To accommodate rapid changes in the real world, cognition system of humans is capable continually learning concepts. On contrary, conventional deep models lack this capability preserving previously learned knowledge. When a neural network fine-tuned to learn new tasks, its performance on trained tasks will significantly deteriorate. Many recent works incremental object detection tackle problem ...

A novel approach to the automatic detection of fault surface images in 3D seismic datasets is presented. Based on the premise that seismic faulting introduces discontinuities into the rock layering (that is, the horizons), a coherency measure is used to detect points of significant horizon discontinuity. A highest confidence first (HCF) merging strategy is then combined with a flexible surface ...

Brain cancer is acknowledged as one of the most aggressive tumors, with a significant impact on patient survival rates. Unfortunately, approximately 70% patients diagnosed this malignant do not survive. This paper introduces method designed to detect and localize brain by proposing an automated approach for detection localization cancer. The utilizes magnetic resonance imaging analysis. By leve...

The staggered-grid finite difference (FD) method demands significantly computational capability and is inefficient for seismic wave modelling in 2-D viscoelastic media on a single PC. To improve computation speedup, a graphic processing units (GPUs) accelerated method was proposed, for modern GPUs have now become ubiquitous in desktop computers and offer an excellent cost-toperformance-ratio pa...