Photographs Of Scenes Research Articles

The uncanniness of interactive photography: Exploring spatial perception in virtual tours and structure from motion

Photography has always been associated with the physical activity of the human body: capturing, editing and viewing photos are all activities that involve the user’s spatial interaction with the technology used. With conventional photography, one aspect of spatial relation with technology is the viewer’s ability to recognize the camera’s location in the photographic scene through visual indications, such as the relative location of objects in the frame to the camera’s point of view, combined with a basic familiarity with the functionality of conventional cameras, relating to the notions of ‘in-front’ and ‘behind’ the camera. Some instances of interactive photography, such as 360-degree imagery and three-dimensional photogrammetry, challenge the spatial connection between the photographer (represented by the camera location) and the viewer due to unique usability and innovative interactions, such as the viewer’s ability to ‘move’ freely in all directions of the three-dimensional, photographic space. The technological affordances of interactive photography create a distinct corporeal experience that challenges the traditional associations between the photographer, the viewer and the photographic imagery, evoking with the viewers the notion of technological uncanniness and, consequently, questioning some of the traditional preconceptions regarding the prime characteristics of the photographic medium.

Studying the Effect of Material and Geometry on Perceptual Outdoor Illumination.

Understanding and modeling perceived properties of sky-dome illumination is an important but challenging problem due to the interplay of several factors such as the materials and geometries of the objects present in the scene being observed. Existing models of sky-dome illumination focus on the physical properties of the sky. However, these parametric models often do not align well with the properties perceived by a human observer. In this work, drawing inspiration from the Hosek-Wilkie sky-dome model, we investigate the perceptual properties of outdoor illumination. For this purpose, we perform a large-scale user study via crowdsourcing to collect a dataset of perceived illumination properties (scattering, glare, and brightness) for different combinations of geometries and materials under a variety of outdoor illuminations, totaling 5,000 distinct images. We perform a thorough statistical analysis of the collected data which reveals several interesting effects. For instance, our analysis shows that when there are objects in the scene made of rough materials, the perceived scattering of the sky increases. Furthermore, we utilize our extensive collection of images and their corresponding perceptual attributes to train a predictor. This predictor, when provided with a single image as input, generates an estimation of perceived illumination properties that align with human perceptual judgments. Accurately estimating perceived illumination properties can greatly enhance the overall quality of integrating virtual objects into real scene photographs. Consequently, we showcase various applications of our predictor. For instance, we demonstrate its utility as a luminance editing tool for showcasing virtual objects in outdoor scenes.

Worth a Thousand Words: Crime Scenes Represented by Photojournalists and Forensic Photographers in Brazil

ABSTRACT This article compares how crime scenes are represented by photojournalists and forensic photographers in the city of Brasília. It starts from the premise that crime scene photography adheres to the social and professional contexts of the professional group that is taking it who, in turn, determines its identities, practices and conventions. The methodology consists of an analysis of 168 photos taken of three cases of femicide that occurred between 2016 and 2019, and in-depth interviews conducted with three photojournalists and three forensic photographers. News photographs place their importance on the selection of information that is going to be shown to their audience, usually choosing people and elements that evoke emotions. Forensic photographers emphasize documentation and control, using high-angle shots to capture everything. Both fields claim objectivity in their production, but in opposite ways, as forensic photography attempts to prove what is shown, and news photography hides and distorts elements to appeal to its audience. This paper looks at the role photography plays in building social reality, showing the same scene from the same incident and how its representation may differ according to who makes the image, the protocols they follow, and also who these images are taken for.

Quality of Colour Rendering in Photographic Scenes Illuminated by Light Sources with Light-Shaping Attachments

In both commercial and scientific photography, achieving accurate colour reproduction is essential. This study investigates the influence of light-shaping attachments on colour rendering indices (CRIs) under various lighting conditions. Through 180 measurements of different lighting combinations, statistical analysis reveals significant interactions between light sources, modifiers, and CRI test colour samples (TCS). Snoots consistently produce superior CRIs, while interactions between light sources and other light modifiers vary, affecting CRIs differently based on spectral distributions and material properties of the modifiers. Combining light sources and modifiers from the same manufacturer is found to enhance colour accuracy. These findings offer valuable insights for photographers, highlighting the importance of understanding light properties and light-shaping attachment effects on colour rendition. By informing best practices, this research contributes to achieving high-quality photographs across commercial and scientific domains, ensuring both accurate data representation and visually appealing results.

Individual differences in internal models explain idiosyncrasies in scene perception

According to predictive processing theories, vision is facilitated by predictions derived from our internal models of what the world should look like. However, the contents of these models and how they vary across people remains unclear. Here, we use drawing as a behavioral readout of the contents of the internal models in individual participants. Participants were first asked to draw typical versions of scene categories, as descriptors of their internal models. These drawings were converted into standardized 3d renders, which we used as stimuli in subsequent scene categorization experiments. Across two experiments, participants' scene categorization was more accurate for renders tailored to their own drawings compared to renders based on others' drawings or copies of scene photographs, suggesting that scene perception is determined by a match with idiosyncratic internal models. Using a deep neural network to computationally evaluate similarities between scene renders, we further demonstrate that graded similarity to the render based on participants' own typical drawings (and thus to their internal model) predicts categorization performance across a range of candidate scenes. Together, our results showcase the potential of a new method for understanding individual differences – starting from participants' personal expectations about the structure of real-world scenes.

The role of local meaning in infants' fixations of natural scenes.

As infants view visual scenes every day, they must shift their eye gaze and visual attention from location to location, sampling information to process and learn. Like adults, infants' gaze when viewing natural scenes (i.e., photographs of everyday scenes) is influenced by the physical features of the scene image and a general bias to look more centrally in a scene. However, it is unknown how infants' gaze while viewing such scenes is influenced by the semantic content of the scenes. Here, we tested the relative influence of local meaning, controlling for physical salience and center bias, on the eye gaze of 4- to 12-month-old infants (N=92) as they viewed natural scenes. Overall, infants were more likely to fixate scene regions rated as higher in meaning, indicating that, like adults, the semantic content, or local meaning, of scenes influences where they look. More importantly, the effect of meaning on infant attention increased with age, providing the first evidence for an age-related increase in the impact of local meaning on infants' eye movements while viewing natural scenes.

Aging attenuates the memory advantage for unexpected objects in real-world scenes

Across the adult lifespan memory processes are subject to pronounced changes. Prior knowledge and expectations might critically shape functional differences; however, corresponding findings have remained ambiguous so far. Here, we chose a tailored approach to scrutinize how schema (in-)congruencies affect older and younger adults’ memory for objects embedded in real-world scenes, a scenario close to everyday life memory demands. A sample of 23 older (52–81 years) and 23 younger adults (18–38 years) freely viewed 60 photographs of scenes in which target objects were included that were either congruent or incongruent with the given context information. After a delay, recognition performance for those objects was determined. In addition, recognized objects had to be matched to the scene context in which they were previously presented. While we found schema violations beneficial for object recognition across age groups, the advantage was significantly less pronounced in older adults. We moreover observed an age-related congruency bias for matching objects to their original scene context. Our findings support a critical role of predictive processes for age-related memory differences and indicate enhanced weighting of predictions with age. We suggest that recent predictive processing theories provide a particularly useful framework to elaborate on age-related functional vulnerabilities as well as stability.

Recognition and Analysis of Scene-Emotion in Photographic Works Based on AI Technology

Emotional effect is highly subjective in people's cognitive process, and a single discrete emotional feeling can hardly support the description of the immersion scene, which also puts forward higher requirements for emotional calculation in photography. Therefore, this article first constructs a photographic scene recognition model, and then establishes a visual emotion analysis model which optimizes the basic structure of vgg19 through CNN, extracts the user's photography situation information from the corresponding image metadata, establishes the mapping relationship between situation and emotion, and obtains the low-dimensional dense vector representation of the situation features through embedding. The authors divided eight emotional categories; accuracy of the model is compared and the feature distribution of scene-emotion in different works is analyzed. The results show that the accuracy of the scene-emotion recognition model of photographic works after multimodal fusion is high, reaching 73.9%, in addition, different shooting scenes can distinguish the emotional characteristics of works.

Disentangling diagnostic object properties for human scene categorization

It usually only takes a single glance to categorize our environment into different scene categories (e.g. a kitchen or a highway). Object information has been suggested to play a crucial role in this process, and some proposals even claim that the recognition of a single object can be sufficient to categorize the scene around it. Here, we tested this claim in four behavioural experiments by having participants categorize real-world scene photographs that were reduced to a single, cut-out object. We show that single objects can indeed be sufficient for correct scene categorization and that scene category information can be extracted within 50 ms of object presentation. Furthermore, we identified object frequency and specificity for the target scene category as the most important object properties for human scene categorization. Interestingly, despite the statistical definition of specificity and frequency, human ratings of these properties were better predictors of scene categorization behaviour than more objective statistics derived from databases of labelled real-world images. Taken together, our findings support a central role of object information during human scene categorization, showing that single objects can be indicative of a scene category if they are assumed to frequently and exclusively occur in a certain environment.

Photographing Crime Scenes in 20th-Century London: Microhistories of Domestic Murder. By Alexa Neale

We are perhaps so used to ‘seeing’ crime scenes and being exposed to the minute details of murder investigations that its development as a method and process of criminal inquiry has been taken for granted. Neale’s excellent Photographing Crime Scenes provides a fascinating, valuable, and important insight into the use of forensic photographs to understand how crime scenes were understood historically. Focusing on domestic murders in London, Neale persuasively argues that the role of photography was most crucial in the courtroom and did not focus in detail, but ‘about visualizing a narrative in the courtroom than investigating and establishing that narrative in police detection’ (p. 2). Drawing attention to the cultural and social production of crime scene photography, Neale’s analysis emphasizes that these sources were not conceived of as visual facts, and draws attention to the ways in which class, race, and gender shaped these narratives and how crimes scenes were presented and received through the prosecution process and in court. Other sources, particularly the press and other forms of cultural media, are woven through Neale’s expert analysis of crime scene photography to demonstrate how these images were themselves a product of broader social and cultural processes.

Physiological and Psychological Effects of Walking in Campus Landscape to Young Adults

Green space has a vital role in the community’s health and well-being. Forest bathing is an effective method of enjoying the forest atmosphere through physical activity or relaxing in a forest landscape. However, until now, no one has declared the benefits of forest therapy in Indonesia. This study clarifies the physiological and psychological effects of walking in a campus landscape. This research was conducted using experimental methods through physical activity survey, self-report questionnaires, Visitor Employed Photography (VEP), and automatic classification based on the image annotation API. The experiment was conducted in a park and an arboretum, and thirty-two young university subjects were tested. The participants walked for fifteen minutes on walking routes and district roads. Their blood pressure was measured before and after walking, and their heart rate was measured continuously. During the walk, the subjects took photographs of striking scenes using the Visitor Employed Photography method. Profile of Mood States (POMS) and State-Trait Anxiety Inventory (STAI) were used to evaluate the psychological responses. Walking in the campus landscape resulted in a lower heart rate, less negative moods, and less anxiety than walking on the district road. Out of 837 photographs, 45% were taken from the Academic Event Plaza, 41.5% from the Arboretum, and the remaining 13.5% from district road, Jalan Raya Dramaga. Two main groups of campus green spaces included man-made landscape consisting of road surface, buildings, plant organs, stairs, and terrestrial plants; and natural landscape consisting of sky, trees, flowers, clouds, and plant community. This study found that walking in campus green space induced physiological and psychological health benefits and prominent landscape elements supported the green campus.

Forensic Photography Concepts and Applications for Better Crime Scene Examination

"A picture is worth a thousand words," and crime scene photography is a case in point. All crime scenes begin with photography, which is done with the almost priority. The most efficient and straight forward way for an investigating officer to depict a crime scene is through photographs and criminal sketches. By documenting the original site and surroundings, crime scene photography serves to offer a true and accurate record of the crime scene and tangible evidence that was present. Regardless of how well a detective can orally describe a crime scene pictures can convey the same information more clearly and effectively because they freeze time and capture the evidence. A trial's forensic photography is an essential component. Additionally, crime scene images are frequently used as the basis for the decision.

Method for Classifying Crime Scene Photographs Based on Convolution Neural Network

Method for Classifying Crime Scene Photographs Based on Convolution Neural Network

Binocular Information Improves the Reliability and Consistency of Pictorial Relief

Binocular disparity is an important cue to three-dimensional shape. We assessed the contribution of this cue to the reliability and consistency of depth in stereoscopic photographs of natural scenes. Observers viewed photographs of cluttered scenes while adjusting a gauge figure to indicate the apparent three-dimensional orientation of the surfaces of objects. The gauge figure was positioned on the surfaces of objects at multiple points in the scene, and settings were made under monocular and binocular, stereoscopic viewing. Settings were used to create a depth relief map, indicating the apparent three-dimensional structure of the scene. We found that binocular cues increased the magnitude of apparent depth, the reliability of settings across repeated measures, and the consistency of perceived depth across participants. These results show that binocular cues make an important contribution to the precise and accurate perception of depth in natural scenes that contain multiple pictorial cues.

AQE-Net: A Deep Learning Model for Estimating Air Quality of Karachi City from Mobile Images

Air quality has a significant influence on the environment and health. Instruments that efficiently and inexpensively detect air quality could be extremely valuable in detecting air quality indices. This study presents a robust deep learning model named AQE-Net, for estimating air quality from mobile images. The algorithm extracts features and patterns from scene photographs collected by the camera device and then classifies the images according to air quality index (AQI) levels. Additionally, an air quality dataset (KARACHI-AQI) of high-quality outdoor images was constructed to enable the model’s training and assessment of performance. The sample data were collected from an air quality monitoring station in Karachi City, Pakistan, comprising 1001 hourly datasets, including photographs, PM2.5 levels, and the AQI. This study compares and examines traditional machine learning algorithms, e.g., a support vector machine (SVM), and deep learning models, such as VGG16, InceptionV3, and AQE-Net on the KHI-AQI dataset. The experimental findings demonstrate that, compared to other models, AQE-Net achieved more accurate categorization findings for air quality. AQE-Net achieved 70.1% accuracy, while SVM, VGG16, and InceptionV3 achieved 56.2% and 59.2% accuracy, respectively. In addition, MSE, MAE, and MAPE values were calculated for our model (1.278, 0.542, 0.310), which indicates the remarkable efficacy of our approach. The suggested method shows promise as a fast and accurate way to estimate and classify pollutants from only captured photographs. This flexible and scalable method of assessment has the potential to fill in significant gaps in the air quality data gathered from costly devices around the world.

How iconic news images travel: republishing and reframing historic photographs in Israeli newspapers

Abstract Iconic photographs are symbolically dense images characterized by broad circulation over time and recognition by large publics. Following this definition, we track the republication and reframing, over nearly 70 years, of 15 news photographs previously identified as most recognized by the Israeli public. Distinguishing between “discrete icons” (singular photographs of particular scenes) and “aggregate icons” (where several variants of the same event are continually reproduced), our quantitative and qualitative analyses show that iconic images are both resistant to the passing of time and kept in motion through renewed media use. We identify four “iconic modalities,” corresponding to different ways in which iconic status and meanings are achieved, transformed, or denied through republishing and reframing. This concept improves our understanding of iconicity as a fluctuating material and symbolic process, whereby the circulation of images not only produces shifts in meaning, but constructs powerful aggregative frameworks of collective visual memory.

Optimization of the driver's seat belt and injury biomechanical analysis in real-world minivan small offset impact accident scenarios.

To minimize injuries and protect the safety of the driver in minivan small offset collisions, an optimized pre-tensioned force-limiting seat belt was proposed herein. An accident with detailed information, such as medical reports, vehicle inspection reports, and accident scene photographs, was reconstructed using HyperMesh software. The effectiveness of both the accident model and the pre-tensioned force-limiting seat belt was evaluated. To obtain the optimal seat belt parameters for driver protection, first, force-limiting A, pre-tensioned force B, and pre-tensioned time C factors were selected in designing an orthogonal test with different factor levels. The influence laws of each factor on the injury biomechanical characteristics of the driver were analyzed via the direct analysis method. Moreover, each kind of critical injury value of the human body was synthesized, and the radial basis function surrogate model was constructed. The three seat belt parameters were optimized using the NSGA-II multi-objective genetic algorithm. The results showed that the optimal balance variable parameter of the seat belt was 4751.618 N-2451.839 N-17.554ms (A-B-C). Finally, the optimal scheme was verified in a system simulating a minivan small offset collision. The results showed that after optimization, the skull von Mises stress was reduced by 36.9%, and the stress of the cervical vertebra cortical bone and cancellous bone decreased by 29.1% and 30.8%, respectively. In addition, the strains of the ribs and lungs decreased by 31.2% and 30.7%, respectively.

The extent and specificity of visual exploration determines the formation of recollected memories in complex scenes.

Our visual memories of complex scenes often appear as robust, detailed records of the past. Several studies have demonstrated that active exploration with eye movements improves recognition memory for scenes, but it is unclear whether this improvement is due to stronger feelings of familiarity or more detailed recollection. We related the extent and specificity of fixation patterns at encoding and retrieval to different recognition decisions in an incidental memory paradigm. After incidental encoding of 240 real-world scene photographs, participants (N = 44) answered a surprise memory test by reporting whether an image was new, remembered (indicating recollection), or just known to be old (indicating familiarity). To assess the specificity of their visual memories, we devised a novel report procedure in which participants selected the scene region that they specifically recollected, that appeared most familiar, or that was particularly new to them. At encoding, when considering the entire scene,subsequently recollected compared to familiar or forgotten scenes showed a larger number of fixations that were more broadly distributed, suggesting that more extensive visual exploration determines stronger and more detailed memories. However, when considering only the memory-relevant image areas, fixations were more dense and more clustered for subsequently recollected compared to subsequently familiar scenes. At retrieval, the extent of visual exploration was more restricted for recollected compared to new or forgotten scenes, with a smaller number of fixations. Importantly, fixation density and clustering was greater in memory-relevant areas for recollected versus familiar or falsely recognized images. Our findings suggest that more extensive visual exploration across the entire scene, with a subset of more focal and dense fixations in specific image areas, leads to increased potential for recollecting specific image aspects.

A Systematic Literature Review on the Usage of Digital Photography at Crime Scene Investigation Process

The use of digital photography medium is dominantly used in the process of investigating criminal cases. Digital photography used in the investigation process is also known as crime scene photography, where photographic images are produced to collect evidence, documenting the scene, and subsequently, be used as a reference for criminal case investigation and prosecution process. Using systematic literature review (SLR), this study identifies the current practice of digital use of photography in the process of investigating criminal cases. Searches via electronic databases of indexed journals, namely, Scopus, Science Direct, Web of Science and Springer were conducted. Data is limited to these journals was produced within the years 2017 until 2021 as the latest research information. The findings consist of 28 papers that had met the criteria by which this study touched on digital photography technology such as DSLR Camera, Drone Photography, and CCTV.

Priming of natural scene categorization during continuous flash suppression

Continuous Flash Suppression (CFS) reduces conscious awareness of stimuli. Whether stimuli suppressed by CFS are processed at categorical or semantic levels is still debated. Here, we approached this question using a large set of indoor and outdoor scene photographs in a priming paradigm. Perceptually suppressed primes were followed by visible targets. Participants rapidly reported whether the targets showed an indoor or an outdoor scene. Responses were faster (and fast responses more accurate) when primes and targets came from a congruent superordinate category (e.g., both were outdoor scenes). During CFS, priming effects were relatively small (up to 10 ms) and modulated by prime visibility and stimulus onset asynchrony (SOA) of prime and target. Without CFS, the stimuli elicited consistent and more robust priming effects (about 24 ms). Our results imply that scene category is processed during CFS, although some residual prime visibility is likely necessary for significant priming effects to occur.