Innovative Expression of Canal Cultural Symbols in Cultural and Creative Design Driven by Virtual Reality Technology

Since the reform and opening up, China’s politics, economy and culture have entered a stage of rapid development. People’s material standard of living has gradually improved, and people not only pay attention to the material level of demand, but also pay more attention to the pursuit of the spiritual level. The rapid development of Internet+ has provided people with a freer and broader choice of products, and the progress of science and technology has provided stable and powerful technical support for the development of commodities. It is worth pondering that while people are enjoying a convenient and fast life, traditional culture is being impacted by the collision of foreign cultures. People enjoy the convenience of cultural diversity, but neglect the organic combination of traditional culture and modern development. The roots of traditional culture are gradually weakening, and the development and inheritance of national traditional culture are facing the impact and challenge of foreign culture [1-4]. How to enhance the cultural self-confidence of the people of the country, inspire the cultural consciousness of the people, and enhance the core position of culture in the construction of the country are the important issues facing the development of traditional Chinese culture. The design and development of cultural and creative products are not only closely related to people’s lives, but also inextricably linked to the inheritance and development of traditional Chinese culture. The inheritance and development of national culture can be realized in the design products through the design concepts, and the design concepts based on cultural self-confidence will be adhered to the attribute of cultural self-confidence and reflected in the specific products [5-8].

The Grand Canal is the main transportation and water conservancy project in ancient China. While the development of the canal promotes the economic prosperity of the north and the south, it brings about the intermingling of the north and the south and the collision of cultures, and forms a lot of canal cultures, canal culture, folk culture, merchant culture, and water culture, etc. The culture of the Grand Canal contains the unique national cultural genes, and it can provide a new development idea for the design of cultural and creative products [9-10]. Meanwhile, as cultural products closely related to people’s lives, they can promote the formation of group identity and influence people’s aesthetic orientation and value judgment. Through the tangible material carrier of cultural and creative products, it can show the connotation of canal culture and stimulate people’s emotional resonance for canal culture [11-12].

With the continuous development and maturity of virtual reality technology, it has been fully utilized in many fields, and the innovative expression of canal cultural symbols in cultural and creative design driven by virtual reality technology is a concrete and valuable attempt of its application [13-14]. Virtual reality technology can visualize and interact with the information of canal culture symbols, simulate the personal feelings as well as the content of the information through the virtual platform, and bring the personal experience to be fully used in the visual communication design, which can be used as a part of the information transmission to let the audience to experience the world of virtual Grand Canal culture [15-16]. The visually rich feeling will further process the information of canal culture symbols, which in turn makes this personalized experience more attractive and more capable of fully expressing the value and effect of canal culture and art, thus making the ancient Grand Canal culture come to life and vitality under the virtual reality technology, showing the unique charm of canal culture and creativity, and awakening the people’s cultural self-confidence in the excellent national culture and national pride [17-20].

In this paper, we design a visual communication system for cultural creation based on image recognition, and firstly, we design the target image recognition module, which is mainly responsible for detecting the target features of the collected images of canal culture symbols and matching the target features, so as to complete the extraction of canal culture symbols and make the combination of canal culture symbols and cultural creation design. The feature detection algorithm for canal culture symbol images is divided into two parts: feature point detection and feature point description. First, two-way matching is integrated into the nearest neighbor matching strategy, and the method is applied to match the feature points of the image. Since the threshold value can only be set so that the matching point is infinitely close to the correct match. Then, a consistent random sampling algorithm is used to filter and purify feature matching points. Finally, the overall framework of the system is designed by combining with virtual reality technology, which assists the visual simulation simulation design of cultural and creative products, and completes the innovative expression of the integration of canal cultural symbols into cultural and creative design.

2

Digital transformation and recreation of traditional symbols

The digitization of traditional culture is an important means of protection, as it transforms excellent traditional culture into a storable and sharable digital form, and enables digital display, development, and platform construction. Canal cultural symbols are cultural products left behind in the long-term development process of the place, and they have profound aesthetic and historical connotations. The process of digital transformation has boosted the innovative development of traditional cultural symbols in modern times, and through digital technology, it will be able to carry out more efficient storage management and virtual display of traditional cultural symbols, thus improving the efficiency of cultural inheritance and protection.

At the same time, digital transformation will also enrich the development of traditional cultural symbols, with the help of diversified social media forms, will be able to expand the dissemination of cultural symbols in the public life, and thus enable modern people to more deeply understand the value behind the cultural heritage. With the deepening trend of digital transformation, cultural symbols are faced with richer possibilities in innovative development and re-creation, relying on digital means will be able to produce richer and more dynamic forms of innovative expression of cultural symbols, thus promoting the innovative interaction between traditional art and modern means. In addition, virtual reality technology allows people to experience the artistic charm of canal culture, breaking time and space limitations, and achieving global dissemination.

The protection and development of canals is also creating new images and symbols of canal culture. In the process of inheritance and development, the deep cultural heritage of the canal and innovative design concepts are used to create the canal culture industry. Mining the historical symbols of the canal, through the transformation of creative design and the integration of handicrafts and practical products, the development of canal cultural creative products. Create an industry dedicated to canal culture, so that people can experience the rich historical culture and spiritual qualities of the canal, while also recognizing its practical value in everyday life, for the benefit of the community.

3

Image-recognition-based visual communication system for cultural creations

Cultivating a vivid and unique canal image and identification culture through the use of cultural and creative design is a strategic goal for the long-term development of canal culture. Image recognition is a strategy for cultural and creative products to embody the cultural symbols of Canal’s cultural and creative products and to differentiate them from other products. The core identification content of image recognition comes from the unique consideration of the cultural symbol information in the appearance form and design concept of the canal, in which image recognition is the expression of the cultural symbols of the canal, and the humanistic information or cultural connotation is the core and source of the appearance and design qualities of the cultural and creative products.

3.1

Image Recognition Module Design

3.1.1

Target Image Recognition Module Framework

For the extraction of canal cultural symbols, designers can take their “shape”, extend their “meaning” and transmit their “spirit”, integrate them into the design and development of cultural and creative products, and fully combine them with the characteristics of the canal culture and reasonably apply them to their cultural and creative visual communication system, so that the cultural and creative products have distinctive regional characteristics and connotations of canal culture. The design of the target image recognition module requires the design of the target image recognition module, which can be used in the design and development of cultural and creative products, fully integrating the characteristics of the canal culture and reasonably applying it to the visual communication system of cultural and creative products, so as to make the cultural and creative products have distinctive regional characteristics and connotations of canal culture.

The design of target image recognition module needs to detect and match the characteristics of cultural symbols and elements of the collected canal images [21]. The primary task of target image recognition is to ensure accuracy and anti-interference in recognition. Therefore, this paper systematically studies the design of a target image recognition module based on the detection and matching of features of image cultural symbols.

The main framework of the target image recognition module designed in this paper is shown in Figure 1:

3.1.2

Feature detection

The feature detection algorithm [22] consists of two parts: feature point detection and feature point description, each part of the feature detection algorithm has its unique detector and descriptor. Feature point detection is the process of detecting specific points with special features in each canal image. The feature point description describes the pixel distribution in the neighborhood surrounding the feature point, providing a descriptor for subsequent feature matching of cultural symbol elements.

The scale space function L(x,y,σ) of the image is defined as the convolution of the scale variable Gaussian function G(x,y,σ) with the input image I(x,y) produces as shown in equation (1): (1) $L (x, y, σ) = G (x, y, σ) * I (x, y)$

where * denotes the convolution operation.

The scale-variable Gaussian function G(x,y,σ) = expression is shown in equation (2) below: (2) $G (x, y, σ) = \frac{1}{2 π σ^{2}} e^{- \frac{x^{2} + y^{2}}{2 σ^{2}}}$

Where σ is the scale factor, the size of which determines the degree of blurring of the image.

In this paper, the feature point detection part of the SURF algorithm is used for feature point detection. This part uses a speckle detector based on Hessian matrix to detect points of interest, which greatly reduces the computational complexity of the algorithm and improves the computational efficiency of the algorithm. Given a pixel point p(x,y) in image I, the Hessian matrix at point p as well as scale σ is defined as shown in equation (3): (3) $H (p, σ) = [\begin{matrix} L_{x x} (p, σ) & L_{x y} (p, σ) \\ L_{x y} (p, σ) & L_{y y} (p, σ) \end{matrix}]$

where L_xx,L_xy,L_xy,L_yy is the convolution of the Gaussian second order derivative with the original image.

Thus, the approximation of the Hessian matrix determinant can be expressed as: (4) $D e t (H) = D_{x x} D_{y y} - {(ω D_{x y})}^{2}$

Local gradient computation using sampled points requires that the sampled points are grouped two by two to form a pair. The binary descriptors of the feature points are obtained by comparing the intensity values of the sampled point pairs of pixels.

Setting G as the descriptor, it can be expressed as: (5) $G = \sum_{0 \leq a < N} 2^{a} T (P_{a})$

Where N denotes the length of the binary descriptor, P_a denotes the pair of sampling points, and T(P_a) denotes a one bit binary test whose expression is shown in Equation (6): (6) $T (P_{a}) = {\begin{matrix} 1 & I (P_{a}^{r_{1}}) - I (P_{a}^{r_{2}}) > 0 \\ 0 & o t h e r w i s e \end{matrix}$

where $I (P_{a}^{t})$ denotes the pixel value of the previous point in the sampled point pair P_a and $I (P_{a}^{F_{2}})$ denotes the pixel value of the latter point. Since this sampling method can obtain a large number of descriptors in a short time, the performance of the descriptors is often mixed. Therefore, while obtaining the binary descriptors of the feature points, the FREAK algorithm proposes to filter, i.e., downscale, the obtained binary descriptors to obtain the optimal descriptors.

In order to solve the rotational invariance problem, the algorithm selects pairs of sampled points with central symmetry and calculates the feature point orientations by summing their local gradients.

Setting o is the local gradient information, and the principal directions of the feature points are calculated according to Equation (7): (7) $\begin{matrix} O = [\begin{matrix} O_{x} \\ O_{y} \end{matrix}] = \frac{1}{M} \sum_{P_{0} \in G} (I (P_{0}^{r_{1}}) - I (P_{0}^{r_{2}})) \frac{P_{0}^{r_{1}} - P_{0}^{r_{2}}}{P_{0}^{r_{1}} - P_{0}^{r_{2}}^{2}} \\ α = \arctan 2 (O_{y}, O_{x}) \end{matrix}$

Where G is the set of sampling point pairs for calculating the feature point direction, M is the number of point pairs in set G, $P_{0}^{r_{1}}$ and $P_{0}^{r_{2}}$ are the sampling point pairs passing through the center of the sampling region, $I (P_{0}^{r_{1}})$ denotes the pixel value of the previous sampling point in the point pair, and $I (P_{0}^{E_{2}})$ denotes the pixel value of the latter sampling point.

After calculating the main direction of the feature point, the rotational invariance needs to be solved, then the coordinates of the neighborhood around the feature point should be rotated to the main direction, and after the rotation, a new sampling region is obtained, the sampling pattern is the same as described in the previous section, and the descriptor of the feature point is calculated.

The algorithm usually selects its medium-length, symmetric pair of sampling points to compute the orientation of the feature point, thus greatly reducing the time required to compute the feature descriptor.

3.1.3

Feature Matching

The Hamming distance is chosen as a metric for the coarse matching of canal culture symbol features, and the Hamming distance between two feature point descriptors is calculated as shown in Equation (8): (8) $H a m (d e s c_{1}, d e s c_{2}) = \sum_{0}^{k} x_{i} \oplus y_{i}$

Where desc_i and desc₂ denote the two feature descriptors to be matched, ⊕ denotes the different-or operation, Ham denotes the Hamming distance between the two feature descriptors, x_i and y_i denote the i th bit of the two descriptors, and k denotes the length of the feature descriptor. The Hamming distance describes the degree to which two binary strings correspond to each bit differently, the smaller the Hamming distance, the higher the degree of matching.

In this paper, we use the matching strategy of nearest-neighbor next-nearest-neighbor ratio combined with two-way matching for feature point matching. For the target image A and the scene image B to be matched, the feature point fine matching steps in this paper are as follows: 1)

A random feature point P_i in image A is selected and the weighted Hamming distance between it and each feature point Q in image B is calculated. After the calculation, the nearest neighbor Q_if and the next nearest neighbor Q_jx from the feature point P_i in image A to image B are selected.

2)

Write the Hamming distances from feature point P_i to nearest neighbor point Q_if and next nearest neighbor point Q_jx as d₁ and d₂, respectively. compute the ratio between nearest neighbor and next nearest neighbor, and only when the ratio between nearest neighbor and next nearest neighbor is less than a threshold (usually chosen), indicating that the nearest neighbor matching point pairs are not close to the next nearest neighbor matching point pairs, do we consider that the feature point P_i to the nearest neighbor point Q_if is a good match.

3)

Iterate through all the feature points in image A according to steps (1) and (2) to obtain a set of all matching point pairs U_A of image A.

4)

Similarly, a random feature point Q_j in image B is selected, and the nearest-neighbor and second-nearest-neighbor ratios between it and each feature point P in image A are computed, and when the nearest-neighbor and second-nearest-neighbor ratios are less than a threshold (the Kan values for bidirectional matching are not the same), the matched pairs of points are counted into set U_B.

5)

When the two-way matching calculation is completed, determine whether a matching point pair (P_i,Q_j) in set U_A and a matching point pair (Q_j,P_i) in set U_B are the same. If they are the same, it means that the two points are the nearest neighbors of each other, and the pair of matching points is retained. Otherwise, the pair is deleted.

3.1.4

Matchpoint purification

Since the threshold can only be set so that the matching point pairs can only make the matching point pairs close to the true and correct match, and can not guarantee complete correctness. Therefore the last step of the feature detection matching algorithm process is to filter and purify the feature matching point pairs using the Random Sampling Consistent Algorithm (RANSAC) [23], i.e., using the algorithm to estimate the coordinate transformation relationship of the feature points between the two images, i.e., the uni-stochasticity matrix H . By using the uni-stochasticity matrix, the specific position of the target image in the scene image is found. The uni-stressful transformation relationship between images is shown in equation (9): (9) $s [\begin{matrix} x^{'} \\ y^{'} \\ z^{'} \end{matrix}] = H [\begin{matrix} x \\ y \\ z \end{matrix}] = [\begin{matrix} h_{11} & h_{12} & h_{13} \\ h_{21} & h_{22} & h_{23} \\ h_{31} & h_{32} & h_{33} \end{matrix}] [\begin{matrix} x \\ y \\ z \end{matrix}]$

Where s denotes the scale parameter of the feature point, (x,y) denotes the coordinates of the feature point in the target image, (x′,y′) denotes the coordinates of the feature point in the scene image, and the image z′ = 1 and z = 1.

Since the image-to-image uni-responsive matrix H is computed, let h₃₃ = 1 then the matrix has 8 unknown parameters and requires at least 4 sets of point pairs to solve.

The RANSAC algorithm was first proposed for estimating the optimal straight line from a point set. At many points, the least squares estimation of the matching model is wrong, and RANSAC can use successive iterations to find the optimal parametric model in a data set containing “outliers”. Therefore, RANSAC is widely used for image matching. The basic principle of the RANSAC algorithm for filtering out false matches is to find an optimal univariate responsiveness matrix H in the data set such that the number of data points satisfying this matrix is maximized.

3.2

Design of cultural and creative visual communication systems

Virtual reality technology can be fully utilized in numerous fields, resulting in a significant impact on people’s production and life. As far as visual communication design is concerned, the high simulation ability of VR technology and the visualization of the conveyed information are the concrete embodiment of its value, and in this paper, image recognition technology is combined with virtual reality technology, and a cultural and creative visual communication system is designed.

3.2.1

Basic Setting and Design Principles

VR virtual reality technology to human-computer interface way to visualize the information interaction, and in accordance with the set program execution of a language form, the technology will be personal feelings as well as the content of the information through the virtual platform to simulate, bring the personal experience fully used in the visual communication design, can be used as part of the information transfer, so that the audience to experience the virtual canal cultural symbols of innovative visual expression. The visually rich feeling will further process the information of the canal’s cultural and creative design, which in turn makes this personalized experience more attractive and more capable of fully expressing the value and effect of the art. This basic setting makes VR virtual technology occupy a prominent position in the cultural and creative visual communication design of the canal and become an inevitable direction for the development of the canal cultural and creative industry.

In the actual cultural and creative visual communication design, some basic design principles need to be fully grasped, which represent the purpose and principles of the whole VR technology in application, and are the guarantee of sufficient influence on every audience. Based on the consideration of technology and use, the design principles cover many aspects: functionality, economy, aesthetics, personalization, comfort, and safety. These design principles need to be emphasized in the actual design to maximize the overall design to meet the needs.

3.2.2

Overall system design framework

When the VR design innovation visual communication is carried out in the cultural and creative visual communication system, it is necessary to reconstruct the spatial information and integrate the modeling advantages of simulation technology, and the cultural and creative products related to the canal cultural symbols can realize the high realism simulation of the virtual view. The function of cultural and creative visual communication display is to transform and process the data of characteristic information of the canal cultural symbols and display them in the way of virtual scenery, while the multimedia display method not only retains the essential features of the virtual scenery content, but also has a great degree of artistic re-creation process, making the display effect stronger than the real situation, and achieving the rich experience feeling that is both real and illusory beyond the time and space. The overall design architecture of the cultural and creative system is based on embedded VisualC+ software technology for in-depth development, and the development process will integrate and utilize the relevant functions of VegaPrime software to realize the virtual visual communication of cultural and creative products.

The design uses preset scene information for product rendering and calling, and simultaneously completes the selection and loading of various commands. All the application information data in the scene database can be used as the data content for the display effect of the cultural and creative products, the data is rendered as needed, the frame information is cached, and the display attributes and effects of the cultural and creative products related to the canal culture symbols are realized according to the hierarchical structure of the data invocation.

3.2.3

System development environment and implementation

In the selection of the system development environment, the main consideration is MultiGenCreator, which is a 3D model creation tool for professional real-time simulation of visual scenery, and makes the whole visual communication display in a full-area mesh state by optimizing the selection of the mesh structure, and pre-setting the vertices in order to structure the design space of the product. The whole system development and design technology integrates PCI bus scheduling, and makes full use of Unity3D technology according to the technical needs, based on the embedded Linux development environment, effectively completes the product development work.

Through the VR virtual simulation technology for the cultural and creative products for visual simulation simulation design, the entire design system from the functional point of view analysis must cover the information acquisition, database, 3D reconstruction, visual simulation, etc., the role of these functional modules to achieve the role of the need to be based on the VR program loaded VR virtual simulation design, the actual development and design, select the Creator menu module for the configuration of the parameters of the various functional modules. The model database stores a variety of parameters for modeling, and the generation design of its geometric module selects LOD 3D deformation technology, the application of which can render the effect of the texture in the visual communication of cultural and creative. The loading and management of the 3D program is mainly realized through the editor software VegaPrime, which can display the virtual simulation of cultural and creative products under different accuracy conditions, render the data of the module, and construct the spatial distribution model based on these standards.

3.3

System application testing

In order to ensure that the designed system has a certain degree of scientificity and reasonableness, after completing the design of software and hardware, the system is tested and analyzed, comparing the degree of image recognition between the traditional system and the designed system in the process of application, so as to accurately analyze and verify the advantages and application effects of the designed system. After clarifying the various data values of the test environment, the corresponding test environment is constructed to carry out the recognition of the images of the cultural and creative designs related to the canal culture symbols and carry out the comparative experimental analysis activities, assuming that Fig. 2 is the sample of the system test, and analyzing the recognition of the original images and comparing the results of the different systems in terms of image recognition. It should be noted that external interference factors or pixel factors should not be used as analysis indexes during the actual test, and only the completeness of image recognition should be studied and compared.

After this test and analysis, Fig. 3 is the result of the image recognition test of the designed system, and Fig. 4 is the result of the test of the image recognition method without match point purification. According to the results of the specific test analysis, it can be found that the method without match point purification has too much blurring in the process of image recognition, and the phenomenon of misidentification of the color of part of the pattern, which identifies the center color of the flower from red to black. In contrast, the designed system does not have any problems with blurring during the image recognition process. At the same time, the designed system can re-establish the image of cultural and creative design samples. The application of the system has a certain superiority, which is worth promoting and popularizing.

3.4

Evaluation of the display of cultural and creative design products

After the experiment, the researchers designed usability and experience questionnaires to evaluate the performance of the VR system and clarify the usability and experience of using it.

Considering the feasibility of the evaluation methods, this study uses the subjective evaluation method to assess the system’s usability and experience. Combining the literature, this study screens and constructs five subjective questionnaires for the experiment: the brain load questionnaire, the sense of presence and immersion questionnaire, the heart flow state questionnaire, the motivation and emotion questionnaire, and the VR motion sickness questionnaire in five aspects for evaluation. Combined with questionnaire results and interviews, the factors of VR system supporting the innovative expression of canal cultural symbols in cultural and creative design were comprehensively analyzed. The scales were all rated on a 7-point Likert scale. Twenty questionnaires were sent out and 20 were returned.

Item analysis of the questionnaire results revealed the following: issues related to the state of mind flow (practice seemed to speed up in the VR learning experience), issues related to motivation and emotion (I actively evaluated the design case for system design. I actively experimented with various prototyping tools. I am willing to explore the canal culture using the VR experience. I enjoy the VR experience and am willing to stay in the VR environment for several hours) have a difference between the high and low subgroups (p<0.05), and the rest of the results do not show a difference (p>0.05), and the data results can be used for descriptive analysis.

Table 1 shows detailed data for the different items of the usability questionnaire. As can be seen from Table 1, the average score of all five areas is above 5, which is a good performance.

Table 1.

System availability basic indicators

Project	Sample size	Minimum value	Maximum value	Mean value	Standard deviation	Median
Motion sickness	20	4.292	6.735	6.119	0.854	6.288
Field feeling	20	4.476	6.056	5.337	0.522	5.381
Motivation and emotion	20	3.864	6.982	5.343	0.915	5.23
Flow state	20	4.213	5.977	5.139	0.588	5.074
Mental load evaluation	20	3.831	5.498	5.423	0.707	5.145

Table 2 shows the scores of the sense of presence survey of the experiencers, the average score of the items related to the sense of presence is 5.337, and the minimum value is 4.476, and its score on the P10 dimension reaches a high score of 6.02. It indicates that the image recognition-based visual expression system for cultural and creative arts provides a good reproduction of the real environment in the visual aspect, provides an excellent sense of realism, and facilitates the occurrence of the user presence experience. It also proves that the design of the system in terms of interaction and operation effectively avoids breaking the user’s immersion in experiencing the canal cultural and creative design activities. The results of all 20 questionnaires reported positive levels of immersion experience.

Table 2.

Questionnaire on presentation

Number	Describe	Mean value	Standard deviation
P01	I know my role in the vr environment.	5.654	1.321
P02	The control device I use looks natural.	5.255	1.278
P03	The way I interact with systems (scene objects, interfaces) in vr environments is natural.	5.126	0.598
P04	I focus on the experience in the vr environment, rather than focusing on the use of the device.	5.256	1.125
P05	In the course of the experience, I can observe the vr environment and the objects very well.	5.669	1.321
P06	I can look at objects that are designed from a multi-perspective.	5.245	0.779
P07	At the end of the experience, I can use the various functions of the vr system well.	5.456	0.498
P08	My experience in the vr environment is consistent with my experience in the real world, not watching the screen and using the controller.	5.458	1.589
P09	I felt myself in the vr environment, so I didn’t notice what was happening around the world.	5.987	0.897
P10	In the vr environment, I have a feeling of being in the world.	6.02	0.798

The scores for motivation and emotion are shown in Table 3, and the average score regarding in user motivation and emotion is 5.343, indicating that the image recognition-based visual communication system for cultural creations effectively promotes user engagement and constructs a positive emotional experience regarding canal cultural and creative design. In more specific questionnaire descriptions, “I would like to use this VR experience as a way to explore the knowledge of canal culture and creativity” received a score of 5.83 “I think it is an interesting way to learn about canal culture and creativity in a VR environment” received a score of 5.67. The descriptions regarding motivation to participate and willingness to use VR both received a mean score of 5.25 or higher. The description “I enjoyed the experience of the VR system and would like to stay in the VR environment for a few hours” scored slightly lower at 4.17, indicating that there is room for improvement in the VR experience.

Table 3.

Motivation and emotional questionnaire results

Number	Describe	Mean value	Standard deviation
M01	I think it is an interesting way to learn about the canal in the vr environment.	5.628	0.789
M02	During the experience, I actively explored all vr situations.	5.432	0.946
M03	In the process of experience, I actively evaluated the design case scheme for system Settings.	5.256	1.256
M04	During the experience, I actively tried various cultural symbols.	5.469	0.797
M05	I think the system conveys a good canal cultural symbol product.	5.436	1.236
M06	When I am experiencing a vr experience, I want to share my experience and feelings with others.	5.511	1.022
M07	I am willing to use this vr experience to explore the canal.	5.897	0.946
M08	I enjoy the experience of the vr system, and I’m willing to stay in the vr environment for a few hours.	4.521	1.398

The scores about the state of mind flow are shown in Table 4, and its average score is 5.139, which shows that the users entered a positive flow state, and among the descriptions about the state of mind flow, “I think that I can use myself to fully control my behavior” received a rating of 5. The descriptions of neglecting external factors, F01, F02 and F03 dimensions, all received a score of 5.4 or higher, side by side demonstrating that the VR system provides an excellent immersion experience. “Time seemed to speed up during the learning experience” and “I became less sensitive to the passage of time while using the system”, the above descriptions received scores of 5 or more, indicating that the majority of the users appeared to have an important characteristic of the flow state: a blurred concept of time. Among the lower scoring items, “I know what to do next every step of the way” scored 4.417, suggesting that VR systems need to pay more attention to the setting of task guidance, especially to help users establish personal activity goals. The lower score of 4.25 for “I don’t worry about the safety of bumping into things” suggests that the disruption of the flow state is mainly due to external physical factors, i.e., the size and scope of the test site, and that the disruption of the flow state can be mitigated by replacing the test site with one of a larger scope of use. Overall, the descriptions related to the flow state all show a positive evaluation trend, and this study’s design of a cultural and creative visual communication system based on image recognition can effectively help users enter the flow state.

Table 4.

The point of the flow state

Number	Describe	Mean value	Standard deviation
F01	I don’t worry about what other people think of me.	5.897	1.462
F02	I became less sensitive when I used the system.	5.546	1.562
F03	I’m not worried about the fact that I’m using vr.	5.544	1.113
F04	The vr learning experience seems to accelerate.	5.565	0.987
F05	I think I control the vr experience.	5.548	0.875
F06	I think I can completely control my behavior.	5.369	0.956
F07	I have some strong positive emotions in the vr experience.	4.489	1.236
F08	I know every step of the way what I’m going to do.	4.456	1.212
F09	In the process of using vr, I’m not worried about security issues that hit other things.	4.489	0.784

The brain load score of the system is shown in Table 5, and its average score is 5.423, which is a high level of brain load, and its score in the aspect of B06 dimension is the highest, which is 5.987, and the user’s perception of brain load is mainly reflected in the completion of the visual exploration of cultural and creative design.

Table 5.

Mental load description score

Number	Describe	Mean value	Standard deviation
B01	I didn’t spend a lot of mental energy in order to complete the exploration task	4.897	1.254
B02	I didn’t spend a lot of energy in order to finish the creative experience	5.447	1.356
B03	I think the t experience is abundant	5.145	1.447
B04	I feel I’m using the vr system to meet expectations	5.233	0.977
B05	I don’t need to give extra mental and physical energy to achieve the desired state	5.189	0.856
B06	I didn’t feel confused, discouraged, irritable, stressed and angry	5.987	0.912

The VR motion sickness scores are shown in Table 6, the overall average VR motion sickness score is 6.119, which has a good motion sickness performance, and the user does not experience serious VR discomfort, and its scores in the S08 and S09 dimensions are 6.745 and 6.748, respectively, which shows that it does not have any dizziness no matter whether it closes its eyes or opens them. The system has good VR usability. Overall, the system has good usability without excessive cerebral load, but it can be difficult to experience severe VR discomfort.

Table 6.

VR motion sickness description and score

Number	Describe	Mean value	Standard deviation
S01	I didn’t feel any discomfort.	6.235	0.978
S02	I don’t feel tired.	6.123	0.786
S03	I don’t feel very tired.	5.123	0.981
S04	My eyes didn’t have a hard time focusing.	5.264	1.123
S05	I don’t feel any headache.	6.359	1.254
S06	I don’t have any feeling of swelling.	6.441	1.236
S07	I didn’t feel blurred.	6.213	0.874
S08	My eyes open, no dizziness.	6.745	0.876
S09	I closed my eyes and felt no dizziness.	6.748	0.772

4

Conclusion

This thesis constructs a more organized system support for the visual innovation communication of canal culture symbol-related cultural and creative products, and through the development practice of the system, the test and evaluation based on the system are carried out.

The method without match point purification has a high degree of fuzziness in the process of image recognition of canal cultural symbols, which cannot completely recognize the images of canal-related cultural symbols, and there is a situation that the color of some parts of the pattern is incorrectly recognized. The image recognition method designed in this paper, on the other hand, clearly identifies canal cultural symbols during the image recognition process and presents the cultural symbol images in a complete manner. This paper’s image recognition method has certain superiority and can be applied to the visual communication system of canal culture and creativity, as shown.

This paper designs a brain load questionnaire, a sense of presence and immersion questionnaire, a heart flow state questionnaire, a motivation and emotion questionnaire, and a VR motion sickness questionnaire for the image recognition-based visual communication system for cultural creations, and the average scores of these questionnaires by the experiencers are over 5, which indicates that the system performs well in the visual communication of canal cultural creations, and that the system not only innovates the expression form of the cultural creations design products but also helps the The system not only innovates the expression form of the cultural and creative design products, but also helps the experiencers to have a deep understanding of the knowledge related to the canal cultural and creative, which contributes to the inheritance of the canal cultural symbols.

Sprache:: Englisch

Zeitrahmen der Veröffentlichung:: 1 Hefte pro Jahr
Fachgebiete der Zeitschrift:: Biologie, Biologie, andere, Mathematik, Angewandte Mathematik, Mathematik, Allgemeines, Physik, Physik, andere

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Innovative Expression of Canal Cultural Symbols in Cultural and Creative Design Driven by Virtual Reality Technology

Zhen Chen

Yu Wang

Online veröffentlicht: 17. März 2025

Eingereicht: 28. Okt. 2024

Akzeptiert: 31. Jan. 2025

DOI: https://doi.org/10.2478/amns-2025-0352

SchlüsselwörterCultural and Creative Design, Virtual Reality, Visual Communication, Image Recognition

© 2025 Zhen Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Cultural and Creative Design, Virtual Reality, Visual Communication, Image Recognition