Dynamic Texture Research Articles

Role of bolus properties in dynamic texture perception of meat analogue and beef patties: Juiciness is driven by serum release during early stages of mastication

The sensory quality of plant-based meat analogues (PBMAs) limits wider consumer acceptance, particularly because of their lack of perceived juiciness. This study aimed to investigate the role of bolus properties at different moments of consumption in dynamic texture perception, especially juiciness, of PBMA and beef patties. Patties were cooked to three core temperatures (60, 70, 80 °C) to obtain specimens differing in juiciness. For PBMA and beef patties, juiciness citation proportions (Temporal-Check-All-That-Apply) peaked within the first third of mastication, then decreased strongly until swallowing. This temporal pattern closely aligned with the serum release during mastication as 75% of serum was released from patties during the first third of mastication. Additional structural breakdown of bolus occurred until the end of mastication accompanied by less than 25% additional serum release. With increasing mastication, PBMA and beef patties showed a significant increase in saliva uptake and number of bolus particles, while bolus particle size and hardness decreased, demonstrating a progressive oral structural breakdown. No significant differences in bolus properties were observed between PBMA patties differing in juiciness, while beef patties varying in juiciness differed significantly in bolus water content and liquid expelled from bolus, as a result of the structural changes of myofibrillar protein upon heating. We conclude that, for the patties used in this study, juiciness perception of PBMA patties is driven by serum release during early stages of mastication and not effected by additional oral structural breakdown, while juiciness of beef patties is affected by initial serum release and differences in bolus properties resulting from additional oral structural breakdown.

An event-triggered background-oriented schlieren technique combined with dynamic projection using dynamic mirror device

Abstract This paper reports a high-frequency event-triggered background-oriented schlieren (BOS) technique using a combination of an event-triggered camera and dynamic projection. To combine the advantages of continuous and pulsed illumination for the event-triggered camera, a novel background pattern is first developed to incorporate static and dynamic textures generated through projection utilizing a dynamic mirror device (DMD). Then, a specific post-processing algorithm is proposed to reconstruct frames with high time accuracy from event data. This technique allows for the continuous observation and capturing of flows at 4000 frames per second (FPS) with a very low cost, breaking through the short operating times of current high-frame-rate BOS. Moreover, the proposed BOS technique can visualize the flow in real-time with high temporal accuracy, a capability that is challenging to achieve with traditional BOS. To examine the proposed technique, BOS experiments were conducted on a sweeping jet actuator with various inlet pressure. The sweeping dynamics and the start-up process of the sweeping jet at various inlet pressure were visualized and investigated. It is found that the proposed event-triggered BOS can continuously visualize and record the jet flow at a resolution of 1280 × 720 pixels with an equivalent frame rate of up to 4000 FPS. The oscillation frequency of the sweeping jet was found to increase linearly with increasing inlet pressure. It reaches 117.2 Hz at an inlet pressure of 0.5 Mpa. Within the first ten milliseconds or so of start-up, the shape of the sweep was found to be symmetrical. Within the next hundred milliseconds, the jet commences to sweep and saturates. The start-up time of the sweeping jet was quantitatively measured and was observed to decrease with increased inlet pressures.

Human Emotion Recognition Based on Spatio-Temporal Facial Features Using HOG-HOF and VGG-LSTM

Human emotion recognition is crucial in various technological domains, reflecting our growing reliance on technology. Facial expressions play a vital role in conveying and preserving human emotions. While deep learning has been successful in recognizing emotions in video sequences, it struggles to effectively model spatio-temporal interactions and identify salient features, limiting its accuracy. This research paper proposed an innovative algorithm for facial expression recognition which combined a deep learning algorithm and dynamic texture methods. In the initial phase of this study, facial features were extracted using the Visual-Geometry-Group (VGG19) model and input into Long-Short-Term-Memory (LSTM) cells to capture spatio-temporal information. Additionally, the HOG-HOF descriptor was utilized to extract dynamic features from video sequences, capturing changes in facial appearance over time. Combining these models using the Multimodal-Compact-Bilinear (MCB) model resulted in an effective descriptor vector. This vector was then classified using a Support Vector Machine (SVM) classifier, chosen for its simpler interpretability compared to deep learning models. This choice facilitates better understanding of the decision-making process behind emotion classification. In the experimental phase, the fusion method outperformed existing state-of-the-art methods on the eNTERFACE05 database, with an improvement margin of approximately 1%. In summary, the proposed approach exhibited superior accuracy and robust detection capabilities.

Multi-Modal Temporal Hypergraph Neural Network for Flotation Condition Recognition.

Efficient flotation beneficiation heavily relies on accurate flotation condition recognition based on monitored froth video. However, the recognition accuracy is hindered by limitations of extracting temporal features from froth videos and establishing correlations between complex multi-modal high-order data. To address the difficulties of inadequate temporal feature extraction, inaccurate online condition detection, and inefficient flotation process operation, this paper proposes a novel flotation condition recognition method named the multi-modal temporal hypergraph neural network (MTHGNN) to extract and fuse multi-modal temporal features. To extract abundant dynamic texture features from froth images, the MTHGNN employs an enhanced version of the local binary pattern algorithm from three orthogonal planes (LBP-TOP) and incorporates additional features from the three-dimensional space as supplements. Furthermore, a novel multi-view temporal feature aggregation network (MVResNet) is introduced to extract temporal aggregation features from the froth image sequence. By constructing a temporal multi-modal hypergraph neural network, we encode complex high-order temporal features, establish robust associations between data structures, and flexibly model the features of froth image sequence, thus enabling accurate flotation condition identification through the fusion of multi-modal temporal features. The experimental results validate the effectiveness of the proposed method for flotation condition recognition, providing a foundation for optimizing flotation operations.

A novel method for identifying aerobic granular sludge state using sorting, densification and clarification dynamics during the settling process

Aerobic granular sludge is one of the most promising biological wastewater treatment technologies, yet maintaining its stability is still a challenge for its application, and predicting the state of the granules is essential in addressing this issue. This study explored the potential of dynamic texture entropy, derived from settling images, as a predictive tool for the state of granular sludge. Three processes, traditional thickening, often overlooked clarification, and innovative particle sorting, were used to capture the complexity and diversity of granules. It was found that rapid sorting during settling indicates stable granules, which helps to identify the state of granules. Furthermore, a relationship between sorting time and granule heterogeneity was identified, helping to adjust selection pressure. Features of the dynamic texture entropy well correlated with the respirogram, i.e., R2 were 0.86 and 0.91 for the specific endogenous respiration rate (SOURe) and the specific quasi-endogenous respiration rate (SOURq), respectively, providing a biologically based approach for monitoring the state of granules. The classification accuracy of models using features of dynamic texture entropy as an input was greater than 0.90, significantly higher than the input of conventional features, demonstrating the significant advantage of this approach. These findings contributed to developing robust monitoring tools that facilitate the maintenance of stable granular sludge operations.

Stereoscopic Video Quality Assessment in the Context of Internet of Things

Under the background of Internet of Things (IoT), users pursue a higher quality of immer-sive visual experience, including virtual reality (VR), augmented reality and stereoscopic information. In order to ensure the quality of experience (QoE), an effective quality assessment method is proposed to accurately determine the quality of information. Specifically, for the large amount of stereo video data, the representative sequence of stereo video is extract to reduce the data volume. Inspired by binocular channel theory, operators of the difference map and the novel fusion map are executed on these sequences. Then a dynamic texture descriptor, known as volume local binary pattern (VLBP), is employed to represent the spatio-temporal domain to predict the stereo video quality. Experiments have been executed on two public databases and results show that our method has excellent quality prediction capability.

Unsupervised learning of style-aware facial animation from real acting performances

This paper presents a novel approach for text/speech-driven animation of a photo-realistic head model based on blend-shape geometry, dynamic textures, and neural rendering. Training a VAE for geometry and texture yields a parametric model for accurate capturing and realistic synthesis of facial expressions from a latent feature vector. Our animation method is based on a conditional CNN that transforms text or speech into a sequence of animation parameters. In contrast to previous approaches, our animation model learns disentangling/synthesizing different acting-styles in an unsupervised manner, requiring only phonetic labels that describe the content of training sequences. For realistic real-time rendering, we train a U-Net that refines rasterization-based renderings by computing improved pixel colors and a foreground matte. We compare our framework qualitatively/quantitatively against recent methods for head modeling as well as facial animation and evaluate the perceived rendering/animation quality in a user-study, which indicates large improvements compared to state-of-the-art approaches.

Representing dynamic textures based on polarized gradient features

Representing dynamic textures based on polarized gradient features

Non-Normality as a Drive for Change An Overview of Systemic Processes

this paper moves from the multifaceted frame of ‘normality' to inquiry how the processes of change work in case of perturbations. The aim is not simply to investigate the opaque region of the world of life that belongs to metamorphosis but to reflect on the possibility to move further obsolete paradigms about what is normal/abnormal/pathological. Through several forms of normalities that result from processes that follow shocking events (social emergencies, severe diagnoses and chronic diseases) the author will highlight the dynamic texture of normality and the transformative power of non-normality. Normality in case of perturbation results not from the limit of the norm, but from the constant effort to re-modulate it. Furthermore, it will be argued that new normalities originate from a process of renormalization, a dynamic which refers to a relation of scale. Deeply intertwined with change, the dy- namic texture of normality cannot be detected by the observer. For a valuable understanding of the opaque region of transformation, it ought to investigate through the lenses of systemic thinking. This approach brings into light two main phases, which are the breaking of a previous balance and the emer- gence of novel, unpredictable conditions. A special concern is addressed to post-traumatic creativity: it will be possible to assume that creativity can be recognized as the capacity to grasp and institute new relations. Finally, it will be questioned whether the term normality in the discourse about health can be substituted by another one, which can better fit the trans- formative power of non-normality and can put into brackets any moral as- sessment of the pathological/abnormal condition of the person. It will be con- cluded that it is not only possible, but also highly recommended in a frame where disease and health can be understood in relation to the harmonic inte- gration among parts and levels.

Efficient bat-inspired block matching algorithm with novel motion energy directional histograms for dynamic texture fast recognition

Efficient bat-inspired block matching algorithm with novel motion energy directional histograms for dynamic texture fast recognition

Dynamic texture analysis using Temporal Gray scale Pattern Image for water surface velocity measurement

Water surface velocity (WSV) is one of the critical parameters in hydrology. The development of non-intrusive measurement of this parameter using cameras is increasing. Traditionally, measuring WSV using a camera utilizes tracking of moving objects on the water surfaces. Recently, another method has emerged that utilizes the movement of water ripples to estimate the WSV. This paper proposes a novel method for estimating WSV based on camera measurements. The authors call this method Temporal Gray-scale Pattern Image (TGPI) since it extracts Gray-scale patterns of pixels of water flow video in the temporal domain using the XOR operator and create new image. The new image pattern formed is then used to predict WSV using predictor. There are four predictors being compared, namely Multiple Input Linear Regression (MILR), Multiple Input Logistic Regression (MILgR), Convolutional Neural Network Regression (CNN-R), and Convolutional Neural Network Classification (CNN-K). CNN-R and CNN-K predict WSV directly from the results of TGPI. Meanwhile, MILR and MILgR predict WSV from five TGPI features. The five features are the Mean and Median of the Histogram, Mean and Median of the Histogram of Oriented Gradient (HOG), and the Maximum Mid-Value of the Fast Fourier Transform (FFT). MILR and CNN-R are predictors for regression problems, so the testing metrics for them are Trend-Line equation and R2 from the predicted WSV and actual values graph. Meanwhile, for MILgR and CNN-K, which are predictors for classification problems, the testing metrics are Confusion Matrix (CM), Accuracy, Precision, Recall, and F1-Score. To test the four methods without distinguishing their predictor types, a 2-dimensional histogram graph is used. The data-set used for training and testing is video footage of water flow with known WSV. The WSV measurement points used in this study are 1.7 m/s, 3.1 m/s, and 4.2 m/s. The video dataset and these three points are generated by the Mini Open Channel Water Flow Simulator (MOCWFS) developed by the author in this study. From the comparison results, it can be seen that the classification type predictor is superior to the regression type. For the regression type predictor, MILR is better than CNN-R. Meanwhile, for the classification type, CNN-K is superior to MILgR. The best accuracy produced by CNN-K is 98.4%. Although there are shortcomings, the TGPI method is quite feasible for predict Water Surface Velocity.

Banana Dream by Hasan Namir

Reviewed by: Banana Dream by Hasan Namir Kate Quealy-Gainer, Editor Namir, Hasan Banana Dream; illus. by Daby Zainab Faidhi. Porter/Holiday House, 2023 [32p] Trade ed. ISBN 9780823451005 $18.99 E-book ed. ISBN 9780823455485 $11.99 Reviewed from digital galleys R 4-7 yrs Mooz’s name means banana in Arabic, and, unfortunately, it’s brought him nothing but trouble, inviting ridicule from cousins and classmates. It’s not just the name they find strange, but what it represents: bananas were a forbidden fruit in Iraq during the Gulf War, when countries limited trade with Mooz’s country. It is for that reason, though, that Mooz’s parents gave him his name; before he was born, his mother was told she couldn’t conceive, but after a dream led his Baba to make [End Page 333] a nine-hour trip to Jordan to get Mama a bunch of bananas, she learned she was pregnant with Mooz. To his parents, his name means hope. Mooz makes a cheerful, appealing narrator, even when lamenting the teasing from his schoolmates, and his voice is absolutely kid-authentic, conveying his personal account while weaving in some of the larger context of his homeland and the challenges he and his family face. The mix of digital, charcoal, and pencil art easily complements Mooz’s narrative approach, with vibrant colors and dynamic textures energizing scenes, while small visual cues—an “out of stock sign” and a war report on the television—place Mooz’s experience within the upheaval of Iraq at that time. An author’s note explains how the book is based on Namir’s childhood experiences and provides a bit more detail on the sanctions and rationing during the Gulf War. Copyright © 2023 The Board of Trustees of the University of Illinois

Nowhere at ease: Listening to Syrian refugee trauma in Christy Lefteri’s The Beekeeper of Aleppo (2019)

The ongoing Syrian civil war has inflicted wounds on the minds and bodies of countless Syrians, and it continues to influence the contemporary global agenda. The purpose of this article is to examine how refugee trauma is depicted at the individual level, and how Syrian refugees attempt to survive it, in Christy Lefteri’s The Beekeeper of Aleppo (2019). This best-selling novel tells a story of the effects of war by following the lives of a bereaved Syrian couple as they make their way through Turkey and Greece to the United Kingdom. We show how Lefteri’s fictionalised portraits of traumatised refugees coincide with the academic literature on trauma. At the same time, we question the applicability of Eurocentric trauma theory to their cases. We argue that refugee trauma presents itself in distinctive ways and embodies a dynamic texture as a result of refugees’ vulnerability and marginalisation in broader society, which in turn prompts them to employ various coping mechanisms in their struggle for survival.

What Can a Robot’s Skin Be? Designing Texture-changing Skin for Human–Robot Social Interaction

Biological skin has numerous functions like protection, sensing, expression, and regulation. On the contrary, a robot’s skin is usually regarded as a passive and static separation between the body and environment. In this article, we explore the design opportunities of a robot’s skin as a socially expressive medium. Inspired by living organisms, we discuss the roles of interactive robotic skin from four perspectives: expression, perception, regulation, and mechanical action. We focus on the expressive function of skin to sketch design concepts and present a flexible technical method for embodiment. The proposed method integrates pneumatically actuated dynamic textures on soft skin, with forms and kinematic patterns generating a variety of visual and haptic expressions. We demonstrate the proposed design space with six texture-changing skin prototypes and discuss their expressive capacities.

Dynamic texture classification based on bag-of-models with mixture of student’s t-hidden Markov models

Hidden Markov model (HMM) is a popular statistical approach for modeling sequential data comprising continuous attributes, and it has been applied in finance, machine vision and other fields. Due to higher tolerance to outliers than Gaussian distribution, the multivariate student’s t-distribution is used to serve as the observation emission distribution in continuous HMM (SHMM), which has been exploited to improve the performance for modeling sequential data. In this paper, a bag-of-models method based on the mixture of SHMMs is proposed to describe dynamic texture for dynamic texture classification, in which codebook is constructed with SHMMs. Specifically, a novel mixture model, mixture of SHMMs, is proposed to model and cluster the observation sequences of the dynamic texture. The parameter learning method is derived by using the expectation maximization (EM) algorithm. Then all components of the mixture of SHMMs are assembled to obtain the codewords and the bag-of-models based features are extracted for describing the dynamic texture. Finally, we demonstrate the effectiveness of the bag-of-models based on the mixture of SHMMs for dynamic texture classification on different benchmark datasets. The experimental results show the potential performance of the proposed approach by comparing with some existing dynamic texture classification methods.

The Courage of the Little Hummingbird: A Tale Told Around the World by Leah Henderson

Reviewed by: The Courage of the Little Hummingbird: A Tale Told Around the World by Leah Henderson Kate Quealy-Gainer, Editor Henderson, Leah The Courage of the Little Hummingbird: A Tale Told Around the World; illus. by Magaly Morales. Abrams, 2023 [32p] Trade ed. ISBN 9781419754555 $18.99 Reviewed from digital galleys R 4-8 yrs In this folkloric picture book inspired by a South America indigenous story, flames begin to threaten the Great Forest. The animals (understandably) flee to safety, but the hummingbird thinks of “each branch and each tree that protected so many of the animals, and the nectar of each flower that fed her,” and she begs the other animals to help her put out the flames. They all say there’s nothing that can be done, but, undeterred, the hummingbird begins to bring water, drop by drop, to the flames. When the animals point out the futility of her efforts, she simply says “I’m doing all I can.” It is a statement both hopeful and desperate, and kids who feel especially powerless in a world that, like the Great Forest, is greatly threatened by forces larger than them, might find both the optimism and the honesty of the tale heartening. The animals do eventually join the hummingbird, but viewers never actually see if they succeed in putting the flames out, underscoring the message that the possibility of failure shouldn’t prevent effort toward a decent outcome. The art has a purposeful stylistic flatness, reminiscent of Tonatiuh’s works (Funny Bones BCCB 10/15, The Princess and the Warrior BCCB 11/16, et.al.), and spreads are fully saturated with vibrant colors and dynamic textures, particularly in the flames and water. Although Henderson indicates the tale may have originated with the Quechua people of South America, more specific sources in an author’s note would have been welcome. An illustrator’s note, glossary, and fun hummingbird facts are included. Copyright © 2023 The Board of Trustees of the University of Illinois

Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis

Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge. However, existing methods cannot effectively learn a synthesis model for high-dimensional DT from a small number of training samples. In this article, we propose a novel DT synthesis method, which makes full use of similarity as prior knowledge to address this issue. Our method is based on the proposed kernel similarity embedding, which can not only mitigate the high dimensionality and small sample issues, but also has the advantage of modeling nonlinear feature relationships. Specifically, we first put forward two hypotheses that are essential for the DT model to generate new frames using similarity correlations. Then, we integrate kernel learning and the extreme learning machine into a unified synthesis model to learn kernel similarity embeddings for representing DTs. Extensive experiments on DT videos collected from the Internet and two benchmark datasets, i.e., Gatech Graphcut Textures and Dyntex, demonstrate that the learned kernel similarity embeddings can provide discriminative representations for DTs. Further, our method can preserve the long-term temporal continuity of the synthesized DT sequences with excellent sustainability and generalization. Meanwhile, it effectively generates realistic DT videos with higher speed and lower computation than the current state-of-the-art methods. The code and more synthesis videos are available at our project page <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://shiming-chen.github.io/Similarity-page/Similarit.html</uri> .

Dynamic Texture Classification Using Directional Binarized Random Features

Dynamic texture description has been studied extensively due to its wide applications in the field of computer vision. Local binary pattern (LBP) and its various variants account for a large part of dynamic texture description methods because of its advantages, such as good discriminability and low computational complexity. However, many LBP-based methods directly extract feature from pixel intensities and only use a proportion of pixels in a local neighborhood. And their good classification performance is usually achieved at the cost of high feature dimensionality, which would limit their application scenarios. We argue that extracting features from the gradient domain will capture more discriminative features due to the additional directional information, and that making use of all the pixels in a local neighborhood would improve performance. In this paper, we propose a simply but effective dynamic texture descriptor that inherits the advantages of LBP while excluding its disadvantages. The proposed method consists of four stages of data processing: 1) gradients extraction; 2) random feature extraction from gradients; 3) binary hashing of directional random features; and 4) histogramming. Gaussian first-order derivatives are used as gradient filters such that stable gradients could be generated. Then random projection is applied to extract random features from each gradients. Both the above two stages are conducted via 3D filtering, and thus they are efficient. Thirdly, the random features from each gradient are binarized and encoded into integer codes, from which a histogram is built. Finally, the histograms from each gradient are concatenated into a feature vector. Because we use 8-bit codes, The feature dimensionality is very low. We evaluate the proposed method on three benchmark dynamic texture datasets with various test protocols. The results demonstrate its effectiveness and efficiency when comparing to many state-of-the-art methods.

Locating robust patterns based on invariant of LTP-based features

• A novel concept of robust patterns (called RLTP) is introduced by considering the invariance of LTP-based features. • A crucial derivative of CLTP (called RCLTP) is proposed to be in accordance with RLTP. • An efficient application is presented to take into account RCLTP for local DT representation. • Local optical-flow-based features are taken into account DT motions for boosting the discrimination. • Just addressing the robust patterns, our RCLTP descriptor obtains very good performance compared to state of the art. Efficiently representing Dynamic Textures (DTs) based on salient features is one of the considerable challenges in computer vision. Locating these features can be obstructed due to the impact of encoding factors. In this article, a novel concept of Robust Local Ternary Patterns (RLTP) is introduced in consideration of the invariance of Local Ternary Patterns (LTP) subject to the deviation of thresholds. Our locating process is able to simultaneously encapsulate the discrimination of local features, and deal with the noise sensibility caused by a small gray-scale change of local neighbors. RLTP is then adapted to the completed LTP model to form an efficient operator for capturing completely properties of RLTP. Finally, RLTP is taken into account for the DT description, where the robust patterns of spatial-temporal features and optical-flow-based motions are exploited to improve the performance. Experiments have clearly corroborated the efficacy of our proposed approach.

Steff: A Novel Spatio-Temporal System for Dynamic Texture Classification Using Efficientnet on Outdoor Scenes

Steff: A Novel Spatio-Temporal System for Dynamic Texture Classification Using Efficientnet on Outdoor Scenes