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Road cycling races, such as the Tour de France, captivate millions of viewers globally, combining competitive sportsmanship with the promotion of regional landmarks. Traditionally, points of interest (POIs) are highlighted during broadcasts using manually created static overlays, a process that is both outdated and labor-intensive. This paper presents a novel, fully automated methodology for detecting and tracking POIs in live helicopter video streams, aiming to streamline the visualization workflow and enhance viewer engagement. Our approach integrates a saliency and Segment Anything-based technique to propose potential POI regions, which are then recognized using a keypoint matching method that requires only a few reference images. This system supports both automatic and semi-automatic operations, allowing video editors to intervene when necessary, thereby balancing automation with manual control. The proposed pipeline demonstrated high effectiveness, achieving over 75% precision and recall in POI detection, and offers two tracking solutions: a traditional MedianFlow tracker and an advanced SAM 2 tracker. While the former provides speed and simplicity, the latter delivers superior segmentation tracking, albeit with higher computational demands. Our findings suggest that this methodology significantly reduces manual workload and opens new possibilities for interactive visualizations, enhancing the live viewing experience of cycling races. [ABSTRACT FROM AUTHOR]

Migration biology is an important chapter of the past decade which includes the wealth and health of descriptive articles that indicate different kinds of emigrational aspects. We must understand the proximate as well as ultimate causes of migration timing, energy distribution, optimized performance, immigration success, and fitness for the purpose of supporting wildlife population management as well as conservation initiatives by putting in place suitable safeguards or controlling environmental variables that affect migration. Different kinds of psychological measurements and dance are an important part by using new and innovative approaches and experimental processes. For hundreds of thousands of generations, humans have been intrigued by birds' extraordinary capacity to travel to faraway locations with accuracy. A unique combination of biological processes and genetic factors organizes this intricate accomplishment. The mechanism of Avi and species are divided into some parts like "Magneto reception", "Landmark Recognition", "Sun and Star Navigation", and "Olfaction". According to the sensory movement theory, which explains how homing pigeons navigate, birds learn how to connect directions from the wind with breeze-borne scents within their homes and, after being moved, calculate their home orientation based on ambient odors in the area around them. The "migratory movements" are determined mostly in the passerine birds which are mostly presumed as the cultural and social factors that play the largest role in the environmental system. [ABSTRACT FROM AUTHOR]

Road cycling races, such as the Tour de France, captivate millions of viewers globally, combining competitive sportsmanship with the promotion of regional landmarks. Traditionally, points of interest (POIs) are highlighted during broadcasts using manually created static overlays, a process that is both outdated and labor-intensive. This paper presents a novel, fully automated methodology for detecting and tracking POIs in live helicopter video streams, aiming to streamline the visualization workflow and enhance viewer engagement. Our approach integrates a saliency and Segment Anything-based technique to propose potential POI regions, which are then recognized using a keypoint matching method that requires only a few reference images. This system supports both automatic and semi-automatic operations, allowing video editors to intervene when necessary, thereby balancing automation with manual control. The proposed pipeline demonstrated high effectiveness, achieving over 75% precision and recall in POI detection, and offers two tracking solutions: a traditional MedianFlow tracker and an advanced SAM 2 tracker. While the former provides speed and simplicity, the latter delivers superior segmentation tracking, albeit with higher computational demands. Our findings suggest that this methodology significantly reduces manual workload and opens new possibilities for interactive visualizations, enhancing the live viewing experience of cycling races.

Inefficient data collection methods hamper tourist behavior research. Focusing on Beijing, this paper employs computer-related video techniques to capture and segment tourism videos posted on platforms like YouTube and Bilibili by domestic and foreign tourists visiting Beijing. The keyframes are then subjected to landmark, common object, and scene recognition for a comparative analysis of spatial behavior of tourists. The results are as follows: first, tourists pay more attention to the folk culture in non-habitual environments; second, Chinese and foreign tourists have different tourism preferences and behaviors; and finally, cultural distance has an inhibitory effect on international tourism activities. [ABSTRACT FROM AUTHOR]

In this paper, we present a new dataset named CityUPlaces, comprising 17,771 images from various campus buildings, which contains 9 major categories and further derives 18 minor categories based on the internal and external scenes of these identities. Each category is not balanced ranging from 344 to 1539 with diverse variations in angle, attractions, views, illumination, etc. Compared to existing large-scale datasets, the proposed dataset shows its strengths in two aspects: (1) it contains a moderate number of both indoor and outdoor images under different conditions for each identity, which enables diverse real-time recognition tasks by featuring hierarchical categorization with reasonable dataset size; (2) the issue of label noise is significantly alleviated for each identity in the dedicated annotation and filtering stages to facilitate the subsequent tasks. This provides great flexibility to perform these vision-based tasks with different learning objectives in a real-time mode. Moreover, we propose a novel lightweight classification framework that outperforms state-of-the-art baselines on the dataset with the relatively low computational complexity of fewer training parameters and floating-point operations per second, by taking advantage of the involved coarse-to-fined learning strategy in a self-transfer manner. This laterally confirms the applicability of the new dataset. We also conduct experiments on the MIT Indoors and Paris datasets, where the proposed method still achieves superior performance that validates its efficacy. The dataset and code will be publicly available in the future. [ABSTRACT FROM AUTHOR]

The unprecedented development of the internet has compelled a growing number of tourists to share their photographs on social media. These images convey valuable memories and points of interest. As photography and content sharing have become commonplace among visitors, pictorial digital footprints represent a prevalent topic in tourism research. Studies on tourists' movement trajectories hold great importance for destination management, marketing, and services. Flickr is a popular source in photo-based tourism research given the digital footprints embedded in photos' metadata; however, the site's bottlenecks (e.g. declining user activity, overly professional photographs) raise concerns. Scholars have instead gradually shifted their attention to emerging photo platforms such as Instagram—yet these pictures do not contain geographical information. Taking Beijing as a focal location, we introduce an approach in which landmark recognition complements the geographical cues in Instagram photos. Instagram check-in data and data identified through landmark recognition are validated. Ultimately, the recognized landmark information appears highly correlated with check-in data. This study demonstrates the feasibility of landmark recognition for extracting tourists' footprints from ordinary content in user-generated photos. Findings also confirm that many photos from general social media platforms can serve as alternative and representative data sources in photo-based tourism research. [ABSTRACT FROM AUTHOR]

Purpose: Evaluating lower extremity alignment using full-leg plain radiographs is an essential step in diagnosis and treatment of patients with knee osteoarthritis. The study objective was to present a deep learning-based anatomical landmark recognition and angle measurement model, using full-leg radiographs, and validate its performance. Methods: A total of 11,212 full-leg plain radiographs were used to create the model. To train the data, 15 anatomical landmarks were marked by two orthopaedic surgeons. Mechanical lateral distal femoral angle (mLDFA), medial proximal tibial angle (MPTA), joint line convergence angle (JLCA), and hip-knee-ankle angle (HKAA) were then measured. For inter-observer reliability, the inter-observer intraclass correlation coefficient (ICC) was evaluated by comparing measurements from the model, surgeons, and students, to ground truth measurements annotated by an orthopaedic specialist with 14 years of experience. To evaluate test–retest reliability, all measurements were made twice by each measurer. Intra-observer ICCs were then derived. Performance evaluation metrics used in previous studies were also derived for direct comparison of the model's performance. Results: Inter-observer ICCs for all angles of the model were 0.98 or higher (p < 0.001). Intra-observer ICCs for all angles were 1.00, which was higher than that of the orthopaedic specialist (0.97–1.00). Measurements made by the model showed no significant systemic variation. Except for JLCA, angles were precisely measured with absolute error averages under 0.52 degrees and proportion of outliers under 4.26%. Conclusions: The deep learning model is capable of evaluating lower extremity alignment with performance as accurate as an orthopaedic specialist with 14 years of experience. Level of evidence: III, retrospective cohort study. [ABSTRACT FROM AUTHOR]

Existing methods focus on destination image construction by textual description or visual content separately. However, descriptions and images are closely related since they are taken from the same reviews and represent tourists impression of the city. It's questionable to study them separately. In this paper, we used both images and descriptions from the reviews to construct Xi'an tourism destination image. More concretely, scene recognition, landmark recognition and food image recognition are utilized to obtain visual image. Lexical analysis is applied to obtain semantic image. We further compared the differences between visual image and semantic image then we proposed the fusion image. Finally, the top 300 key words and differences of the photo contents between the adjacent 2 years are selected to discovering new changes of the destination image. Results show that the visual image and semantic image are significant different from each other and the new changes of semantic image are closely related to the events or things that happened in that year and changes of visual image are not significant. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks have largely benefited computer vision tasks. However, the high computational complexity limits their real-world applications. To this end, many methods have been proposed for efficient network learning, and applications in portable mobile devices. In this paper, we propose a novel Moving-Mobile-Network, named M2Net, for landmark recognition, equipped each landmark image with located geographic information. We intuitively find that M2Net can essentially promote the diversity of the inference path (selected blocks subset) selection, so as to enhance the recognition accuracy. The above intuition is achieved by our proposed reward function with the input of geo-location and landmarks. We also find that the performance of other portable networks can be improved via our architecture. We construct two landmark image datasets, with each landmark associated with geographic information, over which we conduct extensive experiments to demonstrate that M2Net achieves improved recognition accuracy with comparable complexity. [ABSTRACT FROM AUTHOR]

How well do we know our city? It turns out, much more poorly than we might imagine. We used declarative memory and eye-tracking techniques to examine people's ability to detect modifications to real-world landmarks and scenes in Toronto locales with which they have had extensive experience. Participants were poor at identifying which scenes contained altered landmarks, whether the modification was to the landmarks' relative size, internal features, or relation to surrounding context. To determine whether an indirect measure would prove more sensitive, we tracked eye movements during viewing. Changes in overall visual exploration, but not to specific regions of change, were related to participants' explicit endorsement of scenes as modified. These results support the contention that very familiar landmarks are represented at a global or gist level, but not local or fine-grained, level. These findings offer a unified view of memory for gist across verbal and spatial domains, and across recent and remote memory, with implications for hippocampal-neocortical interactions. [ABSTRACT FROM AUTHOR]

The process of urban landmark-based navigation has proven to be difficult to study in a rigorous fashion, primarily due to confounding variables and the problem of obtaining reliable data in real-world contexts. The development of high-resolution, immersive virtual reality technologies has opened exciting new possibilities for gathering data on human wayfinding that could not otherwise be readily obtained. We developed a research platform using a virtual environment and electroencephalography (EEG) to better understand the neural processes associated with landmark usage and recognition during urban navigation tasks. By adjusting the architectural parameters of different buildings in this virtual environment, we isolated and tested specific design features to determine whether or not they served as a target for landmarking. EEG theta band (4–7 Hz) event-related synchronization/desynchronization over posterior scalp areas was evaluated at the time when participants observed each target building along a predetermined self-paced route. A multi-level linear model was used to investigate the effects of salient architectural features on posterior scalp areas. Our results support the conclusion that highly salient architectural features—those that contrast sharply with the surrounding environment—are more likely to attract visual attention, remain in short-term memory, and activate brain regions associated with wayfinding compared with non-salient buildings. After establishing this main aggregate effect, we evaluated specific salient architectural features and neural correlates of navigation processing. The buildings that most strongly associated extended gaze time, location recall accuracy, and changes in theta-band neural patterns with landmarking in our study were those that incorporated rotational twist designs and natural elements such as trees and gardens. Other building features, such as unusual façade patterns or building heights, were to a lesser extent also associated with landmarking.

The process of urban landmark-based navigation has proven to be difficult to study in a rigorous fashion, primarily due to confounding variables and the problem of obtaining reliable data in real-world contexts. The development of high-resolution, immersive virtual reality technologies has opened exciting new possibilities for gathering data on human wayfinding that could not otherwise be readily obtained. We developed a research platform using a virtual environment and electroencephalography (EEG) to better understand the neural processes associated with landmark usage and recognition during urban navigation tasks. By adjusting the architectural parameters of different buildings in this virtual environment, we isolated and tested specific design features to determine whether or not they served as a target for landmarking. EEG theta band (4–7 Hz) event-related synchronization/desynchronization over posterior scalp areas was evaluated at the time when participants observed each target building along a predetermined self-paced route. A multi-level linear model was used to investigate the effects of salient architectural features on posterior scalp areas. Our results support the conclusion that highly salient architectural features—those that contrast sharply with the surrounding environment—are more likely to attract visual attention, remain in short-term memory, and activate brain regions associated with wayfinding compared with non-salient buildings. After establishing this main aggregate effect, we evaluated specific salient architectural features and neural correlates of navigation processing. The buildings that most strongly associated extended gaze time, location recall accuracy, and changes in theta-band neural patterns with landmarking in our study were those that incorporated rotational twist designs and natural elements such as trees and gardens. Other building features, such as unusual façade patterns or building heights, were to a lesser extent also associated with landmarking. [ABSTRACT FROM AUTHOR]

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This project tested three hypotheses conceptualizing the interaction between path integration based on self-motion and piloting based on landmarks in a familiar environment with persistent landmarks. The first hypothesis posits that path integration functions automatically, as in environments lacking persistent landmarks (environment-independent hypothesis). The second hypothesis suggests that persistent landmarks suppress path integration (suppression hypothesis). The third hypothesis proposes that path integration updates the spatial views of the environment (updating-spatial-views hypothesis). Participants learned a specific object's location. Subsequently, they undertook an outbound path originating from the object and then indicated the object's location (homing). In Experiments 1&1b, there were landmarks throughout the first 9 trials. On some later trials, the landmarks were presented during the outbound path but unexpectedly removed during homing (catch trials). On the last trials, there were no landmarks throughout (baseline trials). Experiments 2–3 were similar but added two identical objects (the original one and a rotated distractor) during homing on the catch and baseline trials. Experiment 4 replaced two identical objects with two groups of landmarks. The results showed that in Experiments 1&1b, homing angular error on the first catch trial was significantly larger than the matched baseline trial, undermining the environment-independent hypothesis. Conversely, in Experiment 2–4, the proportion of participants who recognized the original object or landmarks was similar between the first catch and the matched baseline trial, favoring the updating-spatial-views hypothesis over the suppression hypothesis. Therefore, while mismatches between updated spatial views and actual views of unexpected removal of landmarks impair homing performance, the updated spatial views help eliminate ambiguous targets or landmarks within the familiar environment. [ABSTRACT FROM AUTHOR]

This paper proposes an improved Retinex theory based on a weighted guided filter method to enhance images in low-light conditions. The captured images under low illumination can cause dimness, distortion or details loss. We use the weighted guided filter method to perform illumination estimation and the original image is regarded as the guidance image, which can avoid color distortion and over-enhancement. It can adjust the regularization parameter adaptively based on the image content. Perceptual contrast is improved by using an illumination enhancement method with dynamic adjustment. To test the validness of our algorithm, the weighted guided filter method proposed in this paper is compared with bilateral filter and the guided filter method. Finally, experiment under low illumination is implemented on a NAO robot by using the proposed weighted guided filter method based on EKF-SLAM. The experiment result demonstrates that the proposed weighted guided filter method is feasible and effective in low-light environment. [ABSTRACT FROM AUTHOR]

As smartphones and mobile data become universal in modern society, the opportunities to interact with the real world would grow tremendously. Latest Technologies such as Oculus Rift and Google Glass attempt to bridge the gap between the virtual and the material. With advancements in computing speed and image recognition, the idea of augmented reality (AR) becomes more tangible. However, the sheer complexity of image processing and feature recognition is an area of concern for AR. A successful AR system must distinguish among many landmarks and identify or classify the existence of new landmarks. AR algorithms naturally lend themselves to using deep learning because of the adaptability required to various factors. This paper aims to develop and refine a deep learning algorithm that can distinguish landmarks from images using a google landmark database of known landmarks. Instance-level recognition is universally used in areas of Landmark recognition and is also the upcoming research area. Instance-level recognition is the brain behind Landmark recognition. As in Landmarks, the goal is to seek an instance of a common group instead of a group, requiring new deep learning techniques. In this paper, three different VGG16, Inceptionv3, and ResNet50 models are trained using the transfer learning technique and a Pure Convolutional Neural Network (CNN) model is also trained from scratch. This paper proposes a modified version of the ResNet50 model to increase the accuracy and performance of the models used. The revised version of Resnet50 contains an additional Deep Local Features (DeLF) processing layer before generating the final output.

The utility and adoption of indoor localization applications have been limited due to the complex nature of the physical environment combined with an increasing requirement for more robust localization performance. Existing solutions to this problem are either too expensive or too dependent on infrastructure such as Wi-Fi access points. To address this problem, we propose APFiLoc—a low cost, smartphone-based framework for indoor localization. The key idea behind this framework is to obtain landmarks within the environment and to use the augmented particle filter to fuse them with measurements from smartphone sensors and map information. A clustering method based on distance constraints is developed to detect organic landmarks in an unsupervised way, and the least square support vector machine is used to classify seed landmarks. A series of real-world experiments were conducted in complex environments including multiple floors and the results show APFiLoc can achieve 80% accuracy (phone in the hand) and around 70% accuracy (phone in the pocket) of the error less than 2 m error without the assistance of infrastructure like Wi-Fi access points.

A role of perirhinal cortex (PrC) in recognition memory for objects has been well established. Contributions of parahippocampal cortex (PhC) to this function, while documented, remain less well understood. Here, we used fMRI to examine whether the organization of item-based recognition memory signals across these two structures is shaped by object category, independent of any difference in representing episodic context. Guided by research suggesting that PhC plays a critical role in processing landmarks, we focused on three categories of objects that differ from each other in their landmark suitability as confirmed with behavioral ratings (buildings > trees > aircraft). Participants made item-based recognition-memory decisions for novel and previously studied objects from these categories, which were matched in accuracy. Multi-voxel pattern classification revealed category-specific item-recognition memory signals along the long axis of PrC and PhC, with no sharp functional boundaries between these structures. Memory signals for buildings were observed in the mid to posterior extent of PhC, signals for trees in anterior to posterior segments of PhC, and signals for aircraft in mid to posterior aspects of PrC and the anterior extent of PhC. Notably, item-based memory signals for the category with highest landmark suitability ratings were observed only in those posterior segments of PhC that also allowed for classification of landmark suitability of objects when memory status was held constant. These findings provide new evidence in support of the notion that item-based memory signals for objects are not limited to PrC, and that the organization of these signals along the longitudinal axis that crosses PrC and PhC can be captured with reference to landmark suitability. [ABSTRACT FROM AUTHOR]

This article presents a self-localization scheme for indoor mobile robot navigation based on reliable design and recognition of artificial visual landmarks. Each landmark is patterned with a set of concentric circular rings in black and white, which reliably encodes the landmark's identity under environmental illumination. A mobile robot in navigation uses an onboard camera to capture landmarks in the environment. The landmarks in an image are detected and identified using a bilayer recognition algorithm: A global recognition process initially extracts candidate landmark regions across the whole image and tries to identify enough landmarks; if necessary, a local recognition process locally enhances those unidentified regions of interest influenced by illumination and incompleteness and reidentifies them. The recognized landmarks are used to estimate the position and orientation of the onboard camera in the environment, based on the geometric relationship between the image and environmental frames. The experiments carried out in a real indoor environment show high robustness of the proposed landmark design and recognition scheme to the illumination condition, which leads to reliable and accurate mobile robot localization. [ABSTRACT FROM AUTHOR]

Previous studies have showed that spatial memory declines with age but have not clarified the relevance of different landmark cues for specifying heading directions among different age groups. This study examined differences between younger, middle-aged and older adults in route learning and memory tasks after they navigated a virtual maze that contained: (a) critical landmarks that were located at decision points (i.e., intersections) and (b) non-critical landmarks that were located at non-decision points (i.e., the sides of the route). Participants were given a recognition memory test for critical and non-critical landmarks and also given a landmark-direction associative learning task. Compared to younger adults, older adults committed more navigation errors during route learning and were poorer at associating the correct heading directions with both critical and non-critical landmarks. Notably, older adults exhibited a landmark-direction associative memory deficit at decision points; this was the first finding to show that an associative memory deficit exist among older adults in a navigational context for landmarks that are pertinent for reaching a goal, and suggest that older adults may expend more cognitive resources on the encoding of landmark/object features than on the binding of landmark and directional information. This study is also the first to show that older adults did not have a tendency to process non-critical landmarks, which were regarded as distractors/irrelevant cues for specifying the directions to reach the goal, to an equivalent or larger extent than younger adults. We explain this finding in view of the low number of non-critical cues in our virtual maze (relative to a real-world urban environment) that might not have evoked older adults’ usual tendency toward processing or encoding distractors. We explain the age differences in navigational and cognitive performance with regards to functional and structural changes in the hippocampus and parahippocampus, and recommend further investigations into the functional connectivity between the prefrontal cortex and hippocampus for a better understanding of the landmark-direction associative learning among the elderly. Finally, it is hoped that the current behavioral findings will facilitate efforts to identify the neural markers of Alzheimer’s disease, a disease that commonly involves navigational deficits.

The hippocampus is believed to have evolved to support allocentric spatial representations of environments as well as the details of personal episodes that occur within them, whereas other brain structures are believed to support complementary egocentric spatial representations. Studies of patients with adult-onset lesions lend support to these distinctions for newly encountered places but suggest that with time and/or experience, schematic aspects of environments can exist independent of the hippocampus. Less clear is the quality of spatial memories acquired in individuals with impaired episodic memory in the context of a hippocampal system that did not develop normally. Here we describe a detailed investigation of the integrity of spatial representations of environments navigated repeatedly over many years in the rare case of H.C., a person with congenital absence of the mammillary bodies and abnormal hippocampal and fornix development. H.C. and controls who had extensive experience navigating the residential and downtown areas known to H.C. were tested on mental navigation tasks that assess the identity, location, and spatial relations among landmarks, and the ability to represent routes. H.C. was able to represent distances and directions between familiar landmarks and provide accurate, though inefficient, route descriptions. However, difficulties producing detailed spatial features on maps and accurately ordering more than two landmarks that are in close proximity to one another along a route suggest a spatial representation that includes only coarse, schematic information that lacks coherence and that cannot be used flexibly. This pattern of performance is considered in the context of other areas of preservation and impairment exhibited by H.C. and suggests that the allocentric-egocentric dichotomy with respect to hippocampal and extended hippocampal system function may need to be reconsidered.

Previous studies have showed that spatial memory declines with age but have not clarified the relevance of different landmark cues for specifying heading directions among different age groups. This study examined differences between younger, middle-aged and older adults in route learning and memory tasks after they navigated a virtual maze that contained: (a) critical landmarks that were located at decision points (i.e., intersections) and (b) non-critical landmarks that were located at non-decision points (i.e., the sides of the route). Participants were given a recognition memory test for critical and non-critical landmarks and also given a landmark-direction associative learning task. Compared to younger adults, older adults committed more navigation errors during route learning and were poorer at associating the correct heading directions with both critical and non-critical landmarks. Notably, older adults exhibited a landmark-direction associative memory deficit at decision points; this was the first finding to show that an associative memory deficit exist among older adults in a navigational context for landmarks that are pertinent for reaching a goal, and suggest that older adults may expend more cognitive resources on the encoding of landmark/object features than on the binding of landmark and directional information. This study is also the first to show that older adults did not have a tendency to process non-critical landmarks, which were regarded as distractors/irrelevant cues for specifying the directions to reach the goal, to an equivalent or larger extent than younger adults. We explain this finding in view of the low number of non-critical cues in our virtual maze (relative to a real-world urban environment) that might not have evoked older adults' usual tendency toward processing or encoding distractors. We explain the age differences in navigational and cognitive performance with regards to functional and structural changes in the hippocampus and parahippocampus, and recommend further investigations into the functional connectivity between the prefrontal cortex and hippocampus for a better understanding of the landmark-direction associative learning among the elderly. Finally, it is hoped that the current behavioral findings will facilitate efforts to identify the neural markers of Alzheimer's disease, a disease that commonly involves navigational deficits. [ABSTRACT FROM AUTHOR]

Due to the influences of many factors, the aircraft always deviates from the planned path during the process of actual flying. A landmark-based navigation technique is presented to reduce the flight path error and improve navigation precision of aircraft. The method refers to appropriate landmark selection based on the planned path and the view field of imaging, position estimation of aircraft based on the position recognition of landmark and flight parameters, and the flight path correction by comparing with the planned flight path. The robustness of the proposed method is demonstrated with the test data takes different levels of error on pitch by experiments. [ABSTRACT FROM AUTHOR]