12 research outputs found
Pixel Relationships-based Regularizer for Retinal Vessel Image Segmentation
The task of image segmentation is to classify each pixel in the image based
on the appropriate label. Various deep learning approaches have been proposed
for image segmentation that offers high accuracy and deep architecture.
However, the deep learning technique uses a pixel-wise loss function for the
training process. Using pixel-wise loss neglected the pixel neighbor
relationships in the network learning process. The neighboring relationship of
the pixels is essential information in the image. Utilizing neighboring pixel
information provides an advantage over using only pixel-to-pixel information.
This study presents regularizers to give the pixel neighbor relationship
information to the learning process. The regularizers are constructed by the
graph theory approach and topology approach: By graph theory approach, graph
Laplacian is used to utilize the smoothness of segmented images based on output
images and ground-truth images. By topology approach, Euler characteristic is
used to identify and minimize the number of isolated objects on segmented
images. Experiments show that our scheme successfully captures pixel neighbor
relations and improves the performance of the convolutional neural network
better than the baseline without a regularization term
Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model
Amodal completion is a visual task that humans perform easily but which is
difficult for computer vision algorithms. The aim is to segment those object
boundaries which are occluded and hence invisible. This task is particularly
challenging for deep neural networks because data is difficult to obtain and
annotate. Therefore, we formulate amodal segmentation as an out-of-task and
out-of-distribution generalization problem. Specifically, we replace the fully
connected classifier in neural networks with a Bayesian generative model of the
neural network features. The model is trained from non-occluded images using
bounding box annotations and class labels only, but is applied to generalize
out-of-task to object segmentation and to generalize out-of-distribution to
segment occluded objects. We demonstrate how such Bayesian models can naturally
generalize beyond the training task labels when they learn a prior that models
the object's background context and shape. Moreover, by leveraging an outlier
process, Bayesian models can further generalize out-of-distribution to segment
partially occluded objects and to predict their amodal object boundaries. Our
algorithm outperforms alternative methods that use the same supervision by a
large margin, and even outperforms methods where annotated amodal segmentations
are used during training, when the amount of occlusion is large. Code is
publically available at https://github.com/YihongSun/Bayesian-Amodal