| 1. |
- Borga, Magnus, et al.
(författare)
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Semi-Supervised Learning of Anatomical Manifolds for Atlas-Based Segmentation of Medical Images
- 2016
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Ingår i: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR). - : IEEE Computer Society. - 9781509048472 - 9781509048489 ; , s. 3146-3149
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel method for atlas-based segmentation of medical images. The method uses semi- supervised learning of a graph describing a manifold of anatom- ical variations of whole-body images, where unlabelled data are used to find a path with small deformations from the labelled atlas to the target image. The method is evaluated on 36 whole-body magnetic resonance images with manually segmented livers as ground truth. Significant improvement (p < 0.001) was obtained compared to direct atlas-based registration.
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| 2. |
- Danelljan, Martin, 1989-, et al.
(författare)
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Aligning the Dissimilar: A Probabilistic Feature-Based Point Set Registration Approach
- 2016
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Ingår i: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR) 2016. - : IEEE. - 9781509048472 - 9781509048489 ; , s. 247-252
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Konferensbidrag (refereegranskat)abstract
- 3D-point set registration is an active area of research in computer vision. In recent years, probabilistic registration approaches have demonstrated superior performance for many challenging applications. Generally, these probabilistic approaches rely on the spatial distribution of the 3D-points, and only recently color information has been integrated into such a framework, significantly improving registration accuracy. Other than local color information, high-dimensional 3D shape features have been successfully employed in many applications such as action recognition and 3D object recognition. In this paper, we propose a probabilistic framework to integrate high-dimensional 3D shape features with color information for point set registration. The 3D shape features are distinctive and provide complementary information beneficial for robust registration. We validate our proposed framework by performing comprehensive experiments on the challenging Stanford Lounge dataset, acquired by a RGB-D sensor, and an outdoor dataset captured by a Lidar sensor. The results clearly demonstrate that our approach provides superior results both in terms of robustness and accuracy compared to state-of-the-art probabilistic methods.
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| 3. |
- Gladh, Susanna, et al.
(författare)
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Deep motion features for visual tracking
- 2016
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Ingår i: Proceedings of the 23rd International Conference on, Pattern Recognition (ICPR), 2016. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509048472 - 9781509048489 ; , s. 1243-1248
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Konferensbidrag (refereegranskat)abstract
- Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or Color Names. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking. Despite their success, these features only capture appearance information. On the other hand, motion cues provide discriminative and complementary information that can improve tracking performance. Contrary to visual tracking, deep motion features have been successfully applied for action recognition and video classification tasks. Typically, the motion features are learned by training a CNN on optical flow images extracted from large amounts of labeled videos. This paper presents an investigation of the impact of deep motion features in a tracking-by-detection framework. We further show that hand-crafted, deep RGB, and deep motion features contain complementary information. To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking. Comprehensive experiments clearly suggest that our fusion approach with deep motion features outperforms standard methods relying on appearance information alone.
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| 4. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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Compact Multi-scale Periocular Recognition Using SAFE Features
- 2016
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Ingår i: Proceedings - International Conference on Pattern Recognition. - Washington : IEEE. - 9781509048472 ; , s. 1455-1460
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Konferensbidrag (refereegranskat)abstract
- In this paper, we present a new approach for periocular recognition based on the Symmetry Assessment by Feature Expansion (SAFE) descriptor, which encodes the presence of various symmetric curve families around image key points. We use the sclera center as single key point for feature extraction, highlighting the object-like identity properties that concentrates to this unique point of the eye. As it is demonstrated, such discriminative properties can be encoded with a reduced set of symmetric curves. Experiments are done with a database of periocular images captured with a digital camera. We test our system against reference periocular features, achieving top performance with a considerably smaller feature vector (given by the use of a single key point). All the systems tested also show a nearly steady correlation between acquisition distance and performance, and they are also able to cope well when enrolment and test images are not captured at the same distance. Fusion experiments among the available systems are also provided. © 2016 IEEE
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| 5. |
- Asplund, Teo, et al.
(författare)
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A new approach to mathematical morphology on one dimensional sampled signals
- 2016
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Ingår i: Proceedings of the 23rd International Conference on Pattern Recognition ICPR 2016. - Piscataway, NJ : IEEE Communications Society. - 9781509048472 ; , s. 3904-3909
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Konferensbidrag (refereegranskat)abstract
- We present a new approach to approximate continuous-domain mathematical morphology operators. The approach is applicable to irregularly sampled signals. We define a dilation under this new approach, where samples are duplicated and shifted according to the flat, continuous structuring element. We define the erosion by adjunction, and the opening and closing by composition. These new operators will significantly increase precision in image measurements. Experiments show that these operators indeed approximate continuous-domain operators better than the standard operators on sampled one-dimensional signals, and that they may be applied to signals using structuring elements smaller than the distance between samples. We also show that we can apply the operators to scan lines of a two-dimensional image to filter horizontal and vertical linear structures.
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| 6. |
- Brandtberg, Tomas
(författare)
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Virtual hexagonal and multi-scale operator for fuzzy rank order texture classification using one-dimensional generalised Fourier analysis
- 2016
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Ingår i: 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR). - : IEEE COMPUTER SOC. - 9781509048472 ; , s. 2018-2024
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Konferensbidrag (refereegranskat)abstract
- This paper presents a study on a family of local hexagonal and multi-scale operators useful for texture analysis. The hexagonal grid shows an attractive rotation symmetry with uniform neighbour distances. The operator depicts a closed connected curve (1D periodic). It is resized within a scale interval during the conversion from the original square grid to the virtual hexagonal grid. Complementary image features, together with their tangential first-order hexagonal derivatives, are calculated. The magnitude/phase information from the Fourier or Fractional Fourier Transform (FFT, FrFT) are accumulated in thirty different Cartesian (polar for visualisation) and multi-scale domains. Simultaneous phase-correlation of a subset of the data gives an estimate of scaling/rotation relative the references. Similarity metrics are used as template matching. The sample, unseen by the system, is classified into the group with the maximum fuzzy rank order. An instantiation of a 12-point hexagonal operator (radius=2) is first successfully evaluated on a set of thirteen Brodatz images (no scaling/rotation). Then it is evaluated on the more challenging KTH-TIPS2b texture dataset (scaling/rotation, varying pose/illumination). A confusion matrix and cumulative fuzzy rank order summaries show, for example, that the correct class is top-ranked 44 - 50% and top-three ranked 68 - 76% of all sample images. A similar evaluation, using a box-like 12-point mask of square grids, gives overall lower accuracies. Finally, the FrFT parameter is an additional tuning parameter influencing the accuracies significantly.
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| 7. |
- Bylow, Erik, et al.
(författare)
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Robust online 3D reconstruction combining a depth sensor and sparse feature points
- 2016
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Ingår i: Proceedings - International Conference on Pattern Recognition. - 1051-4651. - 9781509048472 ; , s. 3709-3714
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Konferensbidrag (refereegranskat)abstract
- Online 3D reconstruction has been an active research area for a long time. Since the release of the Microsoft Kinect Camera and publication of KinectFusion [11] attention has been drawn how to acquire dense models in real-time. In this paper we present a method to make online 3D reconstruction which increases robustness for scenes with little structure information and little texture information. It is shown empirically that our proposed method also increases robustness when the distance between the camera positions becomes larger than what is commonly assumed. Quantitative and qualitative results suggest that this approach can handle situations where other well-known methods fail. This is important in, for example, robotics applications like when the camera position and the 3D model must be created online in real-time.
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| 8. |
- Chowdhury, Manish, et al.
(författare)
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An Efficient Radiographic Image Retrieval System Using Convolutional Neural Network
- 2016
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Ingår i: 2016 23rd International Conference on Pattern Recognition (ICPR). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509048472 ; , s. 3134-3139
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Konferensbidrag (refereegranskat)abstract
- Content-Based Medical Image Retrieval (CBMIR) is an important research field in the context of medical data management. In this paper we propose a novel CBMIR system for the automatic retrieval of radiographic images. Our approach employs a Convolutional Neural Network (CNN) to obtain high- level image representations that enable a coarse retrieval of images that are in correspondence to a query image. The retrieved set of images is refined via a non-parametric estimation of putative classes for the query image, which are used to filter out potential outliers in favour of more relevant images belonging to those classes. The refined set of images is finally re-ranked using Edge Histogram Descriptor, i.e. a low-level edge-based image descriptor that allows to capture finer similarities between the retrieved set of images and the query image. To improve the computational efficiency of the system, we employ dimensionality reduction via Principal Component Analysis (PCA). Experiments were carried out to evaluate the effectiveness of the proposed system on medical data from the “Image Retrieval in Medical Applications” (IRMA) benchmark database. The obtained results show the effectiveness of the proposed CBMIR system in the field of medical image retrieval.
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| 9. |
- Markus, Nenad, et al.
(författare)
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Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion
- 2016
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Ingår i: 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR). - : IEEE COMPUTER SOC. - 9781509048472 ; , s. 2380-2385
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Konferensbidrag (refereegranskat)abstract
- Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs. However, data of this kind is not always available since detailed keypoint correspondences can be hard to establish. On the other hand, we can often obtain labels for pairs of keypoint bags. For example, keypoint bags extracted from two images of the same object under different views form a matching pair, and keypoint bags extracted from images of different objects form a non-matching pair. On average, matching pairs should contain more corresponding keypoints than non-matching pairs. We describe an end-to-end differentiable architecture that enables the learning of local keypoint descriptors from such weakly-labeled data.
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| 10. |
- Nilsson, Mikael
(författare)
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Sparse coding with unity range codes and label consistent discriminative dictionary learning
- 2017
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Ingår i: 2016 23rd International Conference on Pattern Recognition, ICPR 2016. - 9781509048472 ; , s. 3186-3191
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Konferensbidrag (refereegranskat)abstract
- A novel sparse coding framework with unity range codes and the possibility to produce a discriminative dictionary is presented. The framework is, in contrast to many other works, able to handle unsupervised, supervised and semi-supervised settings. Furthermore, codes are constrained to be in unity range, which is beneficial in many scenarios. The paper presents the framework and solvers used to produce dictionaries and codes. Experiments in image reconstruction and feature learning for classification highlight the benefits with the proposed framework.
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