Unsupervised change detection using RANSAC

Sharma, Bishwajit; Rishabh, Ish; Rakshit, Subrata

doi:10.1049/cp:20060496

Cited by 3 publications

(3 citation statements)

References 11 publications

(13 reference statements)

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“…The first group of image enhancement includes image differencing, image regression, image rationing, vegetation index differencing, change vector analysis (CVA), IRMAD, new kernel based methods, and transformation methods such as Principle Component Analysis (PCA) [4] and RANSAC [5]. The main problem of all the algorithms is to determine the threshold for the change areas.…”

Section: Change Detection From Imagerymentioning

confidence: 99%

Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

Vosselman

Elberink

2015

Remote Sensing

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Abstract:The difficulty associated with the Lidar data change detection method is lack of data, which is mainly caused by occlusion or pulse absorption by the surface material, e.g., water. To address this challenge, we present a new strategy for detecting buildings that are "changed", "unchanged", or "unknown", and quantifying the changes. The designation "unknown" is applied to locations where, due to lack of data in at least one of the epochs, it is not possible to reliably detect changes in the structure. The process starts with classified data sets in which buildings are extracted. Next, a point-to-plane surface difference map is generated by merging and comparing the two data sets. Context rules are applied to the difference map to distinguish between "changed", "unchanged", and "unknown". Rules are defined to solve problems caused by the lack of data. Further, points labelled as "changed" are re-classified into changes to roofs, walls, dormers, cars, constructions above the roof line, and undefined objects. Next, all the classified changes are organized as changed building objects, and the geometric indices are calculated from their 3D minimum bounding boxes. Performance analysis showed that 80%-90% of real changes are found, of which approximately 50% are considered relevant.

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Section: Change Detection From Imagerymentioning

confidence: 99%

Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

Vosselman

Elberink

2015

Remote Sensing

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“…Some structure of the scene is also estimated which is highly related to structure from motion (SFM). Sharma et al [17] uses RANSAC for change detection in remote sensing. The transformation in the dynamic range of the images is estimated.…”

Section: Related Workmentioning

confidence: 99%

Multiple-model RANSAC for ego-motion estimation in highly dynamic environments

Yang

Wang

2009

2009 IEEE International Conference on Robotics and Automation

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Abstract-Robust ego-motion estimation in urban environments is a key prerequisite for making a robot truly autonomous, but is not easily achievable as there are two motions involved: the motions of moving objects and the motion of the robot itself. We proposed a random sample consensus (RANSAC) based ego-motion estimator to deal with highly dynamic environments using one planar laser scanner. Instead of directly sampling on individual measurements, the RANSAC process is performed at a higher level abstraction for systematic sampling and computational efficiency. We proposed a multiplemodel approach to solve the problems of ego-motion estimation and moving object detection jointly in a RANSAC paradigm. To accommodate RANSAC to multiple models -a static environment model for ego-motion estimation and a moving object model for moving object detection, a compact representation models moving object information implicitly is proposed. Moving objects are successfully detected without incorporating any grid maps, that are inherently time and space consuming. The experimental results show that accurate identification of static environments can help classification of moving objects, whereas discrimination of moving objects also yields better egomotion estimation, particularly in environments containing a significant percentage of moving objects.

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“…Once the images are registered, the dynamic range scaling and offset of the images is determined using a new method for the solution of this classical problem, described in a recent work [17]. This method uses a RANSAC based algorithm to estimate the scale and offset, and is found to be more robust than the iterative PCA algorithm.…”

Section: Dynamic Range Equalisationmentioning

confidence: 99%

High Fidelity Inpainting for Critical Applications Using Image Fusion

Mishra

Sharma

Rakshit

2007

2007 International Conference on Computing: Theory and Applications (ICCTA'07)

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We propose a novel image restoration technique based on complementary information present in two or more images. While standalone image inpainting techniques fill the missing information artificially, use of spatiotemporal image sequence is suitable and reliable for critical applications, since it gathers the unavailable information in one image from other images in the sequence. There are two important issues to be dealt with before utilizing such sequences. Firstly, the images should be correctly registered in order to correctly access the undamaged images for inpainting the damaged one. Secondly, the illumination conditions should be taken care of, without which shadowing effects are generated since two images can differ in the illumination conditions, as well as sensor gain settings. In this work, a RANSAC based registration technique is used to perform efficient registration in presence of artefacts. Next, the dynamic range of the images are equalized to achieve illumination coherence. Further, we use fusion methods to incorporate information from all undamaged images into the damaged image. Cosmetic inpainting is used only if no other information is present. This work has uses in annotation removal from military and medical images and video, restoration of damaged video frames, etc. The results confirm the efficiency of the proposed method.

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Unsupervised change detection using RANSAC

Cited by 3 publications

References 11 publications

Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

Multiple-model RANSAC for ego-motion estimation in highly dynamic environments

High Fidelity Inpainting for Critical Applications Using Image Fusion

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