Set Of Scenes Research Articles

Noise Power Spectrum Scene-Dependency in Simulated Image Capture Systems

The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply nonlinear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The sceneand- process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-processdependent Modulation Transfer Functions (SPD-MTF).

Picture-evoked changes in pupil size predict learning success in children

Episodic memory, the ability to remember past events in time and place, develops during childhood. Much knowledge about the underlying neuronal mechanisms has been gained from methods not suitable for children. We applied pupillometry to study memory encoding and recognition mechanisms. Children aged 8 and 9 years (n = 24) and adults (n = 24) studied a set of visual scenes to later distinguish them from new pictures. Children performed worse than adults, demonstrating immature episodic memory. During memorization, picture-related changes in pupil diameter predicted later successful recognition. This prediction effect was also observed on a single-trial level. During retrieval, novel pictures showed stronger pupil constriction than familiar pictures in both age groups. The statistically independent effects of objective familiarity (previously presented pictures) versus subjective familiarity (pictures evaluated as familiar independent of the prior presentation) suggest dissociable underlying brain mechanisms. In addition, we isolated principal components of the picture-related pupil response that were differently affected by the memorization and retrieval effects. Results are discussed in the context of the maturation of the medial temporal lobe and prefrontal networks. Our results demonstrate the dissociation of distinct contributions to episodic memory with a psychophysiological method that is suitable for a wide age spectrum.

On Sugarland: An excerpt and annotation

66 leshea harris’s plays often explore the intricacies of relationships between women. Audiences come to understand the nuances of each character’s personality by observing her in conversation with other women, from mothers and sisters to rivals and mentors. On the page, characters’ affects are also illustrated by Harris’s use of innovative typography; an admonishment might be rendered in large typeface, while a whispered admission might waft down the page in tiny letters. Her new play, On Sugarland, excavates an intergenerational relationship between preadolescent Sadie and her aunt, Odella. For this special issue, we asked Harris to share snippets from the work, annotated with her thoughts on character formation, scene setting, and typographical experimentation. —the editors A On Sugarland An excerpt and annotation Aleshea Harris play 67 excerpt one (A young girl speaks assuredly to herself.) sadie I can make the dead walk.† I can. I can. I can make ’em put theyself back together and remember how to use their muscles and all. I can make ’em take new air into their lungs like it ain’t nothin But I ain’t finna tell nobody how naw What I look like giving out my gifts? I can make the dead walk and I got invisibility O yes Don’t nobody do invisibility like us, huh? Girls like us, we got it down, huh? We kinda like the wind you know we kinda like a shadow you know † I love to begin a work with a juicy image, something strange and suspenseful. This little girl telling us right off the bat that she can do the seemingly impossible should feel like a jolt. we got a frequency other folk can’t pick up on† but we can pick up on each other’s frequency through time, over time, under time our frequencies ain’t stuttin no time can’t nothin stop ’em Nothin I got Great Great Great Gran’s frequency running all through me even though she lived a long time ago Here go her frequency right here in my arm You see it? Here it go in my chest‡ You see it? Sheeid I’m tryna tell you Great Great Great Gran lived on a farm in Civil War times Her mama and daddy were long gone so she had to take care of her little brother and a little sister She wasn’t a slave, technically She had been freed but still had to work for next to nothing and they were all starving so she figured out she could carry messages to the Union soldiers and they’d give her food And didn’t nobody suspect a little black girl † The typography here is sturdy and steady, like a train moving quickly, its boxcars in a row. Sadie doesn’t have time to dilly-dally. The “frequency” moving through her body seems to compel her to speak quickly, too. Like a bullet through a gun, we move. ‡ This line about seeing the frequency in her arm and chest reminds me of a line from Sojourner Truth’s “Ain’t I a Woman” speech, which I once performed in a play. It strikes me that a speech delivered so long ago by Sojourner Truth shows up in some small way in Sadie’s speech here. This transference echoes the effort of Sadie’s monologue to capture the pulse of unnameable energy shared by Black girls and women generations, decades, continents apart. It ain’t stuttin no time! 68 | ALESHEA HARRIS ON SUGARLAND | 69 you know we invisible† you know Didn’t nobody suspect a little black girl was carrying messages. So she told them what she knew and the soldiers gave her hard tack and cheese or whatever they had. So she got her belly full and they got to know when there was whispers of cracker-jack Rebel soldiers taking over people’s drawing rooms and shit. One day, though One day Great Great Great Gran’s headed back from passing along messages Feelin all grown and stuff And she’s got a sack over her shoulder filled with bread and possum meat and maybe even an egg or two so she really feelin...

On the Comparison of Characteristics of Scene Setting Conversation and Free Conversation

On the Comparison of Characteristics of Scene Setting Conversation and Free Conversation

O compartilhamento de ações de estágio como espaço de formação inicial para a docência

O presente artigo apresenta resultados de uma pesquisa de mestrado desenvolvida no campo da Educação Matemática, particularmente sobre o estágio na formação inicial de professores de matemática. Fundamentada na Teoria da Atividade, a investigação foi realizada por meio de pesquisa de campo com situações de acompanhamento em turmas de Estágio Curricular Supervisionado em um curso de Licenciatura em Matemática, tendo como foco os momentos de compartilhamento de ações de estágio. Nesse cenário, investigou-se possibilidades de organização do ensino pelo professor da disciplina de estágio que possam favorecer a inserção dos estudantes à docência no contexto do estágio curricular supervisionado. A pesquisa se deu à luz dos pressupostos do materialismo histórico-dialético. O processo de captação de dados se deu por meio dos diários de bordo, observações e gravações em áudio feitas durante os encontros, fontes para o processo de análise acerca da problemática de pesquisa. No movimento de exposição da análise, organizamos o trabalho em três isolados denominados planejamento, coletividade e professor em atividade. Cada um desses isolados foram discutidos na forma de episódios e constituídos de um conjunto de cenas. Como resultados, constatamos, por meio da análise dos dados coletados que os sujeitos compreenderam a coletividade e o planejamento compartilhado como elementos representativos para a organização do ensino em sala de aula por intermédio dos encontros coletivos semanais. Nessa perspectiva, podemos destacar a importância destes momentos compartilhados de estágio na formação de futuros docentes como fundamentais ao processo de aprendizagem da docência.

PLADE: A Plane-Based Descriptor for Point Cloud Registration With Small Overlap

Traditional point cloud registration methods require large overlap between scans, which imposes strict constraints on data acquisition. To facilitate registration, users have to carefully position scanners to ensure sufficient overlap. In this article, we propose to use high-level structural information (i.e., plane/line features and their interrelationship) for registration, which is capable of registering point clouds with small overlap, allowing more freedom in data acquisition. We design a novel plane-/line-based descriptor dedicated to establishing structure-level correspondences between point clouds. Based on this descriptor, we propose a simple but effective registration algorithm. We also provide a data set of real-world scenes containing a larger number of scans with a wide range of overlap. Experiments and comparisons with state-of-the-art methods on various data sets reveal that our method is superior to existing techniques. Though the proposed algorithm outperforms state-of-the-art methods on the most challenging data set, the point cloud registration problem is still far from being solved, leaving significant room for improvement and future work.

Characterizing and automatically detecting smooth pursuit in a large-scale ground-truth data set of dynamic natural scenes.

Eye movements are fundamental to our visual experience of the real world, and tracking smooth pursuit eye movements play an important role because of the dynamic nature of our environment. Static images, however, do not induce this class of eye movements, and commonly used synthetic moving stimuli lack ecological validity because of their low scene complexity compared to the real world. Traditionally, ground truth data for pursuit analyses with naturalistic stimuli are obtained via laborious hand-labelling. Therefore, previous studies typically remained small in scale. We here present the first large-scale quantitative characterization of human smooth pursuit. In order to achieve this, we first provide a methodological framework for such analyses by collecting a large set of manual annotations for eye movements in dynamic scenes and by examining the bias and variance of human annotators. To enable further research on even larger future data sets, we also describe, improve, and thoroughly analyze a novel algorithm to automatically classify eye movements. Our approach incorporates unsupervised learning techniques and thus demonstrates improved performance with the addition of unlabelled data. The code and data related to our manual and automated eye movement annotation are publicly available via https://web.gin.g-node.org/ioannis.agtzidis/gazecom_annotations/.

The Syntax of Assertion and Presupposition

This paper addresses the question of how assertion and presuppositionare reflected in the grammar. Since Kiparsky & Kiparsky (1970) and Hooper &Thompson (1973), it’s often been suggested that these notions provide the semanticpragmaticunderpinning for a range of complementation patterns, including thelicensing of so-called Main Clause Phenomena [MCP]. This paper presents a newlarge-scale experimental study, investigating the lexical and semantic-pragmaticlicensing conditions of four types of MCP (Verb Second [V2], topicalization, speechact adverbs, and scene setting adverbs) in English, Swedish, and German. Thecentral contribution of this paper is demonstrating what precise dimensions ofassertion and presupposition are reflected in the grammar: for embedded V2, whatmatters is the discourse status of the embedded proposition as new vs. Given (inthe sense of Schwarzschild 1999); a dimension which cross-cuts both factive andnon-factive verbs. The other MCP investigated show no sensitivity to either of thelexical or pragmatic factors investigated. We further show that Givenness is notreflected in a (null) DP-layer, contrary to previous claims.

ML2S-SVM: multi-label least-squares support vector machine classifiers

PurposeImage classification is becoming a supporting technology in several image-processing tasks. Due to rich semantic information contained in the images, it is very popular for an image to have several labels or tags. This paper aims to develop a novel multi-label classification approach with superior performance.Design/methodology/approachMany multi-label classification problems share two main characteristics: label correlations and label imbalance. However, most of current methods are devoted to either model label relationship or to only deal with unbalanced problem with traditional single-label methods. In this paper, multi-label classification problem is regarded as an unbalanced multi-task learning problem. Multi-task least-squares support vector machine (MTLS-SVM) is generalized for this problem, renamed as multi-label LS-SVM (ML2S-SVM).FindingsExperimental results on the emotions, scene, yeast and bibtex data sets indicate that the ML2S-SVM is competitive with respect to the state-of-the-art methods in terms of Hamming loss and instance-based F1 score. The values of resulting parameters largely influence the performance of ML2S-SVM, so it is necessary for users to identify proper parameters in advance.Originality/valueOn the basis of MTLS-SVM, a novel multi-label classification approach, ML2S-SVM, is put forward. This method can overcome the unbalanced problem but also explicitly models arbitrary order correlations among labels by allowing multiple labels to share a subspace. In addition, the multi-label classification approach has a wider range of applications. That is to say, it is not limited to the field of image classification.

GradNet

Monte Carlo (MC) methods for light transport simulation are flexible and general but typically suffer from high variance and slow convergence. Gradientdomain rendering alleviates this problem by additionally generating image gradients and reformulating rendering as a screened Poisson image reconstruction problem. To improve the quality and performance of the reconstruction, we propose a novel and practical deep learning based approach in this paper. The core of our approach is a multi-branch auto-encoder, termed GradNet, which end-to-end learns a mapping from a noisy input image and its corresponding image gradients to a high-quality image with low variance. Once trained, our network is fast to evaluate and does not require manual parameter tweaking. Due to the difficulty in preparing ground-truth images for training, we design and train our network in a completely unsupervised manner by learning directly from the input data. This is the first solution incorporating unsupervised deep learning into the gradient-domain rendering framework. The loss function is defined as an energy function including a data fidelity term and a gradient fidelity term. To further reduce the noise of the reconstructed image, the loss function is reinforced by adding a regularizer constructed from selected rendering-specific features. We demonstrate that our method improves the reconstruction quality for a diverse set of scenes, and reconstructing a high-resolution image takes far less than one second on a recent GPU.

The contribution of ALOS/PALSAR-1 multi-temporal data to map permanently and temporarily flooded coastal wetlands

ABSTRACTAlthough regional wetland mapping studies have mostly relied on optical sensors, synthetic aperture radar (SAR) sensors are being increasingly applied. The aim of this study is to analyse the ability of the Phased Array type L-band Synthetic Aperture Radar on board of the Advanced Land Observing Satellite (ALOS/PALSAR-1) data to identify, delineate and monitor wetlands, and to evaluate the importance of scene selection in a highly unpredictable wetland. Three SAR scene sets (Year A, Year B and Inter-annual) were built for this purpose, considering the intra-annual and inter-annual hydrologic variability and the phenologic variability of the studied coastal wetland. Seven land cover types were defined, including three permanently flooded wetland classes, three temporarily flooded wetland classes and one non-wetland class. An object-based unsupervised classification approach was applied on each multi-temporal set. The obtained clusters were characterized by a temporal signature and assigned to the seven land cover types using a decision tree with user-defined thresholds. The accuracy assessment of each product was performed using a set of 258 data sites, including field collected data and data retrieved from Landsat 8 Operational Land Imager (OLI) imagery acquired during the dates of the field campaign. The Year B set showed the best accuracy (83.4% overall, 75% Kappa coefficient (κ)) and the lowest omission and commission mean errors (16.6% and 16.1% respectively). The classes that were best differentiated are permanently flooded wetlands (PFW) and non-wetlands (NW) in all sets.

Offline Deep Importance Sampling for Monte Carlo Path Tracing

AbstractAlthough modern path tracers are successfully being applied to many rendering applications, there is considerable interest to push them towards ever‐decreasing sampling rates. As the sampling rate is substantially reduced, however, even Monte Carlo (MC) denoisers–which have been very successful at removing large amounts of noise–typically do not produce acceptable final results. As an orthogonal approach to this, we believe that good importance sampling of paths is critical for producing better‐converged, path‐traced images at low sample counts that can then, for example, be more effectively denoised. However, most recent importance‐sampling techniques for guiding path tracing (an area known as “path guiding”) involve expensive online (per‐scene) training and offer benefits only at high sample counts. In this paper, we propose an offline, scene‐independent deep‐learning approach that can importance sample first‐bounce light paths for general scenes without the need of the costly online training, and can start guiding path sampling with as little as 1 sample per pixel. Instead of learning to “overfit” to the sampling distribution of a specific scene like most previous work, our data‐driven approach is trained a priori on a set of training scenes on how to use a local neighborhood of samples with additional feature information to reconstruct the full incident radiance at a point in the scene, which enables first‐bounce importance sampling for new test scenes. Our solution is easy to integrate into existing rendering pipelines without the need for retraining, as we demonstrate by incorporating it into both the Blender/Cycles and Mitsuba path tracers. Finally, we show how our offline, deep importance sampler (ODIS) increases convergence at low sample counts and improves the results of an off‐the‐shelf denoiser relative to other state‐of‐the‐art sampling techniques.

Virtual Humanity—Access, Empathy and Objectivity in VR Film Making

ABSTRACTThis scene setting considers the meaning of immersive technologies for humanitarian encounters, particularly 360 degree video “virtual reality” film making. Drawing on scholarship on the political economy of technology, aesthetics, affect and visual securitisation, as well as a number of notable deployments of VR films as a tool for NGO and IGO project fundraising, it will consider the interchange between immersion, emotion and action within VR as humanitarian praxis, and what kind of politics this may produce. In particular, the article considers the UNICEF film “Clouds Over Sidra” and the African Parks co-sponsored film “The Protectors” which highlight not only the utility of the technology in creating immersive experiences for audiences and donors but also some of the broader politics of empathy and authorial control arising from a visual technology that purports to allow audiences a more objective “see for yourself” style experience.

Research on the Cabin’s Local Orientation Cues of Space Station Oriented to Navigation Enhancement

The large and complex interior space of multi-cabin space station makes the astronauts easily happened to navigational disorder during the travel. With the need to enhance the navigation ability of the astronauts, based on a method study on space station intra-vehicular navigation simulation including both VR visual simulation and physiological HDBR simulation, multiple different sets of sample scenes are designed for the axial orientation cues of non-node cabin and the local orientation cues of node cabin. Through the experimental data analysis, the influence law of the cabin’s local orientation cues of the space station on the navigation task performance and related physiological effects in the cabin are obtained.

Individual differences in visual salience vary along semantic dimensions

What determines where we look? Theories of attentional guidance hold that image features and task demands govern fixation behavior, while differences between observers are interpreted as a "noise-ceiling" that strictly limits predictability of fixations. However, recent twin studies suggest a genetic basis of gaze-trace similarity for a given stimulus. This leads to the question of how individuals differ in their gaze behavior and what may explain these differences. Here, we investigated the fixations of >100 human adults freely viewing a large set of complex scenes containing thousands of semantically annotated objects. We found systematic individual differences in fixation frequencies along six semantic stimulus dimensions. These differences were large (>twofold) and highly stable across images and time. Surprisingly, they also held for first fixations directed toward each image, commonly interpreted as "bottom-up" visual salience. Their perceptual relevance was documented by a correlation between individual face salience and face recognition skills. The set of reliable individual salience dimensions and their covariance pattern replicated across samples from three different countries, suggesting they reflect fundamental biological mechanisms of attention. Our findings show stable individual differences in salience along a set of fundamental semantic dimensions and that these differences have meaningful perceptual implications. Visual salience reflects features of the observer as well as the image.

DeepLight: light source estimation for augmented reality using deep learning

This paper presents a novel method for illumination estimation from RGB-D images. The main focus of the proposed method is to enhance visual coherence in augmented reality applications by providing accurate and temporally coherent estimates of real illumination. For this purpose, we designed and trained a deep neural network which calculates a dominant light direction from a single RGB-D image. Additionally, we propose a novel method for real-time outlier detection to achieve temporally coherent estimates. Our method for light source estimation in augmented reality was evaluated on the set of real scenes. Our results demonstrate that the neural network can successfully estimate light sources even in scenes which were not seen by the network during training. Moreover, we compared our results with illumination estimates calculated by the state-of-the-art method for illumination estimation. Finally, we demonstrate the applicability of our method on numerous augmented reality scenes.

Deep HDR Video from Sequences with Alternating Exposures

AbstractA practical way to generate a high dynamic range (HDR) video using off‐the‐shelf cameras is to capture a sequence with alternating exposures and reconstruct the missing content at each frame. Unfortunately, existing approaches are typically slow and are not able to handle challenging cases. In this paper, we propose a learning‐based approach to address this difficult problem. To do this, we use two sequential convolutional neural networks (CNN) to model the entire HDR video reconstruction process. In the first step, we align the neighboring frames to the current frame by estimating the flows between them using a network, which is specifically designed for this application. We then combine the aligned and current images using another CNN to produce the final HDR frame. We perform an end‐to‐end training by minimizing the error between the reconstructed and ground truth HDR images on a set of training scenes. We produce our training data synthetically from existing HDR video datasets and simulate the imperfections of standard digital cameras using a simple approach. Experimental results demonstrate that our approach produces high‐quality HDR videos and is an order of magnitude faster than the state‐of‐the‐art techniques for sequences with two and three alternating exposures.

Research on the Whole Station’s Interior Orientation Cues Oriented to Navigation Enhancement

The large and complex interior space of multi-cabin space station makes the astronauts easily happened to navigational disorder during the travel.With the need to enhance the navigation ability of the astronauts,based on a method study on space station intra-vehicular navigation simulation including both VR visual simulation and physiological HDBR simulation,multiple different sets of sample scenes are designed for the orientation cues numbers reflected by the whole visual orientation cues that has similar form,and the specific form of the whole visual orientation cues that reflects same orientation numbers.Through the experimental data analysis, the influence law of the whole station’s interior orientation cues on the navigation task performance and associated physiological effects in the cabin are obtained.

Hubungan Outdor Activity dengan Scene Setting dalam Skill Mengajar Pembelajaran IPA Mahasiswa PGSD Universitas Cokroaminoto Palopo

This study generally aims to determine the relationship of outdoor activity with Scene Settings in Elementary School Science Learning PGSD University Students Cokroaminoto Palopo. This research is a quantitative research. The research model used in this study is the correlational model. This research hypothesis was tested using two-way analysis of variance (ANAVA). The sample of this study was PGSD University Cokroaminoto Palopo University. The results of this study indicate that: (1) there is a positive relationship between outdoor activities and scene settings (2) there is a positive positive relationship between outdoor activities and scene settings in science learning of PGSD students at Cokroaminoto Palopo University.

A Low-Cost Portable Polycamera for Stereoscopic 360° Imaging

This paper proposes a low-cost and portable polycamera system and accompanying methods for capturing and synthesizing stereoscopic 360° panoramas. The polycamera consists of only four cameras with fisheye lenses. Synthesizing panoramas from only four views is challenging because the cameras view very differently and the captured images have significant distortions and color degradation including vignetting, contrast loss, and blurriness. For coping with these challenges, this paper proposes methods for rectifying the polyview images, estimating depth of the scene, and synthesizing stereoscopic panoramas. The proposed camera is compact in size, light in weight, and inexpensive. The proposed methods allow the synthesis of visually pleasing stereoscopic 360° panoramas using the images captured with the proposed polycamera. We have built a prototype of the polycamera and tested it on a set of scenes with different characteristics of depth ranges and depth variations. The experiments show that the proposed camera and methods are effective in generating stereoscopic 360° panoramas that can be viewed on popular virtual reality displays.