Image Perspective Transformation using Autoencoders

Journal of Open Source Software

2020

This article incorporates a comprehensive study of autoencoders’ applications related to images. First of all, a vanilla autoencoder is described along with details of its architecture and training procedure. Secondly, main methods for regularization of it are exposed, such as dropout and additive gaussian noise. The applications of autoencoders such as image morphing, reconstruction and search are shown. Then, the VAE (variational autoencoder) is highlighted. Main applications of it such as outliers detection and image generation are described. Finally, it’s shown that using warm-up for VAE with respect to KL loss gives much more plausible results in terms of image generation.

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019

2022

Incorporating geometric transformations that reflect the relative position changes between an observer and an object into computer vision and deep learning models has attracted much attention in recent years. However, the existing proposals mainly focus on affine transformations that cannot fully show viewpoint changes. Furthermore, current solutions often apply a neural network module to learn a single transformation matrix, which ignores the possibility for various viewpoints and creates extra to-be-trained module parameters. In this paper, a layer (PT layer) is proposed to learn the perspective transformations that not only model the geometries in affine transformation but also reflect the viewpoint changes. In addition, being able to be directly trained with gradient descent like traditional layers such as convolutional layers, a single proposed PT layer can learn an adjustable number of multiple viewpoints without training extra module parameters. The experiments and evaluation...

2021

Autoencoders are composed of coding and decoding units, hence they hold the inherent potential of high-performance data compression and signal compressed sensing. The main disadvantages of current autoencoders comprise the following several aspects: the research objective is not data reconstruction but feature representation; the performance evaluation of data recovery is neglected; it is hard to achieve lossless data reconstruction by pure autoencoders, even by pure deep learning. This paper aims for image reconstruction of autoencoders, employs cascade decoders-based autoencoders, perfects the performance of image reconstruction, approaches gradually lossless image recovery, and provides solid theory and application basis for autoencoders-based image compression and compressed sensing. The proposed serial decoders-based autoencoders include the architectures of multi-level decoders and the related optimization algorithms. The cascade decoders consist of general decoders, residual ...

Proceedings of the V International conference Information Technology and Nanotechnology 2019

This paper describes an approach to solving the problem of finding similar images by visual similarity using neural networks on previously unmarked data. We propose to build special architecture of the neural network - autoencoder, through which high-level features are extracted from images. The search for the nearest elements is realized by the Euclidean metric in the generated feature space, after a preliminary decomposition into two-dimensional space. Proposed approach of generate feature space can be applied to the classification task using pre-clustering.

International Journal of Image, Graphics and Signal Processing, 2016

2012

Autoencoders play a fundamental role in unsupervised learning and in deep architectures for transfer learning and other tasks. In spite of their fundamental role, only linear autoencoders over the real numbers have been solved analytically. Here we present a general mathematical framework for the study of both linear and non-linear autoencoders. The framework allows one to derive an analytical treatment for the most non-linear autoencoder, the Boolean autoencoder. Learning in the Boolean autoencoder is equivalent to a clustering problem that can be solved in polynomial time when the number of clusters is small and becomes NP complete when the number of clusters is large. The framework sheds light on the different kinds of autoencoders, their learning complexity, their horizontal and vertical composability in deep architectures, their critical points, and their fundamental connections to clustering, Hebbian learning, and information theory.

Neural Computing and Applications

The presented paper introduces a novel method for enabling appearance modifications for complex image objects. Qualitative visual object properties, quantified using appropriately derived visual attribute descriptors, are subject to alterations. We adopt a basic convolutional autoencoder as a framework for the proposed attribute modification algorithm, which is composed of the following three steps. The algorithm begins with the extraction of attribute-related information from autoencoder’s latent representation of an input image, by means of supervised principal component analysis. Next, appearance alteration is performed in the derived feature space (referred to as ‘attribute-space’), based on appropriately identified mappings between quantitative descriptors of image attributes and attribute-space features. Finally, modified attribute vectors are transformed back to latent representation, and output image is reconstructed in the decoding part of an autoencoder. The method has bee...

Multimedia Tools and Applications, 2018