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Successful commercialization is crucial to innovative firms, but further investigation is needed on how diverse stakeholders can contribute to the commercialization of a radical innovation that requires particular market creation support. This paper aims to, therefore, analyze the key stakeholders and their contributive activities in commercialization and market creation, particularly in the case of radical innovations.

This study relies on qualitative research design including interviews with key stakeholders, such as regulators, scientists, experts, licensing partners, core company representatives and extensive secondary data. This single-case study concerns a functional food product, which is a radical innovation requiring the development of a novel product category positioned between the food and medicine categories in global market settings. Since its market launch in 1995, the involvement of multiple stakeholders was needed for its successful commercialization in over 30 countries.

Results uncover the contributions of diverse stakeholders to commercialization and market creation, particularly of radical innovation. Stakeholders performed market creation activities such as regulating the marketing and labeling of food products, conducting safety assessments, revealing and validating the positive health effects of the novelty and raising awareness of healthy living and cardiovascular health. The commercialization activities included distributing the products overseas, applying the ingredient to different food products and making the products available for users.

This single-case study provides an overview of the positive stakeholder activities with contributions to market creation and commercialization of functional food innovations. Although the user perspective was not included in the empirical part of this study because of our focus on B2B actors, users of the innovation can contribute to R&D activities to a great extent.

The developed framework of stakeholders’ contributive activities in radical innovation commercialization and market creation contributes to literature discussing market creation as well as commercialization within the marketing and innovation management research fields. This work also generates practical advice for managers who commercialize (radical) innovations.

Existing Simultaneous Localization and Mapping (SLAM) algorithms have been relatively well developed. However, when in complex dynamic environments, the movement of the dynamic points on the dynamic objects in the image in the mapping can have an impact on the observation of the system, and thus there will be biases and errors in the position estimation and the creation of map points. The aim of this paper is to achieve more accurate accuracy in SLAM algorithms compared to traditional methods through semantic approaches.

In this paper, the semantic segmentation of dynamic objects is realized based on U-Net semantic segmentation network, followed by motion consistency detection through motion detection method to determine whether the segmented objects are moving in the current scene or not, and combined with the motion compensation method to eliminate dynamic points and compensate for the current local image, so as to make the system robust.

Experiments comparing the effect of detecting dynamic points and removing outliers are conducted on a dynamic data set of Technische Universität München, and the results show that the absolute trajectory accuracy of this paper's method is significantly improved compared with ORB-SLAM3 and DS-SLAM.

In this paper, in the semantic segmentation network part, the segmentation mask is combined with the method of dynamic point detection, elimination and compensation, which reduces the influence of dynamic objects, thus effectively improving the accuracy of localization in dynamic environments.

Presently, 6 Degree of Freedom (6DOF) visual pose measurement methods enjoy popularity in the industrial sector. However, challenges persist in accurately measuring the visual pose of blank and rough metal casts. Therefore, this paper introduces a 6DOF pose measurement method utilizing stereo vision, and aims to the 6DOF pose measurement of blank and rough metal casts.

This paper studies the 6DOF pose measurement of metal casts from three aspects: sample enhancement of industrial objects, optimization of detector and attention mechanism. Virtual reality technology is used for sample enhancement of metal casts, which solves the problem of large-scale sample sampling in industrial application. The method also includes a novel deep learning detector that uses multiple key points on the object surface as regression objects to detect industrial objects with rotation characteristics. By introducing a mixed paths attention module, the detection accuracy of the detector and the convergence speed of the training are improved.

The experimental results show that the proposed method has a better detection effect for metal casts with smaller size scaling and rotation characteristics.

A method for 6DOF pose measurement of industrial objects is proposed, which realizes the pose measurement and grasping of metal blanks and rough machined casts by industrial robots.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

This paper reviews the pros and cons of different parametric modeling methods, which can provide a theoretical reference for parametric reconstruction of 3D human body models for virtual fitting.

In this study, we briefly analyze the mainstream datasets of models of the human body used in the area to provide a foundation for parametric methods of such reconstruction. We then analyze and compare parametric methods of reconstruction based on their use of the following forms of input data: point cloud data, image contours, sizes of features and points representing the joints. Finally, we summarize the advantages and problems of each method as well as the current challenges to the use of parametric modeling in virtual fitting and the opportunities provided by it.

Considering the aspects of integrity and accurate of representations of the shape and posture of the body, and the efficiency of the calculation of the requisite parameters, the reconstruction method of human body by integrating orthogonal image contour morphological features, multifeature size constraints and joint point positioning can better represent human body shape, posture and personalized feature size and has higher research value.

This article obtains a research thinking for reconstructing a 3D model for virtual fitting that is based on three kinds of data, which is helpful for establishing personalized and high-precision human body models.

The development of qualitative research methods addresses the need to explore, understand and interpret complex and subjective phenomena across various fields of study. These methods are guided by methodological frameworks, and data collection involves taking several precautions for observation or interviews. While these guidelines facilitate an emphasis on the objective aspects of discourse, accounting for subjectivity and emotions proves more challenging. However, these subjectivity and emotions are deemed as significant sources of information. In this chapter, we propose an innovative data collection method centred around creating collages and engaging in group discussions to decipher their meaning. Collage serves as a visual medium, and we recommend utilising semiotic analysis tools to comprehend its significance. To gain a more precise understanding of the value of collage as a data collection method, we studied a collage workshop organised by CREE. Through image analysis and exchanges, our findings reveal that collage acts as a physical medium that fosters exchanges, deepens ideas and restricts digressions. Additionally, collage allows for the expression and discussion of emotions linked to the image rather than the individual. The space of intersubjective reflexivity facilitated by collage enables a profound comprehension, critical assessment and augmentation of ideas and the interpretation of emotions without compromising the sensitivity of the author. This chapter’s main contribution is evidently manifested here.

Time of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for identifying and localizing asparagus in the field based on point clouds from ToF imaging. Since the semantics are not included in the point cloud, it contains the geometric information of other objects such as stones and weeds other than asparagus spears. An approach is required for extracting the spear information so that a robotic system can be used for harvesting.

A real-time convolutional neural network (CNN)-based method is used for filtering the point cloud generated by a ToF camera, allowing subsequent processing methods to operate over smaller and more information-dense data sets, resulting in reduced processing time. The segmented point cloud can then be split into clusters of points representing each individual spear. Geometric filters are developed to eliminate the non-asparagus points in each cluster so that each spear can be modelled and localized. The spear information can then be used for harvesting decisions.

The localization system is integrated into a robotic harvesting prototype system. Several field trials have been conducted with satisfactory performance. The identification of a spear from the point cloud is the key to successful localization. Segmentation and clustering points into individual spears are two major failures for future improvements.

Most crop localizations in agricultural robotic applications using ToF imaging technology are implemented in a very controlled environment, such as a greenhouse. The target crop and the robotic system are stationary during the localization process. The novel proposed method for asparagus localization has been tested in outdoor farms and integrated with a robotic harvesting platform. Asparagus detection and localization are achieved in real time on a continuously moving robotic platform in a cluttered and unstructured environment.

The ability to characterise highly conducting objects, that may also be highly magnetic, by the complex symmetric rank–2 magnetic polarizability tensor (MPT) is important for metal detection applications including discriminating between threat and non-threat objects in security screening, identifying unexploded anti-personnel landmines and ordnance and identifying metals of high commercial value in scrap sorting. Many everyday non-threat items have both a large electrical conductivity and a magnetic behaviour, which, for sufficiently weak fields and the frequencies of interest, can be modelled by a high relative magnetic permeability. This paper aims to discuss the aforementioned idea.

The numerical simulation of the MPT for everyday non-threat highly conducting magnetic objects over a broad range of frequencies is challenging due to the resulting thin skin depths. The authors address this by employing higher order edge finite element discretisations based on unstructured meshes of tetrahedral elements with the addition of thin layers of prismatic elements. Furthermore, computer aided design (CAD) geometrical models of the non-threat and threat object are often not available and, instead, the authors extract the geometrical features of an object from an imaging procedure.

The authors obtain accurate numerical MPT characterisations that are in close agreement with experimental measurements for realistic physical objects. The assessment of uncertainty shows the impact of geometrical and material parameter uncertainties on the computational results.

The authors present novel computations and measurements of MPT characterisations of realistic objects made of magnetic materials. A novel assessment of uncertainty in the numerical predictions of MPT characterisations for uncertain geometry and material parameters is included.

This study aims to solve the problems of ambiguous localization, large calculation, poor real-time and limited applicability of bolt thread defect detection.

First, the acquired ultrasound image is used to acquire the larger area of the image, which is set as the compliant threaded area. Second, based on the determined coordinates of the center point in each selected region, the set of coordinates on the left and right sides of the bolts is acquired by DBSCAN method with parameters eps and MinPts, which is determined by data set dimension D and the k-distance curve. Finally, the defect detection boundary line fitting is completed using the acquired coordinate set, and the relationship between the distance from each detection point to the curve and d, which is obtained from the measurement of the standard bolt sample with known thread defect, is used to locate the bolt thread defect simultaneously.

In this paper, the bolt thread defect detection method with ultrasonic image is proposed; meanwhile, the ultrasonic image acquisition system is designed to complete the real-time localization of bolt thread defects.

The detection results show that the method can effectively detect bolt thread defects and locate the bolt thread defect location with wide applicability, small calculation and good real-time performance.

World Tourism Organization (WTO) recognizes image as the main aspect to be considered by a destination in its promotion and marketing process. Cities try to build valued and recognized images, established from an identity defined based on their own values. One of the strategies adopted for this construction is to hold events, through which it is possible to promote tourism, move the economy, improve the infrastructure, change the image and influence intentions to visit the destination. From the point of view of supply and demand, theorists have proposed two categories of destination image: the projected image and the perceived image. In this context, the objective of the research was to propose a model for measuring the Projected and Perceived Image through the Organizational Identity of the Volvo Ocean Race Brazil.

With a quali-quantitative approach, the study universe is composed of in-depth interviews with the main members of the organizing committee, documentary and netnographic analysis of the event's social networks. For the analysis and interpretation of qualitative data, the collective subject discourse was used. Documentary and netnographic analysis were by means of deductive content analysis and correspondence analysis.

The results supported the three secondary hypotheses of the research, leading to confirm the central hypothesis that the constructed organizational identity, projected by the image, is perceived by visitors to the event studied.

It is understood as fundamental the expansion of studies regarding projected and perceived image, identity and the possibility of its application in tourist events, as social representations, as support also for the definition of management and marketing strategies.