In the context of advancing industrial automation, sensor-generated image data often exhibit high dimensionality and complexity, presenting challenges for effective feature extraction. Especially when ...

Feature extraction is the process of transforming an image into a set of features that capture its essential characteristics, such as edges, corners, textures, shapes, colors, or patterns. This ...

Code for Paper Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases - ...

Learn how to compare and combine different image features for predictive modeling. Discover common methods and techniques for feature extraction, comparison, and combination.

Here, we review two promising methods for capturing macro and micro architecture of histology images, Graph Neural Networks, which contextualize patch level information from their neighbors through ...

Traditional knowledge graphs (KGs) are usually comprised of entities, relationships, and attributes. However, they are not designed to effectively store or represent multimodal data. This limitation ...

Charts are commonly used to present data in digital documents such as web pages, research papers, or presentation slides. When the underlying data is not available, it is necessary to extract the data ...

Gershgorin circle theorem-based feature extraction for biomedical signal ... visibility graphs convert each data point in a time series into a node and then connect nodes with an edge if they ...