Search Results (43)

A Sanskrit manuscript of the Prajñāpāramitā or Perfection of Wisdom in eight thousand verses, now in the Cambridge University Library, Add.1643, is one of the most ambitiously designed South Asian manuscripts from the eleventh century, with the highest number of painted panels known among the dated manuscripts from medieval South Asia until 1400 CE. Thanks to the unique occurrence of a caption written next to each painted panel, it is possible to identify most images in this manuscript as representing those of famous pilgrimage sites or auspicious images of specific locales. The iconographic program transforms Add.1643 into a portable device containing famous pilgrimage sites of the Buddhist world known to the makers and users of the manuscript in eleventh-century Nepal. It is one compact colorful package of a book, which can be opened and experienced in its unfolding three-dimensional space, like a virtual or imagined pilgrimage. Building on the recent research focusing on early medieval Buddhist sites across Monsoon Asia and analyzing the representational potentials and ontological values of painting, this essay demonstrates how this early eleventh-century Nepalese manuscript (Add.1643) and its visual program document and remember the knowledge of maritime travels and the transregional and intraregional activities of people and ideas moving across Monsoon Asia. Despite being made in the Kathmandu Valley with a considerable physical distance from the actual sea routes, the sites remembered in the manuscript open a possibility to connect the dots of human movement beyond the known networks and routes of “world systems”. Full article

The rapid advancement of 3D imaging technology and depth cameras has made depth data more accessible for applications such as virtual reality and autonomous driving. However, depth maps typically suffer from lower resolution and quality compared to color images due to sensor limitations. This paper introduces an improved approach to guided depth map super-resolution (GDSR) that effectively addresses key challenges, including the suppression of texture copying artifacts and the preservation of depth discontinuities. The proposed method integrates both local and nonlocal models within a structured framework, incorporating an adaptive bandwidth mechanism that dynamically adjusts guidance weights. Instead of relying on fixed parameters, this mechanism utilizes a distance map to evaluate patch similarity, leading to enhanced depth recovery. The local model ensures spatial smoothness by leveraging neighboring depth information, preserving fine details within small regions. On the other hand, the nonlocal model identifies similarities across distant areas, improving the handling of repetitive patterns and maintaining depth discontinuities. By combining these models, the proposed approach achieves more accurate depth upsampling with high-quality depth reconstruction. Experimental results, conducted on several datasets and evaluated using various objective metrics, demonstrate the effectiveness of the proposed method through both quantitative and qualitative assessments. The approach consistently delivers improved performance over existing techniques, particularly in preserving structural details and visual clarity. An ablation study further confirms the individual contributions of key components within the framework. These results collectively support the conclusion that the method is not only robust and accurate but also adaptable to a range of real-world scenarios, offering a practical advancement over current state-of-the-art solutions. Full article

To enhance the intelligence of robots, constructing accurate object-level instance maps is essential. However, the diversity and clutter of objects in indoor scenes present significant challenges for instance map construction. To tackle this issue, we propose a method for constructing object-level instance maps based on RGBD images. First, we utilize the advanced visual odometer ORB-SLAM3 to estimate the poses of image frames and extract keyframes. Next, we perform semantic and geometric segmentation on the color and depth images of these keyframes, respectively, using semantic segmentation to optimize the geometric segmentation results and address inaccuracies in the target segmentation caused by small depth variations. The segmented depth images are then projected into point cloud segments, which are assigned corresponding semantic information. We integrate these point cloud segments into a global voxel map, updating each voxel’s class using color, distance constraints, and Bayesian methods to create an object-level instance map. Finally, we construct an ellipsoids scene from this map to test the robot’s localization capabilities in indoor environments using semantic information. Our experiments demonstrate that this method accurately and robustly constructs the environment, facilitating precise object-level scene segmentation. Furthermore, compared to manually labeled ellipsoidal maps, generating ellipsoidal maps from extracted objects enables accurate global localization. Full article

Depth information is crucial for perceiving three-dimensional scenes. However, depth maps captured directly by depth sensors are often incomplete and noisy, our objective in the depth-completion task is to generate dense and accurate depth maps from sparse depth inputs by fusing guidance information from corresponding color images obtained from camera sensors. To address these challenges, we introduce transformer models, which have shown great promise in the field of vision, into the task of image-guided depth completion. By leveraging the self-attention mechanism, we propose a novel network architecture that effectively meets these requirements of high accuracy and resolution in depth data. To be more specific, we design a dual-branch model with a transformer-based encoder that serializes image features into tokens step by step and extracts multi-scale pyramid features suitable for pixel-wise dense prediction tasks. Additionally, we incorporate a dual-attention fusion module to enhance the fusion between the two branches. This module combines convolution-based spatial and channel-attention mechanisms, which are adept at capturing local information, with cross-attention mechanisms that excel at capturing long-distance relationships. Our model achieves state-of-the-art performance on both the NYUv2 depth and SUN-RGBD depth datasets. Additionally, our ablation studies confirm the effectiveness of the designed modules. Full article

In the context of agricultural modernization and intelligentization, automated fruit recognition is of significance for improving harvest efficiency and reducing labor costs. The variety of fruits commonly planted in orchards and the fluctuations in market prices require farmers to adjust the types of crops they plant flexibly. However, the differences in size, shape, and color among different types of fruits make fruit recognition quite challenging. If each type of fruit requires a separate visual model, it becomes time-consuming and labor intensive to train and deploy these models, as well as increasing system complexity and maintenance costs. Therefore, developing a general visual model capable of recognizing multiple types of fruits has great application potential. Existing multi-fruit recognition methods mainly include traditional image processing techniques and deep learning models. Traditional methods perform poorly in dealing with complex backgrounds and diverse fruit morphologies, while current deep learning models may struggle to effectively capture and recognize targets of different scales. To address these challenges, this paper proposes a general fruit recognition model based on the Multi-Scale Attention Network (MSA-Net) and a Hough Transform localization compensation mechanism. By generating multi-scale feature maps through a multi-scale attention mechanism, the model enhances feature learning for fruits of different sizes. In addition, the Hough Transform ellipse detection compensation mechanism uses the shape features of fruits and combines them with MSA-Net recognition results to correct the initial positioning of spherical fruits and improve positioning accuracy. Experimental results show that the MSA-Net model achieves a precision of 97.56, a recall of 92.21, and an mAP@0.5 of 94.81 on a comprehensive dataset containing blueberries, lychees, strawberries, and tomatoes, demonstrating the ability to accurately recognize multiple types of fruits. Moreover, the introduction of the Hough Transform mechanism reduces the average localization error by 8.8 pixels and 3.5 pixels for fruit images at different distances, effectively improving the accuracy of fruit localization. Full article

Topological data analysis (TDA) is a method of feature extraction based on data topological structure. Image feature extraction using TDA has been shown to be superior to other feature extraction techniques in some problems, so it has recently received the attention of researchers. In this paper, clothing image retrieval based on topology features and color texture features is studied. The main work is as follows: (1) Based on the analysis of image data by persistent homology, the feature construction method of a topology feature histogram is proposed, which can represent the ruler of image local topological data, and make up for the shortcomings of traditional feature extraction methods. (2) The improvement of Wasserstein distance is presented, while the similarity measure method named topology feature histogram distance is proposed. (3) Because the single feature has some problems such as the incomplete description of image information and poor robustness, the clothing image retrieval is realized by combining the topology feature with the color texture feature. The experimental results show that the proposed algorithm, namely topology feature histogram + corresponding distance, can effectively reduce the computation time while ensuring the accuracy. Compared with the method using only color texture, the retrieval rate of top5 is improved by 14.9%. Compared with the method using cubic complex + Wasserstein distance, the retrieval rate of top5 is improved by 3.8%, while saving 3.93 s computation time. Full article

It is difficult to visually detect internal defects in a large transformer with a metal closure. For convenient internal inspection, a micro-robot was adopted, and an inspection method based on an image-enhancement algorithm and an improved deep-learning network was proposed in this paper. Considering the dim environment inside the transformer and the problems of irregular imaging distance and fluctuating supplementary light conditions during image acquisition with the internal-inspection robot, an improved MSRCR algorithm for image enhancement was proposed. It could analyze the local contrast of the image and enhance the details on multiple scales. At the same time, a white-balance algorithm was introduced to enhance the contrast and brightness and solve the problems of overexposure and color distortion. To improve the target recognition performance of complex carbon-trace defects, the SimAM mechanism was incorporated into the Backbone network of the YOLOv8 model to enhance the extraction of carbon-trace features. Meanwhile, the DyHead dynamic detection Head framework was constructed at the output of the YOLOv8 model to improve the perception of local carbon traces with different sizes. To improve the defect target recognition speed of the transformer-inspection robot, a pruning operation was carried out on the YOLOv8 model to remove redundant parameters, realize model lightness, and improve detection efficiency. To verify the effectiveness of the improved algorithm, the detection model was trained and validated with the carbon-trace dataset. The results showed that the MSH-YOLOv8 algorithm achieved an accuracy of 91.80%, which was 3.4 percentage points higher compared to the original YOLOv8 algorithm, and had a significant advantage over other mainstream target-detection algorithms. Meanwhile, the FPS of the proposed algorithm was up to 99.2, indicating that the model computation and model complexity were successfully reduced, which meets the requirements for engineering applications of the transformer internal-inspection robot. Full article

Human actions that modify terrestrial and aquatic environments contribute to unsustainability, influencing the economy and human health. Urban environments are responsible for the dispersion of pollution and nanoparticles (NPs), which can potentially harm the health of human populations and contaminate the fauna and flora of aquatic ecosystems on a global scale. The objective of this study is to analyze the dissemination of nanoparticles in Cartagena Bay, Colombia, during the strong winds/low runoff season of January 2020 and the weak winds/high runoff season of October 2021. This was accomplished using images from the Sentinel-3B OLCI (Ocean Land Color Instrument) satellite in conjunction with an analytical chemical analysis of sediments collected in the study area in a laboratory with advanced electron microscopy. It was possible to obtain, on average, a sample of suspended sediments (SSs) every 1000 m in the areas of Bocagrande, Isla de Tierra Bomba, and Playa Blanca, which were analyzed in the laboratory with X-ray diffraction (XRD) and electron transmission and scanning microscopies. Images obtained in the summer of 2020 and winter of 2021 by the Sentinel 3B OLCI satellite were selected at a distance of 1 km from each other and analyzed for the following variables: chlorophyll (CHL_NN), water turbidity (TSM_NN), and suspended pollution potential (ADG443_NN). In addition to of evaluating georeferenced maps, they were subjected to an analysis within the statistical and K-Means clustering model. The laboratory analysis of SSs showed the presence of potentially toxic NPs, responsible for contamination that may harm the health of the local population and marine ecosystems. The K-Means and satellite image analysis corroborated the laboratory analyses, revealing the source of the pollution and contamination of Cartagena Bay as the estuary located close to its center. Full article

Color image decolorization can not only simplify the complexity of image processing and analysis, improving computational efficiency, but also help to preserve the key information of the image, enhance visual effects, and meet various practical application requirements. However, with existing decolorization methods it is difficult to simultaneously maintain the local detail features and global smooth features of the image. To address this shortcoming, this paper utilizes singular value decomposition to obtain the hierarchical local features of the image and utilizes quaternion theory to overcome the limitation of existing color image processing methods that ignore the correlation between the three channels of the color image. Based on this, we propose a singular value adaptive weighted fusion quaternion chromaticity contrast preserving decolorization method. This method utilizes the low-rank matrix approximation principle to design a singular value adaptive weighted fusion strategy for the three channels of the color image and implements image decolorization based on singular value adaptive weighting. To address the deficiency of the decolorization result obtained in this step, which cannot maintain global smoothness characteristics well, a contrast preserving decolorization algorithm based on quaternion chromaticity distance is further proposed, and the global weighting strategy obtained by this algorithm is integrated into the image decolorization based on singular value adaptive weighting. The experimental results show that the decolorization method proposed in this paper achieves excellent results in both subjective visual perception and objective evaluation metrics. Full article

Visual navigation based on image matching has become one of the most important research fields for UAVs to achieve autonomous navigation, because of its low cost, strong anti-jamming ability, and high performance. Currently, numerous positioning and navigation methods based on visual information have been proposed for UAV navigation. However, the appearance, shape, color, and texture of objects can change significantly due to different lighting conditions, shadows, and surface coverage during different seasons, such as vegetation cover in summer or ice and snow cover in winter. These changes pose greater challenges for feature-based image matching methods. This encouraged us to overcome the limitations of previous works, which did not consider significant seasonal changes such as snow-covered UAV aerial images, by proposing an image matching method using season-changing UAV aerial images and satellite imagery. Following the pipeline of a two-channel deep convolutional neural network, we first pre-scaled the UAV aerial images, ensuring that the UAV aerial images and satellite imagery had the same ground sampling distance. Then, we introduced attention mechanisms to provide additional supervision for both low-level local features and high-level global features, resulting in a new season-specific feature representation. The similarity between image patches was calculated using a similarity measurement layer composed of two fully connected layers. Subsequently, we conducted template matching to estimate the UAV matching position with the highest similarity. Finally, we validated our proposed method on both synthetic and real UAV aerial image datasets, and conducted direct comparisons with previous popular works. The experimental results demonstrated that our method achieved the highest matching accuracy on multi-temporal and multi-season images. Full article

Chilies undergo multiple stages from field production to reaching consumers, making them susceptible to contamination with foreign materials. Visually similar foreign materials are difficult to detect manually or using color sorting machines, which increases the risk of their presence in the market, potentially affecting consumer health. This paper aims to enhance the detection of visually similar foreign materials in chilies using hyperspectral technology, employing object detection algorithms for fast and accurate identification and localization to ensure food safety. First, the samples were scanned using a hyperspectral camera to obtain hyperspectral image information. Next, a spectral pattern recognition algorithm was used to classify the pixels in the images. Pixels belonging to the same class were assigned the same color, enhancing the visibility of foreign object targets. Finally, an object detection algorithm was employed to recognize the enhanced images and identify the presence of foreign objects. Random forest (RF), support vector machine (SVM), and minimum distance classification algorithms were used to enhance the hyperspectral images of the samples. Among them, RF algorithm showed the best performance, achieving an overall recognition accuracy of up to 86% for randomly selected pixel samples. Subsequently, the enhanced targets were identified using object detection algorithms including R-CNN, Faster R-CNN, and YoloV5. YoloV5 exhibited a recognition rate of over 96% for foreign objects, with the shortest detection time of approximately 12 ms. This study demonstrates that the combination of hyperspectral imaging technology, spectral pattern recognition techniques, and object detection algorithms can accurately and rapidly detect challenging foreign objects in chili peppers, including red stones, red plastics, red fabrics, and red paper. It provides a theoretical reference for online batch detection of chili pepper products, which is of significant importance for enhancing the overall quality of chili pepper products. Furthermore, the detection of foreign objects in similar particulate food items also holds reference value. Full article

With high performances of image capturing tools, image information can be easily obtained by screenshots that make image copyright protection a challenging task. The existing screen-shooting watermarking algorithms suffer from a huge running time, in addition to their low robustness against different screenshot attacks, such as different distances and capturing angles of the screenshots. In this paper, a fast and robust high-capacity flexible watermarking algorithm for screenshot images is proposed. Firstly, Oriented FAST and Rotated BRIEF (ORB) feature points are extracted from the input image. Secondly, the feature points are then sorted in a descending order according to their response values. Then, the first five non-overlapping feature points are selected for the embedding by using Hamming window-based filtering method. Furthermore, we exploit the multi-resolution property of Discrete Wavelet Transform (DWT) and energy compaction property of Singular Value Decomposition (SVD) to embed the watermark. Therefore, the classical DWT combined with Singular Value Decomposition (SVD) are adopted to improve the robustness and capacity of the proposed watermarking algorithm. At the extraction side, the sum of the response values for the three RGB channels of the color-ripped image is calculated to improve the feature point localization accuracy. Experimental results show that the proposed screen-shooting watermarking algorithm improves running speed while ensuring the robustness. Furthermore, it has less time complexity and high robustness compared with the state-of-the-art watermarking algorithms against different screenshot attacks. Full article

The article discusses approaches to solving the problems of detecting, recognizing, and localizing an object with given distinctive features in an aquatic environment using a technical stereo vision system, taking into account restrictions. The stereo vision system is being developed as part of the task in which the AUV, for the purpose of conducting a monitoring mission, follows from the starting point of its route along a given trajectory in order to detect and classify an object with known characteristics and determine its coordinates using a technical stereo vision system at a distance up to 5 m from it with appropriate water clarity. The developed program for the system of the technical stereo vision should provide the AUV with the following information: video sequence; a frame with an image of the detected object; previously unknown characteristics of the object if it is possible to detect them (color, size or shape); distance to the object from the technical stereo vision system; and linear coordinates relative to the technical stereo vision system. Testing of the developed software was carried out on the operating module of the stereo vision installed on the AUV in the underbody compartment. The study was carried out in the pool and in open water. The experiments performed have shown the effectiveness of the developed system when used in conjunction with an underwater robot. Full article

High-resolution (HR) remote sensing images have important applications in many scenarios, and improving the resolution of remote sensing images via algorithms is one of the key research fields. However, current super-resolution (SR) algorithms, which are trained on synthetic datasets, tend to have poor performance in real-world low-resolution (LR) images. Moreover, due to the inherent complexity of real-world remote sensing images, current models are prone to color distortion, blurred edges, and unrealistic artifacts. To address these issues, real-SR datasets using the Gao Fen (GF) satellite images at different spatial resolutions have been established to simulate real degradation situations; moreover, a second-order attention generator adversarial attention network (SA-GAN) model based on real-world remote sensing images is proposed to implement the SR task. In the generator network, a second-order channel attention mechanism and a region-level non-local module are used to fully utilize the a priori information in low-resolution (LR) images, as well as adopting region-aware loss to suppress artifact generation. Experiments on test data demonstrate that the model delivers good performance for quantitative metrics, and the visual quality outperforms that of previous approaches. The Frechet inception distance score (FID) and the learned perceptual image patch similarity (LPIPS) value using the proposed method are improved by 17.67% and 6.61%, respectively. Migration experiments in real scenarios also demonstrate the effectiveness and robustness of the method. Full article

Here, we report a two-dimensional (2D) amorphous photonic structure (APS) discovered in the central layer of the periostracum of the mussel Perna canaliculus, based on field emission scanning electron microscopy, X-ray diffractometer, attenuated total reflection Fourier transform infrared spectroscopy, and fiber optic spectrometry combined with the image processing technology and pair correlation function analysis. This APS contains ~29% in volume of protein fibers embedded in a protein matrix. These fibers, with diameters of 103 ± 17 nm, are densely arranged and unevenly crimped. In addition, they are locally parallel with each other and exhibit short-range order with a nearest-neighbor distance of 189 nm. Interestingly, the APS is humidity-responsive with a vivid green structural color (~530 nm) in the wet state, which disappears in the dry state. Moreover, the APS is sandwiched by two dense layers in the periostracum, which is flexible in wet and can spontaneously or artificially deform into various shapes. We hope this APS may provide new inspirations for the design and synthesis of 2D amorphous photonic materials. Full article