Single-image-based view generation (SIVG) is important for producing 3D stereoscopic content. Here, handling different spatial resolutions as input and optimizing both reconstruction accuracy and processing speed is desirable. Latest approaches are based on convolutional neural network (CNN), and they generate promising results. However, their use of fully connected layers as well as pre-traine...

Facial landmark detection is an important but challenging task for real-world computer vision applications. This paper proposes an accurate and robust approach for facial landmark detection by combining data-driven and modeldriven methods. Firstly, a fully convolutional network (FCN) is trained to generate response maps of all facial landmark points. Such a data-driven method can make full use ...

RESUMEN. Doce ratas Sprague-Dawley fueron sujetas a la lesión de la fimbria fornix (transección de la vía septohipocampal). Seis de estos animales recibieron inyecciones intraventriculares de 3 pg de factor de crecimiento nervioso (FCN) en días alternos y durante dos semanas, mientras que los otros seis recibieron citocromo c en igual esquema y dosis (controles). La actividad específica de la g...

For complex segmentation tasks, fully automatic systems are inherently limited in their achievable accuracy for extracting relevant objects. Especially in cases where only few data sets need to be processed for a highly accurate result, semi-automatic segmentation techniques exhibit a clear benefit for the user. One area of application is medical image processing during an intervention for a si...

Building detection and footprint extraction are highly demanded for many remote sensing applications. Though most previous works have shown promising results, the automatic extraction of building footprints still remains a nontrivial topic, especially in complex urban areas. Recently developed extensions of the CNN framework made it possible to perform dense pixel-wise classification of input i...

Semantic segmentation is a fundamental task in remote sensing image interpretation, which aims to assign semantic label for every pixel the given image. Accurate still challenging due complex distributions of various ground objects. With development deep learning, series networks represented by fully convolutional network (FCN) has made remarkable progress on this problem, but accuracy far from...

In this study, we proposed and validated a multi-atlas diffeomorphism guided 3D fully convolutional network (FCN) ensemble model (M-FCN) for segmenting brain anatomical regions of interest (ROIs) from structural magnetic resonance images (MRIs). A novel based encoding block ROI patches with adaptive sizes were used. the block, both MRI intensity profiles expert priors deformed atlases encoded f...