Ghost Effect Research Articles

The ghost effect: how gamification can hinder genuine learning

The ghost effect: how gamification can hinder genuine learning

FMCW Radar on LiDAR map localization in structural urban environments

AbstractMultisensor fusion‐based localization technology has achieved high accuracy in autonomous systems. How to improve the robustness is the main challenge at present. The most commonly used LiDAR and camera are weather‐sensitive, while the frequency‐modulated continuous wave Radar has strong adaptability but suffers from noise and ghost effects. In this paper, we propose a heterogeneous localization method called Radar on LiDAR Map, which aims to enhance localization accuracy without relying on loop closures by mitigating the accumulated error in Radar odometry in real time. To accomplish this, we utilize LiDAR scans and ground truth paths as Teach paths and Radar scans as the trajectories to be estimated, referred to as Repeat paths. By establishing a correlation between the Radar and LiDAR scan data, we can enhance the accuracy of Radar odometry estimation. Our approach involves embedding the data from both Radar and LiDAR sensors into a density map. We calculate the spatial vector similarity with an offset to determine the corresponding place index within the candidate map and estimate the rotation and translation. To refine the alignment, we utilize the Iterative Closest Point algorithm to achieve optimal matching on the LiDAR submap. The estimated bias is subsequently incorporated into the Radar SLAM for optimizing the position map. We conducted extensive experiments on the Mulran Radar Data set, Oxford Radar RobotCar Dataset, and our data set to demonstrate the feasibility and effectiveness of our proposed approach. Our proposed scan projection descriptors achieves homogeneous and heterogeneous place recognition and works much better than existing methods. Its application to the Radar SLAM system also substantially improves the positioning accuracy. All sequences' root mean square error is 2.53 m for positioning and 1.83° for angle.

Enhanced LDR Detail Rendering for HDR Fusion by TransU- Fusion Network

High Dynamic Range (HDR) images are widely used in automotive, aerospace, AI, and other fields but are limited by the maximum dynamic range of a single data acquisition using CMOS image sensors. High dynamic range images are usually synthesized through multiple exposure techniques and image processing techniques. One of the most challenging task in multiframe Low Dynamic Range (LDR) images fusion for HDR is to eliminate ghosting artifacts caused by motion.In traditional algorithms, optical flow is generally used to align dynamic scenes before image fusion, which can achieve good results in cases of small-scale motion scenes but causes obvious ghosting artifacts when motion magnitude is large. Recently, attention mechanisms have been introduced during the alignment stage to enhance the network’s ability to remove ghosts. However, significant ghosting artifacts still occur in some scenarios with large-scale motion or oversaturated areas. We proposea novel Distilled Feature TransformerBlock (DFTB) structure to distill and re-extract information from deep image features obtained after U-Net downsampling, achieving ghost removal at the semantic level for HDR fusion. We introduce a Feature Distillation Transformer Block (FDTB), based on the Swin-Transformer and RFDB structure. FDTB uses multiple distillation connections to learn more discriminative feature representations. For the multiexposure moving scene image fusion HDR ghost removal task, in the previous method, the use of deep learning to remove the ghost effect in the composite image has been perfect, and it is almost difficult to observe the ghost residue of moving objects in the composite HDR image. The method in this paper focuses more on how to save the details of LDR image more completely after removing the ghost to synthesize high-quality HDR image. After using the proposed FDTB, the edge texture details of the synthesized HDR image are saved more perfectly, which shows that FDTB has a better effect in saving the details of image fusion. Futhermore, we propose a new depth framework based on DFTB for fusing and removing ghosts from deep image features, called TransU-Fusion. First of all, we use the encoder in U-Net to extract image features of different exposures and map them to different dimensional feature spaces. By utilizing the symmetry of the U-Net structure, we can ultimately output these feature images as original size HDR images. Then, we further fuse high-dimensional space features using Dilated Residual Dense Block (DRDB) to expand the receptive field, which is beneficial for repairing over-saturated regions. We use the transformer in DFTB to perform low-pass filtering on low-dimensional space features and interact with global information to remove ghosts. Finally, the processed features are merged and output as an HDR image without ghosting artifacts through the decoder. After testing on datasets and comparing with benchmark and state-of-the-art models, the results demonstrate our model’s excellent information fusion ability and stronger ghost removal capability.

Characteristics of the far-field source signature and its ghost effect of a marine single air gun

The estimation of the air gun source signature is a crucial topic in marine seismic. Typical approaches such as numerical modelling or near-field measurement-based inversion normally work in practice, but suffer from lack of knowledge on the varying sea surface reflection coefficient, the signature directional consistency and the dynamically changed source geometry. For better understanding of the above uncertainties, real far-field air gun source signatures were acquired. A customized approach combined with sparse f-k transformation and adaptive subtraction is applied to deblend the source signature from the sea-floor reflections. Characteristic of the ghost notch distribution and bubble oscillation period indicate an overall good match to the simulated results, except the latter part due to the unexpected air-leakage. Amplitude decay with incidence-angle increase is observed, indicating that the point-source spherical theory for directional signature estimation may be questionable. The sea-surface reflection coefficient appears time-variant and frequency-dependent which challenges the assumption of flat sea surface in signature estimation. An innovative notional source inversion workflow is adapted to the frequency-dependent reflection coefficient. The notional signature inversed through the adapted approach results in less ghost residuals and weaker spectral notch effect comparing with the normal inversion result, i.e., based on a constant reflection coefficient.

Reviewing ViBe, a Popular Background Subtraction Algorithm for Real-Time Applications

Background subtraction or foreground segmentation is commonly the first step in video processing pipelines, where foreground objects and moving entities are detected for further analysis. Background subtraction is still an open problem in computer vision due to the wide range of possible scenarios and challenging cases, such as weather conditions, illumination variation, dynamic backgrounds or low frame-rates. In this article, we review a well-known general algorithm for background subtraction named visual background extractor or ViBe. More than 10 years after its publication, ViBe is still widely used due to its simplicity and low computational load, as its authors claim it can process up to 200 fps. We analyze the algorithm's mechanism to segment foreground objects, comment on its strengths and drawbacks, and describe an effective variant of the method to quickly dissipate ghost effects using 3-frame difference. Furthermore, we provide an easy-to-use demo that allows to quickly test the performance of ViBe on custom data, modify its parameters and download the results. **This is an MLBriefs article, the source code has not been reviewed!**<br> The ViBe algorithm is covered by several patents and its code is not provided with this publication. <a href="https://orbi.uliege.be/handle/2268/145853">Its code is only shared for research purposes.</a><br>

Pseudo-3D Receiver Deghosting of Seismic Streamer Data Based on l1 Norm Sparse Inversion

The ghost effect in marine seismic data causes low-frequency suppression and frequency notch, resulting in incomplete frequency information for seismic records, which poses challenges for high-resolution imaging. The deghosting effect depends on the approximation of the ghost delay operator. Due to the strict requirements of dense sampling, the 2D deghosting method for a densely sampled inline dataset is still the mainstream method for marine data processing. As the trade-off, inversion-based methods are widely used in the industry to reduce the influence of the inaccurate ghost delay operator. In order to overcome the sampling limits and improve the 2D deghosting effect, we propose a pseudo-3D deghosting framework based on an l1 norm sparse inversion. In the framework, the inversion problem is divided into two subproblems, i.e., pseudo-3D operator building and optimization inversion. Considering the data coherence along the shot direction, we derive a pseudo-3D ghost delay operator to deghost simultaneously for multi-shot gathers. We then introduce a sparse inversion method in the pseudo-3D radon domain (multi-shot gathers) to further improve the inversion accuracy. The proposed framework is easy to implement, is not sensitive to noise, and shows superior performance in synthetic and field examples.

P‐3.24: A Content‐adaptive Filtering Algorithm based on Dual‐cell LCDs†

P‐3.24: A Content‐adaptive Filtering Algorithm based on Dual‐cell LCDs†

Multi-species viscous models for tissue growth: incompressible limit and qualitative behaviour.

We introduce two 2D mechanical models reproducing the evolution of two viscous tissues in contact. Their main property is to model the swirling cell motions while keeping the tissues segregated, as observed during vertebrate embryo elongation. Segregation is encoded differently in the two models: by passive or active segregation (based on a mechanical repulsion pressure). We formally compute the incompressible limits of the two models, and obtain strictly segregated solutions. The two models thus obtained are compared. A striking feature in the active segregation model is the persistence of the repulsion pressure at the limit: a ghost effect is discussed and confronted to the biological data. Thanks to a transmission problem formulation at the incompressible limit, we show a pressure jump at the tissues' boundaries.

HLDNet: Abandoned Object Detection Using Hand Luggage Detection Network

Abandoned object detection has recently been studied to detect dangerous situations such as illegally dumping explosives. Existing abandoned object detection used background subtraction, which is sensitive to noise and clutters of a similar size in outdoor environments. Unlike existing approaches, this article presents a deep learning-based detection method that is suitable for use in outdoor environments because it is robust to ghost effects and illumination changes. The proposed method consists of three parts: 1) pedestrian detection and tracking; 2) hand luggage detection (HLD); and 3) abandoned object decision. Since the proposed method detects the abandoned object through HLD instead of directly detecting the abandoned object itself, we could successfully improve the detection accuracy and robustness. The proposed method can also detect dangerous weapons or restricted hand luggage through HLD. This article presents an intelligent abandoned object detection system that can be applied for consumer applications.

Optimization and Fabrication of Multi-Level Microchannels for Long-Term Imaging of Bacterial Growth and Expansion.

Bacteria are unicellular organisms whose length is usually around a few micrometers. Advances in microfabrication techniques have enabled the design and implementation of microdevices to confine and observe bacterial colony growth. Microstructures hosting the bacteria and microchannels for nutrient perfusion usually require separate microfabrication procedures due to different feature size requirements. This fact increases the complexity of device integration and assembly process. Furthermore, long-term imaging of bacterial dynamics over tens of hours requires stability in the microscope focusing mechanism to ensure less than one-micron drift in the focal axis. In this work, we design and fabricate an integrated multi-level, hydrodynamically-optimized microfluidic chip to study long-term Escherichia coli population dynamics in confined microchannels. Reliable long-term microscopy imaging and analysis has been limited by focus drifting and ghost effect, probably caused by the shear viscosity changes of aging microscopy immersion oil. By selecting a microscopy immersion oil with the most stable viscosity, we demonstrate successful captures of focally stable time-lapse bacterial images for ≥72 h. Our fabrication and imaging methodology should be applicable to other single-cell studies requiring long-term imaging.

Phantom Images, Phantom Limbs, Phantoms of Empire

Kader Attia’s Refléchir la Mémoire (Reflecting Memory, 2016) and Cyprien Gaillard’s 3D motion picture Nightlife (2015) engage the politics of memory by invoking the musical practice of dubbing to convey a sense of haunting. Yet this is not a haunting that simply conjures ghostly voices, images or sounds from the past. It is, instead, a practice of mixing, remixing and distorting − making something hidden or invisible critically present. Rather than attempting to restore or repair those who suffer from historical or even physical traumas they focus on transformations that are not simple acts of self-overcoming nor reclaiming one’s sense of self, but ones that complex set of affective relations. These remediated voices and images do not conjure ghosts to make singular demands on the present but produce ghost effects that generate multiple possible readings of the always present politics of memory.

Intelligent driving system at opencast mines during foggy weather

ABSTRACT The fog in mining operations minimises the visibility, preventing drivers from a clear view, causing accidents and vehicle collisions. This paper provides an intelligent driving system for heavy earthmoving machinery operators in opencast mines, including hardware and software. Hardware contains high definition and thermal cameras, a global navigation satellite system (GNSS), radar, laser light, wireless devices, graphical processing unit, touch screen, etc. The software covers image stitching, image enhancement, and convolution neural network-based object detection. The display dashboard is divided into four windows. Each window represents a different view, i.e. 180° panorama view of the driving lane, GNSS tracking map, proximity radar detection view, and rear thermal camera view. An additional colour transfer method has been used in the existing image stitching method to reduce misalignment and ghost effect in the panorama output. The proposed method outperformed the existing methods, namely contrast limited adaptive histogram equalisation (CLAHE) and dark channel prior (DCP). The proposed image enhancement technique has increased contrast, entropy, and colour average by 0.069, 0.43, and 13.96, respectively, than CLAHE, and 0.994, 0.43, and 42.07 than DCP. The accuracy of the object detection model is 97%, and the overall processing time of all the algorithms is 0.44949 seconds.

Spectral Characters: Genre and Materiality on the Modern Stage

Spectral Characters: Genre and Materiality on the Modern Stage

Low Complex Block Level Correlation and Registration for Video Frame Interpolation

In this paper, we propose a low complex block level correlation and registration for Video Frame Interpolation (VFI) algorithm. The proposed method finds the correlation between the corresponding non-overlapping blocks. The correlation coefficients indicate the amount of similarity between the blocks. Using the median of correlation coefficients as threshold, the blocks are blended using image registration process. Compared to existing block based Motion Compensated Frame Interpolation (MCFI) methods, proposed algorithm is a low complex algorithm. In the proposed, there is no need of Motion Estimation (ME), smoothing of unreliable Motion Vectors (MVs) and Motion Prediction. Extensive experiments are conducted on various benchmark video sequences. Subjective assessment shows that the artefacts like blocky artefacts, holes, blurry artefacts, and ghost effects in MCFI are overcome by this algorithm, as there is no ME, preserving the true motion of the object to a greater extent is possible. The metrics PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Metric) provide the objective assessment.

Two-velocity hydrodynamics in fluid mechanics: global existence for 2D case

In this paper, we consider a compressible–incompressible two-velocity hydrodynamic system studied by Bresch et al (2015 J. Math. Pure Appl. 104 762–800) and Lions (1996 Mathematical Topics in Fluid Mechanics (Oxford: OUP)). When the density ρ is a small perturbation of a constant, we establish a priori estimates by using some delicate structure of the nonlinear terms and Hardy space. By using these a priori estimates, we prove the existence of global strong solutions and weak solutions. Our results do not require any constraint between the viscosity and the conductivity and improve the results of Bresch et al (2015) and Lions (1996) in the two-dimensional case. As an application of our results we also establish global strong solutions and weak solutions for a model of gaseous mixture and the ghost effect system.

A arte do vídeo coreano: do televisor à fantasmagoria pós-cibernética

The Korean video scene of the 80’s explored video in its brute form and the 90’s followed up by exploring the image as a property of video itself, demanding that the qualities of the image become exacerbated. By the 2000’s installation was imbedded in video art scene thus making for image to go to great lengths and plaster itself all around museum and gallery walls. Thereafter, image came to inhabit not only the monitor but also every surface upon which it could possibly project itself. Today, virtually generated images dominate every medium and insinuate themselves onto the subconscious of the beholder. In one way, image is galvanizing, in other way, it aims to visually suppress the medium, causing the beholder to watch a spectacle of phantoms, afterimages and Pepper’s Ghost effects. The medium thus becomes what is visible as prophesied by the likes of Jean-Louis Baudry following up from Platão readings.

Automotive plastics: What future is there for polymers in tomorrow's electric and autonomous vehicles?

Polymers have become one of the key materials to manufacture high-performing, lightweight, fuel-efficient , safe, and affordable cars. Accounting for roughly one third of the parts in a vehicle, plastics meet traditional automotive requirements rather well: high specific mechanical properties, impact, thermal and chemical resistance, customization capabilities, easy formability, relatively low cost, and recyclability. However, the growing market for electric vehicles and the advanced digital technologies of autonomous driving are likely to redefine the use of polymers in the auto industry. Fortunately, polymers fit well with this new era. Some of their assets are more than ever crucial: molding parts with complex geometries or multiple functions integration, noise and vibration damping, and weight saving. Electric vehicles emitting less heat than internal combustion engine cars, a broader range of commodity plastics (e.g., polypropylene) could be used in place of costly engineering plastics (e.g., poly amides). In addition to being a light source, headlamps of self-driving cars will integrate electronics and sensors, e.g., lidars, radars and cameras, paving the way for advanced polymers able to warrant optical performance (light diffusion, transmission, colour) and heat management (enhanced importance of flame retar-dancy, thermal conductivity). Polymers can prevent the various waves emitted by the sensors from being disrupted by a barrier object (so-called ghost effect); hence, they are a suitable solution to cover and hide unsightly sensors without impeding radars with any shielding effect. Thanks to transparent materials like polycarbonate, a potential candidate for replacing glass in the windows and windscreens, automotive cockpits could be equipped with large touchscreens and smart surfaces displaying images. Besides, electroactive (i.e., ionic electroactive, liquid-crystal, piezoelectric, fer-roelectric) polymers, such as polyvinylidene fluoride and potentially piezoelectric polylactic acid, might revolutionize acoustic speaker systems thanks to the integration of vibrating surfaces in dashboard or head-rests acting as a resonance chamber. With the development of carsharing, there is also a need for sensory appealing polymers formulated to be dirt-repellent or easy to clean, durable, and scratch-resistant (also in soft-touch surfaces). At last, polymers have a role to play in the next-generation batteries as membranes, separators, elec-trolytes and electrode compounds, a field where research efforts are intensive. Thus, no doubt, polymers have the potential to meet the ever-more-demanding requirements of the reshaped automotive market.

Comparison of Input Image Dimensions for Ship Detection from KOMPSAT-5 SAR Image Using Deep Neural Network

Park, W.; Baek, W.-K.; Won, J.-S., and Jung, H.-S., 2020. Comparison of input image size for ship detection from KOMPSAT-5 SAR image using deep neural networks (DNNs). In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 208-217. Coconut Creek (Florida), ISSN 0749-0208.This study shows the results of ship detection in KOMPSAT-5 X-band SAR images using Deep Learning (DL) based the fast Deep Neural Network (DNN) method with the iterative kernel-based false alram detection algorithm. In addition, this study verifies the detection accuracy according to the size of the input data for are typical errors in SAR images such as backscattering noise, side-lobe, and ghost effect. The test results of the Fast DNN method confirmed that the error caused by the back scattering noise and side-lobe decreased as the input data size increased, however, the error for the ghost effect did not improve significantly. Also, the False Alarm Rate (FAR) was greatly improved using the iterative kernel-based false alram detection algorithm. However, caution is needed when detecting small ships because very small boats disappear. The results of this study showed that the size of the input data was very effective at 51 × 51 or higher when the fast DNN method and the iterative kernel-based false alram detection algorithm were applied to the KOMPSAT-5 images to detect ships, allowing the FAR and Intersaction over Union (IoU) showing relatively high accuracy at 0.270 and 0.611.

Tensor-Based Approach for Background-Foreground Separation in Maritime Sequences

The complexity of a scene in addition to the need for real-time processing are the main challenges that face any background/foreground separation approach for maritime environment. Recent studies on Low-rank and Sparse Separation (LSS) achieved good performance when compared to traditional background subtraction techniques in segregating the foreground from a complex background. However, the issue of maintaining this type of a separation via an updating mechanism is not well addressed by the majority of LSS approaches. The study presents a tensor based singular value decomposition approach for background/foreground separation. The approach is uniquely designed to deal with most challenges related to a maritime environment such as sea dynamics, boat wakes, variety of foreground objects, and camera jitter. Furthermore, the proposed approach operates incrementally via updating the separation components as opposed to reperforming the decomposition on the entire video sequence when a set of frames arrives. Additionally, a forgetting mechanism is employed in the proposed approach to efficiently handle challenges such as Stationary Foreground Objects (SFOs) and ghost effects. The performance of the proposed method with several state-of-the-art LSS and Non-LSS techniques on videos with complex maritime scenarios are evaluated. The results exhibit better performance over most of the tested challenges and also demonstrate the capability of the proposed method to perform the separation in less computational time.

Tracking Objects on Detector Response using Extended Kalman Filter

Video analytics plays a very important role in identification or detection and tracking of objects, this intern find application in many fields and domains. Novel learning methods or techniques built on Neural Networks requires larger dataset for training the results, the output obtained depends on how well the training is done. The proposed method of Weighted Cumulative Summation (WCS) is an approach based on background modelling to segment the moving objects. This method adapts and tunes the background variations instantaneously as the video frame arrives. The segmentation obtained is compared with other basic methods. The result obtained infers improvements in segmentation and in removal of ghost effect in the video. Extended Kalman Filter (EKF) is used to track the detector response. The responses of the detection from WCS are provided as input to EKF to track the moving object. The results are tabulated and represented in the form of graphs for analysis. The results are compared with three different video datasets and the results are noticeably good. The methods WCS can be used in the applications were data set is not available.