618 research outputs found
ASF-Net: Robust Video Deraining via Temporal Alignment and Online Adaptive Learning
In recent times, learning-based methods for video deraining have demonstrated
commendable results. However, there are two critical challenges that these
methods are yet to address: exploiting temporal correlations among adjacent
frames and ensuring adaptability to unknown real-world scenarios. To overcome
these challenges, we explore video deraining from a paradigm design perspective
to learning strategy construction. Specifically, we propose a new computational
paradigm, Alignment-Shift-Fusion Network (ASF-Net), which incorporates a
temporal shift module. This module is novel to this field and provides deeper
exploration of temporal information by facilitating the exchange of
channel-level information within the feature space. To fully discharge the
model's characterization capability, we further construct a LArge-scale RAiny
video dataset (LARA) which also supports the development of this community. On
the basis of the newly-constructed dataset, we explore the parameters learning
process by developing an innovative re-degraded learning strategy. This
strategy bridges the gap between synthetic and real-world scenes, resulting in
stronger scene adaptability. Our proposed approach exhibits superior
performance in three benchmarks and compelling visual quality in real-world
scenarios, underscoring its efficacy. The code is available at
https://github.com/vis-opt-group/ASF-Net
Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review
Medical imaging techniques, such as (cone beam) computed tomography and magnetic resonance imaging, have proven to be a valuable component for oral and maxillofacial surgery (OMFS). Accurate segmentation of the mandible from head and neck (H&N) scans is an important step in order to build a personalized 3D digital mandible model for 3D printing and treatment planning of OMFS. Segmented mandible structures are used to effectively visualize the mandible volumes and to evaluate particular mandible properties quantitatively. However, mandible segmentation is always challenging for both clinicians and researchers, due to complex structures and higher attenuation materials, such as teeth (filling) or metal implants that easily lead to high noise and strong artifacts during scanning. Moreover, the size and shape of the mandible vary to a large extent between individuals. Therefore, mandible segmentation is a tedious and time-consuming task and requires adequate training to be performed properly. With the advancement of computer vision approaches, researchers have developed several algorithms to automatically segment the mandible during the last two decades. The objective of this review was to present the available fully (semi)automatic segmentation methods of the mandible published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field to help develop novel automatic methods for clinical applications
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