This project demonstrates instance segmentation using Mask R-CNN with the OpenCV DNN module. The model is pre-trained on the COCO dataset and can detect and segment multiple object classes in images.

Abstract: Ultrasound imaging is widely used in clinical practice due to its advantages of no radiation and real-time capability. However, its image quality is often degraded by speckle noise, low ...

Abstract: Brain tumors are among the deadliest diseases worldwide and require early and accurate diagnosis via Magnetic Resonance Imaging (MRI). Deep learning techniques, particularly convolutional ...

Abstract: Coronary Heart Disease popularly referred to as CHD is one of the leading causes of death and illness across the global population making it imperative for the identification of an effective ...

Abstract: Aiming at the problem that most thermal error prediction models for machine tools adopt a single neural network architecture, which is difficult to ...

Abstract: Human cognition is robust in estimating depth ordering and occluded regions of objects, including amodal instance segmentation (AIS). Object-centric representation learning (OCRL) is an ...

Abstract: Audio is vital information data for understanding various situations. A multitude of sound features can be explained by analysis through the audio signals. Numerous classification methods ...

Abstract: Efficient image compression is crucial for remote sensing (RS) satellite systems, as it determines the performance of machine vision applications analyzing the downlinked image data at ...

Abstract: ’Fake news’ refers to false, inaccurate, or misleading information that spreads as real news. Fake news primarily aims to affect societies and individuals by spreading false or misleading ...

Abstract: This paper introduces FMCNN, a novel classification method that combines a two-dimensional feature matrix capturing raw data characteristics with a CNN-based classifier. Raw data are the ...