Artificial Texture Research Articles

Efficient Vibration Measurement and Modal Shape Visualization Based on Dynamic Deviations of Structural Edge Profiles.

As a non-contact method, vision-based measurement for vibration extraction and modal parameter identification has attracted much attention. In most cases, artificial textures are crucial elements for visual tracking, and this feature limits the application of vision-based vibration measurement on textureless targets. As a computation technique for visualizing subtle variations in videos, the video magnification technique can analyze modal responses and visualize modal shapes, but the efficiency is low, and the processing results contain clipping artifacts. This paper proposes a novel method for the application of a modal test. In contrast to the deviation magnification that exaggerates subtle geometric deviations from only a single image, the proposed method extracts vibration signals with sub-pixel accuracy on edge positions by changing the perspective of deviations from space to timeline. Then, modal shapes are visualized by decoupling all spatial vibrations following the vibration theory of continuous linear systems. Without relying on artificial textures and motion magnification, the proposed method achieves high operating efficiency and avoids clipping artifacts. Finally, the effectiveness and practical value of the proposed method are validated by two laboratory experiments on a cantilever beam and an arch dam model.

No-Box Universal Adversarial Perturbations Against Image Classifiers via Artificial Textures

No-Box Universal Adversarial Perturbations Against Image Classifiers via Artificial Textures

FeelPen: A Haptic Stylus Displaying Multimodal Texture Feels on Touchscreens

The ever-emerging mobile market induced a blooming interest in stylus-based interactions. Most state-of-the-art styluses either provide no haptic feedback or only deliver one type of sensation, such as vibration or skin stretch. Improving these devices with display abilities of a palette of tactile feels can pave the way for rendering realistic surface sensations, resulting in more natural virtual experiences. However, integrating necessary actuators and sensors while keeping the compact form factor of a stylus for comfortable user interactions challenges their design. This situation also limits the scientific knowledge of relevant parameters for rendering compelling artificial textures for stylus-based interactions. To address these challenges, we developed FeelPen, a haptic stylus that can display multimodal texture properties (compliance, roughness, friction, and temperature) on touchscreens. We validated the texture rendering capability of our design by conducting system identification and psychophysical experiments. The experimental results confirmed that FeelPen could render a variety of modalities with wide parameter ranges necessary to create perceptually salient texture feels, making it a one-of-a-kind stylus. Our unique design and experimental results pave the way for new perspectives with stylus-based interactions on future touchscreens.

Establishment of method distinguishing between defects and artificial texture in defect inspection with phase-shift illumination method

Establishment of method distinguishing between defects and artificial texture in defect inspection with phase-shift illumination method

Attachment ability of combined biomimetic adhesive micro-textures of different shapes

There are various potential applications of biomimetic adhesive solutions including climbing robotic systems, mobile sensor platforms, and biomedical applications such as patches for external use. Achieving resistance to both normal and tangential loads, however, is a critical issue that still needs to be addressed. Some animals have developed exceptional attachment mechanisms based on combined fibrillar elements of different shapes and functions. Experimental investigation of combined biomimetic adhesive micro-textures on tribological performances such as adhesion, friction, and peeling resistance is needed to apply this idea to the design of an artificial texture having similar “biomimetic” properties. In the present study, we demonstrate that combinations of different shapes of biomimetic adhesive micro-textures show increased efficiency under different contact environments and enable long-term adhesive solutions. Our work sheds light on combinations of different element shapes inspired by nature and their adhesive efficiency as a function of the ratio of each biomimetic element, as well as their spatial repartition.

A Novel Dense-Attention Network for Thick Cloud Removal by Reconstructing Semantic Information

The presence of thick clouds in single optical images shows the contamination of interesting objects. Besides, the difficulty of thick cloud removal is mainly the restoration of the weak boundary information from cloud-contaminated areas. Recently, many deep-learning-based frameworks were applied for cloud removal by obtain the related semantic information from the weak boundary information. However, the large-size cloud-contaminated areas lead to the artificial textures in the resulting images. Thus, obtaining the optimal semantic information from finite boundary information is the key to solve this problem. In this work, we design a deep-learning framework for cloud removal, especially large-size clouds removal (i.e., more than 30% coverage of the whole image). First, we design a cloud location model (CLM) which adopted the fully convolutional network to locate the cloud. Second, desired by theory of the coarse-to-fine restoration, we build a dense-attention network (termed as DANet) for restoring cloud contaminated areas. In the DANet, we design a dense block into the coarse network for training the features of restoring directions of each pixel from the weak boundary information. Furthermore, a contextual attention module is built into refinement network for restoring contaminated areas relying on the semantic relationship between the background and foreground information. Compared with the state-of-the-art methods, the proposed DANet achieved greater removal performances and reconstruct more natural image textures.

An Adaptive Vector-Based Vehicles Detection for Urban Intersection Camera Sensors Under Nighttime Illumination

Vehicles detection via surveillance sensors is an intractable and essential task under nighttime urban intersection scenes. To efficiently resolve the problem that most of the current color or texture feature-based vehicles detection models easily suffer contamination from dynamic and sudden or gradual illumination variations on nighttime, an adaptive vector-based background model without complex artificial texture feature is proposed for vehicles detection in night intersection surveillance scenes. For each pixel of image, a series of filters are employed to take the values from the past at the same location and its neighborhood, and a vector is assigned to each filter to represent the neighborhood region information of the pixel. Then, the series of vectors comprising the background model and the vector of the current pixel is compared to the vectors of the background model based on the theorem of linear dependence to describe whether that pixel belongs to foreground or background. Eventually, to adapt the dynamic scenes, a random update scheme is employed to update the model. As our experimental results demonstrated in real-world nighttime urban intersection surveillance scenarios, the proposed model attains superior vehicles detection performance compared to other state-of-the-art algorithms based on extensive qualitative and quantitative evaluations.

Texture analysis using two-dimensional permutation entropy and amplitude-aware permutation entropy

Entropy algorithms have been applied extensively for time series analysis. The entropy value given by the algorithm quantifies the irregularity of the data structure. For higher irregular data structures, the entropy is higher. Both permutation entropy (PE) and amplitude-aware permutation entropy (AAPE) have been previously used to analyze time series. These two metrics have the advantage, over others, of being computationally fast and simple. However, fewer entropy measures have been proposed to process images. Two-dimensional entropy algorithms can be used to study texture and analyze the irregular structure of images. Herein, we propose the extension of AAPE for two-dimensional analysis (AAPE2D). To the best of our knowledge, AAPE2D has never been proposed to analyze texture of images. For comparison purposes, we also study the two-dimensional permutation entropy (PE2D) to analyze the effect of the amplitude consideration in texture analysis. In this study, we compare AAPE2D method with PE2D in terms of irregularity discrimination, parameters sensitivity, and artificial texture differentiation. Both AAPE2D and PE2D appear to be interesting entropy-based approaches for image texture analysis. When applied to a biomedical dataset of chest X-rays with healthy subjects and pneumonia patients, both methods showed to statistically differentiate both groups for P<0.01. Finally, using a SVM model and multiscale entropy values as features, AAPE2D achieves an average of 75.7% accuracy which is slightly better than the results of PE2D. Overall, both entropy algorithms are promising and achieve similar conclusions. This work is a new step towards the development of other entropy-based texture measures.

ArthroNet: a monocular depth estimation technique with 3D segmented maps for knee arthroscopy

BackgroundLack of depth perception from medical imaging systems is one of the long-standing technological limitations of minimally invasive surgeries. The ability to visualize anatomical structures in 3D can improve conventional arthroscopic surgeries, as a full 3D semantic representation of the surgical site can directly improve surgeons’ ability. It also brings the possibility of intraoperative image registration with preoperative clinical records for the development of semi-autonomous, and fully autonomous platforms. This study aimed to present a novel monocular depth prediction model to infer depth maps from a single-color arthroscopic video frame. MethodsWe applied a novel technique that provides the ability to combine both supervised and self-supervised loss terms and thus eliminate the drawback of each technique. It enabled the estimation of edge-preserving depth maps from a single untextured arthroscopic frame. The proposed image acquisition technique projected artificial textures on the surface to improve the quality of disparity maps from stereo images. Moreover, following the integration of the attention-ware multi-scale feature extraction technique along with scene global contextual constraints and multiscale depth fusion, the model could predict reliable and accurate tissue depth of the surgical sites that complies with scene geometry. ResultsA total of 4,128 stereo frames from a knee phantom were used to train a network, and during the pre-trained stage, the network learned disparity maps from the stereo images. The fine-tuned training phase uses 12,695 knee arthroscopic stereo frames from cadaver experiments along with their corresponding coarse disparity maps obtained from the stereo matching technique. In a supervised fashion, the network learns the left image to the disparity map transformation process, whereas the self-supervised loss term refines the coarse depth map by minimizing reprojection, gradients, and structural dissimilarity loss. Together, our method produces high-quality 3D maps with minimum re-projection loss that are 0.0004132 (structural similarity index), 0.00036120156 (L1 error distance) and 6.591908 × 10−5 (L1 gradient error distance). ConclusionMachine learning techniques for monocular depth prediction is studied to infer accurate depth maps from a single-color arthroscopic video frame. Moreover, the study integrates segmentation model hence, 3D segmented maps are inferred that provides extended perception ability and tissue awareness.

The Development of Bacterial Cellulose Biomaterials Using the Material Design-Driven Approach for Packaging Industry

Alternative renewable materials are a possible solution to the rapid depletion of non-renewable resources. Within the renewable materials category, living organisms have been utilised in sustainable material projects. Although the projects are currently speculative, the possibility of utilising living organisms offers an appealing sustainability advantage for product design. Notably, their ability to 'self-build' enables them to become the co-maker of the output materials or products effectively. One of the promising lab-grown materials developed and utilised in product design is bacterial cellulose. Many researchers and designers have focused on improving the cultivation process and the feasibility of the materials for targeted product applications. However, much research is still needed to fill the void of knowledge in developing biomaterials for product design. This paper presents an early development of novel bacterial cellulose biomaterials and their applications using the Material Design Driven (MDD) framework. In this research, three bacterial cellulose biomaterials with unique experiential qualities have been produced through the approach. Notably, the research highlights the innovative potential of bacterial cellulose as a packaging material by incorporating plant fibres as the reinforcement agent and imprinting artificial texture on the material surface.

Optical Aberrations Correction in Postprocessing Using Imaging Simulation

As the popularity of mobile photography continues to grow, considerable effort is being invested in the reconstruction of degraded images. Due to the spatial variation in optical aberrations, which cannot be avoided during the lens design process, recent commercial cameras have shifted some of these correction tasks from optical design to postprocessing systems. However, without engaging with the optical parameters, these systems only achieve limited correction for aberrations. In this work, we propose a practical method for recovering the degradation caused by optical aberrations. Specifically, we establish an imaging simulation system based on our proposed optical point spread function model. Given the optical parameters of the camera, it generates the imaging results of these specific devices. To perform the restoration, we design a spatial-adaptive network model on synthetic data pairs generated by the imaging simulation system, eliminating the overhead of capturing training data by a large amount of shooting and registration. Moreover, we comprehensively evaluate the proposed method in simulations and experimentally with a customized digital-single-lens-reflex camera lens and HUAWEI HONOR 20, respectively. The experiments demonstrate that our solution successfully removes spatially variant blur and color dispersion. When compared with the state-of-the-art deblur methods, the proposed approach achieves better results with a lower computational overhead. Moreover, the reconstruction technique does not introduce artificial texture and is convenient to transfer to current commercial cameras. Project Page: https://github.com/TanGeeGo/ImagingSimulation .

Bioinspiration and Microtopography As Nontoxic Strategies for Marine Bioadhesion Control

AbstractMarine biofouling is the unwanted accumulation of biological origin matters on submerged surfaces. Biofouling is a major economic burden on navy and commercial ships, as well as civil structures, piping systems, sensors, and power generating facilities. Due to the vast variety of marine organisms that colonize submerged surfaces, no surface has been found that is completely resistant to marine biofouling. The development of bioinspired and textured surfaces and their effects on marine biofouling are reviewed here. Different parameters such as mechanical properties, wettability, and surface topography are shown to have an influence on the settlement, attachment, and growth of marine organisms on marine surfaces. The effect of texturation is especially investigated with a special focus on how marine animals are affected. Techniques for creating artificial textures and methods to characterize them are introduced. Bioinspired textures are currently developed using mostly photolithographic technique with patterns showing a high efficiency on single marine species through laboratory bioassays. The efficiency of a texture with spaced features appears clearly dependent on its dimensions in regard of the size of the targeted organisms. On the grounds of how marine species fight against biofoulers, further research on the development of multistrategies antifouling solutions should be conducted.

Going “Live” Again: Reflections on Zoom, Copresence, &amp; Liveness in a (Post)Pandemic World

In April 2020—only weeks after the World Health Organization declared COVID-19 a global pandemic—the New York Times published an article titled “Why Zoom Is Terrible.” Quoting a gustatory simile from Sheryl Brahnam of Missouri State University, the article declared, “In-person communication resembles video conferencing about as much as a real blueberry muffin resembles a packaged blueberry muffin that contains not a single blueberry but artificial flavors, textures and preservatives.”1 It has been a year marked by the absence of “in-person” connection, or in the language of our field, of spatial copresence. The COVID-19 pandemic has fundamentally disrupted our ability to share space. Spatial copresence, it turns out, is what the coronavirus requires to spread. The virus, in this sense, is a phenomenon of the live. While technologies like Zoom have maintained our capacity for temporal copresence, the now ubiquitous status of “Zoom fatigue” points to new ways to consider spatial copresence, and by extension “liveness.”

Centered Multi-Task Generative Adversarial Network for Small Object Detection.

Despite the breakthroughs in accuracy and efficiency of object detection using deep neural networks, the performance of small object detection is far from satisfactory. Gaze estimation has developed significantly due to the development of visual sensors. Combining object detection with gaze estimation can significantly improve the performance of small object detection. This paper presents a centered multi-task generative adversarial network (CMTGAN), which combines small object detection and gaze estimation. To achieve this, we propose a generative adversarial network (GAN) capable of image super-resolution and two-stage small object detection. We exploit a generator in CMTGAN for image super-resolution and a discriminator for object detection. We introduce an artificial texture loss into the generator to retain the original feature of small objects. We also use a centered mask in the generator to make the network focus on the central part of images where small objects are more likely to appear in our method. We propose a discriminator with detection loss for two-stage small object detection, which can be adapted to other GANs for object detection. Compared with existing interpolation methods, the super-resolution images generated by CMTGAN are more explicit and contain more information. Experiments show that our method exhibits a better detection performance than mainstream methods.

The impact of movement sonification on haptic perception changes with aging

Combining multisensory sources is crucial to interact with our environment, especially for older people who are facing sensory declines. Here, we examined the influence of textured sounds on haptic exploration of artificial textures in healthy younger and older adults by combining a tactile device (ultrasonic display) with synthetized textured sounds. Participants had to discriminate simulated textures with their right index while they were distracted by three disturbing, more or less textured sounds. These sounds were presented as a real-time auditory feedback based on finger movement sonification and thus gave the sensation that the sounds were produced by the haptic exploration. Finger movement velocity increased across both groups in presence of textured sounds (Rubbing or Squeaking) compared to a non-textured (Neutral) sound. While young adults had the same discrimination threshold, regardless of the sound added, the older adults were more disturbed by the presence of the textured sounds with respect to the Neutral sound. Overall, these findings suggest that irrelevant auditory information was taken into account by all participants, but was appropriately segregated from tactile information by young adults. Older adults failed to segregate auditory information, supporting the hypothesis of general facilitation of multisensory integration with aging.

An improved multi-focus image fusion algorithm based on multi-scale weighted focus measure

This paper focuses on developing an improved multi-focus image fusion (MFIF) algorithm. Existing spatial domain algorithms dependent on the obtained fusion decision map still lead to unexpected ghosting, blurred, edges as well as blocking effects such that the visual effect of image fusion is seriously degraded. To overcome these shortages, an improved MFIF algorithm is developed with the help of a novel multi-scale weighted focus measure and a decision map optimization technique. First, a novel multi-scale measurement template is designed in order to effectively extract the gradient information of rich texture regions, smooth regions as well as transitional regions between the aforementioned regions simultaneously. Then, an improved calculation scheme of the focus score matrix is designed based on the weighted sum of the focus measure maps in each region window centered on a concerned pixel, under which the advantage of pixel-by-pixel weighting is employed. In what follows, an initial decision map is obtained in light of the focus score matrix combined with threshold filtering, which is employed to eliminate the small isolated regions caused by some misclassified pixels. Furthermore, an accurate decision map is received with the help of the optimization capability of guided filtering to avoid edge unexpected artificial textures. In comparison with block-based fusion algorithms, our algorithm developed in this paper extracts the focus regions pixel-by-pixel, thereby helping to reduce the blocking effects that appear in the fusion image. Finally, some intensive comparison analysis based on common datasets is performed to verify the superiority over state-of-the-art methods in both visual qualitative and quantitative evaluations.

Texture classification based on image (natural and horizontal) visibility graph constructing methods.

Texture classification is widely used in image analysis and some other related fields. In this paper, we designed a texture classification algorithm, named by TCIVG (Texture Classification based on Image Visibility Graph), based on a newly proposed image visibility graph network constructing method by Lacasa et al. By using TCIVG on a Brodatz texture image database, the whole procedure is illustrated. First, each texture image in the image database was transformed to an associated image natural visibility graph network and an image horizontal visibility graph network. Then, the degree distribution measure [P(k)] was extracted as a key characteristic parameter to different classifiers. Numerical experiments show that for artificial texture images, a 100% classification accuracy can be obtained by means of a quadratic discriminant based on natural TCIVG. For natural texture images, 94.80% classification accuracy can be obtained by a linear SVM (Support Vector Machine) based on horizontal TCIVG. Our results are better than that reported in some existing literature studies based on the same image database.

Quality analysis of digital photogrammetric models obtained in low light conditions

The paper presents the results of the study of the photogrammetric models quality depending on the photogrammetric survey. The presented data have been obtained by the Xiaomi Mi Home Security Camera 360. We examined the texture of the captured object and the general geometric parameters of the created photographing basis as the experimental variable. As a result, the paper contains general recommendations for the cameras optimal location for photogrammetric surveying in low light and space limits. The main author’s idea of the improvement of the photogrammetric models quality is projecting a temporary artificial texture on the subject of surveying.

Study of the quality construction of photogrammetric models obtained in low-light conditions

Relevance of the research topic. The paper shows main results of environmental parameters that affect the quality of 3D-models obtained by the photogrammetric method of surveying. Research objective. The article presents: the fundamental possibility of using a small-sized photogrammetric device for underground surveying and the parameters that affect the result of shooting in a confined low-light space. Research methods. Experiments included a set of independent serial photographs of a test site. To increase the results of experiments, object with a simple geometric shape have been used as test objects for extrapolating of the results of the survey to products of standard metal products. In these conditions, imitating abstract underground mine workings: with the lack of lighting, confined spaces, a small number of characteristic points in the background. The texture of the subject of photogrammetric survey and common geometric parameters of photo base accepted as studied factors. Various images were projected onto the test scene to form an artificial texture. Results and its applications. The experimental results confirm the possibility of using the proposed methods for identifying engineering communications nodes, oil and gas production facilities, elements of lifting machines and conductors in vertical mining devices. In addition, the authors performed an idea of improving the quality of photogrammetric models by projecting temporary artificial textures on the subject. The results of the work can be used for the improving of the methods of surveying, using digital photogrammetry technology Findings. It is possible to imrove the quality of digital photogrammetric models of engineering structures by projecting artificial textures onto them. The possibility of the creation of models of the subject increases the accuracy and controllability of the result. With increasing contrast and complexity of the texture being applied, digital photogrammetry algorithms better determine the correspondence between a pair of photographs.

ORIENTATION OF POINT CLOUDS FOR COMPLEX SURFACES IN MEDICAL SURGERY USING TRINOCULAR VISUAL ODOMETRY AND STEREO ORB-SLAM2

Abstract. In photogrammetry, computer vision and robotics, visual odometry (VO) and SLAM algorithms are well-known methods to estimate camera poses from image sequences. When dealing with unknown scenes there is often no reference data available and also the scene needs to be reconstructed for further analysis. In this contribution a trinocular visual odometry approach is implemented and compared to stereo VO and ORB-SLAM2 in an experimental setup imitating the scene of a knee replacement surgery. Two datasets are analysed. While a test-field provides excellent conditions for feature detection algorithms with its artificial texture assembled, extracted images show the knee joint itself solely in order to use only the homogenous, but in real application stable, region of the knee joint. The camera trajectories of VO and ORB-SLAM2 are transformed to corresponding coordinate systems and are subsequently evaluated. The tracking algorithms show poor quality when only the inappropriate surface of the knee is used but perform well when the artificial texture of the test-field is used. The third camera does not lead to a significant advantage in this setup using our implementation. Possible reasons, e.g. less overlap, are discussed in this contribution. Nevertheless, the quality of the oriented point clouds, obtained by trinocular dense matching, is less than 1mm for most of the analysed data. The experiment will be used to focus on further developments, e.g. dealing with specular reflections, and for evaluation purposes using different SLAM/ VO algorithms.