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.

In order to train the joint contrastive representation learning module, we constructe a large Text Annotated Distortion, Appearance and Content (TADAC) image database.

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.

The data used in this study was gathered from various practical applications, specifically sourced from an access control device that incorporates a hardware module. Meanwhile, the data acquired through sampling conducted under 850nm light. The hardware device itself was utilized in diverse scenarios, encompassing indoor and outdoor environments, with and without background influences. Users participated in the data collection process by placing their palms naturally onto the device for multiple captures.