Scene Analysis Research Articles

Analysis of the fusion development of traditional culture concept and interior art design based on 3D scene analysis

Abstract In this paper, by collecting a large number of 3D scenes with traditional cultural styles, after pre-processing, the style analysis task of 3D scenes is realized through two stages of model style element discovery and scene style pattern mining. In the style pattern mining stage, the maximum frequent pattern mining algorithm is used to get the style pattern of the final scene, and the part is visualized. To reduce the user’s selection time, a 3D interior layout recommendation system based on style rule mining is designed using collaborative filtering. Based on the previous work on scene style pattern mining, the style association rules are further mined, and the user is provided with candidate layouts and furniture dynamically and in real-time by introducing user interaction. Comparing the text-improved recommendation algorithm with the traditional recommendation algorithm, the accuracy rate of the traditional algorithm starts to saturate to 65% after TopN>20, while the algorithm in this paper is higher than 80%. In the analysis of traditional cultural contexts of interior art, in terms of the aesthetic acquisition, the highest proportion of aesthetic association is 63.7%. In terms of emotional expression, the proportion of symbolization of emotion is 73.1%. In terms of object comparison, the highest proportion of plurality and flexibility of combinations was 57.5%. Therefore, this paper provides good research ideas for how to better integrate traditional culture into interior design.

Detection and Classification of Buildings by Height from Single Urban High-Resolution Remote Sensing Images

Recent improvements in remote sensing technologies have boosted building detection techniques from rough classifications using moderate resolution imagery to precise extraction from high-resolution imagery. Shadows frequently emerge in high-resolution urban images. To exploit shadow information, we developed a novel building detection and classification algorithm for images of urban areas with large-size shadows, employing only the visible spectral bands to determine the height levels of buildings. The proposed method, building general-classified by height (BGCH), calculates shadow orientation, detects buildings using seed-blocks, and classifies the buildings into different height groups. Our proposed approach was tested on complex urban scenes from Toronto and Beijing. The experimental results illustrate that our proposed method accurately and efficiently detects and classifies buildings by their height levels; the building detection rate exceeded 95%. The precision of classification by height levels was over 90%. This novel building-height-level detection method provides rich information at low cost and is suitable for further city scene analysis, flood disaster risk assessment, population estimation, and building change detection applications.

The Interplay of Dominant Empiricism and Residual Cartesianism in Ann Radcliffe’s A Sicilian Romance

ABSTRACT In response to the supernatural occurrences in the Marquis’s castle, several characters of Ann Radcliffe’s A Sicilian Romance adhere to the tenets of Lockean empiricism to interpret their sensory perception. Conversely, a few characters adopt Cartesian scepticism to dismiss the validity of sensory experience. To date, no exhaustive study of Ann Radcliffe’s fiction has inspected the incorporation of Lockean empiricism and the Cartesian method of René Descartes into her novels. By conducting a historical analysis of the philosophical scene of the Enlightenment era, this paper aims to substantiate that not only does A Sicilian Romance encompass seminal philosophical notions of the age of Enlightenment, but it also points to the shifting philosophical scene within this period, characterised by the dominance of empiricism and the residuality of Cartesianism. By examining the reflection of these philosophies in Radcliffe’s novel, this article challenges the supposition that the Gothic novel necessarily contains anti-Enlightenment impulses.

Advances in Data‐Driven Analysis and Synthesis of 3D Indoor Scenes

AbstractThis report surveys advances in deep learning‐based modelling techniques that address four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We describe different kinds of representations for indoor scenes, various indoor scene datasets available for research in the aforementioned areas, and discuss notable works employing machine learning models for such scene modelling tasks based on these representations. Specifically, we focus on the analysis and synthesis of 3D indoor scenes. With respect to analysis, we focus on four basic scene understanding tasks – 3D object detection, 3D scene segmentation, 3D scene reconstruction and 3D scene similarity. And for synthesis, we mainly discuss neural scene synthesis works, though also highlighting model‐driven methods that allow for human‐centric, progressive scene synthesis. We identify the challenges involved in modelling scenes for these tasks and the kind of machinery that needs to be developed to adapt to the data representation, and the task setting in general. For each of these tasks, we provide a comprehensive summary of the state‐of‐the‐art works across different axes such as the choice of data representation, backbone, evaluation metric, input, output and so on, providing an organized review of the literature. Towards the end, we discuss some interesting research directions that have the potential to make a direct impact on the way users interact and engage with these virtual scene models, making them an integral part of the metaverse.

Neural dynamics underlying successful auditory short-term memory performance.

Listeners often operate in complex acoustic environments, consisting of many concurrent sounds. Accurately encoding and maintaining such auditory objects in short-term memory is crucial for communication and scene analysis. Yet, the neural underpinnings of successful auditory short-term memory (ASTM) performance are currently not well understood. To elucidate this issue, we presented a novel, challenging auditory delayed match-to-sample task while recording MEG. Human participants listened to 'scenes' comprising three concurrent tone pip streams. The task was to indicate, after a delay, whether a probe stream was present in the just-heard scene. We present three key findings: First, behavioural performance revealed faster responses in correct versus incorrect trials as well as in 'probe present' versus 'probe absent' trials, consistent with ASTM search. Second, successful compared with unsuccessful ASTM performance was associated with a significant enhancement of event-related fields and oscillatory activity in the theta, alpha and beta frequency ranges. This extends previous findings of an overall increase of persistent activity during short-term memory performance. Third, using distributed source modelling, we found these effects to be confined mostly to sensory areas during encoding, presumably related to ASTM contents per se. Parietal and frontal sources then became relevant during the maintenance stage, indicating that effective STM operation also relies on ongoing inhibitory processes suppressing task-irrelevant information. In summary, our results deliver a detailed account of the neural patterns that differentiate successful from unsuccessful ASTM performance in the context of a complex, multi-object auditory scene.

REPRESENTASI FEMINISME DALAM IKLAN KECAP ABC “SUAMI SEJATI MAU MASAK”

The kecap ABC advertisement for the suami Sejati Mau Masak edition is a series in the gender equality campaign created by Heinz ABC Indonesia. Through this series of advertisements, it is made as a form of appreciation to women who have done domestic work, especially cooking. In this ad, many messages of feminism are shown visually in every scene. The purpose of this research is to analyze how the message of feminism is contained in the Ketchup ABC advertisement of “suami sejati mau masak” using Rolnad Barthes's semiotic theory approach. In this study, it was carried out using descriptive qualitative research methods by collecting data, conducting analysis, making discussions and conclusions. The results of this study are an analysis of various scenes from the ABC soy sauce of Suami Sejati Mau Masak which is considered to have a feminist message using Roland Barthes's theoretical approach discussing denotative, connotative and myth development.

News and Analysis of the Global Innovation Scene

News and Analysis of the Global Innovation Scene

Adaptive shift of active electroreception in weakly electric fish for troglobitic life

The adaptive-shift hypothesis for the evolution of cave-dwelling species posits that ancestor species in surface habitats had exaptations for subterranean life that were exploited when individuals invaded caves. Weakly electric Gymnotiform fishes, nocturnal South American teleost fishes, have features that appear to be likely exaptations for troglobitic life. These fishes have active electrosensory systems in which fish generate weak electric fields that are detected by specialized electroreceptors. Gymnotiform fishes use their electric fields for navigation, prey capture (scene analysis), and social communication. Although active electrosensory systems appear to be exaptations for troglobitic life, as fish use these systems to “see in the dark”, producing electric fields is energetically costly. Cave habitats, which often are low in resources, may not be able to support such high energetic demands. Eigenmannia vicentespelaea, a species of weakly electric fish that is endemic to the São Vicente II cave in central Brazil, surprisingly generates stronger electric fields than their surface relatives. The increase in strength of electric fields may result simply from differences in size between cave and surface populations, but may also be due to lack of predation pressure in the cave or increases in “sensory volumes” and acuity that improve prey localization and capture. Eigenmannia vicentespelaea exhibits the classical phenotypes of any troglobitic fish: these fish have small to nonexistent eyes and loss of pigmentation. The closest living surface relative, Eigenmannia trilineata, inhabits streams nearby and has eyes and pigmentation. The electrosensory and locomotor behavior of both species of fish were measured in their natural habitats using a grid recording system. Surface Eigenmannia exhibited dramatic circadian changes in social behavior, such as hiding under rocks during the day and foraging in groups at night, while cave Eigenmannia displayed territorial behavior with no apparent circadian modulations. The territorial behavior involved electrical and movement-based interactions that may be a form of boundary patrolling. Electrosocial behavior and scene analysis are mechanistically interlinked because both stem from active sensing tactics.

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, robots have achieved relatively high levels of autonomy. In more unstructured environments, however, the development of fully autonomous mobile robots remains challenging due to the complexity of understanding these environments. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots must understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, rough or smooth terrain, etc.) are required for autonomous robot navigation in unstructured outdoor environments. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges.

The Teaching of Doctrine in Casarosa’s Film “Luca”

Background: Films seen from the element of doctrine are very interesting to be used as positive lessons, because every action can have consequences for choices in attitude. Purpose: This study aims to observe the doctrine in the film "Luca" by Enrico Casarosa, so that it can be known the consequences must be accepted by Luca as the main character, the attitude that must be carried out in response to the doctrine. Method: This research is a literature research. The primary data source was obtained by Casarosa's film "Luca" published in 2021. The data analysis method uses content-based qualitative analysis of dialogue words, sentences, images, cuts, scenes, paragraphs, and descriptive film narratives as well as the responses of the main character "Luca" who have elements of doctrination. Data analysis techniques are carried out through three stages: data reduction, data display, and conclusion pulling/verification Results and Discussion: The results of this study show that there is a doctrine in the film "Luca" by Casarosa. Doctrine against the main character, Luca: Among them are 1) obedience, which is marked by running into hiding; he must not have contact with man; It is not allowed to talk, discuss, and even approach the surface of the sea. As a consequence, if Luca does not comply, he will be captured and killed by humans. 2) Freedom, which is characterized by humans having their own vehicle in life, does not need anyone without pressure. As a consequence, if he does not follow Alberto's character, he will not get freedom, cannot adventure in the human world, and will still feel afraid of everything. 3) Continue to learn, create and innovate, this is indicated by the existence of a telescope that can observe distant objects seen closely; You can travel in the universe and galaxies only through small tools such as telescopes. As a consequence, if he hadn't followed Giulia, he would never have had the experience of a telescope adventure. This tells us about the importance of going to school. Conclusions and Implications: The film "Luca", Casarosa inserts several doctrines to serve as lessons such as: 1) Whenever we see danger or murderers, we need to run to hide. Unless, we will be killed. 2) Whenever we have worries or fears in mind, we need to encourage them all and build our own ways to manage them as a force. Unless, we will continue to feel afraid of facing problems. 3) Dare to learn more, to experience more, to adventure more, especially to the world, universe, and galaxy, then our knowledge will increase. If we just stay and do nothing, we will only continue to be like a frog in a shell. 4) Learn more to increase knowledge, then we will know how to adventure the world and universe. If we do not develop ourselves by learning, then we will only become like people who stutter in knowledge.

Deep-Learning-Based Point Cloud Semantic Segmentation: A Survey

With the rapid development of sensor technologies and the widespread use of laser scanning equipment, point clouds, as the main data form and an important information carrier for 3D scene analysis and understanding, play an essential role in the realization of national strategic needs, such as traffic scene perception, natural resource management, and forest biomass carbon stock estimation. As an important research direction in 3D computer vision, point cloud semantic segmentation has attracted more and more researchers’ attention. In this paper, we systematically outline the main research problems and related research methods in point cloud semantic segmentation and summarize the mainstream public datasets and common performance evaluation metrics. Point cloud semantic segmentation methods are classified into rule-based methods and point-based methods according to the representation of the input data. On this basis, the core ideas of each type of segmentation method are introduced, the representative and innovative algorithms of each type of method are elaborated, and the experimental results on the datasets are compared and analyzed. Finally, some promising research directions and potential tendencies are proposed.

TCANet: three-stream coordinate attention network for RGB-D indoor semantic segmentation

Semantic segmentation plays a vital role in indoor scene analysis. Currently, its accuracy is still limited due to the complex conditions of various indoor scenes. In addition, it is difficult to complete this task solely relying on RGB images. Since depth images can provide additional 3D geometric information to RGB images, researchers chose to incorporate depth images for improving the accuracy of indoor semantic segmentation. However, it is still a challenge to effectively fuse the depth information with the RGB images. To address this issue, a three-stream coordinate attention network is proposed. The presented network reconstructs a multi-modal feature fusion module for RGB-D features, which can realize the aggregation of two modal information along the spatial and channel dimensions. Meanwhile, three convolutional neural network branches are used to construct a parallel three-stream structure, which can, respectively, process the RGB features, depth features and combined features. On one hand, the proposed network can preserve the original RGB and depth feature streams, simultaneously. On the other hand, it can also contribute to utilize and propagate the fusion feature flow better. The embedded ASPP module is used to optimize the semantic information in the proposed network, so as to aggregate the feature information of different scales and obtain more accurate features. Experimental results show that the proposed model can reach a state-of-the-art mIoU accuracy of 50.2% on the NYUDv2 dataset and on the more complex SUN-RGBD dataset.

Utilizing Drone-Based Ground-Penetrating Radar for Crime Investigations in Localizing and Identifying Clandestine Graves.

The decomposition of a body is influenced by burial conditions, making it crucial to understand the impact of different conditions for accurate grave detection. Geophysical techniques using drones have gained popularity in locating clandestine graves, offering non-invasive methods for detecting surface and subsurface irregularities. Ground-penetrating radar (GPR) is an effective technology for identifying potential grave locations without disturbance. This research aimed to prototype a drone system integrating GPR to assist in grave localization and to develop software for data management. Initial experiments compared GPR with other technologies, demonstrating its valuable applicability. It is suitable for various decomposition stages and soil types, although certain soil compositions have limitations. The research used the DJI M600 Pro drone and a drone-based GPR system enhanced by the real-time kinematic (RTK) global positioning system (GPS) for precision and autonomy. Tests with simulated graves and cadavers validated the system's performance, evaluating optimal altitude, speed, and obstacle avoidance techniques. Furthermore, global and local planning algorithms ensured efficient and obstacle-free flight paths. The results highlighted the potential of the drone-based GPR system in locating clandestine graves while minimizing disturbance, contributing to the development of effective tools for forensic investigations and crime scene analysis.

Triangle-Mesh-Rasterization-Projection (TMRP): An Algorithm to Project a Point Cloud onto a Consistent, Dense and Accurate 2D Raster Image.

The projection of a point cloud onto a 2D camera image is relevant in the case of various image analysis and enhancement tasks, e.g., (i) in multimodal image processing for data fusion, (ii) in robotic applications and in scene analysis, and (iii) for deep neural networks to generate real datasets with ground truth. The challenges of the current single-shot projection methods, such as simple state-of-the-art projection, conventional, polygon, and deep learning-based upsampling methods or closed source SDK functions of low-cost depth cameras, have been identified. We developed a new way to project point clouds onto a dense, accurate 2D raster image, called Triangle-Mesh-Rasterization-Projection (TMRP). The only gaps that the 2D image still contains with our method are valid gaps that result from the physical limits of the capturing cameras. Dense accuracy is achieved by simultaneously using the 2D neighborhood information (rx,ry) of the 3D coordinates in addition to the points P(X,Y,V). In this way, a fast triangulation interpolation can be performed. The interpolation weights are determined using sub-triangles. Compared to single-shot methods, our algorithm is able to solve the following challenges. This means that: (1) no false gaps or false neighborhoods are generated, (2) the density is XYZ independent, and (3) ambiguities are eliminated. Our TMRP method is also open source, freely available on GitHub, and can be applied to almost any sensor or modality. We also demonstrate the usefulness of our method with four use cases by using the KITTI-2012 dataset or sensors with different modalities. Our goal is to improve recognition tasks and processing optimization in the perception of transparent objects for robotic manufacturing processes.

Improved Short-term Dense Bottleneck network for efficient scene analysis

Visual scene understanding mainly depends on pixel-wise classification obtained from a deep convolutional neural network. However, existing semantic segmentation models often face difficulties in real-time applications due to their large network architecture. Although there are real-time semantic segmentation models available, their shallow backbone can degrade the performance considerably. This paper introduces SDBNetV2, a lightweight semantic segmentation model designed to improve real-time performance without increasing computational costs. A key contribution is a novel Short-term Dense Bottleneck (SDB) module in the encoder, which provides varied field-of-views to capture different geometrical objects in a complex scene. Additionally, we propose dense feature refinement and improved semantic aggregation modules at the decoder end to enhance contextualization and object localization. We evaluate the proposed model’s performance on several indoor and outdoor datasets in structured and unstructured environments. The results show that SDBNetV2 achieves superior segmentation performance over other real-time models with less than 2 million parameters.

Parvalbumin neurons enhance temporal coding and reduce cortical noise in complex auditory scenes

Cortical representations supporting many cognitive abilities emerge from underlying circuits comprised of several different cell types. However, cell type-specific contributions to rate and timing-based cortical coding are not well-understood. Here, we investigated the role of parvalbumin neurons in cortical complex scene analysis. Many complex scenes contain sensory stimuli which are highly dynamic in time and compete with stimuli at other spatial locations. Parvalbumin neurons play a fundamental role in balancing excitation and inhibition in cortex and sculpting cortical temporal dynamics; yet their specific role in encoding complex scenes via timing-based coding, and the robustness of temporal representations to spatial competition, has not been investigated. Here, we address these questions in auditory cortex of mice using a cocktail party-like paradigm, integrating electrophysiology, optogenetic manipulations, and a family of spike-distance metrics, to dissect parvalbumin neurons’ contributions towards rate and timing-based coding. We find that suppressing parvalbumin neurons degrades cortical discrimination of dynamic sounds in a cocktail party-like setting via changes in rapid temporal modulations in rate and spike timing, and over a wide range of time-scales. Our findings suggest that parvalbumin neurons play a critical role in enhancing cortical temporal coding and reducing cortical noise, thereby improving representations of dynamic stimuli in complex scenes.

Views from 'crabworld': the spatial distribution of light in a tropical mudflat.

Natural scene analysis has been extensively used to understand how the invariant structure of the visual environment may have shaped biological image processing strategies. This paper deals with four crucial, but hitherto largely neglected aspects of natural scenes: (1) the viewpoint of specific animals; (2) the fact that image statistics are not independent of the position within the visual field; (3) the influence of the direction of illumination on luminance, spectral and polarization contrast in a scene; and (4) the biologically relevant information content of natural scenes. To address these issues, I recorded the spatial distribution of light in a tropical mudflat with a spectrographic imager equipped with a polarizing filter in an attempt to describe quantitatively the visual environment of fiddler crabs. The environment viewed by the crabs has a distinct structure. Depending on the position of the sun, the luminance, the spectral composition, and the polarization characteristics of horizontal light distribution are not uniform. This is true for both skylight and for reflections from the mudflat surface. The high-contrast feature of the line of horizon dominates the vertical distribution of light and is a discontinuity in terms of luminance, spectral distribution and of image statistics. On a clear day, skylight intensity increases towards the horizon due to multiple scattering, and its spectral composition increasingly resembles that of sunlight. Sky-substratum contrast is highest at short wavelengths. I discuss the consequences of this extreme example of the topography of vision for extracting biologically relevant information from natural scenes.

Safety BIM intelligent modeling analysis of prestressed concrete continuous girder bridge construction scene

BIM intelligent modeling is used to analyze the safety of the construction scene of prestressed concrete continuous girder bridge, so as to realize the safety management of the bridge construction scene and improve the construction efficiency. The construction safety analysis system is used to analyze the binding data of construction safety, and the data is combined with the self-applicable equilibrium control and the game equilibrium control to build the construction scene safety objective function model. On this basis, combined with the control constraints of the whole life cycle, the statistical analysis regression model is used to build the scene safety analysis model based on BIM. The whole life cycle safety intelligent analysis of the construction scene is realized, and the improved particle swarm optimization algorithm is used to solve the model by adaptive differential evolution, so as to shorten the calculation time of the model. The experimental results show that the safety management performance of the proposed method is high, and the safety management evaluation grade is 285. The identification accuracy of main beam stress change is high. Under the conditions of unbalanced load, combination of unbalanced load and prestress, combination of wind load and prestress and unbalanced load, the safety analysis of upper edge stress and lower edge stress of main beam can be effectively completed, and the construction safety of prestressed concrete continuous beam bridge can be realized.

Multi-Branch Deep Learning Framework for Land Scene Classification in Satellite Imagery

Land scene classification in satellite imagery has a wide range of applications in remote surveillance, environment monitoring, remote scene analysis, Earth observations and urban planning. Due to immense advantages of the land scene classification task, several methods have been proposed during recent years to automatically classify land scenes in remote sensing images. Most of the work focuses on designing and developing deep networks to identify land scenes from high-resolution satellite images. However, these methods face challenges in identifying different land scenes. Complex texture, cluttered background, extremely small size of objects and large variations in object scale are the common challenges that restrict the models to achieve high performance. To tackle these challenges, we propose a multi-branch deep learning framework that efficiently combines global contextual features with multi-scale features to identify complex land scenes. Generally, the framework consists of two branches. The first branch extracts global contextual information from different regions of the input image, and the second branch exploits a fully convolutional network (FCN) to extract multi-scale local features. The performance of the proposed framework is evaluated on three benchmark datasets, UC-Merced, SIRI-WHU, and EuroSAT. From the experiments, we demonstrate that the framework achieves superior performance compared to other similar models.

News and Analysis of the Global Innovation Scene

News and Analysis of the Global Innovation Scene