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Introduction: Motion blur, primarily caused by rapid camera movements, significantly challenges the robustness of feature point tracking in visual odometry (VO). Methods: This paper introduces a robust and efficient approach for motion blur detection and recovery in blur-prone environments (e.g., with rapid movements and uneven terrains). Notably, the Inertial Measurement Unit (IMU) is utilized for motion blur detection, followed by a blur selection and restoration strategy within the motion frame sequence. It marks a substantial improvement over traditional visual methods (typically slow and less effective, falling short in meeting VO's realtime performance demands). To address the scarcity of datasets catering to the image blurring challenge in VO, we also present the BlurVO dataset. This publicly available dataset is richly annotated and encompasses diverse blurred scenes, providing an ideal environment for motion blur evaluation. [ABSTRACT FROM AUTHOR]

Robots in tomato greenhouses need to perceive the plant and plant parts accurately to automate monitoring, harvesting, and de-leafing tasks. Existing perception systems struggle with the high levels of occlusion in plants and often result in poor perception accuracy. One reason for this is because they use fixed cameras or predefined camera movements. Next-best-view (NBV) planning presents an alternate approach, in which the camera viewpoints are reasoned and strategically planned such that the perception accuracy is improved. However, existing NBV-planning algorithms are agnostic to the task-at-hand and give equal importance to all the plant parts. This strategy is inefficient for greenhouse tasks that require targeted perception of specific plant parts, such as the perception of leaf nodes for de-leafing. To improve targeted perception in complex greenhouse environments, NBV planning algorithms need an attention mechanism to focus on the task-relevant plant parts. In this paper, the role of attention in improving targeted perception using an attention-driven NBV planning strategy was investigated. Through simulation experiments using plants with high levels of occlusion and structural complexity, it was shown that focusing attention on task-relevant plant parts can significantly improve the speed and accuracy of 3D reconstruction. Further, with real-world experiments, it was shown that these benefits extend to complex greenhouse conditions with natural variation and occlusion, natural illumination, sensor noise, and uncertainty in camera poses. The results clearly indicate that using attention-driven NBV planning in greenhouses can significantly improve the efficiency of perception and enhance the performance of robotic systems in greenhouse crop production. • Method to plan the next-best viewpoint to reconstruct the nodes of tomato plants. • Adding attention mechanism improves the efficiency of next-best-view planning. • Performed systematic evaluations with both simulation and real-world tomato plants. • Effectively handles occlusion in complex greenhouse environments. [ABSTRACT FROM AUTHOR]

In the history of film studies, few developments have proven to be as fascinating as film noir, while few aesthetic devices have proven to be as perplexing as camera movement. In this article, I articulate the terms of a unique noir aesthetic predicated on the moving camera in the films of the Béla Tarr. A renowned exponent of what is known as slow cinema, Tarr's work has captivated scholars interested in camera movement and long takes. However, for as much attention as Tarr has received for the style of his films, their substance has received scant attention. To redress this, I elaborate on Tarr's unique style, and in particular his inspired use of camera movement, by considering it in conjunction with his equally unique noir sensibility, and in particular his thematic interest in classic noir themes such as fate and free will. In films such as Damnation (1988) and The Man from London (2007), Tarr provides profound meditations on enduring noir themes, and by virtue of detailed analyses of these exemplary slow noir films, I demonstrate the uniqueness of Tarr's films while also contributing to greater understandings of the power of the moving camera and the vicissitudes of film noir. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed moving camera method allows for 2D kinematical swimming analysis with no field of view limitation and with better accuracy and precision than those using a single static camera. Despite its limitations, 2D kinematical analysis remains a simple and valid alternative for swimming analysis. This analysis is limited by the length of the field of view, and the reconstruction errors are proportional to the calibrated area. A possible solution for these problems is the usage of moving cameras that allow for tracking an object across a larger area without the concerns of the calibration area. The purpose of this study was to verify the viability of the utilization of moving cameras for underwater 2D kinematical analysis. Two calibration processes were evaluated: (i) obtaining the extrinsic parameters for every frame based on pool markers (M1) and; (ii) constraining the degrees of freedom of the camera's movements and tracking translation based on the principal point (M2). M1 obtained better accuracy in reconstructing the coordinates of static markers (mean error: 12.1 against 14.2 mm from a static camera) and in the estimation of the rod length (−2.6 vs. 12.6 mm). M2 obtained better accuracy when evaluating the distance between the points (−0.3 mm) than that for position estimation (58.6 mm). The results indicate that moving cameras are a viable alternative for 2D underwater kinematic analysis, but M2 had limitations about real position extraction. [ABSTRACT FROM AUTHOR]

In recent years, infrared images have attracted widespread attention, due to their extensive application in low-visibility search and rescue, forest fire monitoring, ground target monitoring, and other fields. Infrared small target detection technology plays a vital role in these applications. Although there has been significant research over the years, accurately detecting infrared small targets in complex backgrounds remains a significant challenge. Multi-frame detection methods can significantly improve detection performance in these cases. However, current multi-frame methods face difficulties in balancing the number of input frames and detection speed, and cannot effectively handle the background motion caused by movement of the infrared camera. To address these issues, we propose an adaptive frame sampling method and a detection network aligned at the feature level. Our adaptive frame sampling method uses mutual information to measure motion changes between adjacent frames, construct a motion distribution, and sample frames with uniform motion based on the averaged motion distribution. Our detection network handles background motion by predicting a homography flow matrix that aligns features at the feature level. Extensive evaluation of all components showed that the proposed method can more effectively perform multi-frame infrared small target detection. [ABSTRACT FROM AUTHOR]

Josephine Botting's book on Adrian Brunel provides a comprehensive exploration of the British filmmaker's career, drawing extensively from his paper archive held by the BFI. The book examines Brunel's reputation as a highbrow filmmaker and his relationships with collaborators, contrasting them with Hitchcock's ability to build a personal brand. While the book focuses on Brunel's work during the 1920s, it acknowledges his later films, many of which are lost. The book also analyzes key films in Brunel's career, offering insights into their production, critical reception, and box office performance. Overall, the book serves as an engaging introduction to the work of an overlooked British filmmaker during a transformative period in the industry. [Extracted from the article]

In the realm of video salient object detection (VSOD), the majority of research has traditionally been centered on third‐person perspective videos. However, this focus overlooks the unique requirements of certain first‐person tasks, such as autonomous driving or robot vision. To bridge this gap, a novel dataset and a camera‐based VSOD model, CaMSD, specifically designed for egocentric videos, is introduced. First, the SalEgo dataset, comprising 17,400 fully annotated frames for video salient object detection, is presented. Second, a computational model that incorporates a camera movement module is proposed, designed to emulate the patterns observed when humans view videos. Additionally, to achieve precise segmentation of a single salient object during switches between salient objects, as opposed to simultaneously segmenting two objects, a saliency enhancement module based on the Squeeze and Excitation Block is incorporated. Experimental results show that the approach outperforms other state‐of‐the‐art methods in egocentric video salient object detection tasks. Dataset and codes can be found at https://github.com/hzhang1999/SalEgo. [ABSTRACT FROM AUTHOR]

The increasing demand for laparoscopic surgery due to its cosmetic benefits and rapid post-surgery recovery is juxtaposed with a shortage of surgical support staff. This juxtaposition highlights the necessity for improved camera management in laparoscopic procedures, encompassing positioning, zooming, and focusing. Our feasibility study introduces the information and communications technology (ICT) laparoscopy system designed to aid solo laparoscopic surgery. This system tracks a surgeon's body motion using a controller, manipulating an embedded camera to focus on specific surgical areas. It comprises a camera module, a camera movement controller, and a motor within the main body, operating connected wires according to controller commands for camera movement. Surgeon movements are detected by an inertial measurement unit (IMU) sensor, facilitating precise camera control. Additional features include a foot pedal switch for motion tracking, a dedicated trocar for main body stability, and a display module. The system's effectiveness was evaluated using an abdomen phantom model and animal experimentation with a porcine model. The camera responded to human movement within 100 ms, a delay that does not significantly affect procedural performance. The ICT laparoscopy system with advanced motion-tracking technology is a promising tool for solo laparoscopic surgery, potentially improving surgical outcomes and overcoming staff shortages. [ABSTRACT FROM AUTHOR]

Multi-Camera Multi-Vehicle Tracking (MCMVT) is a critical task in Intelligent Transportation Systems (ITS). Differently to in urban environments, challenges in highway tunnel MCMVT arise from the changing target scales as vehicles traverse the narrow tunnels, intense light exposure within the tunnels, high similarity in vehicle appearances, and overlapping camera fields of view, making highway MCMVT more challenging. This paper presents an MCMVT system tailored for highway tunnel roads incorporating road topology structures and the overlapping camera fields of view. The system integrates a Cascade Multi-Level Multi-Target Tracking strategy (CMLM), a trajectory refinement method (HTCF) based on road topology structures, and a spatio-temporal constraint module (HSTC) considering highway entry–exit flow in overlapping fields of view. The CMLM strategy exploits phased vehicle movements within the camera's fields of view, addressing such challenges as those presented by fast-moving vehicles and appearance variations in long tunnels. The HTCF method filters static traffic signs in the tunnel, compensating for detector imperfections and mitigating the strong lighting effects caused by the tunnel lighting. The HSTC module incorporates spatio-temporal constraints designed for accurate inter-camera trajectory matching within overlapping fields of view. Experiments on the proposed Highway Surveillance Traffic (HST) dataset and CityFlow dataset validate the system's effectiveness and robustness, achieving an IDF1 score of 81.20% for the HST dataset. [ABSTRACT FROM AUTHOR]

Interpolation between camera positions is a standard problem in computer graphics and can be considered the foundation of camera path planning. As the basis for a new interpolation method, we introduce a new Riemannian metric in camera space, which measures the 3D image flow under a small movement of the camera. Building on this, we define a linear interpolation between two cameras as shortest geodesic in camera space, for which we provide a closed‐form solution after a mild simplification of the metric. Furthermore, we propose a geodesic Catmull‐Rom interpolant for keyframe camera animation. We compare our approach with several standard camera interpolation methods and obtain consistently better camera paths especially for cameras with extremely varying scales. [ABSTRACT FROM AUTHOR]

The real-time simulation technology of large-scale open sea surfaces has been of great importance in fields such as computer graphics, ocean engineering, and national security. However, existing technologies typically have performance requirements or platform limitations, and the two are often impossible to balance. Based on the camera-view-based screen space level of detail strategy and virtual camera pose adaptive filtering strategy proposed in this article, we have developed a fast and comprehensive solution for rendering large-scale open sea surfaces. This solution is designed to work without the need for special hardware extensions, making it easy to deploy across various platforms. Additionally, it enhances the degrees of freedom of virtual camera movement. After conducting performance tests under various camera poses, our filtering strategy was found to be effective. Notably, the time cost of simulation using 60 waves at the height of 6 m above sea level was only 0.184 ms. In addition, we conducted comparative experiments with four state-of-the-art algorithms currently in use, and our solution outperformed the others with the best performance and suboptimal visual effects. These results demonstrate the superiority of our approach in terms of both efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Copyright of Polish Otorhinolaryngological Review / Polski Przegląd Otorynolaryngologiczny (Index Copernicus) is the property of Index Copernicus International and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Imagofagia is the property of Imagofagia - AsAECA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The emergence of novel AI technologies and increasingly portable wearable devices have introduced a wider range of more liberated avenues for communication and interaction between human and virtual environments. In this context, the expression of distinct emotions and movements by users may convey a variety of meanings. Consequently, an emerging challenge is how to automatically enhance the visual representation of such interactions. Here, a novel Generative Adversarial Network (GAN) based model, AACOGAN, is introduced to tackle this challenge effectively. AACOGAN model establishes a relationship between player interactions, object locations, and camera movements, subsequently generating camera shots that augment player immersion. Experimental results demonstrate that AACOGAN enhances the correlation between player interactions and camera trajectories by an average of 73%, and improves multi‐focus scene quality up to 32.9%. Consequently, AACOGAN is established as an efficient and economical solution for generating camera shots appropriate for a wide range of interactive motions. Exemplary video footage can be found at https://youtu.be/Syrwbnpzgx8. [ABSTRACT FROM AUTHOR]

In the sports field auxiliary recognition system, the light motion capture device monitors the activity on the sports field in real time by sensing the body position and movement track of the exercisers. However, the current light motion capture device has some problems, such as not high precision, large delay, and so on, which need to be further improved. The aim of this research is to develop a light motion capture device based on Android intelligent platform, and apply it to the sports field auxiliary recognition system. It is hoped that the device can realize accurate recognition and real-time position tracking of sports players, and improve the performance and reliability of the auxiliary recognition system of sports field. In this paper, an optical motion capture device is designed and developed based on the Android intelligent platform. The device uses the camera to sense the movement of the mover, and extracts the key point information of the mover through image processing and algorithm analysis, so as to realize the accurate recognition and tracking of its position and movement trajectory. The experimental results show that the light motion capture device developed in this study has achieved good results in the field identification system. The device has high accuracy and low delay, and can accurately identify and track the body position and movement trajectory of the movement. [ABSTRACT FROM AUTHOR]

Controlling a laparoscopic camera during robotic surgery represents a multifaceted challenge, demanding considerable physical and cognitive exertion from operators. While manual control presents the advantage of enabling optimal viewing angles, it is offset by its taxing nature. In contrast, current autonomous camera systems offer predictability in tool tracking but are often rigid, lacking the adaptability of human operators. This research investigates the potential of two distinct network architectures: a dense neural network (DNN) and a recurrent network (RNN), both trained using a diverse dataset comprising autonomous and human-driven camera movements. A comparative assessment of network-controlled, autonomous, and human-operated camera systems is conducted to gauge network efficacies. While the dense neural network exhibits proficiency in basic tool tracking, it grapples with inherent architectural limitations that hinder its ability to master the camera's zoom functionality. In stark contrast, the recurrent network excels, demonstrating a capacity to sufficiently replicate the behaviors exhibited by a mixture of both autonomous and human-operated methods. In total, 96.8% of the dense network predictions had up to a one-centimeter error when compared to the test datasets, while the recurrent network achieved a 100% sub-millimeter testing error. This paper trains and evaluates neural networks on autonomous and human behavior data for camera control. [ABSTRACT FROM AUTHOR]

Dynamic photogrammetry is an established method for acquiring 3D information of deforming objects or dynamic scenes in various close-range applications. A crucial impact has occlusions caused by object deformations, obstacles or camera movements. Temporal occlusions are highly application-specific and sometimes difficult to predict, resulting in a significant reduction of reconstruction quality or the aborting of image sequence processing. Previous approaches usually model such occlusions as semantic information and consider them using image masks. However, generating these image masks requires complex methods and extensive training data. Due to the unpredictability of the complexity and movements of dynamic scenes, generating training data is challenging in many applications. Therefore, this paper proposes an alternative modelling approach, which can be part of a spatio-temporal matching process. Based on the characteristic high redundancy, occlusions can be detected using robust estimation methods and considered in the optimisation. Therefore, no information about the occlusions and further processing steps are necessary. We evaluate our approach with synthetic and real data of an industrial application regarding the accuracy and ability to detect occlusion simultaneously. The evaluation of the proposed approach shows that the impact of occlusion can be eliminated, and the quality of the results is comparable to conventional methods. [ABSTRACT FROM AUTHOR]

When running on a curve, the lower limbs interact with the ground to redirect the trajectory of the centre of mass of the body (CoM). The goal of this paper is to understand how the trajectory of the CoM and the work done to maintain its movements relative to the surroundings (Wcom) are modified as a function of running speed and radius of curvature. Eleven participants ran at different speeds on a straight line and on circular curves with a 6 m and 18 m curvature. The trajectory of the CoM and Wcom were calculated using force-platforms measuring the ground reaction forces and infrared cameras recording the movements of the pelvis. To follow a circular path, runners overcompensate the rotation of their trajectory during contact phases. The deviation from the circular path increases when the radius of curvature decreases and speed increases. Interestingly, an asymmetry between the inner and outer lower limbs emerges as speed increases. The method to evaluate Wcom on a straight-line was adapted using a referential that rotates at heel strike and remains fixed during the whole step cycle. In an 18 m radius curve and at low speeds on a 6 m radius, Wcom changes little compared to a straight-line run. Whereas at 6 m s-1 on a 6 m radius, Wcom increases by ~25%, due to an augmentation in the work to move the CoM laterally. Understanding these adaptations provides valuable insight for sports sciences, aiding in optimizing training and performance in sports with multidirectional movements. [ABSTRACT FROM AUTHOR]

The article focuses on a semiotic analysis of camera movement in Disney animation, co-authored by Seyed Mohammad Jamasbi, Mohammad Ali Safoora, and Mostafa Goudarzi, published in the Quarterly Review of Film & Video. It provides bibliographic details including volume, issue, pagination, publication date, and DOI, highlighting scholarly discourse on the use of camera techniques in Disney's animated works.

Copyright of Laser Technology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Signa is the property of Editorial UNED and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

RGBT tracking combines visible and thermal infrared images to achieve tracking and faces challenges due to motion blur caused by camera and target movement. In this study, we observe that the tracking in motion blur is significantly affected by both frequency and spatial aspects. And blurred targets exhibit sharp texture details that are represented as high-frequency information. But existing trackers capture low-frequency components while ignoring high-frequency information. To enhance the representation of sharp information in blurred scenes, we introduce multi-frequency and multi-spatial information in network, called FSBNet. First, we construct a modality-specific unsymmetrical architecture and integrate an adaptive soft threshold mechanism into a DCT-based multi-frequency channel attention adapter (DFDA). DFDA adaptively integrates rich multi-frequency information. Second, we propose a masked frequency-based translation adapter (MFTA) to refine drifting failure boxes caused by camera motion. Moreover, we find that small targets get more affected by motion blur compared to larger targets, and we mitigate this issue by designing a cross-scale mutual conversion adapter (CFCA) between the frequency and spatial domains. Extensive experiments on GTOT, RGBT234 and LasHeR benchmarks demonstrate the promising performance of our method in the presence of motion blur. [ABSTRACT FROM AUTHOR]

This paper introduces an edge AI-based scene-specific object detection system for long-term traffic management, focusing on analyzing congestion and movement via cameras. It aims to balance fast processing and accuracy in traffic flow data analysis using edge computing. We adapt the YOLOv5 model, with four heads, to a scene-specific model that utilizes the fixed camera’s scene-specific properties. This model selectively detects objects based on scale by blocking nodes, ensuring only objects of certain sizes are identified. A decision module then selects the most suitable object detector for each scene, enhancing inference speed without significant accuracy loss, as demonstrated in our experiments. [ABSTRACT FROM AUTHOR]

The task of production design is to create locations where cinematic events occur. Since the subjective experiences of the characters in the film are mainly observable externally, the entire film practice aims to express them through secondary means, for example by design and cinematography. The filmmaker can choose what she brings to the foreground from the character's experience and what aspects she emphasizes. In the case of production design, it is possible to express metaphorical correlations through the spatialization process of cinematic events while anticipating the embodied situatedness of the camera and the eventual film viewer. The notion of situated authorship is discussed through iconic design examples from Gone With the Wind and The Birds, which have historically shaped the concept of production design practice. In the case study referring to my work as a designer, I intend to evaluate the schemas used in the spatialization process in the context of Embodied Simulation Theory (EST) and Conceptual Metaphor Theory (CMT). Here, I propose the idea of a dynamic staging scenario that indicates how cinematic movement – especially the action of the scene and the movement of the camera onset – is considered in spatialization. With the discussion, I conduct a design analysis with which the properties of the space can be evaluated based on the concepts of image schema, primary metaphor, and conceptual metaphor. [ABSTRACT FROM AUTHOR]

"All the President's Men" is a film analyzed by Christian Keathley and Robert B. Ray in their book. The authors argue that the film focuses on the experience of disorientation and uncertainty rather than the outcome of the investigation. The film, which follows the reporters Bob Woodward and Carl Bernstein as they investigate the Watergate scandal, recreates the feeling of being lost in a maze of clues and information. The book provides a close reading of the film's narrative structure, mise-en-scène, and cinematography, and argues for its status as an American classic. The authors also discuss the blend of art movie, classical studio-era, and New Hollywood elements in the film, as well as the deliberate choice to obscure and omit relevant story information. The book offers valuable insights for students of close reading methods and scholars of Hollywood history and style. [Extracted from the article]

In this article, a hierarchical method for action recognition based on temporal and spatial features is proposed. In current HAR methods, camera movement, sensor movement, sudden scene changes, and scene movement can increase motion feature errors and decrease accuracy. Another important aspect to take into account in a HAR method is the required computational cost. The proposed method provides a preprocessing step to address these challenges. As a preprocessing step, the method uses optical flow to detect camera movements and shots in input video image sequences. In the temporal processing block, the optical flow technique is combined with the absolute value of frame differences to obtain a time saliency map. The detection of shots, cancellation of camera movement, and the building of a time saliency map minimise movement detection errors. The time saliency map is then passed to the spatial processing block to segment the moving persons and/or objects in the scene. Because the search region for spatial processing is limited based on the temporal processing results, the computations in the spatial domain are drastically reduced. In the spatial processing block, the scene foreground is extracted in three steps: silhouette extraction, active contour segmentation, and colour segmentation. Key points are selected at the borders of the segmented foreground. The last used features are the intensity and angle of the optical flow of detected key points. Using key point features for action detection reduces the computational cost of the classification step and the required training time. Finally, the features are submitted to a Recurrent Neural Network (RNN) to recognise the involved action. The proposed method was tested using four well-known action datasets: KTH, Weizmann, HMDB51, and UCF101 datasets and its efficiency was evaluated. Since the proposed approach segments salient objects based on motion, edges, and colour features, it can be added as a preprocessing step to most current HAR systems to improve performance. [ABSTRACT FROM AUTHOR]

Purpose: The integration of artificial intelligence (AI) into surgical laparoscopy has shown promising results in recent years. This survey aims to investigate the inconveniences of current conventional laparoscopy and to evaluate the attitudes and desires of surgeons in Germany towards new AI-based laparoscopic systems. Methods: A 12-item web-based questionnaire was distributed to 38 German university hospitals as well as to a Germany-wide voluntary hospital association (CLINOTEL) consisting of 66 hospitals between July and November 2022. Results: A total of 202 questionnaires were completed. The majority of respondents (88.1%) stated that they needed one assistant during laparoscopy and rated the assistants' skillfulness as "very important" (39.6%) or "important" (49.5%). The most uncomfortable aspects of conventional laparoscopy were inappropriate camera movement (73.8%) and lens condensation (73.3%). Selected features that should be included in a new laparoscopic system were simple and intuitive maneuverability (81.2%), automatic de-fogging (80.7%), and self-cleaning of camera (77.2%). Furthermore, AI-based features were improvement of camera positioning (71.3%), visualization of anatomical landmarks (67.3%), image stabilization (66.8%), and tissue damage protection (59.4%). The reason for purchasing an AI-based system was to improve patient safety (86.1%); the reasonable price was €50.000–100.000 (34.2%), and it was expected to replace the existing assistants' workflow up to 25% (41.6%). Conclusion: Simple and intuitive maneuverability with improved and image-stabilized camera guidance in combination with a lens cleaning system as well as AI-based augmentation of anatomical landmarks and tissue damage protection seem to be significant requirements for the further development of laparoscopic systems. [ABSTRACT FROM AUTHOR]

A visual servoing system is a type of control system used in robotics that employs visual feedback to guide the movement of a robot or a camera to achieve a desired task. This problem is addressed using deep models that receive a visual representation of the current and desired scene, to compute the control input. The focus is on early fusion, which consists of using additional information integrated into the neural input array. In this context, we discuss how ready-to-use information can be directly obtained from the current and desired scenes, to facilitate the learning process. Inspired by some of the most effective traditional visual servoing techniques, we introduce early fusion based on image moments and provide an extensive analysis of approaches based on image moments, region-based segmentation, and feature points. These techniques are applied stand-alone or in combination, to allow obtaining maps with different levels of detail. The role of the extra maps is experimentally investigated for scenes with different layouts. The results show that early fusion facilitates a more accurate approximation of the linear and angular camera velocities, in order to control the movement of a 6-degree-of-freedom robot from a current configuration to a desired one. The best results were obtained for the extra maps providing details of low and medium levels. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to compare motions of the laparoscope tip during a laparoscopic task in a training box using a recent joystick-guided robotic scope holder to those manipulated by human scopists. We hypothesized that laparoscopic manipulation could be positively affected by robotic scope holders due to the elimination of unintentional movement. Methods: Twelve surgeons participated as operators, and eight medical doctors participated in this study. Among the human scopists, five were trained surgeons and three were novices who had no experience with laparoscopic surgery. A validated laparoscopic task was used to evaluate the path length of the laparoscope tip using an optical position tracker and operative time. The operators performed the designated camera task under three different laparoscopic manipulations: using a joystick-guided robotic scope holder, expert human scopists, and novice scopists. Results: The median path lengths (cm) of the laparoscopic tip were 94.0, 110.0, and 122.2 in the robotic scope holder, expert, and novice groups, respectively. The path lengths in the robotic scope holder group were significantly shorter than those in the other groups (P <.01). The median operative times (seconds) were 136.6, 66.4, and 62.3 in the robotic scope holder, expert, and novice groups, respectively. The operative time of the robotic scope holder group was significantly longer than that of the other groups (P <.001). Conclusion: A robotic scope holder can provide shorter camera movement owing to the stable holding and intentional scope manipulation by the operator, although it requires a longer operative time than a human assistant. [ABSTRACT FROM AUTHOR]

Optical camera communication (OCC) is one of the most promising optical wireless technology communication systems. This technology has a number of benefits compared to radio frequency, including unlimited spectrum, no congestion due to high usage, and low operating costs. OCC operates in order to transmit an optical signal from a light-emitting diode (LED) and receive the signal with a camera. However, identifying, detecting, and extracting data in a complex area with very high mobility is the main challenge in operating the OCC. In this paper, we design and implement a real-time OCC system that can communicate in high mobility conditions, based on You Only Look Once version 8 (YOLOv8). We utilized an LED array that can be identified accurately and has an enhanced data transmission rate due to a greater number of source lights. Our system is validated in a highly mobile environment with camera movement speeds of up to 10 m/s at 2 m, achieving a bit error rate of 10 − 2 . In addition, this system achieves high accuracy of the LED detection algorithm with mAP0.5 and mAP0.5:0.95 values of 0.995 and 0.8604, respectively. The proposed method has been tested in real time and achieves processing speeds up to 1.25 ms. [ABSTRACT FROM AUTHOR]

The present study explores resemiotisation in political discourse through an investigation of a video by Chokri Belaid, a Tunisian politician, lawyer and left-wing activist. Belaid, who was invited to a panel by Nessma TV in January 2012 to talk about a specific legal case, known as the "Persepolis case", was assassinated a year later on February 6th 2013 by religious extremists. Building on these contextual factors, the present paper highlights two levels of resemiotisation in this video. At a first level, Belaid repurposed the original legal purpose of the panel to assume several roles (politician, thinker, and lawyer) in order to infuse his ideology and political views. Second, the literal death of the author has induced several re-interpretations of this talk. The present study undertakes a multimodal analysis of the different frames and shots of this talk to argue that these two levels of resemiotisation are facilitated and highlighted by the integrated use of semiotic resources (gaze, gestures, camera movements, sound features and language) and other emergent semiotic resources exploited by secondary sign-makers for the re-interpretation of this video. [ABSTRACT FROM AUTHOR]

Occupancy models have been extended to account for either multiple spatial scales or species interactions in a dynamic setting. However, as interacting species (e.g., predators and prey) often operate at different spatial scales, including nested spatial structure might be especially relevant to models of interacting species. Here we bridge these two model frameworks by developing a multi-scale, two-species occupancy model. The model is dynamic, i.e. it estimates initial occupancy, colonization and extinction probabilities—including probabilities conditional to the other species' presence. With a simulation study, we demonstrate that the model is able to estimate most parameters without marked bias under low, medium and high average occupancy probabilities, as well as low, medium and high detection probabilities, with only a small bias for some parameters in low-detection scenarios. We further evaluate the model's ability to deal with sparse field data by applying it to a multi-scale camera trapping dataset on a mustelid-rodent predator–prey system. Most parameters are estimated with low uncertainty (i.e. narrow posterior distributions). More broadly, our model framework creates opportunities to explicitly account for the spatial structure found in many spatially nested study designs, and to study interacting species that have contrasting movement ranges with camera traps.Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

In the process of making a movie, directors constantly care about where the spectator will look on the screen. Shot composition, framing, camera movements, or editing are tools commonly used to direct attention. In order to provide a quantitative analysis of the relationship between those tools and gaze patterns, we propose a new eye-tracking database, containing gaze-pattern information on movie sequences, as well as editing annotations, and we show how state-of-the-art computational saliency techniques behave on this dataset. In this work, we expose strong links between movie editing and spectators gaze distributions, and open several leads on how the knowledge of editing information could improve human visual attention modeling for cinematic content. The dataset generated and analyzed for this study is available at https://github.com/abruckert/eye_tracking_filmmaking [ABSTRACT FROM AUTHOR]

"The Bad and the Beautiful" is a film by Warren Sonbert that features couples engaged in various activities in their apartments and other locations. The film showcases Sonbert's admiration for the love between couples and explores the relationship between the subjects and the filmmaker. While some critics have negative views of the film, others appreciate its representation of human interactions and its ability to capture moments of tenderness. Sonbert's work is seen as a departure from traditional cinematic techniques and is considered a significant contribution to amateur cinema. [Extracted from the article]

This article provides a brief overview of Warren Sonbert's cinematic development, focusing on his early films. Sonbert, born in 1947 in Brooklyn, New York, showed an early interest in film and music. He gained an education in film culture by attending screenings at the Bleecker Street Cinema, where he was exposed to experimental films and Hollywood classics. Sonbert was associated with the Warhol Factory scene and incorporated diverse cinematic strategies in his films, including in-camera editing and montage. He paid homage to various filmmakers in his work, blending elements of commercial narrative filmmaking with his own unique style. [Extracted from the article]

Video stabilization aims to eliminate camera jitter and improve the visual experience of shaky videos. Video stabilization methods often ignore the active movement of the foreground objects and the camera, and may result in distortion and over-smoothing problems. To resolve these issues, this paper proposes a novel video stabilization method based on motion decomposition. Since the inter-frame movement of foreground objects is different from that of the background, we separate foreground feature points from background feature points by modifying the classic density based spatial clustering method of applications with noise (DBSCAN). The movement of background feature points is consistent with the movement of the camera, which can be decom-posed into the camera jitter and the active movement of the camera. And the movement of foreground feature points can be decomposed into the movement of the camera and the active movement of foreground objects. Based on motion decomposition, we design first-order and second-order trajectory smoothing constraints to eliminate the high-frequency and low-frequency components of the camera jitter. To reduce content distortion, shape-preserving constraints, and regularization constraints are taken to generate stabilized views of all feature points. Experimental results demonstrate the effectiveness and robustness of the proposed video stabilization method on a variety of challenging videos. [ABSTRACT FROM AUTHOR]