Introduction to Dynamic Text

The results revealed that the dynamic texture influences expectations for saltiness, spiciness, temperature, deliciousness, price, and appetite before eating the meal and perceived saltiness, spiciness, and appetite when eating.

cz) is a web-based service designed to mutually compare and rank (recently nearly 200) different static and dynamic texture and image segmenters, to find optimal parametrization of a segmenter and support the development of new segmentation and classification methods.

The purpose of this work was to characterize the dynamic texture perception and study the mechanisms occurring in bolus from chewing to swallowing during white bread oral processing.

Therefore, we propose a dynamic texture feature named LBP on the TOP and GLCM Histograms (LTGH) which integrates the local binary patterns (LBPs) and gray-level co-occurrence matrix (GLCM) histograms on the three orthogonal planes (TOP).

Highlights Wearable and smart textiles are constructed by integrating embroidery technology and 5G cloud communication, showing promising applications in temperature management and real-time dynamic textile displays.

Distance-based dynamic texture recognition is an important research field in multimedia processing with applications ranging from retrieval to segmentation of video data.

” Moving fluidly across a range of framings and concepts, this dynamic text has not only stood the test of time, it has shaped the field in both direct and indirect ways, which becomes more apparent as one moves through the remaining contributions to this special issue.

To address this issue, we propose a model that conducts dynamic text planning and surface realization alternately.

Our character model also features a learned dynamic texture model that accounts for photo-realistic motion-dependent appearance details, as well as view-dependent lighting effects.

In particular, it tackles the task of dynamic texture segmentation.

Understanding dynamic textures (DTs) is a challenge in various computer vision applications due to the negative impacts of noise, changes of environment, illumination, and scales on capturing turbulent characteristics.

State-of-the-art relied on estimating dynamic textures features and convolutional neural network features to derive spatio-temporal features.

Based on a year-long ethnographic study of one teacher’s practice and her students’ multilingual/multimodal compositions, we highlight two findings: (1) this pedagogy afforded students a more dynamic text production process and (2) it expanded the range of meanings and identities they constructed and enacted during literacy instruction.

A dynamic texture refers to a sequence of images with regular and periodic changes in appearance from one image to another.

Nevertheless, the recognition of dynamic texture is challenging compared with the static signature image due to their unknown spatial and temporal nature.

, traffic, pedestrian, Dynamic Texture videos, and radar images, show the superiority of our proposed approach compared with previous approaches.

The frame image is further divided into blocks, and the average gradient of each block is calculated to construct the film dynamic texture.

Initially, in each frame, dynamic textures are detected through three different fractal analysis methods and thresholding techniques.

However, when dealing with video contents containing dynamic textures (DT), the existing prediction schemes usually failed due to temporal variability and randomness of DT, which results in more bit cost on residual coding compared with other contents.

A subsequent experiment using a dynamic texture in which elements moved in a variable range of random directions showed that the variegated motion direction plays a critical role in promoting visual unpleasantness.

Dynamic text presentation methods may improve reading ability in patients with central vision loss (CVL) by eliminating the need for accurate eye movements.

The diversification of water appearances makes their classification in the same class a quite difficult task using the general dynamic texture recognition methods.

We design two schemes in our network to respectively attack the broad frequency spectrum and the dynamic texture of moire: a multi-scale structure to process images at different spatial resolutions and a dynamic feature encoding module to encode the texture dynamically.

The main challenge of dynamic texture synthesis lies in how to maintain spatial and temporal consistency in synthesized videos.

For validation, we apply our proposed approach in the classification of dynamic textures and the recognition of infrared actions.

In this paper, we propose RSCA: a Real-time Segmentation-based Context-Aware model for arbitrary-shaped scene text detection, which sets a strong baseline for scene text detection with two simple yet effective strategies: Local Context-Aware Upsampling and Dynamic TextSpine Labeling, which model local spatial transformation and simplify label assignments separately.

, dynamic text and speech output upon selection of a graphic symbol within the grid display) on the acquisition of 12 personally relevant single words for individuals with severe autism spectrum disorder (ASD) who had minimal or no speech.

First, we propose a new dynamic feature descriptor, namely, the local directional structural pattern from three orthogonal planes (LDSP-TOP), which analyzes the structural aspects of the local dynamic texture.

Thus, to address this problem, we propose a compact keyframe-based representation that decouples a dynamic texture into a basic static texture and a set of multiplicative changing maps.

A common problem in the 4D reconstruction of people from multi-view video is the quality of the captured dynamic texture appearance which depends on both the camera resolution and capture volume.

Our work is divided into three contributions: first, we extracted the features from Local Binary Patterns, Local Phase Quantisation, and dynamic texture descriptor LPQTOP with two distinct leveraged network models from different CNN architectures for local deep visual learning for AU image analysis.

Dynamic texture refers to the content in video sequences that is characterized by spatial repetition and temporal variation, such as swaying foliage and flowing water.

For the identification of these emotions, there is a mixture of models that make use of the feature representation that are gradient based, a mixture of various dynamic textures along with contextual information.

Most could choreograph interactive visualizations and dynamic text, although some struggled with advanced uses requiring unstructured code editing.

Given the pose information, the first CNN predicts a dynamic texture map that contains time-coherent high-frequency details, and the second CNN conditions the generation of the final video on the temporally coherent output of the first CNN.

Efficiently representing spatio-temporal features of dynamic textures (DTs) in videos has been restricted due to negative impacts of the well-known issues of environmental changes, illumination, and noise.

We present a novel method for modelling dynamic texture appearance from a minimal set of cameras.

Reading women's tanci through the intersectional lenses of early modernity, queer theory, and narrativity, this study examines such narratives as an inspiration to initiate a more contextualized epistemological, historical, and methodical understanding of the dynamic textual spaces that harbor same-sex intimacies, erotic desires, and clandestine longings in vernacular traditions.

, supervised action recognition, self-supervised action recognition, and dynamic texture recognition.

Being a statistical method, it is computationally less intensive and domain independent method, and could be of great utility in organizing dynamic text collection and other applications.

It performs video authentication on simple videos with a static camera without using dynamic texture.

Aiming at the difficult problem of dynamic texture feature extraction in complex scenes, this paper proposes a dynamic texture modeling method based on multi-scale convolutional autoencoder.

Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain.

The proposed approach applies the concept of dynamic texture to the domain of person identification through dynamic facial patterns modeled by means of the Volume Local Binary Pattern (VLBP) descriptor, which effectively combines local features and movement.

Dynamic texture (DT) classification has attracted extensive attention in the field of image sequence analysis.

In this paper, we present a novel, simple but effective approach for dynamic texture recognition using 3D random features.

In this paper, we propose dynamic texture recognition from video snippets by constructing temporal intensity co-occurrence histograms for feature representation and learning.

The paper presents a new spatio-temporal learning-based descriptor called binarised statistical dynamic features (BSDF) for representation and classification of dynamic texture.

Given the limited reading skills of many adults with intellectual and developmental disabilities (IDD) and limited speech, the current study evaluated the effects of using an augmentative and alternative communication (AAC) app, featuring dynamic text and speech output embedded in visual scene displays, on the single-word recognition performance of six adults with IDD who demonstrated limited speech.

Thus, the paper develops a Dynamic Text Analytical model for Contract and Correspondence “DTA-CC” through utilizing BIM.

The TDS tool allows understanding the dynamic textural sensation in the mouth.

Dynamic texture is a field of research that has gained considerable interest from computer vision community due to the explosive growth of multimedia databases.

A dynamic texture coarseness algorithm is proposed, which combines 3-D data with 2-D Gaussian filtering and texture coarseness characteristics to transform 3-D roughness into visual image texture coarseness.

I proxy for these costs with the cross-industry labor mobility of a firm’s workers using a novel dynamic textual measure for this mobility based on network centrality.

This paper proposes a methodology for early fire detection based on visual smoke characteristics such as movement, color, gray tones and dynamic texture, i.

This paper aims to unlock the potential of dynamic textile patterns in future-driven fashion design using computer graphics, which enables changes to the visual appearance of a textile for aesthetic, expressive or communicative purposes.

This paper proposes a method of artifact reduction in compressed videos using dynamic texture map together with artifact maps and 3D - fuzzy filters.

This paper presents a new approach to dynamic texture classification based on deterministic partially self-avoiding (DPS) walks on complex networks (or graphs).

Dynamic texture (DT) is the annexation of an altering series of images where temporal regularity is present.

Describing the dynamic textures has attracted growing attention in the field of computer vision and pattern recognition.

These modules were: 1) point-based texture mapping; 2) deformation simulation; 3) cutting simulation; 4) tearing simulation; 5) dynamic texture mapping; and 6) 3-D display.

In this paper, we focus on Dynamic textures (DT).

This paper introduces a novel and appealing face spoof detection technique, which is primarily based on the study of contrast and dynamic texture features of both seized and spoofed photos.

This study used a single-subject, multiple-probe, across-participants design, to investigate the effects of a new software feature, dynamic text and speech output, on the acquisition of sight words by three pre-literate preschoolers with ASD during shared digital book reading experiences.

Although the volume LBP (VLBP) and the LBP-three orthogonal planes (LPB-TOP) have the capability of describing dynamic texture, they are seldom used as descriptors for VAD because 1) both VLBP and LBP-TOP are more suitable for natural scenes with rich dynamic textures, but sensitive to noise in the scenes with less dynamic textures, 2) the combination of motion and appearance not only limits their capability of motion characterizing but also brings the irrelevant appearance information such as background, and 3) high dimensionality is another drawback.

Exemplar-based dynamic texture synthesis (EDTS) is targeted to generate new samples of high quality that are perceptually similar to a given input dynamic texture exemplar.

Our system can deal with multiple, possibly crossing objects, with complex motions, and with dynamic textures.

This paper proposes a robust computer vision system to classify terrain types using three main algorithms, which extract features from UAV’s downwash effect: Static texturesGray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM) and Dynamic texturesOptical Flow method.

Particularly, the dynamic texture (DT) exhibits complex appearance and motion changes that remain a challenge to deal with.

The paper presents a new spatio-temporal learning-based descriptor called binarised statistical dynamic features (BSDF) for representation and classification of dynamic texture.

On the other hand, an arbitrary computable language is be shown to be nonrandom under certain dynamic texts.

While the previous approaches can produce good results, the accurate detection of motion remains a challenging task due to the difficulties raised by illumination variations, occlusion, camouflage, sudden motions appearing in burst, dynamic texture, and environmental changes such as weather conditions, sunlight changes during a day, and so on.

Dynamic textures (DT) constitute of objects characterized by stationary properties in time such as how leaves move in a windy day.

We validate our model with two challenging applications; image categorization and dynamic texture categorization.

Moire spreads across various frequency bands of images and is a dynamic texture with varying colors and shapes, which pose two main challenges in demoireing—an important task in image restoration.

Our proposed approach has two main steps: the first step tries to find candidate plagiarised fragments and focuses on high recall, followed by a more precise similarity analysis based on dynamic text alignment that will filter the results by finding alignments between the identified fragments.

In the CaroLIn (carbon fibre nonwovens optimised for aircraft interior components) research project a novel aerodynamic textile process is developed, in order to produce highly orientated non-wovens form recycled carbon fibres.

Recent works used spatiotemporal local binary pattern (STLBP) for micro-expression recognition and considered dynamic texture information to represent face images.

Dynamic texture (DT) is temporal extension of static texture.

Two different types of features are concatenated to accurately detect the face liveness, including depth features and dynamic texture features.

Dynamic texture analysis has been the focus of intensive research in recent years.

We present a deep learning framework for video classification applicable to face recognition and dynamic texture recognition.

We conclude that (1) eating behaviour of ice creams is captured better by video recordings than self-reporting and that (2) oral processing behaviour considerably changes dynamic texture and flavour perception of ice creams.

Details of the averaging procedure can be found in standard electrodynamic textbooks, for instance, in Jackson, Classical Electrodynamics, Wiley, New York, 1999, [3].

Local binary descriptors, such as local binary pattern (LBP) and its various variants, have been studied extensively in texture and dynamic texture analysis due to their outstanding characteristics, such as grayscale invariance, low computational complexity and good discriminability.

Second, we present attention-based dynamic texture images (att-DTIs) as pose-guided video frame feature.

The noninvasive grading of glioma tumors is obtained using multiple radiomic texture features including dynamic texture analysis, multifractal detrended fluctuation analysis, and multiresolution fractal Brownian motion in structural MRI.

We propose to address the problem of dynamic text classification by replacing the traditional, fixed-size output layer with a learned, semantically meaningful metric space.

For the identification of these emotions, there is a mixture of models that make use of the feature representation that are gradient based, a mixture of various dynamic textures along with contextual information.

Compared with a 3D mask, a real face displays different facial motion patterns that are reflected by different facial dynamic textures.

An effective model, which jointly captures shape and motion cues, for dynamic texture (DT) description is introduced by taking into account advantages of volumes of blurred-invariant features in three main following stages.

The latter involves dynamic textures extraction of video sequences extended in a temporal domain.

In this paper, we present a novel approach for dynamic text effects transfer by using example-based texture synthesis.

In the presented study, the proposed framework extends a Gaussian Mixture Model of Dynamic Textures detection (GMM-of-DT) technique using Kalman filtering for the hierarchical motion representation of the crowds, simultaneously at micro (individual crowd members) and macro (groups of individuals) levels.

By regarding the multispectral irradiance sequence of fire as dynamic texture and studying the sequence with the Linear Dynamic System (LDS) model, we represent the sequence with the parameters of the LDS model.

To explain their L2 learning potentials, the chapter will conceptualize digital games as dynamic texts, affinity spaces, and semiotic ecologies, and discuss the implications of each conceptualization for gamebased L2 learning and teaching.

In this paper dynamic texture recognition focuses on outdoor scenarios where a crisis event might occur (i.

These publications are evidence of a dynamic textual exchange, an overlapping of translation and adaptation, a blurring of media and genre, an interlingual and intercultural metissage.