Image Processing
The dataset is generated specifically to simulate the essential components for driving environments in a virtual campus of Chulalongkorn University, including street blocks, various pavements, lane markings, traffic signs, lamp poles, and pedestrians, among other features. We selected this campus for our simulation due to its distinctive road and pavement environments, which are unique to Thailand and other Asian countries. This choice contrasts with many widely cited datasets that predominantly feature environments from European or other regions.
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Arabic handwritten letters Dataset (AHLD) consists of 8,000 handwritten Arabic letter images of size 128x128 pixels, distributed into 28 classes (Arabic alphabets). This dataset is derived from processing 582 images, each containing several letters,
written by 15 individuals. The dataset creation involves a series of image processing operations: image acquisition, grayscale conversion, binarization, noise reduction, segmentation, normalization, skeletonization, and dataset labeling.
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Experimental results and analysis demonstrate that the proposed scheme is secure, reliable, easy to implement, and capable of handling binary, grayscale, and color secret images as well as cover images.The characteristics and effectiveness of the proposed algorithm are demonstrated through experiments and comparative analysis. Specifically, we illustrate its application by considering four cover images that share one secret image, implemented using Matlab 2018 programming. The experimental images are shown , and each size is 128´128.
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Chronic wounds pose an ongoing health concern globally, largely due to the prevalence of conditions such as diabetes and leprosy's disease. The standard method of monitoring these wounds involves visual inspection by healthcare professionals, a practice that could present challenges for patients in remote areas with inadequate transportation and healthcare infrastructure. This has led to the development of algorithms designed for the analysis and follow-up of wound images, which perform image-processing tasks such as classification, detection, and segmentation.
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The dataset aims to compile images of buildings with structural damage for analysis. The images can be classified by the severity of damage to building facades after seismic events using deep learning techniques, particularly pre-trained convolutional neural networks and transfer learning. The analysis can precisely identify structural damage levels, aiding in effective evaluation and response strategies.
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In order to train the joint contrastive representation learning module, we constructe a large Text Annotated Distortion, Appearance and Content (TADAC) image database.
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The LuFI-RiverSnap dataset includes close-range river scene images obtained from various devices, such as UAVs, surveillance cameras, smartphones, and handheld cameras, with sizes up to 4624 × 3468 pixels. Several social media images, which are typically volunteered geographic information (VGI), have also been incorporated into the dataset to create more diverse river landscapes from various locations and sources.
Please see the following links:
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628 Views
The paper assesses the effectiveness of sharpness metrics for monitoring the cleanliness of in-line thermographic systems and enabling self-diagnosis, in order to prevent degradation of metrologic performance and increase of measurement uncertainty. When optical measurement systems are installed in harsh industrial environments, external contaminants may compromise their operation conditions. Various types of dust may settle on the lens or protective window, deteriorating the quality of the generated signal.
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This study used boots, aircraft, cells, pliers, and guitars from 2D shapes included in the literature as data sets to test modeling success. These 2D shapes, which are mostly not publicly available data sets, form the target curves of IP. In this study, hand drawings with a curved structure were used in modeling, where the success of fitting precision could be better determined.
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