3 research outputs found
An Abstraction Model for Semantic Segmentation Algorithms
Semantic segmentation is a process of classifying each pixel in the image.
Due to its advantages, sematic segmentation is used in many tasks such as
cancer detection, robot-assisted surgery, satellite image analysis,
self-driving car control, etc. In this process, accuracy and efficiency are the
two crucial goals for this purpose, and there are several state of the art
neural networks. In each method, by employing different techniques, new
solutions have been presented for increasing efficiency, accuracy, and reducing
the costs. The diversity of the implemented approaches for semantic
segmentation makes it difficult for researches to achieve a comprehensive view
of the field. To offer a comprehensive view, in this paper, an abstraction
model for the task of semantic segmentation is offered. The proposed framework
consists of four general blocks that cover the majority of majority of methods
that have been proposed for semantic segmentation. We also compare different
approaches and consider the importance of each part in the overall performance
of a method.Comment: 6 pages 2 figure
Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions
Image retargeting aims to alter the size of the image with attention to the
contents. One of the main obstacles to training deep learning models for image
retargeting is the need for a vast labeled dataset. Labeled datasets are
unavailable for training deep learning models in the image retargeting tasks.
As a result, we present a new supervised approach for training deep learning
models. We use the original images as ground truth and create inputs for the
model by resizing and cropping the original images. A second challenge is
generating different image sizes in inference time. However, regular
convolutional neural networks cannot generate images of different sizes than
the input image. To address this issue, we introduced a new method for
supervised learning. In our approach, a mask is generated to show the desired
size and location of the object. Then the mask and the input image are fed to
the network. Comparing image retargeting methods and our proposed method
demonstrates the model's ability to produce high-quality retargeted images.
Afterward, we compute the image quality assessment score for each output image
based on different techniques and illustrate the effectiveness of our approach.Comment: 18 pages, 5 figure