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6 results for “Size Objectivity”
Image Dataset for Object Detection of Small Size Construction Tools
<p> This is an image dataset established as input data for object detection model of small-sized construction tools. In the dataset, there are 12 classes of target tools (bucket, cutter, drill, grinder, hammer, knife, saw, shovel, spanner, tacker, trowel, and wrench) which are typically used at indoor construction sites. 25,084 sets of image and the corresponding label data have been established and shared. </p> <p> The diversity of objects in the images of the 12 small tools was considered by photographing tools of various shapes, sizes, and colors. In addition, to improve the model performance, images were also captured with various changes (e.g., image resolution, occlusion, lighting, and background). Among the 25,084 images in the dataset, 6,258 (25%) were obtained from the actual construction site. </p> <p> Object annotations in each image were done by bounding boxes and were saved into a text file. The coordinates of the bounding box have the form of (Class, Center X, Center Y, Width, Height). Class refers to one of 12 construction tool types. Center X and Center Y are the center coordinates of the bounding box for an object from an image when the resolution of the image has min-max normalized. Width and Height are the width and height of the bounding box for an object, respectively, also from the image with the min-max normalized resolution.</p> <p> </p> <p>The peer-reviewed publication for this dataset has now been published in " KSCE Journal of Civil Engineering" a Springer journal as follows:</p> <p><strong>* Lee, K., Jeon, C., and Shin, D. (2023, In press) "Small Tool Image Database and Object Detection Approach for Indoor Construction Site Safety" <em>KSCE Journal of Civil Engineering</em>. DOI: https://doi.org/10.1007/s12205-022-1011-7</strong></p> <p>Please cite this reference when using the dataset.</p>
Cortical idiosyncrasies predict the perception of object size
<p>This archive contains preprocessed pRF data from our manuscript "Cortical idiosyncrasies predict the perception of object size" (originally name "Mapping the subjective experience of object size"). This has been published in Nature Communications in 2016: http://www.nature.com/ncomms/2016/160630/ncomms12110/full/ncomms12110.html. Stimulus presentation and data analysis code is available on FigShare: https://dx.doi.org/10.6084/m9.figshare.1579442.v5</p>
Instances and solutions for the multi-depot electric vehicle scheduling problem with the objective of minimizing the fleet size (EVSP-MD-FS)
<p>The set of instances and corresponding solutions, which were used in the computational study of the paper “Multi-depot electric vehicle scheduling in in-plant production logistics considering non-linear charging models”.</p>
Göhringer, F., Löhr-Limpens, M., Hesse, C., & Schenk, T. (2019) 'Grasping discriminates between object sizes less not more accurately than the perceptual system.' Vision. DOI 10.3390/vision3030036
<p>This repository contains 6 data files and 1 readme file.</p>
DATASET - Automated grain sizing from UAV imagery of a gravel-bed river: benchmarking of three object-based methods and analysis of particle-size clustering
<p>Dataset used to compute the grain size distributions from in-field line sampling and digitally on orthoimages with automated methodologies and by manual labelling. It also contains the data used to produce spatial statistics.</p>
Smaller is better? Unduly nice accuracy assessments in image classification due to spatial autocorrelation in identification of small sized objects
<p>Deriving the thematic accuracy of models is a fundamental part of image classification analyses. However, due to high spatial autocorrelation in remotely sensed imagery, accuracy assessments can be biased, which leads to relevant overestimation of accuracies. </p>
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