-
Amodal Completion 如何在部分被遮挡的情况下划分整个物体
图像分割方法
传统方法
基于阈值的分割
固定阈值分割
直方图双峰法
Prewitt et al.
迭代阈值图像分割
自适应阈值图像风格
大津法/OTSU/最大类间方差法
- Otsu的实现
def otsu(src):
```
input: a single channel image
output: a binary image, using otsu thresholding
```
hist, _ = np.histogram(src, bins=list(range(257)))
# OTSU method to estimate the threshold
hist_norm = hist/hist.sum()
Q = hist_norm.cumsum()
bins = np.arange(256)
fn_min = np.inf
thresh = -1
for i in range(255):
p1,p2 = np.hsplit(hist_norm,[i]) # probabilities
q1,q2 = Q[i],Q[255]-Q[i] # cum sum of classes
if q1 < 1.e-6 or q2 < 1.e-6:
continue
b1,b2 = np.hsplit(bins,[i]) # weights
# finding means and variances
m1,m2 = np.sum(p1*b1)/q1, np.sum(p2*b2)/q2
v1,v2 = np.sum(((b1-m1)**2)*p1)/q1,np.sum(((b2-m2)**2)*p2)/q2
# calculates the minimization function
fn = v1*q1 + v2*q2
if fn < fn_min:
fn_min = fn
thresh = i
# print("Threshold is %d"%thresh)
# ## if you want to check the histogram
# plt.hist(v.ravel(),256,[0,256])
# plt.show()
[height, width] = src.shape
binary = np.zeros([height, width])
for iy in range(height):
for ix in range(width):
if src[iy, ix] > thresh:
binary[iy, ix] = 255
else:
binary[iy, ix] = 0
return binary均值法
最佳阈值
应用聚类算法
K-means和GMM
DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
超像素Superpixels
- https://cm_westwood.gitee.io/image_processing_homework/#header-n54
- 算法的提出 Ren, Malik. Learning a classification model for segmentation[C]. international conference on computer vision, 2003: 10-17.
- 评价标准 Den Bergh M V, Boix X, Roig G, et al. SEEDS: Superpixels Extracted Via Energy-Driven Sampling[J]. International Journal of Computer Vision, 2015, 111(3): 298-314.
经典算法 SLIC
- Stutz D, Hermans A, Leibe B, et al. Superpixels: An evaluation of the state-of-the-art[J]. Computer Vision and Image Understanding, 2018: 1-27.
改进算法SEEDS，ETPS
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Yao J, Boben M, Fidler S, et al. Real-time coarse-to-fine topologically preserving segmentation[C]. computer vision and pattern recognition, 2015: 2947-2955.
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https://zhuanlan.zhihu.com/p/30732385
- 基于阈值的分割
- 固定阈值分割
- 直方图双峰法Prewitt et al.
- 迭代阈值图像分割
- 自适应阈值图像风格
- 大津法/OTSU/最大类间方差法
- 均值法
- 最佳阈值
- 基于边缘的分割
- Canny边缘检测器
- 需要两个参数maxVal和minVal
- Harris角点检测器
- SIFT检测器
- SURF检测器
- Canny边缘检测器
- 基于区域的分割
- 种子区域生长法， Levine et al.
- 区域分裂合并法Gonzalez et al.
- 分水岭法Meyer et al.
- 基于图论的分割
- GraphCut
- 基于能量泛函的分割
- 基于阈值的分割
通过机器学习的方法
FCN
- CVPR 2015
ReSeg
U-net
ParseNet
DeepMask
SegNet
Instance-Aware Segmentation
DeepLab, DeepLabv3
- 参考文献（2018）
- Pytorch实现
RefineNet
Pyramid Scene Parsing Network (PSPNet)
Dense-Net
Mask-Lab
PANet
- Path aggregation network for instance segmentation
DANet
- Dual attention network for scene segmentation
FastFCN
- FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation
Gated-SCNN
- Gated-SCNN: Gated Shape CNNs for Semantic Segmentation
OneFormer
- OneFormer: One Transformer to Rule Universal Image Segmentation
PSPNet-ResNet50_PSSL
- Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models