Precise Edge Research Articles

Development of high-performance, chemical pretreatment-free dye-based inks for digital printing on polyester fabric

An innovative thermal-sensitive ink has been developed and thoroughly tested on polyester fabric. Unlike conventional methods, this new ink eliminates the need for chemical pretreatment and post-printing washing of the fabric before or after printing. It utilizes high-temperature disperse dyes and a biodegradable carrier molecule called a triblock copolymer, specifically Poly(ε-caprolactone-lactic acid)-poly(ethylene glycol)-poly(ε-caprolactone-lactic acid) (PCLA-PEG-PCLA). This remarkable copolymer comprises dihydroxy polyethylene glycol (PEG), ε-caprolactone (CL), and L‐lactide (LA). Upon contact with a heated surface, such as a fiber or textile, the ink droplets swiftly transition rapidly, transforming into a gel-like state. This transition enhances the ink's viscosity, effectively preventing undesired spreading and resulting in precise edge definition on the fabric. To comprehensively evaluate the performance of polyester fabric printed with this innovative ink, a series of meticulous evaluations have been conducted. These evaluations encompass pH levels, particle size, surface tension, viscosity, ink droplet formation, as well as real-time photo-rheological and LF‐NMR analyses conducted on the surface of the polyester fabric. The newly developed inks demonstrate exceptional effectiveness and are highly suitable for digital printing applications across various materials. Furthermore, the implementation of this new ink can significantly reduce the reliance on chemicals and minimize wastewater production during the manufacturing process.

FPGA-Based Improved Sobel Operator Edge Detection

Image edge detection is an important field in image processing, with its primary goal being to identify edge information of various objects in images. Traditional edge detection programs are mostly executed serially, resulting in low detection efficiency. This paper proposes an FPGA-based improved Sobel operator edge detection method, harnessing the parallel processing capability of FPGA to enhance detection efficiency. Building upon the Sobel operator in edge detection algorithms, this method increases the convolution calculation dimensions of the Sobel operator, expanding it from 2 directions to 8 directions for more precise edge information. Images captured by OV5640 are processed within the FPGA and displayed via VGA. Compared to other edge detection algorithms, the FPGA-based improved Sobel operator edge detection method efficiently and rapidly detects image edges, ensuring stable image display and accurate edge information.

Image processing‐based Athletes' injury prevention mechanism

AbstractThis study proposes a compressed domain video action recognition to prevent athlete injuries. The main idea is to use the motion vector and residual of the compressed video to construct new spatiotemporal features while extracting compressed code stream information. The new spatiotemporal features have the spatiotemporal relationship between motion vectors and residuals and the characteristic of precise object edges. Through verification on mainstream action recognition datasets (HMDB‐51, UCF‐101), the proposed method has a more negligible computational overhead than action recognition based on the traditional pixel domain and higher recognition accuracy than action recognition based on the video compression domain. Experiments have shown that the new spatiotemporal features based on the compressed domain have advantages such as solid spatiotemporal relationships and high information density, which can make action recognition results with high accuracy, detect abnormalities or patterns associated with injuries, and analyze an athlete's movements and provide personalized training recommendations.

Effect of GaN-Based Distributed Bragg Reflector on Optical Properties of CH3NH3PbBr3 Crystals

As a photoelectric material, the luminescent efficiency improvement of organic–inorganic perovskite material is a hot topic. This work fabricated a nanoporous distributed Bragg reflector based on GaN with a periodic structure using electrochemical etching methods. Considering the fact that hybrid perovskite materials are difficult to prepare on an inorganic GaN-based substrate, ultraviolet ozone treatment was implemented to enhance the surface activity of the prepared distributed Bragg reflector substrate. Cubic CH3NH3PbBr3 crystals with smooth surfaces and precise edges were successfully prepared on the ozone-treated distributed Bragg reflector substrate by a two-step immersion method in the air environment. The structural property of the prepared CH3NH3PbBr3 crystals was investigated using X-ray diffraction, scanning electron microscopy and Fourier-transform infrared spectroscopy. The structural analysis results showed that CH3NH3PbBr3 crystals grown on the prepared distributed Bragg reflector and reference substrates have the same structure, with a good crystal quality. The photoluminescence intensity of CH3NH3PbBr3 crystals grown on the distributed Bragg reflector was significantly enhanced. The enhancement is approximately 3.11-fold compared with CH3NH3PbBr3 crystals grown on the unetched reference GaN substrate. Moreover, there is a 7.2 nm spectral blue shift. The enhancement of the photoluminescence intensity could be attributed to the out-coupling of emission light in the prepared crystals, and the blue shift could be attributed to the stress relaxation caused by the nanoporous GaN structure of the distributed Bragg reflector substrate.

Segmentation of tumor region in respiratory disease by extended algorithm

Introduction: The expansion of pulmonary tumors and their alterations take place in a dynamic manner, so that vigorous segmentation of the obtained images is accurately required. Methods: In this research, an extended algorithm in region growing was executed on CT lung tumors to investigate precise tumor region and edges. First, a new threshold via definition of greater target region around the initial tumor was implemented in MATLAB software. Second, nearby points were settled in an array and then these points were updated established upon the tumor growth to delineate the fresh tumor edges. Here, farthest distance from the center of color intensity point of the initial tumor was selected to grow the region in the algorithm. Third, fresh tumor boundary was determined via an interpolation between these fresh points by sketching lines from the tumor midpoint. Then, the edge correction was implemented and the fresh region was attached to the principal region to attain a segmented tumor exterior. Results: The proposed technique enhanced the tumor recognition by 96% and 91% maximum and minimum accuracy, respectively, in comparison with basilar method. In inclusive algorithm, the percentage of conformity had a positive effect on realization of the threshold value and renewal of the relative amount by 13% enhancement over accuracy assessment. Also when compared to basilar algorithm, it was found that at least 12% of the percentage differences in conformity segment the tumor area in lung CT images. The proposed algorithm with sufficient accuracy accelerates the segmentation process to delineate and improve the tumor edges by growing multiple selected regions. The algorithm also guarantees the independence of the results from the starting point. Conclusion: According to the definition of the center of mass of the tumor color intensity, the proposed extended algorithm may be generalized to the 3D images regardless of the matrix size and the image thickness. The combination of techniques such as machine learning is expected to improve segmentation accuracy for different types of nodule and tumor CT images. Implications for practice: Proposed extended algorithm with sufficient accuracy accelerates the segmentation process to delineate and improve the tumor edges by growing multiple selected regions.

A straightforward and analytical method for precise X-ray CT magnification correction

X-ray Computed Tomography (XCT) has evolved over the last two decades from a medical imaging device to an industrial tool for dimensional metrology and quality control. Employing XCT for these purposes relies on the ability to generate an accurate 3D representation of the scanned object. An essential component of this is inputting the exact geometric magnification into the reconstruction algorithm, where failure to do so results in inaccurate dimensions, distortions, and/or image artifacts. Here, we propose a simple and accurate approach to correct the magnification of a rotation stage based XCT system using a single spherical reference sphere of well-known size. The method uses geometric relationships developed between the source, rotation stage, and detector to relate the sphere's position based on projection edges at three fixed locations on the rotation stage. The typical projection edge is difficult to locate as it is obscured by the air-to-object gray value gradient. Here, the precise edge locations are found through the convergence of several geometrically derived equations describing the sphere's edges from different perspectives. The results indicate that the method repeatedly delivered micron-level determination of geometric magnification, resulting in micron-level precision in dimensional reconstruction. The method is simple and easy to employ, without the need of multiple projections with a complex phantom or the use of a secondary instrument. It is simple and rapid enough to be performed on a scan-by-scan basis for any rotation stage based XCT instrument.

State-Aware High-Order Diffusion Method for Edge Detection in the Wavelet Domain

This paper addresses how to use high-order diffusion to restore the wavelet coefficients in the wavelet domain. To avoid image distortion, wavelets with symmetry are used for image decomposition to obtain the wavelet coefficients of each sub-band. Due to the influence of noise, it is particularly important to obtain the wavelet coefficients, which can accurately reflect the image information. According to the characteristics of wavelet threshold shrinkage and the advantages of the high-order variational method in denoising, a wavelet coefficient restoration scheme is proposed. The theoretical basis of our proposed method is established through the analysis of wavelet threshold theory. To keep the original structure of wavelet coefficients unchanged, we introduce the concept of state quantity of wavelet coefficients and obtain the corresponding state quantity of wavelet coefficients using normalization. The denoising wavelet coefficient is obtained by performing a fourth-order anisotropic diffusion of the state quantities. This paper takes image edge feature extraction as the experimental content and image edges are detected by the module of the wavelet coefficients. The effectiveness of the proposed algorithm is objectively verified from three aspects: denoising effect, edge continuity, and accuracy. The experimental results show that the proposed algorithm can obtain continuous and precise image edges. The algorithm presented in this paper also applies to texture images. Compared with other algorithms, the edges image obtained by this scheme shows advantages in terms of noise removal and edge protection.

Canny Algorithm Enabling Precise Offline Line Edge Roughness Acquisition in High-Resolution Lithography.

The line edge roughness (LER) is one of the most critical indicators of photoresist imaging performance, and its measurement using a reliable method is of great significance for lithography. However, most studies only investigate photoresist resolution and sensitivity because LER measurements require an expensive and not widely available critical dimension scanning electron microscopy (SEM) technology; thus, the imaging performance of photoresist has not been adequately evaluated. Here, we report an image processing software developed for offline calculation of LER that can analyze lithographic patterns with resolutions up to ∼15 nm. This software can effectively process all graphic files obtained from commonly used SEM machines by utilizing the adjustable double threshold. To realize the effective detection of high-resolution patterns in advanced lithography, we used SEM images generated from extreme ultraviolet and electron beam lithography to develop and validate the software's graphic recognition algorithm. This image processing software can process typical SEM images and produce reliable LER in an efficient and user-friendly manner, constituting a powerful tool for promoting the development of high-performance photoresist materials.

Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking

Most research on computer vision (CV)-based vibration measurement is limited to the determination of discrete or coarse mode shapes of the structure. The continuous edge of the structure in images has rich optical features, and thus, by identifying and tracking the movement of the structure’s edge, it is possible to determine high-resolution full-field mode shapes of the structure without a preset target. The present study proposes a CV-based method of full-field mode shape identification using the subpixel edge detection and tracking techniques. Firstly, the Canny operator is applied on each frame of the structure vibration video to extract the pixel-level edges, and the improved Zernike orthogonal moment (ZOM) subpixel edge detection technique is adopted to relocate the precise structure edges. Then, all the detected edge points are tracked to obtain the full-field dense displacement time history that is subsequently used to determine the structure frequencies and compute full-field mode shapes by combining the covariance driven stochastic subspace identification (SSI-COV) with the hierarchical cluster analysis. Finally, the proposed method is verified on the aluminum cantilever beam in the laboratory and the Humen Bridge in the field. The results show that the proposed method is able to detect more precise structure edges and identify the full-field displacement and mode shapes of structures without the need for installing artificial targets on the structure in advance, which provides valuable information for the structural condition assessment, especially for structures with small-amplitude vibrations.

Superpixel Consistency Saliency Map Generation for Weakly Supervised Semantic Segmentation of Remote Sensing Images

The weakly supervised semantic segmentation (WSSS) method aims to assign semantic labels to each image pixel from weak (image-level) instead of strong (pixel-level) labels, which can greatly reduce human labor costs. However, there are some problems in WSSS of remote sensing images such as how to locate labels accurately, and how to get precise segmentation edges. To address these issues, we propose a novel framework directly transferring the scene classification model to perform semantic segmentation. We first train a multi-label scene classification network as the encoder to obtain the pre-trained model, then the feature learned by the model is transferred to the decoder. Different from other methods, we propose a saliency map generator instead of the Class Activation Map for more accurate location information by making pixels belonging to the same class lie close together while different classes are separated in feature space. Meanwhile, we take the superpixel patch as processing unit to provide precise boundary inhibition for the saliency map. To assign semantic labels for each patch, combined with extracted salient region, we propose a module responsible for exploiting the consistency of spatial and semantic similarity between different patches. Finally, we incorporate the above two modules to supervise the training process of the decoder without generating pseudo labels as most methods do, thus simplifying the training process. Experimental results show that our method outperforms other weakly supervised approaches on DLRSD and WHDLD datasets with at least a 3% improvement on mean intersection over union.

Reinforcement Shrink-Mask for Text Detection

Existing real-time text detectors reconstruct text contours by shrink-masks only. Though they simplify the framework and can make the model run fast, the strong dependence on shrink-masks leads to unreliable detection results (e.g., miss detection and overdetection). Moreover, these methods ignore the information from surrounding pixels, which causes sensitive shrink-masks and accelerates the reliability decline of detection results. Considering the above problems, we construct an effective and efficient text detection network, termed as Reinforcement Shrink-Mask for Text Detection (RSMTD), which strengthens the model's ability to recognize texts while enjoying a high detection speed. Specifically, an effective text representation strategy (Reinforcement Shrink-Mask, RSM) is designed to decouple texts and shrink-masks. RSM builds texts through shrink-masks and reinforcement offsets to ensure stable detection results encountering shrink-masks that deviate from the ground-truth. It is worth noting that reinforcement offsets can force our method to focus on the foreground shapes to bring precise shrink-mask edges. For the robustness improvement of shrink-masks, Super-pixel Window (SPW) is proposed to encourage RSMTD to utilize the surroundings of each pixel to predict shrink-masks. Particularly, SPW treats the interval regions between texts and shrink-masks as background, which helps to suppress interval regions and to avoid text adhesion. Moreover, a lightweight feature merging branch is constructed to further accelerate the inference process. As demonstrated in the experiments, our method is superior to existing state-of-the-art (SOTA) methods in both detection accuracy and speed on multiple benchmarks.

Building Extraction From High Spatial Resolution Remote Sensing Images of Complex Scenes by Combining Region-Line Feature Fusion and OCNN

Building extraction from remote sensing imagery has been a research hotspot for some time with the advancement of AI in remote sensing. However, the edges of buildings extracted using existing techniques are commonly broken and inaccurate for the complex scenes in suburban and rural areas. This study proposes a framework for extracting structures by combining region-line feature fusion with object-based convolutional neural networks to solve this problem. First, a building edge detection network known as the Multichannel Attention-based Dense Extreme Inception Network for Edge Detection (MA-DexiNed) is constructed, which is considered more accurate for building edge extraction in complicated image scenes. Second, the probability map of the building edges obtained by MA-DexiNed is refined. According to rule judgment, breakpoints are linked by an edge thinning connection algorithm to obtain single-pixel, contiguous building line features. Third, the geometric boundaries of buildings are obtained by combining region attributes derived by unsupervised image segmentation and line features obtained from deep learning supervised segmentation. Finally, the pretrained AlexNet is employed to identify the class characteristics of buildings. The suggested framework was used for two GF-2 images and one Google Earth image from various regions and with numerous types of complicated scenes. The experimental findings demonstrated that this approach could extract more precise and complete building edges for complex image scenes compared with several existing methods. This advancement results from constrained regional image segmentation using deep semantic edge features. This methodology can offer a benchmark for subsequent building extraction tasks from high resolution imagery.

Deep Hypersphere Feature Regularization for Weakly Supervised RGB-D Salient Object Detection.

We propose a weakly supervised approach for salient object detection from multi-modal RGB-D data. Our approach only relies on labels from scribbles, which are much easier to annotate, compared with dense labels used in conventional fully supervised setting. In contrast to existing methods that employ supervision signals on the output space, our design regularizes the intermediate latent space to enhance discrimination between salient and non-salient objects. We further introduce a contour detection branch to implicitly constrain the semantic boundaries and achieve precise edges of detected salient objects. To enhance the long-range dependencies among local features, we introduce a Cross-Padding Attention Block (CPAB). Extensive experiments on seven benchmark datasets demonstrate that our method not only outperforms existing weakly supervised methods, but is also on par with several fully-supervised state-of-the-art models. Code is available at https://github.com/leolyj/DHFR-SOD.

Multi-SUAV Collaboration and Low-Altitude Remote Sensing Technology-Based Image Registration and Change Detection Network of Garbage Scattered Areas in Nature Reserves

Change detection is an important task in remote sensing image processing and analysis. However, due to position errors and wind interference, bi-temporal low-altitude remote sensing images collected by SUAVs often suffer from different viewing angles. The existing methods need to use an independent registration network for registration before change detection, which greatly reduces the integrity and speed of the task. In this work, we propose an end-to-end network architecture RegCD-Net to address change detection problems in the bi-temporal SUAVs’ low-altitude remote sensing images. We utilize global and local correlations to generate an optical flow pyramid and realize image registration through layer-by-layer optical flow fields. Then we use a nested connection to combine the rich semantic information in deep layers of the network and the precise location information in the shallow layers and perform deep supervision through the combined attention module to finally achieve change detection in bi-temporal images. We apply this network to the task of change detection in the garbage-scattered areas of nature reserves and establish a related dataset. Experimental results show that our RegCD-Net outperforms several state-of-the-art CD methods with more precise change edge representation, relatively few parameters, fast speed, and better integration without additional registration networks.

Termination of graphene edges created by hydrogen and deuterium plasmas

Edge engineering is important for both fundamental research and applications as the device size decreases to nanometer scale. This is especially the case for graphene because a graphene edge shows totally different electronic properties depending on the atomic structure and the termination. It has recently been shown that an atomically precise zigzag edge can be obtained by etching graphene and graphite using hydrogen (H) plasma. However, edge termination had not been studied directly. In this study, termination of edges created by H-plasma is studied by high-resolution electron energy loss spectroscopy to show that the edge is sp2 bonded and the edge carbon atom is terminated by only one H atom. This suggests that an ideal zigzag edge, which is not only atomically precise but also sp2 bonding, can be obtained by H-plasma etching. Etching of the graphite surface with plasma of a different isotope, deuterium (D), is also studied by scanning tunneling microscopy to show that D-plasma anisotropically etches graphite less efficiently, although it can make defects more efficiently, than H-plasma.

Guided Filtering Combining Neighborhood Variance and Anisotropic Window

Aiming at the problem that guided filtering would cause halo near the edge and it is difficult to identify fine edges, a guided filtering combining neighborhood variance and anisotropic window is proposed. Firstly, the directional selectivity of the anisotropic Gaussian filter is used for the recognition of the fine edge, and the narrow spatial structure of the filter can benefit the information fusion of different pixels inside the local window to suppress the edge blur and halo effect. Secondly, based on the local structure similarity, the neighborhood variance is introduced to optimize the parameters of local linear transformation, so as to achieve the maximum diffusion at non-edge region while preserving strong edges. The experimental results on 102 Category Flower Dataset show that, compared with other edge-preserving filtering methods, the proposed method is superior to other methods in visual effect and quantitative evaluation (PSNR and SSIM), and the distortion of the test image is 46.72%, 48.64% and 29.61% smaller than guided filtering, weighted guided filtering and anisotropic guided filtering respectively. It can effectively suppress the occurrence of artifacts while recognizing precise edges.

Precise and stable edge orientation signaling by human first-order tactile neurons.

Fast-adapting type 1 (FA-1) and slow-adapting type 1 (SA-1) first-order neurons in the human tactile system have distal axons that branch in the skin and form many transduction sites, yielding receptive fields with many highly sensitive zones or 'subfields.' We previously demonstrated that this arrangement allows FA-1 and SA-1 neurons to signal the geometric features of touched objects, specifically the orientation of raised edges scanned with the fingertips. Here, we show that such signaling operates for fine edge orientation differences (5-20°) and is stable across a broad range of scanning speeds (15-180 mm/s); that is, under conditions relevant for real-world hand use. We found that both FA-1 and SA-1 neurons weakly signal fine edge orientation differences via the intensity of their spiking responses and only when considering a single scanning speed. Both neuron types showed much stronger edge orientation signaling in the sequential structure of the evoked spike trains, and FA-1 neurons performed better than SA-1 neurons. Represented in the spatial domain, the sequential structure was strikingly invariant across scanning speeds, especially those naturally used in tactile spatial discrimination tasks. This speed invariance suggests that neurons' responses are structured via sequential stimulation of their subfields and thus links this capacity to their terminal organization in the skin. Indeed, the spatial precision of elicited action potentials rationally matched spatial acuity of subfield arrangements, which corresponds to a spatial period similar to the dimensions of individual fingertip ridges.

Integration of Object-Based Image Analysis and Convolutional Neural Network for the Classification of High-Resolution Satellite Image: A Comparative Assessment

During the past decade, deep learning-based classification methods (e.g., convolutional neural networks—CNN) have demonstrated great success in a variety of vision tasks, including satellite image classification. Deep learning methods, on the other hand, do not preserve the precise edges of the targets of interest and do not extract geometric features such as shape and area. Previous research has attempted to address such issues by combining deep learning with methods such as object-based image analysis (OBIA). Nonetheless, the question of how to integrate those methods into a single framework in such a way that the benefits of each method complement each other remains. To that end, this study compared four integration frameworks in terms of accuracy, namely OBIA artificial neural network (OBIA ANN), feature fusion, decision fusion, and patch filtering, according to the results. Patch filtering achieved 0.917 OA, whereas decision fusion and feature fusion achieved 0.862 OA and 0.860 OA, respectively. The integration of CNN and OBIA can improve classification accuracy; however, the integration framework plays a significant role in this. Future research should focus on optimizing the existing CNN and OBIA frameworks in terms of architecture, as well as investigate how CNN models should use OBIA outputs for feature extraction and classification of remotely sensed images.

DPNet: Detail-preserving image deraining via learning frequency domain knowledge

The quality of images captured in rainy days is severely degraded, which affects the accuracy of subsequent computer vision tasks. Recently, many deep learning-based methods have demonstrated superior performance for single image deraining. However, there are still many issues left. Since real-world rain images and their corresponding ground truths are difficult to collect, models trained on limited data may lead to overfitting. Meanwhile, although many methods can remove part of the rain streaks, most of them cannot reconstruct precise edges and textures. For the first issue, we use the transfer learning approach. Loading pre-trained parameters trained on the ImageNet enables the network to have robust feature representation, which improves the generalization of the network. For the second issue, we restore clear details by making full use of the frequency domain information of the image. Specifically, we design a novel frequency domain residual block (FRDB) and use an efficient fusion strategy in FRDB to fuse spatial and frequency domain features. Then, we propose a frequency domain reconstruction loss function (FDR loss) to restore details by reducing the differences in high-frequency space. Finally, a simple detail enhancement attention module (DEAM) is used to further enhance the image details. Extensive experimental results demonstrate that our DPNet has superior performance on both synthetic and real data. Furthermore, we verify the effectiveness of our method on downstream computer vision tasks. The source codes will be open at https://github.com/noxsine/DPNet.

Graph-based image gradients aggregated with random forests

Gradient methods subject images to a series of operations to enhance some characteristics and facilitate image analysis, usually the contours of large objects. We argue that a gradient must show other characteristics, such as minor components and large uniform regions, particularly for the image segmentation task where subjective concepts such as region coherence and similarity are hard to interpret from the pixel information. This work extends the formalism of a previously proposed graph-based image gradient method that uses edge-weighted graphs aggregated with Random Forest (RF) to create descriptive gradients. We aim to explore more extensive input image areas and make changes driven by the RF mechanics. We evaluated the proposals on the edge and segmentation tasks, analyzing the gradient characteristics that most impacted the final segmentation. The experiments indicated that sharp thick contours are crucial, whereas fuzzy maps yielded the worst results even when created from deep methods with more precise edge maps. Also, we analyzed how uniform regions and small details impacted the final segmentation. Statistical analysis on the segmentation task demonstrated that the gradients created by the proposed are significantly better than most of the best edge maps methods and validated our original choices of attributes.