3D Object Recognition in Cluttered Scenes With Robust Shape Description and Correspondence Selection

Tang, Keke; Song, Peng; Chen, Xiaoping

doi:10.1109/access.2017.2658681

Cited by 33 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, they are robust to clutter and occlusions and thus can be used for 3D object recognition in cluttered scenes. There is a large variety of approaches based on local features, such as scale-invariant feature transform (SIFT) [ 14 ], signature of geometric centroids (SGCs) [ 15 ], signature of histograms of orientations (SHOTS) [ 16 ] or rotational contour signature (RCS) [ 17 ]. Local key point detection requires a detailed object resolution in order to extract the key points, leading to poor descriptiveness and computational demanding approaches [ 18 ].…”

Section: Related Workmentioning

confidence: 99%

Realworld 3D Object Recognition Using a 3D Extension of the HOG Descriptor and a Depth Camera

Vilar

Krug

O’Nils

2021

Sensors

View full text Add to dashboard Cite

3D object recognition is an generic task in robotics and autonomous vehicles. In this paper, we propose a 3D object recognition approach using a 3D extension of the histogram-of-gradients object descriptor with data captured with a depth camera. The presented method makes use of synthetic objects for training the object classifier, and classify real objects captured by the depth camera. The preprocessing methods include operations to achieve rotational invariance as well as to maximize the recognition accuracy while reducing the feature dimensionality at the same time. By studying different preprocessing options, we show challenges that need to be addressed when moving from synthetic to real data. The recognition performance was evaluated with a real dataset captured by a depth camera and the results show a maximum recognition accuracy of 81.5%.

show abstract

Section: Related Workmentioning

confidence: 99%

Realworld 3D Object Recognition Using a 3D Extension of the HOG Descriptor and a Depth Camera

Vilar

Krug

O’Nils

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The last database is Kth-tips2-a, 4 which consists of 11 images (samples). Each class includes 396 200*200 samples captured from different direction, lighting conditions, and viewing angle at least.…”

Section: A Experimental Settings and Texture Datasets 1) Experimentamentioning

confidence: 99%

Locally Directional and Extremal Pattern for Texture Classification

et al. 2019

View full text Add to dashboard Cite

An image texture was defined in terms of pixel intensities and directionality. However, most of the current texture representation methods did not consider the two key factors simultaneously. To effectively capture the directional and pixel intensity information of texture, in this paper, we propose a novel and robust local descriptor, named locally directional and extremal pattern (LDEP), for texture classification. It extracts directional local difference count pattern (DLDCP) being made up of DLDCP in the odd positions and DLDCP in the even positions to express directional information in the local area in the first place. Furthermore, to acquire the extremum information remained by DLDCP, by concatenating extremum location pattern (ELP), extremum difference pattern (EDP), and extremum compression pattern (ECP) from the sampling points, we extract a neighbors extremum related local pattern (NERLP). The experimental results obtained from four representative texture databases (Prague, Stex, UIUC, Kth-tips2-a, Brodatz, and CUReT) demonstrate that our proposed LDEP descriptor can achieve comparable accurate classification rates in different conditions (rotation, illumination, scale variation, viewpoint variation, and noise) with ten typical texture classification methods. INDEX TERMS Directional pattern, extremal pattern, local pattern, texture representation, texture classification.

show abstract

“…They use patches with adaptive size to detect salient regions on the surface of a 3D model. Tang et al [6] have proposed a local descriptor based on geometric centroids. Another method have been introduced by Maes et al [7] as an extension of SIFT descriptor [8] to the 3D domain.…”

Section: Related Workmentioning

confidence: 99%

Scale and Resolution Invariant Spin Images for 3D Object Recognition

H’roura¹,

Bekkari²,

Mammass³

et al. 2019

IJACSA

View full text Add to dashboard Cite

Until the last decades, researchers taught that teaching a computer how to recognize a bunny, for example, in a complex scene is almost impossible. Today, computer vision system do it with a high score of accuracy. To bring the real world to the computer vision system, real objects are represented as 3D models (point clouds, meshes), which adds extra constraints that should be processed to ensure a good recognition, for example the resolution of the mesh. In this work, based on the state of the art method called Spin Image, we introduce our contribution to recognize 3D objects. Our motivation is to ensure a good recognition under different conditions such as rotation, translation and mainly scaling, resolution changes, occlusions and clutters. To that end we have analyzed the spin image algorithm to propose an extended version robust to scale and resolution changes, knowing that spin images fails to recognize 3D objects in that case. The key idea is to approach the representation of spin images of the same object under different conditions by the mean of normalization, either these conditions result in linear or non-linear correlation between images. Our contribution, unlike spin image algorithm, allows to recognize objects with different resolutions and scale. Plus it shows a good robustness to occlusions up to 60% and clutters up to 50%, tested on two datasets:

show abstract

3D Object Recognition in Cluttered Scenes With Robust Shape Description and Correspondence Selection

Cited by 33 publications

References 23 publications

Realworld 3D Object Recognition Using a 3D Extension of the HOG Descriptor and a Depth Camera

Realworld 3D Object Recognition Using a 3D Extension of the HOG Descriptor and a Depth Camera

Locally Directional and Extremal Pattern for Texture Classification

Scale and Resolution Invariant Spin Images for 3D Object Recognition

Contact Info

Product

Resources

About