

A range of compression algorithms for generative adversarial networks (GANs) have been explored to alleviate the significant overhead involved in deploying GANs. However, in lightweight tasks, the role of the discriminator is usually ignored, resulting in an imbalance in training. Simultaneously, the ability of compact GANs to generate high-frequency information also lags behind teacher models. Addressing these challenges, we propose a wavelet-based knowledge distillation scheme with an adaptive discriminator (StyleWA). In StyleWA, the adaptive discriminator dynamically activates or inhibits convolutional channels based on the assessment of convolutional kernel contributions, ensuring more stable network training while preserving discriminator efficiency. Moreover, existing research primarily concentrates on the compression of conditional GANs, with limited exploration of unconditional GANs (uncGANs), known for their superior performance. We introduce wavelet analysis for the first time in the knowledge distillation of uncGANs, enhancing the model’s capability to control image details and augmenting distillation quality. Results demonstrate that the StyleWA scheme not only aids in maintaining Nash equilibrium between the discriminator and lightweight generator during training to mitigate pattern collapse but also facilitates the distillation model in learning intricate high-frequency details. This, in turn, enhances the overall generation quality and editing efficacy of the uncGAN model.

Multiscale features have attracted widespread attention in person reidentification due to their capability to enhance the model information processing. However, the use of multiscale features often leads to model redundancy and low operational efficiency, greatly affecting the practical application of person reidentification. To address the above issue, we propose an efficient and lightweight multiscale network for person reidentification. Specifically, we construct a baseline network for lightweight person reidentification called the multiscale efficient network (MSENet). It comprises three primary stages, with each stage employing the multiscale efficient block, consisting of varying numbers of lightweight efficient convolution blocks. The proposed network efficiently achieves pedestrian image retrieval while maintaining low model complexity. Subsequently, we propose a lightweight pyramid feature fusion module to aggregate multilayer features of the MSENet, enhancing feature diversity and obtaining robust features. Finally, we design a contour branch that focuses on the overall feature extraction of pedestrian images, effectively reducing the interference of background information. Extensive experiments conducted on three popular datasets have demonstrated that the proposed method has excellent recognition accuracy and low model complexity.

Occluded person re-identification (re-id) is a task in pedestrian retrieval where occluded person images are matched with holistic person images. Most methods leverage semantic cues from external models to align the availability of visible parts in the feature space. However, presenting visible parts while discarding occluded parts can lead to the loss of semantics in the occluded regions, and in severely crowded regions of occlusion, it will introduce inaccurate features that pollute the overall person features. Thus, constructing person features for occluded regions based on the features of its holistic parts has the potential to address the above issues. In this work, we propose a pose-guided node and trajectory construction transformer (PNTCT). The part feature extraction module extracts parts feature of the person and incorporates pose information to activate key visible local features. However, this is not sufficient to completely separate occluded regions. To further distinguish visible and occluded parts, the skeleton graph module adopts a graph topology to represent local features as graph nodes, enhancing the network’s sensitivity to local features by constructing a skeleton feature graph, which is further utilized to weaken the occlusion noise. The node and trajectory construction module (NTC) mines the relationships between skeleton nodes and aggregates the information of the person’s skeleton to construct a novel skeleton graph. The features of the occluded regions can be reconstructed via the features of the corresponding nodes in the novel skeleton graph. Extensive experiments and analyses confirm the effectiveness and superiority of our PNTCT method.

Various daily behaviors usually exert pressure on the contact surface, such as lying, walking, and sitting. Obviously, the pressure data from the contact surface contain some important biological information for an individual. Recently, a computer vision task, i.e., pose estimation from contact pressure (PECP), has received more and more attention from researchers. Although several deep learning-based methods have been put forward in this field, they cannot achieve accurate prediction using the limited pressure information. To address this issue, we present a multi-task-based PECP model. Specifically, the autoencoder is introduced into our model for reconstructing input pressure data (i.e., the additional task), which can help our model generate high-quality features for the pressure data. Moreover, both the mean squared error and the spectral angle distance are adopted to construct the final loss function, whose aim is to eliminate the Euclidean distance and angle differences between the prediction and ground truth. Extensive experiments on the public dataset show that our method outperforms existing methods significantly in pose prediction from contact pressure.

Contrastive learning for unpaired image-to-image translation (CUT) aims to learn a mapping from source to target domain with an unpaired dataset, which combines contrastive loss to maximize the mutual information between real and generated images. However, the existing CUT-based methods exhibit unsatisfactory visual quality due to the wrong locating of objects and backgrounds, particularly where it incorrectly transforms the background to match the object pattern in layout-changing datasets. To alleviate the issue, we present background-focused contrastive learning for unpaired image-to-image translation (BFCUT) to improve the background’s consistency between real and its generated images. Specifically, we first generate heat maps to explicitly locate the objects and backgrounds for subsequent contrastive loss and global background similarity loss. Then, the representative queries of objects and backgrounds rather than randomly sampling queries are selected for contrastive loss to promote reality of objects and maintenance of backgrounds. Meanwhile, global semantic vectors with less object information are extracted with the help of heat maps, and we further align the vectors of real images and their corresponding generated images to promote the maintenance of the backgrounds in global background similarity loss. Our BFCUT alleviates the wrong translation of backgrounds and generates more realistic images. Extensive experiments on three datasets demonstrate better quantitative results and qualitative visual effects.

The Joint Video Experts Team has recently finalized the versatile video coding (VVC) standard, which incorporates various advanced encoding tools. These tools ensure great enhancements in the coding efficiency, leading to a bitrate reduction up to 50% when compared to the previous standard, high-efficiency video coding. However, this enhancement comes at the expense of high computational complexity. Within this context, we address the new quadtree (QT) with nested multitype tree partition block in VVC for all-intra configuration. In fact, we propose a fast intra-coding unit (CU) partition algorithm using various convolution neural network (CNN) classifiers to directly predict the partition mode, skip unnecessary split modes, and early exit the partitioning process. The proposed approach first predicts the QT depth at a CU of size 64×64 by the corresponding CNN classifier. Then four CNN classifiers are applied to predict the partition decision tree at a CU of size 32×32 using multithreshold values and ignore the rate-distortion optimization process to speed up the partition coding time. Thus the developed method is implemented on the reference software VTM 16.2 and tested for different video sequences. The experimental results confirm that the proposed solution achieves an encoding time reduction of about 46% in average, reaching up to 67.3% with an acceptable increase in bitrate and an unsignificant decrease in quality.

As the elderly population in need of assisted living arrangements continues to grow, the imperative to ensure their safety is paramount. Though effective, traditional surveillance methods, notably RGB cameras, raise significant privacy concerns. This paper highlights the advantages of a surveillance system addressing these issues by utilizing skeleton joint sequences extracted from depth data. The focus on non-intrusive parameters aims to mitigate ethical and privacy concerns. Moreover, the proposed work prioritizes resource efficiency, acknowledging the often limited computing resources in assisted living environments. We strive for a method that can run efficiently even in the most resource-constrained environments. Performance evaluation and a prototypical implementation of our method on a resource-constraint device confirm the efficacy and suitability of the proposed method in real-world applications.

Storage space and security concerns on multimedia images have emerged as a global issue in recent years. Image encryption algorithm via compressed sensing (CS) is an effective method for data security and reducing storage space. However, the existing CS-based image encryption still faces problems, such as weak resistance to attacks and extensive data storage. We design a high-compression image encryption algorithm that combines fractal and semi-tensor product compressed sensing. First, a measurement matrix required for CS is generated using fractal blocks combined with the semi-tensor product method, which enhances security while reducing the size of the measurement matrix. Then, the measurements obtained from the sampling are used to define the product features of their mean and standard deviation. Exclusion criteria are set, and fractal codes are obtained through matched searching. Finally, the fractal code undergoes scrambling and diffusion, providing triple-layer protection and further improving the security of the secret image. In comparison to conventional methods, our proposed method has greatly improved the compression efficiency through compressed sampling and has the advantages of better concealment and enhanced robustness. Experiments show that we substantiate the effectiveness and superior performance of our method, all while upholding image quality and security.

The complexity of clouds, particularly in terms of texture detail at high resolutions, has not been well explored by most existing cloud detection networks. We introduce the high-resolution cloud detection network (HR-cloud-Net), which utilizes a hierarchical high-resolution integration approach. HR-cloud-Net integrates a high-resolution representation module, layer-wise cascaded feature fusion module, and multiresolution pyramid pooling module to effectively capture complex cloud features. This architecture preserves detailed cloud texture information while facilitating feature exchange across different resolutions, thereby enhancing the overall performance in cloud detection. Additionally, an approach is introduced wherein a student view, trained on noisy augmented images, is supervised by a teacher view processing normal images. This setup enables the student to learn from cleaner supervisions provided by the teacher, leading to an improved performance. Extensive evaluations on three optical satellite image cloud detection datasets validate the superior performance of HR-cloud-Net compared with existing methods.

Neuromorphic vision sensors (NVS) with the features of small data redundancy and transmission latency are widely implemented in Internet of Things applications. Previous studies have developed various object detection algorithms based on NVS’s unique event data format. However, most of these methods are only adaptive for scenarios with stationary backgrounds. Under dynamic background conditions, NVS can also acquire the events of non-target objects due to its mechanism of detecting pixel intensity changes. As a result, the performance of existing detection methods is greatly degraded. To address this shortcoming, we introduce an extra refinement process to the conventional histogram-based (HIST) detection method. For the proposed regions from HIST, we apply a practical decision condition to categorize them as either object-dominant or background-dominant cases. Then, the object-dominant regions undergo a second-time HIST-based region proposal for precise localization, while background-dominant regions employ an upper outline determination strategy for target object identification. Finally, the refined results are tracked using a simplified Kalman filter approach. Evaluated in an outdoor drone surveillance with an event camera, the proposed scheme demonstrates superior performance in both intersection over union and F1 score metrics compared to other methods.