图像分割毕业设计

论文：

1. "Fully Convolutional Networks for Semantic Segmentation" by Jonathan Long, Evan Shelhamer, and Trevor Darrell

2. "UNet: Convolutional Networks for Biomedical Image Segmentation" by Olaf Ronneberger, Philipp Fischer, and Thomas Brox

3. "Mask RCNN" by Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick

编程代码：

以下是使用Python和TensorFlow实现的图像分割代码的示例：

1. FCN：

```

import tensorflow as tf

Define the fully convolutional network architecture

def fcn(input_tensor):

conv1 = tf.layers.conv2d(input_tensor, filters=64, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)

conv2 = tf.layers.conv2d(conv1, filters=64, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)

pool1 = tf.layers.max_pooling2d(conv2, pool_size=2, strides=2)

conv3 = tf.layers.conv2d(pool1, filters=128, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)

conv4 = tf.layers.conv2d(conv3, filters=128, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)

pool2 = tf.layers.max_pooling2d(conv4, pool_size=2, strides=2)

conv5 = tf.layers.conv2d(pool2, filters=256, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)

conv6 = tf.layers.conv2d(conv5, filters=256, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)

conv7 = tf.layers.conv2d(conv6, filters=256, kernel_size=1, strides=1, padding='same', activation=tf.nn.relu)

pool3 = tf.layers.max_pooling2d(conv7, pool_size=2, strides=2)

conv8 = tf.layers.conv2d(pool3, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)

conv9 = tf.layers.conv2d(conv8, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)

conv10 = tf.layers.conv2d(conv9, filters=512, kernel_size=1, strides=1, padding='same', activation=tf.nn.relu)

pool4 = tf.layers.max_pooling2d(conv10, pool_size=2, strides=2)

conv11 = tf.layers.conv2d(pool4, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)

conv12 = tf.layers.conv2d(conv11, filters=512, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)

conv13 = tf.layers.conv2d(conv12, filters=512, kernel_size=1, strides=1, padding='same', activation=tf.nn.relu)

pool5 = tf.layers.max_pooling2d(conv13, pool_size=2, strides=2)

fc1 = tf.layers.conv2d(pool5, filters=4096, kernel_size=7, strides=1, padding='valid', activation=tf.nn.relu)

fc2 = tf.layers.conv2d(fc1, filters=4096, kernel_size=1, strides=1, padding='valid', activation=tf.nn.relu)

score = tf.layers.conv2d(fc2, filters=num_classes, kernel_size=1, strides=1, padding='valid', activation=None)

return score

Initialize a new TF session

sess = tf.Session()

Define the input and output placeholders

input_tensor = tf.placeholder(tf.float32, [None, height, width, channels])

target_tensor = tf.placeholder(tf.int32, [None, height, width])

num_classes = 10

Define the neural network

score = fcn(input_tensor)

Define the loss function

loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=score, labels=target_tensor))

Define the optimizer

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)

train_op = optimizer.minimize(loss)

Initialize the variables

sess.run(tf.global_variables_initializer())

Train the network

for i in range(num_iterations):

feed_dict = {input_tensor: images, target_tensor: targets}

sess.run(train_op, feed_dict=feed_dict)

```

2. UNet：

```

import tensorflow as