.png){kind=icon}
返回
Segmentation Mask Refinement Using Image Transformations
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2017.2772269.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper discusses object proposal generation, which is a crucial step of instance-level semantic segmentation (instance segmentation). Known as a challenging computer vision task, the instance segmentation requires jointly detecting and segmenting individual instances of objects in an image. A common approach to this task is first to propose a set of class-agnostic object candidates in the forms of segmentation masks, which represent both object locations and boundaries, and then to perform classification on each object candidate. In this paper, we propose an effective refinement process that employs image transformations and mask matching to increase the accuracy of object segmentation masks. The proposed refinement process is applied to three state-of-the-art object proposal methods (DeepMask, SharpMask, and FastMask), and is evaluated on two standard benchmarks (Microsoft COCO and PASCAL VOC). Both the quantitative and qualitative results show the effectiveness of the process across various experimental settings.
Keyword:
Instance segmentation
object proposal
segmentation mask
convolutional neural networks
deep learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
分享收藏Using the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders With Youth Exhibiting Anger and Irritability使用统一的协议对表现出愤怒和易怒的年轻人进行情绪障碍的诊断治疗
PREAI
分享收藏
查看更多内容