A Nonparametric Approach for Histogram Segmentation

Delon, Julie; Desolneux, Agnès; Lisani, José Luis; Petro, Ana Belén

doi:10.1109/tip.2006.884951

Cited by 101 publications

(71 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The neighborhood is centered on each point location and its size is proportional to the scale of the point. We extract the main orientations of this histogram with the non parametric analysis proposed in [10]. A polar localization grid as in [30] is then used to segment the neighborhood into M non overlapping regions.…”

Section: Methodsmentioning

confidence: 99%

A Statistical Approach to the Matching of Local Features

Rabin¹,

Delon²,

Gousseau³

2009

SIAM J. Imaging Sci.

View full text Add to dashboard Cite

Abstract. This paper focuses on the matching of local features between images. Given a set of query descriptors and a database of candidate descriptors, the goal is to decide which ones should be matched. This is a crucial issue, since the matching procedure is often a preliminary step for object detection or image matching. In practice, this matching step is often reduced to a specific threshold on the Euclidean distance to the nearest neighbor.Our first contribution is a robust distance between descriptors, relying on the adaptation of the Earth Mover's Distance to circular histograms. It is shown that this distance outperforms classical distances for comparing SIFT-like descriptors, while its time complexity remains reasonable. Our second and main contribution is a statistical framework for the matching procedure, which yields validation thresholds automatically adapted to the complexity of each query descriptor and to the diversity and size of the database. The method makes it possible to detect multiple occurrences, as well as to deal with situations where the target is not present. Its performances are tested through various experiments on a large image database.Key words. Statistical analysis of matching processes, local feature matching, dissimilarity measure, Earth Mover's Distance, a contrario.AMS subject classifications. 62H35, 68T45, 68T10 [26,16], and 3D object modeling [19]. One of the most classical approaches to this problem consists in using local features around interest points or regions. The locality of the features ensures robustness to occlusion or context change, while the coding of the features should be invariant or robust to various geometrical, photometric or radiometric changes. Numerous local approaches have been proposed in the literature, the exhaustive study of which is beyond the scope of the present paper. In two relatively recent comparative studies [30,33], the SIFT descriptor [26] has been proven to be one of the most robust and invariant representation methods. As a result, the problem of finding correspondences between images often boils down to the matching of such local features. Nevertheless, whereas the extraction and representation of local descriptors has been thoroughly studied (see e.g. the references in [30]), their matching has not been the object of a systematic study. In practice, the matching step relies on simple but somehow limited procedures, as detailed further in the paper.In many applications, this matching procedure is yet a crucial preliminary step. It can for instance be used as a pre-processing stage (before resorting to some geometric consistency algorithm like RANSAC [9,43,5] or some mean square error minimization [26]) for finding common objects between images. The matching step is at the core of many recent methods relying on image similarities, see e.g.

show abstract

Section: Methodsmentioning

confidence: 99%

A Statistical Approach to the Matching of Local Features

Rabin¹,

Delon²,

Gousseau³

2009

SIAM J. Imaging Sci.

View full text Add to dashboard Cite

show abstract

“…For each u R s , a 210 depth histogram h s of B bins is built. This histogram is automatically segmented into C s classes using the a-contrario technique presented in Delon et al (2007). This technique presents the advantage of segmenting a 1D-histogram without any prior assumption, e.g.…”

Section: Range Histogram Segmentation Techniquementioning

confidence: 99%

Range-Image: Incorporating Sensor Topology for Lidar Point Cloud Processing

Biasutti¹,

Aujol²,

Brédif³

et al. 2018

photogramm eng remote sensing

View full text Add to dashboard Cite

This paper proposes a novel methodology for LiDAR point cloud processing that takes advantage of the implicit topology of various LiDAR sensors to derive 2D images from the point cloud while bringing spatial structure to each point. The interest of such a methodology is then proved by addressing the problems of segmentation and disocclusion of mobile objects in 3D LiDAR scenes acquired via street-based Mobile Mapping Systems (MMS). Most of the existing lines of research tackle those problems directly in the 3D space. This work promotes an alternative approach by using this image representation of the 3D point cloud, taking advantage of the fact that the problem of disocclusion has been intensively studied in the 2D image processing community over the past decade. Using the image derived from the sensor data by exploiting the sensor topology, a semi-automatic segmentation procedure based on depth histograms is presented. Then, a variational image inpainting technique is introduced to reconstruct the areas that are occluded by objects.Experiments and validation on real data prove the effectiveness of this methodology both in terms of accuracy and speed.

show abstract

“…For each u R s , a depth histogram hs of B bins is built. This histogram is automatically segmented into Cs classes using the a-contrario technique presented in (Delon et al, 2007). This technique presents the advantage of segmenting a 1D-histogram without any prior assumption, e.g.…”

Section: Point Cloud Segmentationmentioning

confidence: 99%

Disocclusion of 3d Lidar Point Clouds Using Range Images

Biasutti

Aujol

Brédif³

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:This paper proposes a novel framework for the disocclusion of mobile objects in 3D LiDAR scenes aquired via street-based Mobile Mapping Systems (MMS). Most of the existing lines of research tackle this problem directly in the 3D space. This work promotes an alternative approach by using a 2D range image representation of the 3D point cloud, taking advantage of the fact that the problem of disocclusion has been intensively studied in the 2D image processing community over the past decade. First, the point cloud is turned into a 2D range image by exploiting the sensor's topology. Using the range image, a semi-automatic segmentation procedure based on depth histograms is performed in order to select the occluding object to be removed. A variational image inpainting technique is then used to reconstruct the area occluded by that object. Finally, the range image is unprojected as a 3D point cloud. Experiments on real data prove the effectiveness of this procedure both in terms of accuracy and speed.

show abstract

A Nonparametric Approach for Histogram Segmentation

Cited by 101 publications

References 18 publications

A Statistical Approach to the Matching of Local Features

A Statistical Approach to the Matching of Local Features

Range-Image: Incorporating Sensor Topology for Lidar Point Cloud Processing

Disocclusion of 3d Lidar Point Clouds Using Range Images

Contact Info

Product

Resources

About