Visual Information Research Articles

A Visual Navigation Algorithm for UAV Based on Visual-Geography Optimization

The estimation of Unmanned Aerial Vehicle (UAV) poses using visual information is essential in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose a UAV visual navigation algorithm based on visual-geography Bundle Adjustment (BA) to address the challenge of missing geolocation information in monocular visual navigation. This algorithm presents an effective approach to UAV navigation and positioning. Initially, Visual Odometry (VO) was employed for tracking the UAV’s motion and extracting keyframes. Subsequently, a geolocation method based on heterogeneous image matching was utilized to calculate the geographic pose of the UAV. Additionally, we introduce a tightly coupled information fusion method based on visual-geography optimization, which provides a geographic initializer and enables real-time estimation of the UAV’s geographical pose. Finally, the algorithm dynamically adjusts the weight of geographic information to improve optimization accuracy. The proposed method is extensively evaluated in both simulated and real-world environments, and the results demonstrate that our proposed approach can accurately and in real-time estimate the geographic pose of the UAV in a GNSS-denied environment. Specifically, our proposed approach achieves a root-mean-square error (RMSE) and mean positioning accuracy of less than 13 m.

Widespread, perception-related information in the human brain scales with levels of consciousness

Abstract How does the human brain generate coherent, subjective perceptions—transforming yellow and oblong visual sensory information into the perception of an edible banana? This is a hard problem. According to the standard viewpoint, processing in groups of dedicated regions—identified as active “blobs” when using functional magnetic resonance imaging (fMRI)—gives rise to perception. Here, our research reveals a new organizational concept by discovering stimulus-specific information distributed throughout the whole brain. Using fMRI, we found stimulus-specific information across the neocortex, even in voxels previously considered “noise,” challenging traditional analytical approaches. Surprisingly, these stimulus-specific signals were also present in the subcortex and cerebellum and could be detected from across-subject variances. Additionally, we observed that the widespread signal in brain regions beyond the primary and secondary sensory cortices is influenced by sedation levels, suggesting a connection to perception rather than sensory encoding. We hypothesize that these widespread, stimulus-specific, and consciousness level-dependent signals may underlie coherent and subjective perceptions.

In the Eye of the Beholder: Visualizing Strengths, Burdens and Desires Through the Lens of Neurodivergent Children, Their Parents, and Professionals Using the Yucel Method

Abstract Objectives In mental healthcare, there is a growing emphasis on one’s strengths and context instead of focusing primarily on individual deficiencies, pathologies, risks, and negative emotions. Using the visual and tactile Yucel method, this study gains insight into strengths, burdens, and desires of neurodivergent children in residential care and their parents from different perspectives. Methods In this qualitative study, a total of 45 interviews with the Yucel method were conducted with nine neurodivergent children, their parents, and involved professionals. All built a constellation of the child and family’s strengths, burdens, and desires using the Yucel method. To explore if by applying this visual and tactile method additional information is provided to that gathered in the standard diagnostic process, the information of the Yucel method was compared to the information found in the multidisciplinary reports of the children. Results Thematic analysis showed that besides common mentioned strengths (e.g., mother, sports and activities) and burdens (mental health problems), differences in the informants' perspectives were found. Parents mentioned strengths and burdens that were not addressed by other informants and a diversity in needs was found. The comparison of the results from the interviews with the Yucel method with the information in the multidisciplinary reports showed that the Yucel method provided additional information. Conclusions The Yucel method brought more strengths and perspectives into view than diagnostics as usual in the first three months of residential care. This study contributes to the knowledge regarding personal, strength-based, and family-oriented diagnostics in mental healthcare of neurodivergent children and their families.

In the Eye of the Beholder-Visual Search Behavior in Equestrian Dressage Judges.

This study investigated the visual search behavior of equestrian dressage judges at different expertise levels during the assessment of Grand Prix horse-rider combinations. Twenty judges (11 foundational level, 9 advanced level) participated in the study, with their eye movements recorded using Tobii Fusion Eyetracker as they evaluated video recordings of dressage tests. Fixation metrics, namely Total Duration of Fixation (TDF), Average Duration of Fixation (ADF), and Total Number of Fixations (TNF), were analyzed across four Areas of Interest (AOIs): front, back, rider, and horse's feet. Statistical analysis utilized linear mixed-effects models. Results demonstrated that judges consistently focused more on the front of the horse, with additional differences in fixation duration and frequency based on judge experience and specific movements. Advanced judges focused more on the horses' feet, suggesting they draw meaning from specific areas indicative of performance quality. Conversely, foundational level judges focused more on the rider, reflecting different evaluative priorities at lower levels of the sport. These findings suggest that judges focus on a limited number of highly relevant areas, differing across movements and expertise levels. The study underscores the necessity of understanding both gaze behavior and subsequent interpretations of visual information to increase judging transparency, fairness, and equine welfare.

A survey of vision-based condition monitoring methods using deep learning: A synthetic fiber rope perspective

Computer vision technology has attracted significant interest in the condition monitoring (CM) community due to its potential to automate visual inspection and analysis of structures and components. By facilitating the processing and interpretation of visual information, including images and video data, computer vision holds promise for CM applications. However, it is essential to distinguish computer vision from non-contact CM techniques regarding their underlying principles and methods. While computer vision enables non-contact, remote monitoring, and condition assessment with minimal disruption to daily operations, it is distinct from non-contact CM techniques, which utilize various sensors to assess the condition of assets without physical contact or interference. Building upon the potential of computer vision technology, this survey paper presents a comprehensive overview of the current state-of-the-art CM methods based on computer vision and deep learning (DL) techniques, focusing on their application in monitoring synthetic fiber ropes (SFRs). SFRs are a viable alternative to steel wire ropes for underwater equipment and cranes that handle heavy loads. This is due to their high resistance to frictional wear, high tensile strength, lightweight, and flexibility. New materials, technologies, and processes for CM are being developed to meet the growing demand for SFRs. The paper explores ongoing research in applications that monitor the wear and aging of materials, as well as estimate their remaining useful life. The survey briefly discusses the traditional non-destructive testing and machine learning (ML) methods for CM applications. More importantly, DL-based methods, including supervised, unsupervised, semi-supervised, and self-supervised methods, are discussed in detail, together with the use of deep generative models and the recently developed diffusion models in the generation of synthetic datasets. Furthermore, the paper addresses the difficulties present in DL-based CM applications, including the scarcity of labeled data and the complexity and variety of the models used. The article ends by discussing the benefits of employing DL-based visual methods to understand SFR degradation processes, particularly in monitoring and maintenance.

Development of Promising Interventions to Improve Human Papillomavirus Vaccination in a School-Based Program in Quebec, Canada: Results From a Formative Evaluation Using a Mixed Methods Design.

Despite the availability of school-based human papillomavirus (HPV) vaccination programs, disparities in vaccine coverage persist. Barriers to HPV vaccine acceptance and uptake include parental attitudes, knowledge, beliefs, and system-level barriers. A total of 3 interventions were developed to address these barriers: an in-person presentation by school nurses, an email reminder with a web-based information and decision aid tool, and a telephone reminder using motivational interviewing (MI) techniques. Here we report on the development and formative evaluation of interventions to improve HPV vaccine acceptance and uptake among grade 4 students' parents in Quebec, Canada. In the summer of 2019, we conducted a formative evaluation of the interventions to assess the interventions' relevance, content, and format and to identify any unmet needs. We conducted 3 focus group discussions with parents of grade 3 students and nurses. Interviews were recorded, transcribed, and analyzed for thematic content using NVivo software (Lumivero). Nurses received training on MI techniques and we evaluated the effect on nurses' knowledge and skills using a pre-post questionnaire. Descriptive quantitative analyses were carried out on data from questionnaires relating to the training. Comparisons were made using the proportions of the results. Finally, we developed a patient decision aid using an iterative, user-centered design process. The iterative refinement process involved feedback from parents, nurses, and experts to ensure the tool's relevance and effectiveness. The evaluation protocol and data collection tools were approved by the CHU (Centre Hospitalier Universitaire) de Québec Research Ethics Committee (MP-20-2019-4655, May 16, 2019). The data collection was conducted from April 2019 to March 2021. Following feedback (n=28) from the 3 focus group discussions in June 2019, several changes were made to the in-person presentation intervention. Experts (n=27) and school nurses (n=29) recruited for the project appreciated the visual and simplified information on vaccination in it. The results of the MI training for school nurses conducted in August 2019 demonstrated an increase in the skills and knowledge of nurses (n=29). School nurses who took the web-based course (n=24) filled out a pretest and posttest questionnaire to evaluate their learning. The rating increased by 19% between the pretest and posttest questionnaires. Several changes were made between the first draft of the web-based decision-aid tool and the final version during the summer of 2019 after an expert consultation of experts (n=3), focus group participants (n=28), and parents in the iterative process (n=5). More information about HPV and vaccines was added, and users could click if more detail is desired. We developed and pilot-tested 3 interventions using an iterative process. The interventions were perceived as potentially effective to increase parents' knowledge and positive attitudes toward HPV vaccination, and ultimately, vaccine acceptance. Future research will assess the effectiveness of these interventions on a larger scale.

MR–CT image fusion method of intracranial tumors based on Res2Net

BackgroundInformation complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation.PurposeThe purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method.MethodsThis paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image.ResultsQualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics.ConclusionThe proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

The features analysis of hemoglobin expression on visual information transmission pathway in early stage of Alzheimer’s disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized primarily by cognitive impairment. The motivation of this paper is to explore the impact of the visual information transmission pathway (V–H pathway) on AD, and the following feature were observed: Hemoglobin expression on the V–H pathway becomes dysregulated as AD occurs so as to the pathway becomes dysfunctional. According to the feature, the following conclusion was proposed: As AD occurs, abnormal tau proteins penetrate bloodstream and arrive at the brain regions of the pathway. Then the tau proteins or other toxic substances attack hemoglobin molecules. Under the attack, hemoglobin expression becomes more dysregulated. The dysfunction of V–H pathway has an impact on early symptoms of AD, such as spatial recognition disorder and face recognition disorder.

Mapping olfactory cues for wayfinding – a theoretical approach and an empirical study

ABSTRACT Nowadays, navigation systems are widely used to find the shortest path between a starting point and a destination. In order to guide the user these systems rely on visual information. However, consider, e.g. visually impaired or elderly people. They may not be able to navigate using only visual information. Additionally, sometimes there is no visual information (i.e. visual landmark) available. In such a case olfactory information could be a helpful route guidance. To identify possible olfactory cues, we performed two online studies as well as a focus-group interview and identified 13 different smell classes usable for navigation and wayfinding. Based on our results, we gathered data for these classes in an urban environment. We collected 201 smells and mapped their values for the properties temporary, intensity, and notes. We underline the findings of previous studies that the majority of odours in an urban area are gastronomy and waste-related odours. We find a number of smells that have daily or seasonal dependencies and conclude that the temporary property should be captured in more detail in future studies. We also discuss additional properties of smells and smell mapping that should be considered in future work.

Visual information and appearance: The impact of visual attributes of user-generated photos on review helpfulness

Images have become integral to consumers’ sharing of consumption experiences due to their abilities of carrying rich and vivid information. Drawing from the perspective of information theory and the elaboration likelihood model (ELM), this study investigates impacts of visual information and visual appearance of user-generated photos (UGPs) on customers’ perceived review helpfulness. We utilize deep learning techniques to calculate the breadth and depth of photos (visual information), and evaluate the aesthetic value of the photos (visual appearance). By collecting a substantial amount of review, reviewer and restaurant information from the Yelp platform, our results demonstrate that the visual breadth and visual depth of review photos have a significant positive impact on review helpfulness. Notably, reviewer status and review length moderate these effects. These insights offer valuable strategies for both restaurant managers and online restaurant platforms regarding UGP management.

A task‐centric knowledge graph construction method based on multi‐modal representation learning for industrial maintenance automation

AbstractMaintenance manuals are crucial information sources for maintenance and repair. Prior studies explored factual knowledge extraction from textual documents. However, maintenance knowledge in manuals is more task‐centric rather than factual knowledge and often documented in an unstructured Portable Document Format (PDF), posing challenges for knowledge extraction. Addressing this, this research develops effective methods to extract task‐centric maintenance knowledge from unstructured PDF manuals. A new Task‐centric Knowledge Graph (TCKG) schema centralized on maintenance task components (MTCs) is proposed to address the need for structured knowledge representation. A method (Heterogeneous Graph‐based Method, HGM) for knowledge extraction is then proposed, which is enhanced by incorporating visual and spatial information. In the experiments, the proposed HGM exhibits robust performance in the knowledge extraction process, surpassing the baseline Graph‐based Interaction Model with a Tracker (GIT) method in MTCs extraction by 13.3%, and the baseline Translate Embedding (TransE) method in MTCs' relation extraction by 3.8%. A series of ablation studies also prove that including visual and spatial information through the proposed method can improve the relation extraction performance by over 10%. This research supplies valuable insights for future developments in information extraction from maintenance manuals.

A Framework for Extensive Content-Based Image Retrieval System Incorporating Relevance Feedback and Query Suggestion

Over the last decade, there is a huge amount of images in which visual information has become increasingly important. If the images are to be utilized, they must be organized into databases, from there it can be searched based on different criteria. Content-based Image retrieval (CBIR) frameworks have turned into a mainstream subject of exploration; for their ability to retrieve images based on the real visual content rather than by manually connected textual descriptions. In the CBIR framework, analysis and interpretation of image information in large and diverse image databases is evidently complex because there is no prior information on the size or scale of individual structures within the images to be analyzed. In CBIR, retrieval is based on visual image features, which can be extracted automatically from the images with the help of human intervention, namely a technique called relevance feedback. Nonetheless, an efficient way of differentiating the visual content of images is complex to produce. Therefore, rather than a perfect solution CBIR systems must be able to exploit a partial solution to the problem of image understanding. In this paper, there is implementation of a CBIR framework is introduced that not only tries to efficiently capture the user intent based on the feedback but also provide query suggestions that can help its users to pose better queries in order to retrieve desired results in an efficient manner. The proposed technique is straightforward to implement and scopes efficiently to huge datasets. Extensive experiments on diverse real datasets with image similarity measures have revealed the dominance of the proposed method over original algorithms.

A survey for table recognition based on deep learning

Tables, as an important information interaction media containing much structured information, are popularly adopted in our life. There are a lot of table recognition methods for understanding and applying these information. Among them, table recognition based on deep learning is the mainstream. Therefore, we conducted this survey to summarize the latest research on table recognition based on deep learning in recent years. Table recognition can be divided into three sub-tasks, namely table detection, table structure recognition and table content recognition, so we firstly introduce the importance and significance of table recognition research and analyze the current challenges faced by each sub-task. Then, we list the existing public table recognition datasets and introduce commonly used evaluation metrics. Furthermore, we review deep learning-based solutions proposed in recent years for three different sub-tasks and reclassify them. Especially for table structure recognition, we reclassify them according to their annotation schemes. Finally, we report the performance of some excellent methods, such as CascadeTabNet, TSRFormer, SEM, etc, on public datasets, and describe the future research directions, including table recognition in natural scenes, question and answer about tables, visual information extraction, domain adaptation etc. In summary, this survey reviews the data, evaluations, methods, and development directions of table recognition. Table understanding and domain adaptation need as well to be focused on. The former can predict the relationships between two table elements, which helps us to understand the table. The latter can enhance the generalization ability of table recognition methods, thereby obtaining more robust recognition performance. Both are important development directions for table recognition in the future.

From sight to insight: A multi-task approach with the visual language decoding model

Visual neural decoding aims to unlock the mysteries of how the human brain interprets the visual world through predicting perceived visual information from visual neural activity. While early studies made some progress in decoding visual activity for singular type of information, they failed to concurrently reveal the multi-level interweaving linguistic information in the brain. Here, we developed a novel Visual Language Decoding Model (VLDM) that can simultaneously decode the main categories, semantic labels, and textual descriptions of visual stimuli from visual activities. The large-scale NSD dataset was utilized to ensure the efficiency of the decoding model in joint training and evaluation across multiple tasks. For category decoding, we achieved the effective classification of 12 categories with an accuracy of nearly 70 %, significantly surpassing the chance level. For label decoding, we attained the precise prediction of 80 specific semantic labels with a 16-fold improvement over the chance level. For text decoding, the scores of the decoded text surpassed the corresponding baseline levels by remarkable margins on six evaluation metrics. These results highlight the complexity of how the brain processes visual information and the close connection between visual perception and language cognition. This study contributes significantly to extensive applications in multi-layered brain-computer interfaces, potentially leading to more natural and efficient human-computer interaction experiences.

A scene with an invisible wall - navigational experience shapes visual scene representation.

Human navigation heavily relies on visual information. Although many previous studies have investigated how navigational information is inferred from visual features of scenes, little is understood about the impact of navigational experience on visual scene representation. In this study, we examined how navigational experience influences both the behavioral and neural responses to a visual scene. During training, participants navigated in the virtual reality (VR) environments which we manipulated navigational experience while holding the visual properties of scenes constant. Half of the environments allowed free navigation (navigable), while the other half featured an 'invisible wall' preventing the participants to continue forward even though the scene was visually navigable (non-navigable). During testing, participants viewed scene images from the VR environment while completing either a behavioral perceptual identification task (Experimentl) or an fMRI scan (Experiment2). Behaviorally, we found that participants judged a scene pair to be significantly more visually different if their prior navigational experience varied, even after accounting for visual similarities between the scene pairs. Neurally, multi-voxel pattern of the parahippocampal place area (PPA) distinguished visual scenes based on prior navigational experience alone. These results suggest that the human visual scene cortex represents information about navigability obtained through prior experience, beyond those computable from the visual properties of the scene. Taken together, these results suggest that scene representation is modulated by prior navigational experience to help us construct a functionally meaningful visual environment.

Factorized visual representations in the primate visual system and deep neural networks.

Object classification has been proposed as a principal objective of the primate ventral visual stream and has been used as an optimization target for deep neural network models (DNNs) of the visual system. However, visual brain areas represent many different types of information, and optimizing for classification of object identity alone does not constrain how other information may be encoded in visual representations. Information about different scene parameters may be discarded altogether ('invariance'), represented in non-interfering subspaces of population activity ('factorization') or encoded in an entangled fashion. In this work, we provide evidence that factorization is a normative principle of biological visual representations. In the monkey ventral visual hierarchy, we found that factorization of object pose and background information from object identity increased in higher-level regions and strongly contributed to improving object identity decoding performance. We then conducted a large-scale analysis of factorization of individual scene parameters - lighting, background, camera viewpoint, and object pose - in a diverse library of DNN models of the visual system. Models which best matched neural, fMRI, and behavioral data from both monkeys and humans across 12 datasets tended to be those which factorized scene parameters most strongly. Notably, invariance to these parameters was not as consistently associated with matches to neural and behavioral data, suggesting that maintaining non-class information in factorized activity subspaces is often preferred to dropping it altogether. Thus, we propose that factorization of visual scene information is a widely used strategy in brains and DNN models thereof.

Optimal gamma-band entrainment of visual cortex.

Visual entrainment is a powerful and widely used research tool to study visual information processing in the brain. While many entrainment studies have focused on frequencies around 14-16 Hz, there is renewed interest in understanding visual entrainment at higher frequencies (e.g., gamma-band entrainment). Notably, recent groundbreaking studies have demonstrated that gamma-band visual entrainment at 40 Hz may have therapeutic effects in the context of Alzheimer's disease (AD) by stimulating specific neural ensembles, which utilize GABAergic signaling. Despite such promising findings, few studies have investigated the optimal parameters for gamma-band visual entrainment. Herein, we examined whether visual stimulation at 32, 40, or 48 Hz produces optimal visual entrainment responses using high-density magnetoencephalography (MEG). Our results indicated strong entrainment responses localizing to the primary visual cortex in each condition. Entrainment responses were stronger for 32 and 40 Hz relative to 48 Hz, indicating more robust synchronization of neural ensembles at these lower gamma-band frequencies. In addition, 32 and 40 Hz entrainment responses showed typical patterns of habituation across trials, but this effect was absent for 48 Hz. Finally, connectivity between visual cortex and parietal and prefrontal cortices tended to be strongest for 40 relative to 32 and 48 Hz entrainment. These results suggest that neural ensembles in the visual cortex may resonate at around 32 and 40 Hz and thus entrain more readily to photic stimulation at these frequencies. Emerging AD therapies, which have focused on 40 Hz entrainment to date, may be more effective at lower relative to higher gamma frequencies, although additional work in clinical populations is needed to confirm these findings. PRACTITIONER POINTS: Gamma-band visual entrainment has emerged as a therapeutic approach for eliminating amyloid in Alzheimer's disease, but its optimal parameters are unknown. We found stronger entrainment at 32 and 40 Hz compared to 48 Hz, suggesting neural ensembles prefer to resonate around these relatively lower gamma-band frequencies. These findings may inform the development and refinement of innovative AD therapies and the study of GABAergic visual cortical functions.

Retinal bipolar cells borrow excitability from electrically coupled inhibitory interneurons to amplify excitatory synaptic transmission.

Bipolar cells of the retina carry visual information from photoreceptors in the outer retina to retinal ganglion cells (RGCs) in the inner retina. Bipolar cells express L-type voltage-gated Ca2+ channels at the synaptic terminal, but generally lack other types of channels capable of regenerative activity. As a result, the flow of information from outer to inner retina along bipolar cell processes is generally passive in nature, with no opportunity for signal boost or amplification along the way. Here we report the surprising discovery that blocking voltage-gated Na+ channels profoundly reduces the synaptic output of one class of bipolar cell, the type 6 ON bipolar cell (CBC6), despite the fact that the CBC6 itself does not express voltage-gated Na+ channels. Instead, CBC6 borrows voltage-gated Na+ channels from its neighbor, the inhibitory AII amacrine cell, with whom it is connected via an electrical synapse. Thus, an inhibitory neuron aids in amplification of an excitatory signal as it moves through the retina, ensuring that small changes in the membrane potential of bipolar cells are reliably passed onto downstream RGCs.

Reconfiguration of the visual code and retinal cell type complement in closely related diurnal and nocturnal mice.

How does evolution act on neuronal populations to match computational characteristics to functional demands? We address this problem by comparing visual code and retinal cell composition in closely related murid species with different behaviours. Rhabdomys pumilio are diurnal and have substantially thicker inner retina and larger visual thalamus than nocturnal Mus musculus. High-density electrophysiological recordings of visual response features in the dorsal lateral geniculate nucleus (dLGN) reveals that Rhabdomys attains higher spatiotemporal acuity both by denser coverage of the visual scene and a selective expansion of elements of the code characterised by non-linear spatiotemporal summation. Comparative analysis of single cell transcriptomic cell atlases reveals that realignment of the visual code is associated with increased relative abundance of bipolar and ganglion cell types supporting OFF and ON-OFF responses. These findings demonstrate how changes in retinal cell complement can reconfigure the coding of visual information to match changes in visual needs.

Influence of visual information on sniffing behavior in a routinely trichromatic primate.

Most catarrhine primates are considered to be strongly visually oriented, obtaining information about conspecifics and their environment from a diversity of visual cues. Other sensory modalities may provide information that is redundant and/or complimentary to visual cues. When cues from multiple sensory modalities are available, these may reinforce or suppress each other, as shown in several taxa ranging from insects to humans. Here, we tested how the presence and ambiguity of visual information affect the use of olfactory cues when exploring food and non-food items in semi-free-ranging Barbary macaques at Affenberg Salem, Germany. We presented monkeys with pipes containing food (peanuts, popcorn), non-food (stones, feces), or no items in transparent or opaque containers and assessed whether animals looked, sniffed, and/or grabbed into the pipes depending on the visibility of the contents (experiment 1). Visual information had no robust effect on sniffing probability, but monkeys were more likely to sniff before any other form of inspection if the can was opaque than if it was transparent. Both visual and olfactory information affected, whether or not monkeys attempted to retrieve the items from the pipes, whereby monkeys showed an overall decrease in the propensity to grab after sniffing. Furthermore, we manipulated the visual appearance of familiar food items (popcorn) with food colorant (experiment 2), which resulted in substantially increased olfactory inspections compared to unmanipulated popcorn. Taken together, reliance on the olfactory sense was modulated by the available visual information, emphasizing the interplay between different sensory modalities for obtaining information about the environment.