2014[edit | edit source]

Robust 3D Tracking with Descriptor Fields[edit | edit source]

[36] A. Crivellaro; V. Lepetit. Robust 3D Tracking with Descriptor Fields. 2014. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'14), 2014. doi:10.1109/CVPR.2014.436[1]

Abstract We introduce a method that can register challenging images from specular and poorly textured 3D environments, on which previous approaches fail. We assume that a small set of reference images of the environment and a partial 3D model are available. Like previous approaches, we register the input images by aligning them with one of the reference images using the 3D information. However, these approaches typically rely on the pixel intensities for the alignment, which is prone to fail in presence of specularities or in absence of texture. Our main contribution is an efficient novel local descriptor that we use to describe each image location. We show that we can rely on this descriptor in place of the intensities to significantly improve the alignment robustness at a minor increase of the computational cost, and we analyze the reasons behind the success of our descriptor.

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2013[edit | edit source]

Automated Edge Detection Using Convolutional Neural Network[edit | edit source]

[37] M. A. El-Sayed, Y. A. Estaitia, M. A. Khafagy. Automated Edge Detection Using Convolutional Neural Network. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No. 10, pp. 11-17, 2013. DOI:10.14569/IJACSA.2013.041003[2]

Abstract The edge detection on the images is so important for image processing. It is used in various fields of applications ranging from real-time video surveillance and traffic management to medical imaging applications. Currently, there is not a single edge detector that has both efficiency and reliability. Traditional differential filter-based algorithms have the advantage of theoretical strictness, but require excessive post-processing. Proposed CNN technique is used to realize edge detection task it takes the advantage of momentum features extraction, it can process any input image of any size with no more training required, the results are very promising when compared to both classical methods and other ANN based methods.

Object discovery in 3D scenes via shape analysis[edit | edit source]

[38] A. Karpathy, S. Miller, L. Fei-Fei. Object discovery in 3D scenes via shape analysis. 2013 IEEE International Conference on Robotics and Automation. DOI: 10.1109/ICRA.2013.6630857[3]

Abstract We present a method for discovering object models from 3D meshes of indoor environments. Our algorithm first decomposes the scene into a set of candidate mesh segments and then ranks each segment according to its "objectness" - a quality that distinguishes objects from clutter. To do so, we propose five intrinsic shape measures: compactness, symmetry, smoothness, and local and global convexity. We additionally propose a recurrence measure, codifying the intuition that frequently occurring geometries are more likely to correspond to complete objects. We evaluate our method in both supervised and unsupervised regimes on a dataset of 58 indoor scenes collected using an Open Source implementation of Kinect Fusion [1]. We show that our approach can reliably and efficiently distinguish objects from clutter, with Average Precision score of .92. We make our dataset available to the public.

Different Approaches for Extracting Information from the Co-Occurrence Matrix[edit | edit source]

[39] Nanni L., Brahnam S., Ghidoni S., Menegatti E., Barrier T. Different Approaches for Extracting Information from the Co-Occurrence Matrix. PLoS ONE 8(12): e83554, 2013. DOI:10.1371/journal.pone.0083554.[4]

Abstract In 1979 Haralick famously introduced a method for analyzing the texture of an image: a set of statistics extracted from the co-occurrence matrix. In this paper we investigate novel sets of texture descriptors extracted from the co-occurrence matrix; in addition, we compare and combine different strategies for extending these descriptors. The following approaches are compared: the standard approach proposed by Haralick, two methods that consider the co-occurrence matrix as a three-dimensional shape, a gray-level run-length set of features and the direct use of the co-occurrence matrix projected onto a lower dimensional subspace by principal component analysis. Texture descriptors are extracted from the co-occurrence matrix evaluated at multiple scales. Moreover, the descriptors are extracted not only from the entire co-occurrence matrix but also from subwindows. The resulting texture descriptors are used to train a support vector machine and ensembles. Results show that our novel extraction methods improve the performance of standard methods. We validate our approach across six medical datasets representing different image classification problems using the Wilcoxon signed rank test.

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2012[edit | edit source]

3D Textureless Object Detection and Tracking: An Edge-based Approach[edit | edit source]

[40] C. Choi, H. Christensen. 3D Textureless Object Detection and Tracking: An Edge-based Approach. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012. doi:10.1109/IROS.2012.6386065[5]

Abstract This paper presents an approach to textureless object detection and tracking of the 3D pose. Our detection and tracking schemes are coherently integrated in a particle filtering framework on the special Euclidean group, SE(3), in which the visual tracking problem is tackled by maintaining multiple hypotheses of the object pose. For textureless object detection, an efficient chamfer matching is employed so that a set of coarse pose hypotheses is estimated from the matching between 2D edge templates of an object and a query image. Particles are then initialized from the coarse pose hypotheses by randomly drawing based on costs of the matching. To ensure the initialized particles are at or close to the global optimum, an annealing process is performed after the initialization. While a standard edge-based tracking is employed after the annealed initialization, we employ a refinement process to establish improved correspondences between projected edge points from the object model and edge points from an input image. Comparative results for several image sequences with clutter are shown to validate the effectiveness of our approach.

Local quadrics surface approximation for real-time tracking of textureless 3D rigid curved objects[edit | edit source]

[41] M.Oikawa, M.Fujisawa. Local quadrics surface approximation for real-time tracking of textureless 3D rigid curved objects. 14th Symposium on Virtual and Augmented Reality, 2012. doi:10.1109/SVR.2012.3[6]

Abstract This paper addresses the problem of tracking textureless rigid curved objects. A common approach uses polygonal meshes to represent curved objects and use them inside an edge-based tracking system. However, in order to accurately recover their shape, high quality meshes are required, creating a trade-off between computational efficiency and tracking accuracy. To solve this issue, we suggest the use of quadrics for each patch in the mesh to give local approximations of the object shape. The novelty of our research lies in using curves that represent the quadrics projection in the current viewpoint for distance evaluation instead of using the standard method which compares edges from mesh and detected edges in the video image. This representation allows to considerably reduce the level of detail of the polygonal mesh and led us to the development of a novel method for evaluating the distance between projected and detected features. The experiments results show the comparison between our approach and the traditional method using sparse and dense meshes. They are presented using both synthetic and real image data.

Distribution fields for tracking[edit | edit source]

[42] L. Sevilla-Lara, E. Learned-Miller. Distribution fields for tracking. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1910-1917. DOI:10.1109/CVPR.2012.6247891.[7]

Abstract Visual tracking of general objects often relies on the assumption that gradient descent of the alignment function will reach the global optimum. A common technique to smooth the objective function is to blur the image. However, blurring the image destroys image information, which can cause the target to be lost. To address this problem we introduce a method for building an image descriptor using distribution fields (DFs), a representation that allows smoothing the objective function without destroying information about pixel values. We present experimental evidence on the superiority of the width of the basin of attraction around the global optimum of DFs over other descriptors. DFs also allow the representation of uncertainty about the tracked object. This helps in disregarding outliers during tracking (like occlusions or small misalignments) without modeling them explicitly. Finally, this provides a convenient way to aggregate the observations of the object through time and maintain an updated model. We present a simple tracking algorithm that uses DFs and obtains state-of-the-art results on standard benchmarks.

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2011[edit | edit source]

Adaptive tool-path generation of rapid prototyping for complex product models[edit | edit source]

[43] G.Q. Jin, W.D. Li, C.F.Tsai, L.Wang. Adaptive tool-path generation of rapid prototyping for complex product models. Journal of Manufacturing Systems, Volume 30, Issue 3, 2011, pp. 154-164. DOI:10.1016/j.jmsy.2011.05.007.[8]

Abstract Rapid prototyping (RP) provides an effective method for model verification and product development collaboration. A challenging research issue in RP is how to shorten the build time and improve the surface accuracy especially for complex product models. In this paper, systematic adaptive algorithms and strategies have been developed to address the challenge. A slicing algorithm has been first developed for directly slicing a Computer-Aided Design (CAD) model as a number of RP layers. Closed Non-Uniform Rational B-Spline (NURBS) curves have been introduced to represent the contours of the layers to maintain the surface accuracy of the CAD model. Based on it, a mixed and adaptive tool-path generation algorithm, which is aimed to optimize both the surface quality and fabrication efficiency in RP, has been then developed. The algorithm can generate contour tool-paths for the boundary of each RP sliced layer to reduce the surface errors of the model, and zigzag tool-paths for the internal area of the layer to speed up fabrication. In addition, based on developed build time analysis mathematical models, adaptive strategies have been devised to generate variable speeds for contour tool-paths to address the geometric characteristics in each layer to reduce build time, and to identify the best slope degree of zigzag tool-paths to further minimize the build time. In the end, case studies of complex product models have been used to validate and showcase the performance of the developed algorithms in terms of processing effectiveness and surface accuracy.

Robust 3D visual tracking using particle filtering on the SE(3) group[edit | edit source]

[44] C. Choi, H.I. Christensen. Robust 3D visual tracking using particle filtering on the SE(3) group. 2011 IEEE International Conference on Robotics and Automation. DOI: 10.1109/ICRA.2011.5980245.[9]

Abstract In this paper, we present a 3D model-based object tracking approach using edge and keypoint features in a particle filtering framework. Edge points provide 1D information for pose estimation and it is natural to consider multiple hypotheses. Recently, particle filtering based approaches have been proposed to integrate multiple hypotheses and have shown good performance, but most of the work has made an assumption that an initial pose is given. To remove this assumption, we employ keypoint features for initialization of the filter. Given 2D-3D keypoint correspondences, we choose a set of minimum correspondences to calculate a set of possible pose hypotheses. Based on the inlier ratio of correspondences, the set of poses are drawn to initialize particles. For better performance, we employ an autoregressive state dynamics and apply it to a coordinate-invariant particle filter on the SE(3) group. Based on the number of effective particles calculated during tracking, the proposed system re-initializes particles when the tracked object goes out of sight or is occluded. The robustness and accuracy of our approach is demonstrated via comparative experiments.

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Authors Aliaksei Petsiuk
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Created May 14, 2022 by Irene Delgado
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  1. A. Crivellaro; V. Lepetit. Robust 3D Tracking with Descriptor Fields. 2014. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'14), 2014. doi:10.1109/CVPR.2014.436
  2. M. A. El-Sayed, Y. A. Estaitia, M. A. Khafagy. Automated Edge Detection Using Convolutional Neural Network. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No. 10, pp. 11-17, 2013. DOI:10.14569/IJACSA.2013.041003
  3. A. Karpathy, S. Miller, L. Fei-Fei. Object discovery in 3D scenes via shape analysis. 2013 IEEE International Conference on Robotics and Automation. DOI: 10.1109/ICRA.2013.6630857
  4. Nanni L., Brahnam S., Ghidoni S., Menegatti E., Barrier T. Different Approaches for Extracting Information from the Co-Occurrence Matrix. PLoS ONE 8(12): e83554, 2013. DOI:10.1371/journal.pone.0083554.
  5. C. Choi, H. Christensen. 3D Textureless Object Detection and Tracking: An Edge-based Approach. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012. doi:10.1109/IROS.2012.6386065
  6. M.Oikawa, M.Fujisawa. Local quadrics surface approximation for real-time tracking of textureless 3D rigid curved objects. 14th Symposium on Virtual and Augmented Reality, 2012. doi:10.1109/SVR.2012.3
  7. L. Sevilla-Lara, E. Learned-Miller. Distribution fields for tracking. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1910-1917. DOI:10.1109/CVPR.2012.6247891.
  8. G.Q. Jin, W.D. Li, C.F.Tsai, L.Wang. Adaptive tool-path generation of rapid prototyping for complex product models. Journal of Manufacturing Systems, Volume 30, Issue 3, 2011, pp. 154-164. DOI:10.1016/j.jmsy.2011.05.007.
  9. C. Choi, H.I. Christensen. Robust 3D visual tracking using particle filtering on the SE(3) group. 2011 IEEE International Conference on Robotics and Automation. DOI: 10.1109/ICRA.2011.5980245.
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