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9 results for “odor detection”
The Object Detection for Olfactory References (ODOR) Dataset
<p><strong>The Object Detection for Olfactory References (ODOR) Dataset</strong></p> <p>Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. </p> <p>Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. </p> <p>It has challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. </p> <p>Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.</p> <p><strong>How to use</strong></p> <p>The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes or image descriptions, as well as formal annotations, such as artist, license, or creation year. </p> <p>In addition to a zip containing the dataset images, we provide links to their source collections in the metadata file and a Python script to conveniently download the artwork images (`download_imgs.py`).</p> <p>The mapping between the `images` array of the `annotations.json` and the `metadata.csv` file can be accomplished via the `file_name` attribute of the elements of the `images` array and the unique `File Name` column of the `metadata.csv` file, respectively.</p>
The Object Detection for Olfactory References (ODOR) Dataset.
<p>Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. Existing datasets provide instance-level<br> annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The proposed ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. Conducting a statistical analysis, we showcase challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. Furthermore, we provide an extensive baseline analysis for object detection models and highlight the challenging properties of the dataset through a set of secondary studies. Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.</p> <p><strong>How to use</strong></p> <p>The annotations are provided in COCO JSON format. To represent the two-level hierarchy of the object classes, we make use of the supercategory field in the categories array as defined by COCO. In addition to the object-level annotations, we provide an additional CSV file with image-level metadata, which includes content-related fields, such as Iconclass codes [72 , 73]) or image<br> descriptions, as well as formal annotations, such as artist, license, or creation year. For the sake of license compliance, we do not publish the images directly (although most of the images are public domain). Instead, we provide links to their source collections in the metadata file (meta.csv) and a python script to download the artwork images (download_images.py).</p> <p> </p>
Does the tail show when the nose knows? AI-enhanced analysis of tail kinematics outperforms human experts at predicting when detection dogs find their target odor
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Does scent attractiveness reveal women's ovulatory timing? Evidence from signal detection analyses and endocrine predictors of odor attractiveness
<p>Odor cues associated with shifts in ovarian hormones indicate ovulatory timing in females of many nonhuman species. Although prior evidence supports women's body odors smelling more attractive on days when conception is possible, that research has left ambiguous how diagnostic of ovulatory timing odor cues are, as well as whether shifts in odor attractiveness are correlated with shifts in ovarian hormones. Here, 46 women each provided 6 overnight scent and corresponding day saliva samples spaced 5 days apart, and completed luteinizing hormone tests to determine ovulatory timing. Scent samples collected near ovulation were rated more attractive, on average, relative to samples from the same women collected on other days. Importantly, however, signal detection analyses showed that rater discrimination of fertile window timing from odor attractiveness ratings was very poor. Within-women shifts in salivary estradiol and progesterone were not significantly associated with within-women shifts in odor attractiveness. Between-women, mean estradiol was positively associated with mean odor attractiveness. Our findings suggest that raters cannot reliably detect women's ovulatory timing from their scent attractiveness. The between-women effect of estradiol raises the possibility that women's scents provide information about overall cycle fecundity, though further research is necessary to rigorously investigate this possibility.</p>
Does scent attractiveness reveal women’s ovulatory timing? Evidence from signal detection analyses and endocrine predictors of odor attractiveness
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Detection of prey odors underpins dietary specialization in a Neotropical top-predator: how army ants find their ant prey
<p>1. Deciphering the mechanisms that underpin dietary specialization and niche partitioning is crucial to understanding the maintenance of biodiversity. New world army ants live in species-rich assemblages throughout the Neotropics and are voracious predators of other arthropods. They are therefore an important and potentially informative group for addressing how diverse predator assemblages partition available prey resources. 2. New World army ants are largely specialist predators of other ants, with each species specializing on different ant genera. However, the mechanisms of prey choice are unknown. In this study, we addressed whether the army ant Eciton hamatum: 1) can detect potential prey odors, 2) can distinguish between odors of prey and non-prey, and 3) can differentiate between different types of odors associated with its prey. 3. Using field experiments, we tested the response of army ants to the following four odor treatments: alarm odors, dead ants, live ants, and nest material. Each treatment had a unique combination of odor sources and included some movement in two of the treatments (alarm and live ants). Odor treatments were tested for both prey and non-prey ants. These data were used to determine the degree to which E. hamatum are using specific prey stimuli to detect potential prey and direct their foraging. 4. Army ants responded strongly to odors derived from prey ants, which triggered both increased localized recruitment and slowed advancement of the raid as they targeted the odor source. Odors from non-prey ants were largely ignored. Additionally, the army ants had the strongest response to the nest material of their preferred prey, with progressively weaker responses across the live ant, dead ant, and alarm odors treatments, respectively. 5. This study reveals that the detection of prey odors, and especially the most persistent odors related to the prey's nest, provides a mechanism for dietary specialization in army ants. If ubiquitous across the Neotropical army ants, then this olfaction-based ecological specialization may facilitate patterns of resource partitioning and coexistence in these diverse predator communities. 08-Jan-2020</p>
Detection of prey odors underpins dietary specialization in a Neotropical top-predator: how army ants find their ant prey
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A conserved odor detection pathway via modulation of chromatin and cellular gene expression
GEO Series GSE116502. Arabidopsis thaliana; Mus musculus; Drosophila melanogaster. 34 samples. Type: Expression profiling by high throughput sequencing.
Non Invasive Detection of Lung and Breast Cancer by Odor Signature
ClinicalTrials.gov study NCT02195076. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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