An Optimized Mean Shift Filtering Technique to Image Representation Through Disparity Map for Large Scale Stereo Images

2017 ◽  
Vol 2 (1) ◽  
pp. 36-45
Author(s):  
Kavitha ◽  
Balakrishnan
Keyword(s):  
Large Scale ◽  
Mean Shift ◽  
Image Representation ◽  
Stereo Images ◽  
Disparity Map ◽  
Filtering Technique

scholarly journals A Survey of Learning Approaches and Application for 3D Vision

2018 ◽  
Vol 173 ◽  
pp. 03053
Author(s):  
Luanhao Lu
Keyword(s):  
Stereo Matching ◽  
Mean Shift ◽  
Three Dimensional ◽  
Image Synthesis ◽  
Learning Approaches ◽  
Median Filtering ◽  
Stereo Images ◽  
Disparity Map ◽  
3D Vision ◽  
2D Images

Three-dimensional (3D) vision extracted from the stereo images or reconstructed from the two-dimensional (2D) images is the most effective topic in computer vision and video surveillance. Three-dimensional scene is constructed through two stereo images which existing disparity map by Stereo vision. Many methods of Stereo matching which contains median filtering, mean-shift segmentation, guided filter and joint trilateral filters [1] are used in many algorithms to construct the precise disparity map. These methods committed to figure out the image synthesis range in different Stereo matching fields and among these techniques cannot perform perfectly every turn. The paper focuses on 3D vision, introduce the background and process of 3D vision, reviews several classical datasets in the field of 3D vision, based on which the learning approaches and several types of applications of 3D vision were evaluated and analyzed.

scholarly journals Accurate hierarchical stereo matching based on 3D plane labeling of superpixel for stereo images from rovers

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142110021
Author(s):  
Haichao Li ◽  
Zhi Li ◽  
Jianbin Huang ◽  
Bo Meng ◽  
Zhimin Zhang
Keyword(s):  
Large Scale ◽  
Stereo Matching ◽  
State Of The Art ◽  
The State ◽  
Normal Vector ◽  
Stereo Images ◽  
Disparity Map ◽  
Matching Method ◽  
Search Range ◽  
Coarse To Fine

An accurate hierarchical stereo matching method is proposed based on continuous 3D plane labeling of superpixel for rover’s stereo images. This method can infer the 3D plane label of each pixel combined with the slanted-patch matching strategy and coarse-to-fine constraints, which is especially suitable for large-scale scene matching with low-texture or textureless regions. At every level, the stereo matching method based on superpixel segmentation makes the iteration convergence faster and avoids huge redundant computations. In the coarse-to-fine matching scheme, we propose disparity constraint and 3D normal vector constraint between adjacent levels through which the disparity map and 3D normal vector map at a coarser level are used to restrict the search range of disparity and normal vector at a fine level. The experimental results with the Chang’e-3 rover dataset and the KITTI dataset show that the proposed stereo matching method is efficiently and accurately compared with the state-of-the-art 3D labeling algorithm, especially in low-texture or textureless regions. The computational efficiency of this method is about five to six times faster than the state-of-the-art 3D labeling method, and the accuracy is better.

scholarly journals Disparity estimation using Graph cuts for road applications

2021 ◽  
Vol 297 ◽  
pp. 01055
Author(s):  
Mohamed El Ansari ◽  
Ilyas El Jaafari ◽  
Lahcen Koutti
Keyword(s):  
Stereo Matching ◽  
Optimal Solution ◽  
Similarity Criterion ◽  
Graph Cuts ◽  
Search Space ◽  
Disparity Estimation ◽  
Stereo Images ◽  
Disparity Map ◽  
Current Frame ◽  
Edge Points

This paper proposes a new edge based stereo matching approach for road applications. The new approach consists in matching the edge points extracted from the input stereo images using temporal constraints. At the current frame, we propose to estimate a disparity range for each image line based on the disparity map of its preceding one. The stereo images are divided into multiple parts according to the estimated disparity ranges. The optimal solution of each part is independently approximated via the state-of-the-art energy minimization approach Graph cuts. The disparity search space at each image part is very small compared to the global one, which improves the results and reduces the execution time. Furthermore, as a similarity criterion between corresponding edge points, we propose a new cost function based on the intensity, the gradient magnitude and gradient orientation. The proposed method has been tested on virtual stereo images, and it has been compared to a recently proposed method and the results are satisfactory.

Research of Remote Sensing Image Segmentation Based on Mean Shift and Region Merging

2011 ◽  
Vol 90-93 ◽  
pp. 2836-2839 ◽  
Author(s):  
Jian Cui ◽  
Dong Ling Ma ◽  
Ming Yang Yu ◽  
Ying Zhou
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Large Scale ◽  
Mean Shift ◽  
Image Features ◽  
Small Scale ◽  
Region Merging ◽  
Segmentation Method ◽  
Shift Method ◽  
Scale Parameters

In order to extract ground information more accurately, it is important to find an image segmentation method to make the segmented features match the ground objects. We proposed an image segmentation method based on mean shift and region merging. With this method, we first segmented the image by using mean shift method and small-scale parameters. According to the region merging homogeneity rule, image features were merged and large-scale image layers were generated. What’s more, Multi-level image object layers were created through scaling method. The test of segmenting remote sensing images showed that the method was effective and feasible, which laid a foundation for object-oriented information extraction.

scholarly journals Mastcam Image Resolution Enhancement with Application to Disparity Map Generation for Stereo Images with Different Resolutions

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3526 ◽  
Author(s):  
Ayhan ◽  
Kwan
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Image Enhancement ◽  
Image Resolution ◽  
Map Estimation ◽  
Stereo Images ◽  
Disparity Map ◽  
Camera Image ◽  
The Right ◽  
The Impact

In this paper, we introduce an in-depth application of high-resolution disparity map estimation using stereo images from Mars Curiosity rover’s Mastcams, which have two imagers with different resolutions. The left Mastcam has three times lower resolution as that of the right. The left Mastcam image’s resolution is first enhanced with three methods: Bicubic interpolation, pansharpening-based method, and a deep learning super resolution method. The enhanced left camera image and the right camera image are then used to estimate the disparity map. The impact of the left camera image enhancement is examined. The comparative performance analyses showed that the left camera enhancement results in getting more accurate disparity maps in comparison to using the original left Mastcam images for disparity map estimation. The deep learning-based method provided the best performance among the three for both image enhancement and disparity map estimation accuracy. A high-resolution disparity map, which is the result of the left camera image enhancement, is anticipated to improve the conducted science products in the Mastcam imagery such as 3D scene reconstructions, depth maps, and anaglyph images.

A Disparity Refinement in Stereo Matching based on Mean-shift Segmentation and Spatiotemporal Domain

2020 ◽  
Vol 64 (2) ◽  
pp. 20505-1-20505-12
Author(s):  
Hui-Yu Huang ◽  
Zhe-Hao Liu
Keyword(s):  
Stereo Matching ◽  
Mean Shift ◽  
Disparity Map ◽  
Video Streams ◽  
Segmentation Strategy ◽  
Information Matching ◽  
Color Similarity ◽  
Spatial Domains ◽  
Disparity Maps ◽  
The Cost

Abstract A stereo matching algorithm is used to find the best match between a pair of images. To compute the cost of the matching points from the sequence of images, the disparity maps from video streams are estimated. However, the estimated disparity sequences may cause undesirable flickering errors. These errors result in low visibility of the synthesized video and reduce video coding. In order to solve this problem, in this article, the authors propose a spatiotemporal disparity refinement on local stereo matching based on the segmentation strategy. Based on segmentation information, matching point searching, and color similarity, adaptive disparity values to recover the disparity errors in disparity sequences can be obtained. The flickering errors are also effectively removed, and the boundaries of objects are well preserved. The procedures of the proposed approach consist of a segmentation process, matching point searching, and refinement in the temporal and spatial domains. Experimental results verify that the proposed approach can yield a high quantitative evaluation and a high-quality disparity map compared with other methods.

Topic modeling and improvement of image representation for large-scale image retrieval

2016 ◽  
Vol 366 ◽  
pp. 99-120 ◽  
Author(s):  
Nguyen Anh Tu ◽  
Dong-Luong Dinh ◽  
Mostofa Kamal Rasel ◽  
Young-Koo Lee
Keyword(s):  
Image Retrieval ◽  
Topic Modeling ◽  
Large Scale ◽  
Image Representation ◽  
Large Scale Image Retrieval

Sparse disparity map estimation on stereo images

2012 ◽  
Author(s):  
Omer C. Gurol ◽  
Secil Oztürk ◽  
Burak Acar ◽  
Bulent Sankur ◽  
Mehmet Guney
Keyword(s):  
Map Estimation ◽  
Stereo Images ◽  
Disparity Map

DMESH: FAST DEPTH-IMAGE MESHING AND WARPING

2004 ◽  
Vol 04 (04) ◽  
pp. 653-681 ◽  
Author(s):  
RENATO PAJAROLA ◽  
MIGUEL SAINZ ◽  
YU MENG
Keyword(s):  
Large Scale ◽  
Image Representation ◽  
Depth Image ◽  
Mesh Segmentation ◽  
Object Representations ◽  
Triangle Mesh ◽  
Depth Images ◽  
Segmentation Methods ◽  
Real Time Applications ◽  
Visualization Systems

In this paper we present a novel and efficient depth-image representation and warping technique called DMesh which is based on a piece-wise linear approximation of the depth-image as a textured and simplified triangle mesh. We describe the application of a hierarchical multiresolution triangulation method to generate adaptively triangulated depth-meshes efficiently from reference depth-images, discuss depth-mesh segmentation methods to avoid occlusion artifacts and propose a new hardware accelerated depth-image rendering technique that supports per-pixel weighted blending of multiple depth-images in real-time. Applications of our technique include image-based object representations and the use of depth-images in large scale walk-through visualization systems.

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