Design of Sanda Action Reconstruction Model Based on 3D Images

In order to improve the training level of Sanda movement, this article uses an image analysis method to reconstruct the detailed characteristics of the movement and apply them to the actual training process. Since the traditional wavelet reconstruction method is affected by the accuracy of the decomposition scale, this paper proposes an improved method of Sanda action based on 3D image reconstruction. First, the method relies on frame adjacent phase compensation and digital image stabilization techniques to perform digital frame operations on the image. Then, scanning and corner detection are used for image reconstruction, where adjacent phase compensation methods are used to match feature points and gray pixels. Image extraction is performed by extracting key feature points of the action 3D image, and a fast frame detection method is used to stabilize the image of the digital image, thereby improving the image quality of image reconstruction. The experimental results show that the method has good image output effect and has a high application value in Sanda guidance and optimization.

Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

We have developed an open-source software called bi-channel image registration and deep-learning segmentation (BIRDS) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image preprocessing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to the other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment-incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments.

RESEARCH AND APPLICATION OF HISTORICAL 3D MODELING BASED ON ARCHIVED NON-OBLIQUE AERIAL PHOTOS

Nowadays, the oblique and multi-view, large-overlap aerial photography and airborne LiDAR are the main sources to build the 3D scene model. However, most of our archived aerial photos are acquired by non-oblique, normal photography. Because of low resolution, low overlay and poor model texture, there were less relative research and application. With the development of pixel-level matching technology, especially the application of Semi-Global Matching (SGM) and Multi-View Stereo (MVS) algorithm, the normal (non-oblique, non-large overlap) aerial photos could also be explored to restore the dense Digital Surface Model (DSM) and 3D scene model. In this paper, the method of the 3D scene modelling with the non-oblique aerial photos are summarized into 4 steps consisting of Data preprocessing, Ground Control Points (GCPs) collection and aerial triangulation (AT), DSM extraction and editing, 3D modelling and visualization. For the archived non-oblique aerial photos, including the aerial photographic films, digital frame photos and push-broom aerial data, the key steps of the 3D modelling method with these non-oblique aerial photos are discussed. Based on the experiments, the method can effectively explore the archived normal aerial data for large range restoration, 3D restoration, time series change detection and etc., providing new valuable spatio-temporal data for the urban historical research.

DEVELOPING AN IMAGE BASED LOW-COST MOBILE MAPPING SYSTEM FOR GIS DATA ACQUISITION

The aim of this study is to develop a low-cost mobile mapping system (MMS) with the integration of vehicle-based navigation data and stereo images acquired along vehicle paths. The system consists of a dual frequency GNSS board combined with a low-cost INS unit and two machine vision cameras that collect colour image data for road and roadside objects. The navigation data and the image acquisition are properly synchronized to associate position and attitude to each digital frame captured. In this way, upon pixel location of objects appearing on the video frames, their absolute geographical coordinates can be extracted by employing standard photogrammetric methods. Several calibration steps are implemented before survey operation: camera calibration, relative orientation between cameras and determination of rotation angles and offsets between vehicle and cameras reference frames. A software tool has been developed to facilitate and speed up the calibration procedures. Furthermore, easy object coordinate extraction is supported, either in auto mode, where the conjugate image coordinates are obtained in real time using image correlation techniques. Several surveying experiments were executed to certify and check the accuracy and efficiency of the system. From the achieved results, the developed system is efficient for collecting and positioning road spatial objects such as such as road boundaries, traffic lights, road signs, power poles, etc, more rapidly and less expensively. The obtained absolute positional accuracy is less than 1 meter, depending on the availability and quality of the GPS signal.

Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

We have developed an open-source software called BIRDS (bi-channel image registration and deep-learning segmentation) for the mapping and analysis of 3D microscopy data of mouse brain. BIRDS features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full pipeline including image pre-processing, bi-channel registration, automatic annotation, creation of 3D digital frame, high-resolution visualization, and expandable quantitative analysis (via link with Imaris). The new bi-channel registration algorithm is adaptive to various types of whole brain data from different microscopy platforms and shows obviously improved registration accuracy. Also, the attraction of combing registration with neural network lies in that the registration procedure can readily provide training data for network, while the network can efficiently segment incomplete/defective brain data that are otherwise difficult for registration. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality whole-brain datasets, or real-time inference-based image segmentation for various brain region of interests. Jobs can be easily implemented on Fiji plugin that can be adapted for most computing environments.

Implications of the selfie for marketing management practice in the era of celebrity

Purpose The purpose of this paper is to explore the implications of the selfie for marketing management in the era of celebrity. The purpose is to show that the facilitation of the creative performance of consumer identity is a key element of the marketing management task for the media convergence era. Design/methodology/approach The paper uses the selfie, the picture of oneself taken by oneself, as a metaphor to develop a conceptual exploration of the nature of marketing in the light of the dominance of celebrity and entertainment in contemporary media and entertainment. Findings The paper suggests that marketing management in the era of convergence should facilitate consumers’ identity projects through participatory and engaging social media initiatives. Marketers must furnish and facilitate not only the props for consumers mediated identity performances, but also the scripts, sets and scenes, plot devices, cinematographic and other visual techniques, costumes, looks, movements, characterizations and narratives. Research limitations/implications This is a conceptual paper that sketches out the beginning of a re-framed, communication-focussed vision of marketing management in the era of media convergence. Practical implications Marketing managers can benefit from thinking about consumer marketing as the stage management of consumer visual, physical, virtual, sensory and psychic environments that enable consumers to actively participate in celebrity culture. Originality/value This paper suggests ways in which marketing practice can emerge from its pre-digital frame to embrace the new digital cultures of consumption.

CONTINUOUS MAPPING OF TUNNEL WALLS IN A GNSS-DENIED ENVIRONMENT

The need for reliable systems for capturing precise detail in tunnels has increased as the number of tunnels (e.g., for cars and trucks, trains, subways, mining and other infrastructure) has increased and the age of these structures and, subsequent, deterioration has introduced structural degradations and eventual failures. Due to the hostile environments encountered in tunnels, mobile mapping systems are plagued with various problems such as loss of GNSS signals, drift of inertial measurements systems, low lighting conditions, dust and poor surface textures for feature identification and extraction. A tunnel mapping system using alternate sensors and algorithms that can deliver precise coordinates and feature attributes from surfaces along the entire tunnel path is presented. This system employs image bridging or visual odometry to estimate precise sensor positions and orientations. The fundamental concept is the use of image sequences to geometrically extend the control information in the absence of absolute positioning data sources. This is a non-trivial problem due to changes in scale, perceived resolution, image contrast and lack of salient features. The sensors employed include forward-looking high resolution digital frame cameras coupled with auxiliary light sources. In addition, a high frequency lidar system and a thermal imager are included to offer three dimensional point clouds of the tunnel walls along with thermal images for moisture detection. The mobile mapping system is equipped with an array of 16 cameras and light sources to capture the tunnel walls. Continuous images are produced using a semi-automated mosaicking process. Results of preliminary experimentation are presented to demonstrate the effectiveness of the system for the generation of seamless precise tunnel maps.

CONTINUOUS MAPPING OF TUNNEL WALLS IN A GNSS-DENIED ENVIRONMENT

The need for reliable systems for capturing precise detail in tunnels has increased as the number of tunnels (e.g., for cars and trucks, trains, subways, mining and other infrastructure) has increased and the age of these structures and, subsequent, deterioration has introduced structural degradations and eventual failures. Due to the hostile environments encountered in tunnels, mobile mapping systems are plagued with various problems such as loss of GNSS signals, drift of inertial measurements systems, low lighting conditions, dust and poor surface textures for feature identification and extraction. A tunnel mapping system using alternate sensors and algorithms that can deliver precise coordinates and feature attributes from surfaces along the entire tunnel path is presented. This system employs image bridging or visual odometry to estimate precise sensor positions and orientations. The fundamental concept is the use of image sequences to geometrically extend the control information in the absence of absolute positioning data sources. This is a non-trivial problem due to changes in scale, perceived resolution, image contrast and lack of salient features. The sensors employed include forward-looking high resolution digital frame cameras coupled with auxiliary light sources. In addition, a high frequency lidar system and a thermal imager are included to offer three dimensional point clouds of the tunnel walls along with thermal images for moisture detection. The mobile mapping system is equipped with an array of 16 cameras and light sources to capture the tunnel walls. Continuous images are produced using a semi-automated mosaicking process. Results of preliminary experimentation are presented to demonstrate the effectiveness of the system for the generation of seamless precise tunnel maps.