Advances in experimental medicine and biology, 2015
This chapter describes a novel way of carrying out image analysis, reconstruction and processing ... more This chapter describes a novel way of carrying out image analysis, reconstruction and processing tasks using cloud based service provided on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) infrastructure. The toolbox allows users free access to a wide range of useful blocks of functionalities (imaging functions) that can be connected together in workflows allowing creation of even more complex algorithms that can be re-run on different data sets, shared with others or additionally adjusted. The functions given are in the area of cellular imaging, advanced X-ray image analysis, computed tomography and 3D medical imaging and visualisation. The service is currently available on the website www.cloudimaging.net.au .
ABSTRACT Segment based disparity estimation methods have been proposed in many different ways. Mo... more ABSTRACT Segment based disparity estimation methods have been proposed in many different ways. Most of these studies are built upon the hypothesis that no large disparity jump exists within a segment. When this hypothesis does not hold true, it is difficult for these methods to estimate disparities correctly. Therefore, these methods work well only when the images are initially over segmented but do not work well for under segmented cases. To solve this problem, we present a new segment based stereo matching method which consists of two algorithms: a cost volume watershed algorithm (CVW) and a region merging (RM) algorithm. For incorrectly under segmented regions where pixels on different objects are grouped into one segment, the CVW algorithm regroups the pixels on different objects into different segments and provides disparity estimation to the pixels in different segments accordingly. For unreliable and occluded regions, we merge them into neighboring reliable segments for robust disparity estimation. The comparison between our method and the current state-of-the-art methods shows that our method is very competitive and is robust particularly when the images are initially under segmented.
Proceedings of the 27th Conference on Image and Vision Computing New Zealand - IVCNZ '12, 2012
ABSTRACT We present a new method for dense stereo matching based on a tree structural cost volume... more ABSTRACT We present a new method for dense stereo matching based on a tree structural cost volume watershed (TSCVW) and a region combination (RC) process. Given a cost volume as the data cost and an initial segmentation result, the proposed TSCVW method reliably estimates the disparities in a segment by using energy optimization to control plane segmentation and plane fitting. Then the disparities in the incorrectly fitted and occluded regions are refined using our RC process. Experimental results show that our method is very robust to different initial segmentation results and the shape of a segment. The comparison between our algorithm and the current state-of-the-art algorithms on the Middlebury website shows that our algorithm is very competitive.
2012 International Conference on Digital Image Computing Techniques and Applications (DICTA), 2012
ABSTRACT This paper presents a new method for finding feature correspondences between a pair of s... more ABSTRACT This paper presents a new method for finding feature correspondences between a pair of stereo images, which can be used to perform 3D reconstruction and object recognition. This paper uses epiploar geometry to determine the location of potential matching points which allow the finding of the correspondences faster and more accurately. An adaptive smoothness algorithm is also proposed to filter out false matches based on the disparity jump in neighboring correspondences. More correspondences are then identified in such a way that they are evenly distributed in the images. Experimental results show that the proposed method effectively improve the percentage of correct matches, total number of correct matches, and even distribution of the correspondences.
Advances in experimental medicine and biology, 2015
This chapter describes a novel way of carrying out image analysis, reconstruction and processing ... more This chapter describes a novel way of carrying out image analysis, reconstruction and processing tasks using cloud based service provided on the Australian National eResearch Collaboration Tools and Resources (NeCTAR) infrastructure. The toolbox allows users free access to a wide range of useful blocks of functionalities (imaging functions) that can be connected together in workflows allowing creation of even more complex algorithms that can be re-run on different data sets, shared with others or additionally adjusted. The functions given are in the area of cellular imaging, advanced X-ray image analysis, computed tomography and 3D medical imaging and visualisation. The service is currently available on the website www.cloudimaging.net.au .
ABSTRACT Segment based disparity estimation methods have been proposed in many different ways. Mo... more ABSTRACT Segment based disparity estimation methods have been proposed in many different ways. Most of these studies are built upon the hypothesis that no large disparity jump exists within a segment. When this hypothesis does not hold true, it is difficult for these methods to estimate disparities correctly. Therefore, these methods work well only when the images are initially over segmented but do not work well for under segmented cases. To solve this problem, we present a new segment based stereo matching method which consists of two algorithms: a cost volume watershed algorithm (CVW) and a region merging (RM) algorithm. For incorrectly under segmented regions where pixels on different objects are grouped into one segment, the CVW algorithm regroups the pixels on different objects into different segments and provides disparity estimation to the pixels in different segments accordingly. For unreliable and occluded regions, we merge them into neighboring reliable segments for robust disparity estimation. The comparison between our method and the current state-of-the-art methods shows that our method is very competitive and is robust particularly when the images are initially under segmented.
Proceedings of the 27th Conference on Image and Vision Computing New Zealand - IVCNZ '12, 2012
ABSTRACT We present a new method for dense stereo matching based on a tree structural cost volume... more ABSTRACT We present a new method for dense stereo matching based on a tree structural cost volume watershed (TSCVW) and a region combination (RC) process. Given a cost volume as the data cost and an initial segmentation result, the proposed TSCVW method reliably estimates the disparities in a segment by using energy optimization to control plane segmentation and plane fitting. Then the disparities in the incorrectly fitted and occluded regions are refined using our RC process. Experimental results show that our method is very robust to different initial segmentation results and the shape of a segment. The comparison between our algorithm and the current state-of-the-art algorithms on the Middlebury website shows that our algorithm is very competitive.
2012 International Conference on Digital Image Computing Techniques and Applications (DICTA), 2012
ABSTRACT This paper presents a new method for finding feature correspondences between a pair of s... more ABSTRACT This paper presents a new method for finding feature correspondences between a pair of stereo images, which can be used to perform 3D reconstruction and object recognition. This paper uses epiploar geometry to determine the location of potential matching points which allow the finding of the correspondences faster and more accurately. An adaptive smoothness algorithm is also proposed to filter out false matches based on the disparity jump in neighboring correspondences. More correspondences are then identified in such a way that they are evenly distributed in the images. Experimental results show that the proposed method effectively improve the percentage of correct matches, total number of correct matches, and even distribution of the correspondences.
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Papers by Changming Sun