An improved vehicle panoramic image generation algorithm

Zhang, Jindong; Yin, Xuelong; Luan, Jing; Liu, Tong

doi:10.1007/s11042-019-07890-w

Cited by 19 publications

(5 citation statements)

References 33 publications

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“…Image feature matching is one of the fundamental operations in image processing, used in various vision and robotic applications such as stereo matching [1], image mosaicking [2], specific object recognition [3], feature-based robot localization [4], and SLAM (Simultaneous localization and mapping) [5], among others. Although many robust features extraction algorithms have been proposed such as Scale -Invariant Feature Transform (SIFT) [6,7] Speeded-Up Robust Features (SURF) [8,9], and AKAZE [10], they do not work well for feature extraction in degraded images.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Different Image Enhancement and Feature Extraction Methods

et al. 2022

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This paper describes an image enhancement method for reliable image feature matching. Image features such as SIFT and SURF have been widely used in various computer vision tasks such as image registration and object recognition. However, the reliable extraction of such features is difficult in poorly illuminated scenes. One promising approach is to apply an image enhancement method before feature extraction, which preserves the original characteristics of the scene. We thus propose to use the Multi-Scale Retinex algorithm, which is aimed to emulate the human visual system and it provides more information of a poorly illuminated scene. We experimentally assessed various combinations of image enhancement (MSR, Gamma correction, Histogram Equalization and Sharpening) and feature extraction methods (SIFT, SURF, ORB, AKAZE) using images of a large variety of scenes, demonstrating that the combination of the Multi-Scale Retinex and SIFT provides the best results in terms of the number of reliable feature matches.

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Section: Introductionmentioning

confidence: 99%

Analysis of Different Image Enhancement and Feature Extraction Methods

et al. 2022

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“…Finally, the Outliers are excluded to fit the detection line.In the marine field, [5]used the RANSAC algorithm combined with k-means to segment the image, divided the image into non-uniform segments, and calculated the row mean gradient trough, which achieved the effect of accurately detecting the sea-sky line in the complex sea-land-sky background , but increased time consumption. Reference [6] applied the random sample consistency RANSAC algorithm hierarchically to detect horizon horizons in ocean images under different conditions, resulting in enhanced accuracy, but also increased detection time.In the field of lane detection, the literature [7] uses the RANSAC algorithm to perform straight line fitting on the extracted road boundary points, and combines Kalman filtering to track the straight line to realize real-time road boundary detection.In [8], a line labeling method was proposed to reduce the computational processing time and improve the efficiency of RANSAC fitting straight lines.Reference [9] proposed a block matching-based RANSAC algorithm to further eliminate the mismatched points in the keypoint matching process.In order to further improve the performance of the RANSAC algorithm, the literature [10] used the constrained RANSAC algorithm to select the region of interest and filter the background data, and combined with the curb detection method based on the density of line segment points to classify the road points and curb points, so that the detection effect is better.…”

Section: Introductionmentioning

confidence: 99%

An improved RANSAC algorithm for detection, extraction, and enhancement of noisy indoor house type image lines

Wang¹,

Wang²,

Wang³

2022

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022)

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For noisy indoor house type images obtained by infrared scanning and other methods, they usually need to be further optimized before they can be used. This paper proposes an improved RANSAC algorithm based on the conventional RANSAC algorithm,and combines the least squares method to detect,extract and enhance lines of noisy indoor house type image.The binarization of the image is completed by threshold judgment division to avoid the excessive dispersion of pixels, which leads to the deviation of the subsequent RANSAC extraction model.Then, a recursive-based desalination method is proposed to desalinate the image, desalt the noise and improve the accuracy.When detecting a straight line, a sequential recording method is proposed to record and process the multi-segment line segments that are in the same straight line but not connected to ensure that there will be no omission or error. Finally, the extraction of multiple straight lines and line segments is realized by cyclically removing interior points, so as to complete the straight line detection and extraction enhancement process of the entire noisy indoor house type image. The actual results show that the method has high accuracy and timeliness, and can make the line detection and extraction completion rate reach about 90%, and can still maintain a very low error rate even in a heavily noisy environment.

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“…Compared with the existing methods, this method effectively obtained more accurate mosaic results. J. Zhang et al proposed a RANSAC algorithm based on block matching in the reference [13] to eliminate mismatch points in the process of key point matching and this algorithm had strong robustness. L. Li et al proposed a suture detection algorithm [14], which can effectively hide the artifacts caused by dynamic objects and geometric misalignment.…”

Section: Introductionmentioning

confidence: 99%

Image Mosaic Research and Realization Based on LoFTR Algorithm

Tian

Wang

Zhang

et al. 2021

Preprint

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Aiming at the problem of inaccurate extraction of feature points by the traditional image matching method, low robustness, and problems such as diffculty in inentifying feature points in area with poor texture. This paper proposes a new local image feature matching method, which replaces the traditional sequential image feature detection, description and matching steps. First, extract the coarse features with a resolution of 1/8 from the original image, then tile to a one-dimensional vector plus the positional encoding, feed them to the self-attention layer and cross-attention layer in the Transformer module, and finally get through the Differentiable Matching Layer and confidence matrix, after setting the threshold and the mutual closest standard, a Coarse-Level matching prediction is obtained. Secondly the fine matching is refined at the Fine-level match, after the Fine-level match is established, the image overlapped area is aligned by transforming the matrix to a unified coordinate, and finally the image is fused by the weighted fusion algorithm to realize the unification of seamless mosaic of images. This paper uses the self-attention layer and cross-attention layer in Transformers to obtain the feature descriptor of the image. Finally, experiments show that in terms of feature point extraction, LoFTR algorithm is more accurate than the traditional SIFT algorithm in both low-texture regions and regions with rich textures. At the same time, the image mosaic effect obtained by this method is more accurate than that of the traditional classic algorithms, the experimental effect is more ideal.

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An improved vehicle panoramic image generation algorithm

Cited by 19 publications

References 33 publications

Analysis of Different Image Enhancement and Feature Extraction Methods

Analysis of Different Image Enhancement and Feature Extraction Methods

An improved RANSAC algorithm for detection, extraction, and enhancement of noisy indoor house type image lines

Image Mosaic Research and Realization Based on LoFTR Algorithm

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