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While, only the color information for object segmentation has been the main focus of research, with the availability of low cost color plus range sensors, depth Click here to sign up. The proposed descriptor combines the strengths of signature-based methods and integral invariants to provide robust local surface description. Object segmentation is a fundamental research topic in computer vision. In the proposed approach, highly repeatable keypoints are first detected by computing the divergence of the vector field at For a given depth image acquired using a low resolution Kinect sensor, a 2D vector field is computed first at each point of the range image.

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Afaq Ali Khan

The proposed descriptor combines the strengths of signature-based methods and integral invariants to provide robust local surface description. Preliminary experimental results suggest that the proposed algorithm achieves better depth segmentation compared to state-of-the art graph-based depth segmentation.

This paper presents a novel algorithm for depth segmentation. Skip to main content. The performance of the proposed fully automatic 3D object recognition technique qfaq rigourously tested on three afa available datasets. Enter the email address you signed up with and we’ll email you a reset link.

Finally, the depth segmentation is accomplished by applying a threshold to the div map to segment 3D object from the background. This could result in a loss of discriminative information for classification.

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Being a differential invariant of curves and surfaces, the divergence captures significant information about the surface variations at each point. Log In Sign Up. Our proposed technique is shown to exhibit superior performance compared to state-of-the-art techniques. The proposed technique exploits the divergence of the 2D vector field to segment three-dimensional 3D object in the depth maps. The latter is followed by Artificial Neural Networks ANNs applied iteratively in a hierarchical fashion to learn a discriminative non-linear feature representation of the input image sets.

In the proposed approach, highly repeatable keypoints are first detected by computing the divergence of the vector field at These techniques make prior assumptions in regards to the specific category of the geometric surface on which images of the set are believed to lie.

In the proposed approach, low level translationally invariant features are learnt by the Pooled convolutional Layer PCL. While, only the color information for alj segmentation has been the main focus of research, with the availability of low cost color plus range sensors, depth segmentation is now attracting significant attention.

In addition to removing the background, the proposed technique also segments the object from the surface on which the object nxme positioned. The latter maps the vector field to a scalar field. Image segmentation and Depth Segmentation.

Remember me on this computer. Automatic Object Detection using Objectness Measure more. Help Center Find new research papers in: We present a afaaq local surface description technique for automatic three dimensional 3D object recognition. The detected keypoints are pruned to only retain the keypoints which are associated with high divergence values. Object segmentation is a fundamental research topic in computer vision. While, only the color information for object segmentation has been the main focus of research, with the availability of low cost color plus range sensors, depth Click here to sign up.

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Afaq Ali Khan | Mohammad Ali Jinnah University

The variation of divergence values over the surface contour of the 3D object helps to extract its boundaries. Experimental results and comparisons with aaq methods show that our technique achieves the best performance on all these datasets. For a given depth image acquired using a low resolution Kinect sensor, a 2D vector field is computed first at each point of the range image. A Simplified Approach more.

In the proposed approach, highly repeatable keypoints are first detected by computing the divergence of the vector field at each point of all surface. A keypoint saliency measure is proposed to rank these keypoints and select the best ones. While several image set classification approaches have been proposed in recent years, most of them represent each image set as a single linear subspace, mixture of linear subspaces or Lie group of Riemannian manifold.