scholarly journals The Improved Algorithm of Fast Panorama Stitching for Image Sequence and Reducing the Distortion Errors

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Zhong Qu ◽  
Si-Peng Lin ◽  
Fang-Rong Ju ◽  
Ling Liu
Keyword(s):  
Feature Detection ◽  
Image Sequence ◽  
Input Image ◽  
Coordinate Space ◽  
Reference Image ◽  
Image Stitching ◽  
Feature Points ◽  
Improved Method ◽  
Sift Feature ◽  
Improved Algorithm

The traditional image stitching result based on the SIFT feature points extraction, to a certain extent, has distortion errors. The panorama, especially, would get more seriously distorted when compositing a panoramic result using a long image sequence. To achieve the goal of creating a high-quality panorama, the improved algorithm is proposed in this paper, including altering the way of selecting the reference image and putting forward a method that can compute the transformation matrix for any image of the sequence to align with the reference image in the same coordinate space. Additionally, the improved stitching method dynamically selects the next input image based on the number of SIFT matching points. Compared with the traditional stitching process, the improved method increases the number of matching feature points and reduces SIFT feature detection area of the reference image. The experimental results show that the improved method can not only accelerate the efficiency of image stitching processing, but also reduce the panoramic distortion errors, and finally we can obtain a pleasing panoramic result.

Multiple Image Based Human Joint Angle Computation

2017 ◽  
Vol 865 ◽  
pp. 547-553 ◽  
Author(s):  
Ji Hun Park
Keyword(s):  
Nonlinear Programming ◽  
Joint Angle ◽  
Video Stream ◽  
Input Image ◽  
Reference Image ◽  
Computation Method ◽  
Feature Points ◽  
Center Of Rotation ◽  
Volumetric Reconstruction ◽  
Human Joint

This paper presents a new computation method for human joint angle. A human structure is modelled as an articulated rigid body kinematics in single video stream. Every input image consists of a rotating articulated segment with a different 3D angle. Angle computation for a human joint is achieved by several steps. First we compute internal as well as external parameters of a camera using feature points of fixed environment using nonlinear programming. We set an image as a reference image frame for 3D scene analysis for a rotating articulated segment. Then we compute angles of rotation and a center of rotation of the segment for each input frames using corresponding feature points as well as computed camera parameters using nonlinear programming. With computed angles of rotation and a center of rotation, we can perform volumetric reconstruction of an articulated human body in 3D. Basic idea for volumetric reconstruction is regarding separate 3D reconstruction for each articulated body segment. Volume reconstruction in 3D for a rotating segment is done by modifying transformation relation of world-to-camera to adjust an angle of rotation of a rotated segment as if there were no rotation for the segment. Our experimental results for a single rotating segment show our method works well.

A Fast Underwater Image Stitching Algorithm with Adaptive Adjustment of Attitude Angle

2021 ◽  
Author(s):  
Lizhou Jiang ◽  
Zhijie Tang ◽  
Zhihang Luo ◽  
Chi Wang
Keyword(s):  
Feature Point ◽  
Reference Image ◽  
Image Stitching ◽  
Panoramic Image ◽  
Feature Points ◽  
Image Capture ◽  
Matching Process ◽  
Underwater Image ◽  
Adaptive Adjustment ◽  
The Impact

In underwater image acquisition process, due to the impact of water currents and other disturbances, the movement posture of the underwater machine will be unstable, which could lead to unusual problems such as twisting of underwater image capture. These factors will increase the error rate of feature point matching and lead to the failure of panoramic image mosaic. In this regard, we propose a new, highly applicable underwater image stitching algorithm. Firstly, the posture angle adjustment link is added to the underwater image processing, and the angle deflection problem of the underwater image is effectively improved by using the posture angle information. Secondly, the feature points of underwater images are extracted based on the accelerated robust feature (SURF) algorithm. Then, the reference image is matched with the feature points of the image to be registered, and effective feature point pairs are obtained by screening. Finally, the images are stitched based on OpenCV to obtain a good panoramic image. Afterexperimental analysis and comparison, our method can increase the number of matching feature point pairs between images. In addition, the Euclidean distance is significantly shortened during the matching process, which further makes the matching of feature points more accurate. Our method satisfactorily overcomes the adverse effects of actual underwater operations and has a better application prospect.

scholarly journals Incident Light Frequency-Based Image Defogging Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Wenbo Zhang ◽  
Xiaorong Hou
Keyword(s):  
Incident Light ◽  
Input Image ◽  
Dark Channel Prior ◽  
Improved Method ◽  
Color Channel ◽  
Original Algorithm ◽  
Image Defogging ◽  
Dark Channel ◽  
Natural Result ◽  
Improved Algorithm

To solve the color distortion problem produced by the dark channel prior algorithm, an improved method for calculating transmittance of all channels, respectively, was proposed in this paper. Based on the Beer-Lambert Law, the influence between the frequency of the incident light and the transmittance was analyzed, and the ratios between each channel’s transmittance were derived. Then, in order to increase efficiency, the input image was resized to a smaller size before acquiring the refined transmittance which will be resized to the same size of original image. Finally, all the transmittances were obtained with the help of the proportion between each color channel, and then they were used to restore the defogging image. Experiments suggest that the improved algorithm can produce a much more natural result image in comparison with original algorithm, which means the problem of high color saturation was eliminated. What is more, the improved algorithm speeds up by four to nine times compared to the original algorithm.

Image Copy-Move Forgery Detection Based on SIFT and Gray Level

2012 ◽  
Vol 263-266 ◽  
pp. 3021-3024 ◽  
Author(s):  
Xuan Jing Shen ◽  
Ye Zhu ◽  
Ying Da Lv ◽  
Hai Peng Chen
Keyword(s):  
Feature Vector ◽  
Experimental Results ◽  
Gray Level ◽  
Forgery Detection ◽  
Improved Method ◽  
Sift Feature ◽  
Key Points ◽  
Gaussian Blur ◽  
Copy Move Forgery Detection ◽  
Improved Algorithm

In order to reduce the false matching rate when detecting copy-move forgeries, an improved method based on SIFT and gray level was proposed in this study. Firstly, extract SIFT key points, and establish SIFT feature vector for every key point; Secondly, extract the gray level feature and combine it with SIFT feature to found a feature vector with size of 129D; Finally, match the above feature vector between every two different key points and then the copy-move regions would be detected. The experimental results showed that the improved algorithm reduced false matching rate even when an image was distorted by Gaussian blur.

scholarly journals Improved Parallax Image Stitching Algorithm Based on Feature Block

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 348 ◽  
Author(s):  
Huaitao Shi ◽  
Lei Guo ◽  
Shuai Tan ◽  
Gang Li ◽  
Jie Sun
Keyword(s):  
Feature Matching ◽  
Computational Cost ◽  
Reference Image ◽  
Image Stitching ◽  
Feature Points ◽  
Shape Distortion ◽  
Scale Invariant ◽  
Point Matching ◽  
Feature Point Matching ◽  
Scale Invariant Feature

Image stitching aims at generating high-quality panoramas with the lowest computational cost. In this paper, we present an improved parallax image-stitching algorithm using feature blocks (PIFB), which achieves a more accurate alignment and faster calculation speed. First, each image is divided into feature blocks using an improved fuzzy C-Means (FCM) algorithm, and the characteristic descriptor of each feature block is extracted using scale invariant feature transform (SIFT). The feature matching block of the reference image and the target image are matched and then determined, and the image is pre-registered using the homography calculated by the feature points in the feature block. Finally, the overlapping area is optimized to avoid ghosting and shape distortion. The improved algorithm considering pre-blocking and block stitching effectively reduced the iterative process of feature point matching and homography calculation. More importantly, the problem that the calculated homography matrix was not global has been solved. Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image. The performance of the proposed approach is demonstrated using several challenging cases.

A Synthetically Improved Method Based on Apriori Algorithm

2012 ◽  
Vol 546-547 ◽  
pp. 1495-1500
Author(s):  
Min Zhang ◽  
Yu Hou ◽  
Liang Wei Yao
Keyword(s):  
Apriori Algorithm ◽  
Improved Method ◽  
Pruning Strategy ◽  
Improved Algorithm

There are some disadvantages of the Apriori algorithm，such as too many scan of the database and many redundant middle itemsets to be generated. In this paper, we propose an improved algorithm, OApriori, with a synthetical method: (1) pruning strategy, (2) connection strategy, (3) reducing the scanning scale of database. We have performed extensive experiments and compared the performance of two algorithms. It was found that the improved algorithm reduces the counts of unnecessary candidate itemsets, accelerates the speed of the algorithm.

scholarly journals No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features

2021 ◽  
Vol 7 (7) ◽  
pp. 112
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Multiple Scales ◽  
Digital Images ◽  
Input Image ◽  
Perceptual Quality ◽  
Reference Image ◽  
Benchmark Datasets ◽  
In The Wild

The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ).

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