scholarly journals A Two-Step Global Alignment Method for Feature-Based Image Mosaicing

2016 ◽  
Vol 21 (3) ◽  
pp. 30 ◽  
Author(s):  
Armagan Elibol
Keyword(s):  
Global Alignment ◽  
Alignment Method ◽  
Image Mosaicing ◽  
Feature Based

scholarly journals Robust Cylindrical Panorama Stitching for Low-Texture Scenes Based on Image Alignment Using Deep Learning and Iterative Optimization

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5310
Author(s):  
Lai Kang ◽  
Yingmei Wei ◽  
Jie Jiang ◽  
Yuxiang Xie
Keyword(s):  
Deep Learning ◽  
Image Alignment ◽  
Wide Field ◽  
Camera Motion ◽  
Alignment Method ◽  
Feature Correspondence ◽  
Iterative Optimization ◽  
Scene Representation ◽  
Feature Based ◽  
High Resolution Images

Cylindrical panorama stitching is able to generate high resolution images of a scene with a wide field-of-view (FOV), making it a useful scene representation for applications like environmental sensing and robot localization. Traditional image stitching methods based on hand-crafted features are effective for constructing a cylindrical panorama from a sequence of images in the case when there are sufficient reliable features in the scene. However, these methods are unable to handle low-texture environments where no reliable feature correspondence can be established. This paper proposes a novel two-step image alignment method based on deep learning and iterative optimization to address the above issue. In particular, a light-weight end-to-end trainable convolutional neural network (CNN) architecture called ShiftNet is proposed to estimate the initial shifts between images, which is further optimized in a sub-pixel refinement procedure based on a specified camera motion model. Extensive experiments on a synthetic dataset, rendered photo-realistic images, and real images were carried out to evaluate the performance of our proposed method. Both qualitative and quantitative experimental results demonstrate that cylindrical panorama stitching based on our proposed image alignment method leads to significant improvements over traditional feature based methods and recent deep learning based methods for challenging low-texture environments.

scholarly journals Performance analysis on color image mosaicing techniques on FPGA

2019 ◽  
Vol 9 (3) ◽  
pp. 1630
Author(s):  
Jayalaxmi H ◽  
S. Ramachandran
Keyword(s):  
Image Registration ◽  
Color Image ◽  
Image Sequences ◽  
Phase Correlation ◽  
Corner Detection ◽  
Global Alignment ◽  
Surveillance Systems ◽  
Image Mosaicing ◽  
Single Frame ◽  
Field Programmable

Today, the surveillance systems and other monitoring systems are considering the capturing of image sequences in a single frame. The captured images can be combined to get the mosaiced image or combined image sequence. But the captured image may have quality issues like brightness issue, alignment issue (correlation issue), resolution issue, manual image registration issue etc. The existing technique like cross correlation can offer better image mosaicing but faces brightness issue in mosaicing. Thus, this paper introduces two different methods for mosaicing i.e., (a) Sliding Window Module (SWM) based Color Image Mosaicing (CIM) and (b) Discrete Cosine Transform (DCT) based CIM on Field Programmable Gate Array (FPGA). The SWM based CIM adopted for corner detection of two images and perform the automatic image registration while DCT based CIM aligns both the local as well as global alignment of images by using phase correlation approach. Finally, these two methods performances are analyzed by comparing with parameters like PSNR, MSE, device utilization and execution time. From the analysis it is concluded that the DCT based CIM can offers significant results than SWM based CIM.

scholarly journals Feature-based Similarity Method for Aligning the Malay and English News Document

2013 ◽  
Vol 11 (4) ◽  
pp. 2410-2421
Author(s):  
Nurul Amelina Nasharuddin ◽  
Muhamad Taufik Abdullah ◽  
Azreen Azman ◽  
Rabiah Abdul Kadir ◽  
Enrique Herrera-Viedma
Keyword(s):  
Machine Translation ◽  
Statistical Features ◽  
Alignment Method ◽  
Average Precision ◽  
Named Entities ◽  
Low Resource ◽  
European Languages ◽  
Feature Based ◽  
The Creation ◽  
Similarity Method

Corpus-based translation approach can be used to obtain reliable translation knowledge in addition to the use of dictionaries or machine translation. But the availability of such corpus is very limited especially for the low-resources languages. Many works have been reported for the alignments of multilingual documents especially among the European languages, but less focusing on the languages with less linguistics resources. One of the challenges is to align the available multilingual documents for the creation of comparable corpus for these kinds of languages. This article describes an alignment method that utilized the statistical features of the documents such as the documents’ titles, texts of the contents, and also the named entities present in each document. This method will be focusing on the English and Malay news documents, in which in which the Malay language is considered as a low-resource language. Source and target documents were then compared in a pair. Accuracy, precision, and recall measurements were used in evaluating the results with the inclusion of three relevance scales; Same story, Shared aspect and Unrelated, to assess the alignment pairs. The results indicate that the method performed well in aligning the news documents with the accuracy of 96% and average precision of 81%.

scholarly journals An In Depth Review Paper on Numerous Image Mosaicing Approaches and Techniques

2018 ◽  
Vol 8 (02) ◽  
Author(s):  
Mohamed Abdul – Rahim ◽  
Zheng – Sheng Yu
Keyword(s):  
Review Paper ◽  
Resolution Enhancement ◽  
Research Area ◽  
Parameters Estimation ◽  
Corner Detection ◽  
Image Mosaicing ◽  
Current Image ◽  
The Past ◽  
Wide Range ◽  
Feature Based

Image mosaicing is one of the most important subjects of research in computer vision at current. Image mocaicing requires the integration of direct techniques and feature based techniques. Direct techniques are found to be very useful for mosaicing large overlapping regions, small translations and rotations while feature based techniques are useful for small overlapping regions. Feature based image mosaicing is a combination of corner detection, corner matching, motion parameters estimation and image stitching. Furthermore, image mosaicing is considered the process of obtaining a wider field-of-view of a scene from a sequence of partial views, which has been an attractive research area because of its wide range of applications, including motion detection, resolution enhancement, monitoring global land usage, and medical imaging. Numerous algorithms for image mosaicing have been proposed over the last two decades. In this paper the authors present a review on different approaches for image mosaicing and the literature over the past few years in the field of image masaicing methodologies. The authors take an overview on the various methods for image mosaicing. This review paper also provides an in depth survey of the existing image mosaicing algorithms by classifying them into several groups. For each group, the fundamental concepts are first clearly explained. Finally this paper also reviews and discusses the strength and weaknesses of all the mosaicing groups.

THE HUFFMAN-LIKE ALIGNMENT IN MANIFOLD LEARNING

2014 ◽  
Vol 28 (04) ◽  
pp. 1451005 ◽  
Author(s):  
ZHENGMING MA ◽  
JING CHEN
Keyword(s):  
Manifold Learning ◽  
Huffman Coding ◽  
Dimensional Euclidean Space ◽  
Current Phase ◽  
Global Alignment ◽  
Alignment Method ◽  
Real World Data ◽  
Progressive Alignment ◽  
Open Set ◽  
Local Coordinates

In manifold learning, the neighborhood is often called a patch of the manifold, and the corresponding open set is called the local coordinate of the patch. The so-called alignment is to align the local coordinates in the d-dimensional Euclidean space to get the global coordinate of the manifold. There are two kinds of alignment methods: global and progressive alignment methods. The global alignment methods align the local coordinates of the manifold all at one time by solving an eigenvalue problem. The progressive alignment methods often take the local coordinate of a patch as the basic local coordinate and then attach other local ordinates to the basic local coordinate patch-by-patch until the basic local coordinate evolves into the global coordinate of the manifold. In this paper, a new progressive alignment method is proposed, where only the local coordinates of the two patches with the largest intersection at the current stage of progressive alignment will be aligned into a larger local coordinate. It is inspired by the famous Huffman coding, where two random events with the smallest probabilities at the current phase will be merged into a random event with a larger probability. Therefore, the proposed method is a Huffman-like alignment method. The experiments on benchmark data show that the proposed method outperforms both the global alignment methods and the other progressive alignment methods and is more robust to the changes of data size. The experiments on real-world data show the feasibility of the proposed method.

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