Abstract Gaze estimation is a fundamental task in many applications of cognitive sciences, human–computer interaction, and robotics. The purely data-driven appearance-based gaze methods may suffer from lack interpretability, which prevents their applicability to pervasive scenarios. In this study, feature fusion method with multi-level information elements proposed improve the comprehensive per...

Image fusion operation is beneficial to many applications and also one of the most common critical computer vision challenges. The perfect infrared visible image results should include important targets while preserving textural detail information as much possible. A novel framework proposed for this purpose. In paper, network (MIFFuse) an end-to-end, multi-level-based images. presented approac...

Information about urban land use is important for planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability remotely sensed (RS) development new methods integration to provide opportunities mapping types use. However, modes RS GBD are diverse due differences in data, study areas, classifiers, etc. In this context, aims summarize main evaluate t...

The segmentation of ground-based cloud images is the basis for obtaining numerous parameters. To achieve high-precision adaptive image requirements, this study designs a lightweight method named CloudDeepLabV3+ that integrates multi-scale features aggregation and multi-level attention feature enhancement. Firstly, novel EfficientNetV2-S designed as extraction backbone to reduce network Secondly...

Automatic image annotation is a process in which computer systems automatically assign the textual tags related with visual content to a query image. In most cases, inappropriate tags generated by the users as well as the images without any tags among the challenges available in this field have a negative effect on the query's result. In this paper, a new method is presented for automatic image...

objective: diabetes is one of the most common metabolic diseases. earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...

This paper presents a novel feature selection method called Feature Quality (FQ) measure based on the quality measure of individual features. We also propose novel combinations of two level and multi level dimensionality reduction methods which are based on the feature selection like mutual correlation, FQ measure and feature extraction methods like PCA(Principal Component Analysis)/LPP(Localit...

Variety of feature selection methods have been developed in the literature, which can be classified into three main categories: filter, wrapper and hybrid approaches. Filter methods apply an independent test without involving any learning algorithm, while wrapper methods require a predetermined learning algorithm for feature subset evaluation. Filter and wrapper methods have their drawbacks and...