Image segmentation remains one of the major challenges in image analysis.And soft image segmentation has been widely used due to its good effect.Fuzzy clustering algorithms are very popular in soft segmentation.A new ...Image segmentation remains one of the major challenges in image analysis.And soft image segmentation has been widely used due to its good effect.Fuzzy clustering algorithms are very popular in soft segmentation.A new soft image segmentation method based on center-free fuzzy clustering is proposed.The center-free fuzzy clustering is the modified version of the classical fuzzy C-means ( FCM ) clustering.Different from traditional fuzzy clustering , the center-free fuzzy clustering does not need to calculate the cluster center , so it can be applied to pairwise relational data.In the proposed method , the mean-shift method is chosen for initial segmentation firstly , then the center-free clustering is used to merge regions and the final segmented images are obtained at last.Experimental results show that the proposed method is better than other image segmentation methods based on traditional clustering.展开更多
基于深度学习的目标检测方法是目前计算机视觉领域的热点,在目标识别、跟踪等领域发挥了重要的作用.随着研究的深入开展,基于深度学习的目标检测方法主要分为有锚框的目标检测方法和无锚框的目标检测方法,其中无锚框的目标检测方法无需...基于深度学习的目标检测方法是目前计算机视觉领域的热点,在目标识别、跟踪等领域发挥了重要的作用.随着研究的深入开展,基于深度学习的目标检测方法主要分为有锚框的目标检测方法和无锚框的目标检测方法,其中无锚框的目标检测方法无需预定义大量锚框,具有更低的模型复杂度和更稳定的检测性能,是目前目标检测领域中较前沿的方法.在调研国内外相关文献的基础上,梳理基于无锚框的目标检测方法及各场景下的常用数据集,根据样本分配方式不同,分别从基于关键点组合、中心点回归、Transformer、锚框和无锚框融合等4个方面进行整体结构分析和总结,并结合COCO(Common objects in context)数据集上的性能指标进一步对比.在此基础上,介绍了无锚框目标检测方法在重叠目标、小目标和旋转目标等复杂场景情况下的应用,聚焦目标遮挡、尺寸过小和角度多等关键问题,综述现有方法的优缺点及难点.最后对无锚框目标检测方法中仍存在的问题进行总结并对未来发展的应用趋势进行展望.展开更多
基金Supported by the National Natural Science Foundation of China(61103058,61233011)
文摘Image segmentation remains one of the major challenges in image analysis.And soft image segmentation has been widely used due to its good effect.Fuzzy clustering algorithms are very popular in soft segmentation.A new soft image segmentation method based on center-free fuzzy clustering is proposed.The center-free fuzzy clustering is the modified version of the classical fuzzy C-means ( FCM ) clustering.Different from traditional fuzzy clustering , the center-free fuzzy clustering does not need to calculate the cluster center , so it can be applied to pairwise relational data.In the proposed method , the mean-shift method is chosen for initial segmentation firstly , then the center-free clustering is used to merge regions and the final segmented images are obtained at last.Experimental results show that the proposed method is better than other image segmentation methods based on traditional clustering.
文摘基于深度学习的目标检测方法是目前计算机视觉领域的热点,在目标识别、跟踪等领域发挥了重要的作用.随着研究的深入开展,基于深度学习的目标检测方法主要分为有锚框的目标检测方法和无锚框的目标检测方法,其中无锚框的目标检测方法无需预定义大量锚框,具有更低的模型复杂度和更稳定的检测性能,是目前目标检测领域中较前沿的方法.在调研国内外相关文献的基础上,梳理基于无锚框的目标检测方法及各场景下的常用数据集,根据样本分配方式不同,分别从基于关键点组合、中心点回归、Transformer、锚框和无锚框融合等4个方面进行整体结构分析和总结,并结合COCO(Common objects in context)数据集上的性能指标进一步对比.在此基础上,介绍了无锚框目标检测方法在重叠目标、小目标和旋转目标等复杂场景情况下的应用,聚焦目标遮挡、尺寸过小和角度多等关键问题,综述现有方法的优缺点及难点.最后对无锚框目标检测方法中仍存在的问题进行总结并对未来发展的应用趋势进行展望.
