Camera Angles Research Articles

Multi-modal Metaphorical Analysis of Children’s Picturebook <i>Little Stone Lion</i>

Little Stone Lion stands out as a member of Xiong Liang's "Chinese Illustrated Book" series. This work tells the story from the perspective of the stone lion at the bridgehead of the town, recounting its observations, experiences, and feelings. This book integrates traditional Chinese ink painting techniques with cultural elements, presenting a Chinese-style visual creation. Drawing upon a qualitative research approach, the study employed a comprehensive analysis of Little Stone lion, focusing on identifying and interpreting the verbal, pictorial, and multimodal metaphors employed in the illustrations and their relation to the accompanying text. The study delved into the interplay between visual and textual elements, examining how they contribute to the creation of metaphoric meaning and literary depth in the picturebook. This research sought to uncover metaphorical meanings derived from the situational and conceptual-cognitive contexts as well as the underlying thematic and emotional layers embedded within the picturebook. As the only guardian deity of the town, the little stone lion has witnessed the growth and departures of countless generations, silently guarding this land and its people. Multimodal metaphors identified from camera angles, colors, and its plain monologue are enough to touch the heartstrings and evoke a deep sense of nostalgia for one's hometown and loved ones.

Stereo-PIV measurements within the canopy in open-channel flows

Measuring the flow field not just above but also inside the canopy of rough beds in open-channel flows is challenging when optical access from below or the side is not possible. To this end, a new stereo-PIV arrangement was implemented and tested. To optically access the area in between the roughness elements, a top-viewing stereo-PIV system was installed at a steep viewing angle of 70∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}. Furthermore, a glass plate was installed at the water surface to avoid random image distortions from surface waves. Finally, in-house-produced low-cost fluorescent particles were used to filter out laser reflections on the canopy’s surface. To validate the stereo-PIV measurements with respect to the steep viewing angle of the stereo cameras, the instantaneous flow field above a bed of spheres was measured simultaneously with the stereo-PIV system and with a third side-looking camera for standard 2D-PIV processing. Two different water depths and two different Reynolds numbers were investigated under uniform flow conditions. Finally, to assess the influence of the glass plate on the flow, 2D-PIV measurements were performed with and without the glass plate. It is shown that the steep viewing stereo PIV can properly reconstruct the instantaneous, mean and turbulent statistics except for the vertical normal turbulent stresses, which are overestimated due to peak-locking errors. Also, the boundary layer developing below the glass plate induces a slight acceleration in the bulk flow, which can be considered negligible for higher water depths. In all instances, the flow acceleration does not affect the near-bed region.Graphical abstract

Enhancing Reliability of Advanced Driver-Assistance Systems through Predictive Maintenance and Data-Driven Insights

The advancement of Advanced Driver Assistance Systems (ADAS) marks a pivotal evolution in automotive technology, aiming to enhance road safety and driving efficiency through a wide array of functionalities like blind spot detection, emergency braking, and adaptive cruise control. This research paper delves into the operational integrity, performance metrics, and maintenance strategies of ADAS components, underpinned by a comprehensive methodology involving data collection, pre-processing, feature engineering, machine learning model development, and rigorous validation processes. Systematic inspection of ADAS components indicates their importance in vehicle safety and reliability. The visibility, distance, speed, and steering angle of front cameras, LiDAR, radar, and ultrasonic sensors are carefully evaluated. Maintenance logs show proactive error code management, boosting efficiency. SVM, Gradient Boosting, and Random Forest machine learning models predicted ADAS component failures during validation and testing. Random Forest scored 90% accuracy, 92% precision, 88% recall, and 90% F1. Gradient Boosting was the most accurate, with 93% accuracy, 94% precision, 91% recall, and 92% F1. SVM predicted ADAS component failures with 88% accuracy, 90% precision, 85% recall, and 87% F1 score. Machine learning helps shift from reactive to proactive maintenance. Modelling sensor signal quality, actuator reaction times, error code frequencies, and maintenance intervals enables predictive maintenance and failure detection. Feature engineering builds predictive models using maintenance logs and operational KPIs. The models predict ADAS component failures, boosting vehicle safety and dependability. Using external data improves predictive maintenance models. The maintenance model's adaptability and forecast accuracy are proved by ADAS operation after traffic, accident, and manufacturer upgrades. Predictive maintenance and machine learning improve ADAS dependability and safety, the study found. Advanced analytics and data-driven insights can reduce automotive system failures, improving safety and reliability.

Evaluation and analysis of feature point detection methods based on vSLAM systems

Feature point detection is identified as a significant issue in the field of computer vision. Quantitative conclusions have been established regarding the performance evaluation of feature point detection using manually annotated datasets. However, these datasets, obtained through affine transformations with preset parameters on images, exhibit limitations in terms of variety, quantity, and challenges, differing from real-world application scenarios. In actual scenes, Vision Simultaneous Localization and Mapping (vSLAM) systems are extensively applied, and the precision of their localization and mapping is directly linked to the performance of feature points. To profoundly understand the performance of feature point detection in practical applications, vSLAM systems are chosen for evaluation in this study. More diverse and challenging datasets are utilized, encompassing real datasets covering variations in lighting, rotation, occlusion, and camera angle changes, as well as synthetic datasets reflecting complex conditions such as time, season, motion patterns, and environmental textures. Based on the evaluation results, the applicability of various feature point detection methods in different environments is thoroughly discussed, and the underlying principles are analyzed (Table 13). The conclusions drawn from this research provide references for the development of new feature point detection methods, the selection of such methods in vSLAM systems, and other related studies in the field of computer vision.

Notion of Beauty in the Selected Pakistani Advertisements: A Multimodal Critical Discourse Analysis

Beauty advertisement is one of the most potent and successful tools for significantly influencing consumer purchasing behavior as it modifies individuals’ views of beauty, social conventions, and gender roles. From a Multimodal Critical Discourse Analysis standpoint, this study looks at the beauty advertisements in a few chosen social media (Instagram) posts. The study’s goal is to concentrate on how the idea of beauty is used in these Pakistani advertisements to control and influence consumers through both language as well as images. For this particular reason, four ads from different Instagram pages have been chosen via purposive sampling and they have undergone both linguistic and visual analysis. Fairclough’s Three-Dimensional Model (1995) serves as the foundation for the linguistic examination while Kress and van Leeuwen’s Grammar of Visual Design (2006) is applied for the visual analysis of these ads. The nature of this study is qualitative. The findings reveal that advertisers have employed a variety of discursive techniques like surreal representation, scientific evidence, celebrity endorsement, & self-representation etc., and linguistic devices, such as pronouns, catchy phrases, adjectives, repetitions, etc. in addition to visual tactics, e.g., modality, gaze, social distance, salience, and camera angles etc. to influence women by portraying an idealized version of beauty in the chosen advertisements. Additionally, it shows how advertisers—who are essentially powerful people with vested interests—marginalize and restrict the status of women in society to propagate the ideology of beauty just for boosting the sales of their products. Thus, the companies and creators of these commercials employ images and language as a means of controlling Pakistan’s female consumer base by luring them to get on their items. It is anticipated that this study will increase awareness of the usefulness of multimodal critical discourse analysis and open the door for future researchers to investigate the language and pictures used in advertisements for a variety of other products.

DVANet: Disentangling View and Action Features for Multi-View Action Recognition

In this work, we present a novel approach to multi-view action recognition where we guide learned action representations to be separated from view-relevant information in a video. When trying to classify action instances captured from multiple viewpoints, there is a higher degree of difficulty due to the difference in background, occlusion, and visibility of the captured action from different camera angles. To tackle the various problems introduced in multi-view action recognition, we propose a novel configuration of learnable transformer decoder queries, in conjunction with two supervised contrastive losses, to enforce the learning of action features that are robust to shifts in viewpoints. Our disentangled feature learning occurs in two stages: the transformer decoder uses separate queries to separately learn action and view information, which are then further disentangled using our two contrastive losses. We show that our model and method of training significantly outperforms all other uni-modal models on four multi-view action recognition datasets: NTU RGB+D, NTU RGB+D 120, PKU-MMD, and N-UCLA. Compared to previous RGB works, we see maximal improvements of 1.5%, 4.8%, 2.2%, and 4.8% on each dataset, respectively. Our code can be found here: https://github.com/NyleSiddiqui/MultiView_Actions

O^2-Recon: Completing 3D Reconstruction of Occluded Objects in the Scene with a Pre-trained 2D Diffusion Model

Occlusion is a common issue in 3D reconstruction from RGB-D videos, often blocking the complete reconstruction of objects and presenting an ongoing problem. In this paper, we propose a novel framework, empowered by a 2D diffusion-based in-painting model, to reconstruct complete surfaces for the hidden parts of objects. Specifically, we utilize a pre-trained diffusion model to fill in the hidden areas of 2D images. Then we use these in-painted images to optimize a neural implicit surface representation for each instance for 3D reconstruction. Since creating the in-painting masks needed for this process is tricky, we adopt a human-in-the-loop strategy that involves very little human engagement to generate high-quality masks. Moreover, some parts of objects can be totally hidden because the videos are usually shot from limited perspectives. To ensure recovering these invisible areas, we develop a cascaded network architecture for predicting signed distance field, making use of different frequency bands of positional encoding and maintaining overall smoothness. Besides the commonly used rendering loss, Eikonal loss, and silhouette loss, we adopt a CLIP-based semantic consistency loss to guide the surface from unseen camera angles. Experiments on ScanNet scenes show that our proposed framework achieves state-of-the-art accuracy and completeness in object-level reconstruction from scene-level RGB-D videos. Code: https://github.com/THU-LYJ-Lab/O2-Recon.

Human Activity Detection Events Through Human Eye Reflection using Bystander Analyzer

ABSTRACT Automatic video-based human activity recognition has shown promise and is mostly used in video surveillance applications for diverse purposes. However, there are still substantial performance issues that make real-world implementation challenging. The main barrier to the accurate detection of human movement remains to the perspective problem, which results from the fact that video sequences are frequently shot from random camera angles. Therefore, the main focus of this study is on how human eyesight reflects in different camera views that are used to detect human activity or identify intruders. First, a gaussian mixture model that previously used the Minimal Spanning Tree for segmentation is used for pre-processing. Using the pixel-based Kruskal methodology and this method, the input data set’s minimal weight is precisely determined. Segmentation comes next. Independent discriminant features are extracted using transformation using Karhunen-Loeve expansion, which isolates human behavior based on the Person Correlation Coefficient. To effectively identify data, Deep Lens Classifier is also employed to look for any suspect human behavior. With an impressive 78.8774% accuracy, 28.6961% sensitivity, 98.50% specificity, 75.6734% precision, 48.781% recall, and 65.10% F-measure, this approach is unique in the field of detecting human activity. Finally, our proposed system’s MATLAB performance retrieves the accurate detection.

Design and Development of Model to Detect Garbage from CCTV

Urban waste management is a pressing concern globally, necessitating innovative solutions for effective sanitation and sustainability. In response, this project introduces a Garbage Detection System leveraging deep learning techniques and web-based monitoring for real-time surveillance of city-wide Garbage accumulation. Utilizing the YOLOv7 object detection model, the system achieves an accuracy rate of 81% in identifying Garbage objects across diverse environmental conditions and camera angles. The accompanying web application dashboard provides intuitive access to live camera feeds, empowering stakeholders with proactive monitoring capabilities and facilitating prompt response to sanitation issues. Integration with MongoDB ensures efficient data storage and retrieval, enabling seamless access to historical Garbage detection data for analysis and optimization. The iterative refinement process, guided by periodic model updates and user feedback loops, contributes to continuous improvement in system performance. Through its comprehensive approach, this project demonstrates the feasibility and effectiveness of utilizing advanced computer vision technology for enhancing urban cleanliness and sustainability. Future endeavours will focus on further enhancing accuracy, scalability, and integration with existing waste management infrastructure to realize holistic urban waste management solutions. Key Words: YOLOv7, neural network, object detection, deep learning, web-based monitoring

Vision-based seam tracking for GMAW fillet welding based on keypoint detection deep learning model

Pre-programmed welding robots significantly improved the efficiency and quality of the welds in large batch production. In small and medium batch production, the robots need appropriate sensors to perform well and adapt to the changes and uncertainties in a noisy welding environment. Vision-based sensors enabled by machine learning are making it possible to sense in process previously not measurable. One challenge is developing artificial intelligent models capable of real-time seam tracking, particularly in fillet joints where visual analysis is hindered by non-perpendicular camera angles and arc reflections. In this paper, we propose a vision system that enables automated seam tracking with a collaborative robot. The vision-based deep learning classification model detects the tacks, where the seam is not visible. It is based on a keypoint detection deep learning model that addresses the challenges in distorted and noisy images of fillet joints between the pipes and flanges during the real-time Gas Metal Arc Welding to track the location of the seam in non tack images. The system is optimized for real time seam tracking by proposing the appropriate input image size. Multiple images and multiple points are also considered to provide a controllable signal of the location of the seam with less errors and outliers. Our proposed model can track the seam with more than 80 percent accuracy for errors less than 0.3 mm in fillet joints. The high accuracy of the proposed method would result in fewer flaws and defects and reduced rework, resulting in significant cost saving in manufacturing. The real-time monitoring also enables the adaptability to slight variations in gap.

Assessing radiometric calibration methods for multispectral UAV imagery and the influence of illumination, flight altitude and flight time on reflectance, vegetation index and inversion of winter wheat AGB and LAI

Radiometric correction is essential to unmanned aerial vehicle (UAV) precision agriculture applications, especially for crop multi-period analysis and quantitative inversion of phenotypic and physiological parameters. In this study, we investigated the performance of four radiometric correction methods: “Camera only” (A), “Camera and sun irradiance” (B) and “Camera, sun irradiance and sun angle” (C) methods provided by Pix4d Mapper and an irradiance sensor-based method (D) implemented with the R software. UAV campaigns were conducted at flight altitudes of 30 m, 60 m and 90 m under clear and cloudy skies at different times over an experimental field of winter wheat. The influences of illumination, flight altitude and flight time on reflectance and vegetation indexes derived from different radiometric calibration methods were analyzed. Moreover, we further assessed the impact of reflectance and vegetation index variations on the above-ground biomass (AGB) and leaf area index (LAI) estimations of winter wheat. Results showed that method D is a promising approach for radiometric calibration, which produced consistent reflectance with method A at noon (e.g., 10:44:41 AM March 17 and 11:39:37 AM April 7) and mid-afternoon (e.g., 15:30:02 PM May 1) and more reliable reflectance in the morning (e.g., 7:15:44 AM March 17). Methods B and C seriously underestimated the reflectance at 10:44:41 AM March 17 and 11:39:37 AM April 7. This was caused by the incorrect attitude angles of the irradiance sensor and further miscalculation of the solar irradiance by Pix4d Mapper. Interestingly, these errors were eliminated in the calculation of the ratio type of vegetation indexes. Methods B and C yielded vegetation indexes that were almost the same as those of methods A and D. Under cloudy sky, when direct sunlight is not blocked by clouds, method D was effective; with cloud occlusion, results on April 24 indicated that the method failed to estimate the total sunlight and direct sunlight ratio. Reflectance and vegetation indexes obtained from different radiometric correction methods were all obviously affected by flight altitude. Furthermore, the variations of NDVI with flight altitude increasing were different with reflectance. Changes in reflectance and vegetation indexes with flight altitude had little effect on the correlation coefficients yielded with AGB, but showed a significant influence on those of LAI. Through analyzing the variation of observed solar radiation at different time intervals, caution is recommended when using the assumption that solar radiation is stable during the UAV flight. The longer the flight, the greater the variation in solar radiation, especially at sunrise and sunset. Even at noon, when the flight duration is 30 min, the change in solar radiation may exceed 10 % (e.g., 16.5 % on March 20).

Perception and Action Augmentation for Teleoperation Assistance in Freeform Telemanipulation

Teleoperation enables controlling complex robot systems remotely, providing the ability to impart human expertise from a distance. However, these interfaces can be complicated to use as it is difficult to contextualize information about robot motion in the workspace from the limited camera feedback. Thus, it is required to study the best manner in which assistance can be provided to the operator that reduces interface complexity and effort required for teleoperation. Some techniques that provide assistance to the operator while freeform teleoperating include: (1) perception augmentation, like augmented reality visual cues and additional camera angles, increasing the information available to the operator; (2) action augmentation, like assistive autonomy and control augmentation, optimized to reduce the effort required by the operator while teleoperating. In this article, we investigate: (1) which aspects of dexterous telemanipulation require assistance; (2) the impact of perception and action augmentation in improving teleoperation performance; and (3) what factors impact the usage of assistance and how to tailor these interfaces based on the operators’ needs and characteristics. The findings from this user study and resulting post-study surveys will help identify task-based and user-preferred perception and augmentation features for teleoperation assistance.

Multimodality In Stop Motion Animation Films

Stop motion animation films use a complicated process of giving life to inanimate objects through visuals and cinematic experience that are created by using puppets made of clay, resin, silicon, wool etc. In the world of digital communication and technology, there has been rise of using multiple modes in order to give deeper meaning to the visuals of a film. Today, apart from language, stop animation film uses various semiotic modes of sound, music, visual metaphors, set design, camera angles, color theory, gestural, spatial and haptic illusion that helps in disseminating ideas, and concepts that are difficult to communicate through a film. The researcher has conducted a content analysis of three Oscar Nominated stop motion animation films including Negative Space, Sister and Lost and Found to identify the various semiotic modes used in the narrative of the film and a quantitative study was conducted to investigate the perception of the young adults including college students of undergraduate and post graduates and professionals between age group of (18-24) living in Mumbai regarding these modes.

Early Fire Detection System by Using Automatic Synthetic Dataset Generation Model Based on Digital Twins

Fire is amorphous and occurs differently depending on the space, environment, and material of the fire. In particular, the early detection of fires is a very important task in preventing large-scale accidents; however, there are currently almost no learnable early fire datasets for machine learning. This paper proposes an early fire detection system optimized for certain spaces using a digital-twin-based automatic fire learning data generation model for each space. The proposed method first automatically generates realistic particle-simulation-based synthetic fire data on an RGB-D image matched to the view angle of a monitoring camera to build a digital twin environment of the real space. In other words, our method generates synthetic fire data according to various fire situations in each specific space and then performs transfer learning using a state-of-the-art detection model with these datasets and distributes them to AIoT devices in the real space. Synthetic fire data generation optimized for a space can increase the accuracy and reduce the false detection rate of existing fire detection models that are not adaptive to space.

A Study on the Effects of Photogrammetry by the Camera Angle of View Using Computer Simulation

During the decommissioning activities, a movie was shot inside the reactor building during the investigation of the primary containment vessel by applying photogrammetry, which is one of the methods for three-dimensional (3D) reconstruction from images, to the images from this movie, it is feasible to perform 3D reconstruction of the environment around the primary containment vessel. However, the images from this movie may not be suitable for 3D reconstruction because they were shot remotely by robots owing to limited illumination, high-dose environments, etc. Moreover, photogrammetry has the disadvantage of easily changing 3D reconstruction results by simply changing the shooting conditions. Therefore, this study investigated the accuracy of the 3D reconstruction results obtained by photogrammetry with changes in the camera angle of view under shooting conditions. In particular, we adopted 3D computer graphics software to simulate shooting target objects for 3D reconstruction in a dark environment while illuminating them with light for application in decommissioning activities. The experimental results obtained by applying artificial images generated by simulation to the photogrammetry method showed that more accurate 3D reconstruction results can be obtained when the camera angle of view is neither too wide nor too narrow when the target objects are shot and surrounded. However, the results showed that the accuracy of the obtained results is low during linear trajectory shooting when the camera angle of view is wide.

Football referee gesture recognition algorithm based on YOLOv8s.

Gesture serves as a crucial means of communication between individuals and between humans and machines. In football matches, referees communicate judgment information through gestures. Due to the diversity and complexity of referees' gestures and interference factors, such as the players, spectators, and camera angles, automated football referee gesture recognition (FRGR) has become a challenging task. The existing methods based on visual sensors often cannot provide a satisfactory performance. To tackle FRGR problems, we develop a deep learning model based on YOLOv8s. Three improving and optimizing strategies are integrated to solve these problems. First, a Global Attention Mechanism (GAM) is employed to direct the model's attention to the hand gestures and minimize the background interference. Second, a P2 detection head structure is integrated into the YOLOv8s model to enhance the accuracy of detecting smaller objects at a distance. Third, a new loss function based on the Minimum Point Distance Intersection over Union (MPDIoU) is used to effectively utilize anchor boxes with the same shape, but different sizes. Finally, experiments are executed on a dataset of six hand gestures among 1,200 images. The proposed method was compared with seven different existing models and 10 different optimization models. The proposed method achieves a precision rate of 89.3%, a recall rate of 88.9%, a mAP@0.5 rate of 89.9%, and a mAP@0.5:0.95 rate of 77.3%. These rates are approximately 1.4%, 2.0%, 1.1%, and 5.4% better than those of the newest YOLOv8s, respectively. The proposed method has right prospect in automated gesture recognition for football matches.

Enhancing Historical Narrative through Application of Staging Techniques in 3D Animation “How Islam Spread Around the World”

This research addresses the challenge of effectively conveying the historical narrative of Islam's spread through 3D animation. The primary objective is to explore the potential of staging techniques in enhancing message delivery and audience engagement. Staging, a method that emphasizes scene element arrangement, character placement, and camera perspective, is pivotal for a clear and impactful narrative. Through this technique, elements are organized to guide viewers' attention and emphasize the intended message. This study demonstrates that careful alignment of characters, backgrounds, and camera angles, combined with visual symbolism representing Islamic values, can significantly enhance the narrative's depth and viewer comprehension. Experiments reveal that strategic staging not only strengthens the storyline but also boosts audience understanding. The research underscores the importance of staging in 3D animation, especially for intricate narratives like the spread of Islam. It offers insights into the advantages of staging over traditional methods, emphasizing its role in narrative comprehension, deeper meaning conveyance, and audience engagement.

Penerapan Dutch Angle Dalam Enggambarkan Emosional Pada Film “Tolong Aku”

Film, as a genre in the world of cinema, offers a short but captivating audiovisual experience. Films have the potential to create creative, entertaining works and have high artistic value. In the filmmaking process, various techniques and elements are used to improve the quality of the final result. Films are able to convey messages and emotions through various means, including dialogue, visuals, music and special effects. This research focuses on the important role of shooting techniques or cameras in film making. One of the techniques taught is Dutch Angle or Dutch Tilt, which involves tilting the camera angle on a vertical or horizontal axis. This technique is often used in the thriller and horror genres to create a dramatic effect on the audience. The film "Help Me" is an interesting case study, utilizing the Dutch Angle to produce emotions such as sadness, fear, anger, and surprise in the audience. The story of this film tells the story of a student named Dinda who is trapped in an organization that adheres to devil beliefs. Through the use of Dutch Angle, the audience is presented with an unstable atmosphere, creating tension and anxiety that blends with the main character's experiences. The aim of making the film "Help Me" is to apply the Dutch Angle technique to arouse the audience's emotions. In this way, the author hopes to strengthen the message and emotional nuances conveyed through this film. This research also aims to provide an overview of how shooting techniques in films can influence the audience's perception and emotions towards the work.

On the “mise-en-scène” of Pinoocchio 1940 and 2022 films' versions

This study examines the film Pinocchio (2022) by Robert Zemeckis in comparison to the 1940 version of old animation on the basis of mise-en-scène. The analysis was done using the descriptive analysis method with the film approach and the films are analyzed on both the narrative and cinematographic aspect. The result showed that two films show a lot of similarities since they were both made on the same story. The analysis of the narrative aspect showed that they have different opening and conclusion in plot, have extra more characters in the new film, as well as show different social setting of the film. The cinematic examination proved that the new film put more details on the props of cuckoo clocks made by Geppetto and the lighting analysis showed that the 1940 version used constant high-key lighting while the new version used both low-key and high-key lighting. The 2022 version also used various camera angles and movements, while the 1940 version used constant standard camera angles throughout the film. The overall analysis showed that while the old version is made simply for young audiences, the new film targets audiences of not only young children, but also young adults.

Penerapan Teknik Objektive Camera Angle Dalam Membangkitkan Dramatis Film Pulang

The film "Pulang" is a short drama genre film with a family style, This film was made as an entertainment medium that can provide information to audience about a family where Karin's mother has a an illness that made him forget his memory even of his own child, however The child is still trying to take care of and make the mother's memory return to normal. The final result that will be produced is a work of fiction film entitled "Pulang" the process of taking pictures uses the application of objective techniques camera angle by using the audience's point of view so that the audience can feel the same thing experienced by Karin as the main character in this film