| 1. |
- Berg, Amanda, 1988-, et al.
(författare)
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Detecting Rails and Obstacles Using a Train-Mounted Thermal Camera
- 2015
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Ingår i: Image Analysis. - Cham : Springer. - 9783319196640 - 9783319196657 ; , s. 492-503
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Konferensbidrag (refereegranskat)abstract
- We propose a method for detecting obstacles on the railway in front of a moving train using a monocular thermal camera. The problem is motivated by the large number of collisions between trains and various obstacles, resulting in reduced safety and high costs. The proposed method includes a novel way of detecting the rails in the imagery, as well as a way to detect anomalies on the railway. While the problem at a first glance looks similar to road and lane detection, which in the past has been a popular research topic, a closer look reveals that the problem at hand is previously unaddressed. As a consequence, relevant datasets are missing as well, and thus our contribution is two-fold: We propose an approach to the novel problem of obstacle detection on railways and we describe the acquisition of a novel data set.
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| 2. |
- Danelljan, Martin, 1989-, et al.
(författare)
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Coloring Channel Representations for Visual Tracking
- 2015
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Ingår i: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings. - Cham : Springer. - 9783319196640 - 9783319196657 ; , s. 117-129
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Konferensbidrag (refereegranskat)abstract
- Visual object tracking is a classical, but still open research problem in computer vision, with many real world applications. The problem is challenging due to several factors, such as illumination variation, occlusions, camera motion and appearance changes. Such problems can be alleviated by constructing robust, discriminative and computationally efficient visual features. Recently, biologically-inspired channel representations \cite{felsberg06PAMI} have shown to provide promising results in many applications ranging from autonomous driving to visual tracking.This paper investigates the problem of coloring channel representations for visual tracking. We evaluate two strategies, channel concatenation and channel product, to construct channel coded color representations. The proposed channel coded color representations are generic and can be used beyond tracking.Experiments are performed on 41 challenging benchmark videos. Our experiments clearly suggest that a careful selection of color feature together with an optimal fusion strategy, significantly outperforms the standard luminance based channel representation. Finally, we show promising results compared to state-of-the-art tracking methods in the literature.
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| 3. |
- Khan, Fahad Shahbaz, et al.
(författare)
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Deep Semantic Pyramids for Human Attributes and Action Recognition
- 2015
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Ingår i: Image Analysis. - Cham : Springer. - 9783319196657 - 9783319196640 ; , s. 341-353
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Konferensbidrag (refereegranskat)abstract
- Describing persons and their actions is a challenging problem due to variations in pose, scale and viewpoint in real-world images. Recently, semantic pyramids approach [1] for pose normalization has shown to provide excellent results for gender and action recognition. The performance of semantic pyramids approach relies on robust image description and is therefore limited due to the use of shallow local features. In the context of object recognition [2] and object detection [3], convolutional neural networks (CNNs) or deep features have shown to improve the performance over the conventional shallow features.We propose deep semantic pyramids for human attributes and action recognition. The method works by constructing spatial pyramids based on CNNs of different part locations. These pyramids are then combined to obtain a single semantic representation. We validate our approach on the Berkeley and 27 Human Attributes datasets for attributes classification. For action recognition, we perform experiments on two challenging datasets: Willow and PASCAL VOC 2010. The proposed deep semantic pyramids provide a significant gain of 17.2%, 13.9%, 24.3% and 22.6% compared to the standard shallow semantic pyramids on Berkeley, 27 Human Attributes, Willow and PASCAL VOC 2010 datasets respectively. Our results also show that deep semantic pyramids outperform conventional CNNs based on the full bounding box of the person. Finally, we compare our approach with state-of-the-art methods and show a gain in performance compared to best methods in literature.
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| 4. |
- Meneghetti, Giulia, et al.
(författare)
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Image alignment for panorama stitching in sparsely structured environments
- 2015
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Ingår i: Image Analysis. SCIA 2015.. - Cham : Springer. - 9783319196640 - 9783319196657 ; , s. 428-439
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Konferensbidrag (refereegranskat)abstract
- Panorama stitching of sparsely structured scenes is an open research problem. In this setting, feature-based image alignment methods often fail due to shortage of distinct image features. Instead, direct image alignment methods, such as those based on phase correlation, can be applied. In this paper we investigate correlation-based image alignment techniques for panorama stitching of sparsely structured scenes. We propose a novel image alignment approach based on discriminative correlation filters (DCF), which has recently been successfully applied to visual tracking. Two versions of the proposed DCF-based approach are evaluated on two real and one synthetic panorama dataset of sparsely structured indoor environments. All three datasets consist of images taken on a tripod rotating 360 degrees around the vertical axis through the optical center. We show that the proposed DCF-based methods outperform phase correlation-based approaches on these datasets.
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| 5. |
- Oskarsson, Magnus, et al.
(författare)
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Democratic Tone Mapping Using Optimal K-means Clustering
- 2015
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Ingår i: Lecture Notes in Computer Science (Image Analysis, 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings)). - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783319196657 - 9783319196640 ; 9127, s. 354-365
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Konferensbidrag (refereegranskat)abstract
- The field of high dynamic range imaging addresses the problem of capturing and displaying the large range of luminance levels found in the world, using devices with limited dynamic range. In this paper we present a novel tone mapping algorithm that is based on $K$-means clustering. Using dynamic programming we are able to, not only solve the clustering problem efficiently, but also find the global optimum. Our algorithm runs in O(N^2K) for an image with N luminance levels and K output levels. We show that our algorithm gives comparable result to state-of-the-art tone mapping algorithms, but with the additional large benefit of a total lack of parameters. We test our algorithm on a number of standard high dynamic range images, and give qualitative comparisons to a number of state-of-the-art tone mapping algorithms.
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| 6. |
- Oskarsson, Magnus, et al.
(författare)
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Regularizing Image Intensity Transformations Using the Wasserstein Metric
- 2015
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Ingår i: Lecture Notes in Computer Science (Image Analysis, 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings)). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319196657 - 9783319196640 ; 9127, s. 275-286
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Konferensbidrag (refereegranskat)abstract
- In this paper we direct our attention to the problem of discretization effects in intensity transformations of images. We propose to use the Wasserstein metric (also known as the Earth mover distance) to bootstrap the transformation process. The Wasserstein metric gives a mapping between gray levels that we use to direct our image mapping. In order to spatially regularize the image mapping we apply anisotropic filtering and use this to steer our mapping. We describe a general framework for intensity transformation, and investigate the application of our method on a number of special problems, namely histogram equalization, color transfer and bit depth expansion. We have tested our algorithms on real images, and we show that we get state-of-the-art results.
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| 7. |
- Alvén, Jennifer, 1989, et al.
(författare)
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Überatlas: Robust Speed-Up of Feature-Based Registration and Multi-Atlas Segmentation
- 2015
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319196640 ; 9127, s. 92-102
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Konferensbidrag (refereegranskat)abstract
- Registration is a key component in multi-atlas approaches to medical image segmentation. Current state of the art uses intensitybased registration methods, but such methods tend to be slow, which sets practical limitations on the size of the atlas set. In this paper, a novel feature-based registration method for affine registration is presented. The algorithm constructs an abstract representation of the entire atlas set, an uberatlas, through clustering of features that are similar and detected consistently through the atlas set. This is done offline. At runtime only the feature clusters are matched to the target image, simultaneously yielding robust correspondences to all atlases in the atlas set from which the affine transformations can be estimated efficiently. The method is evaluated on 20 CT images of the heart and 30 MR images of the brain with corresponding gold standards. Our approach succeeds in producing better and more robust segmentation results compared to two baseline methods, one intensity-based and one feature-based, and significantly reduces the running times.
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| 8. |
- Danielsson, Oscar Martin
(författare)
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Category-sensitive hashing and bloom filter based descriptors for online keypoint recognition
- 2015
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Ingår i: 19th Scandinavian Conference on Image Analysis, SCIA 2015. - Cham : Springer. - 9783319196640 ; , s. 329-340
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Konferensbidrag (refereegranskat)abstract
- In this paper we propose a method for learning a categorysensitive hash function (i.e. a hash function that tends to map inputs from the same category to the same hash bucket) and a feature descriptor based on the Bloom filter. Category-sensitive hash functions are robust to intra-category variation. In this paper we use them to produce descriptors that are invariant to transformations caused by for example viewpoint changes, lighting variation and deformation. Since the descriptors are based on Bloom filters, they support a ”union” operation. So descriptors of matched features can be aggregated by taking their union.We thus end up with one descriptor per keypoint instead of one descriptor per feature (By keypoint we refer to a world-space reference point and by feature we refer to an image-space interest point. Features are typically observations of keypoints and matched features are observations of the same keypoint). In short, the proposed descriptor has data-defined invariance properties due to the category-sensitive hashing and is aggregatable due to its Bloom filter inheritance. This is useful whenever we require custom invariance properties (e.g. tracking of deformable objects) and/or when we make multiple observations of each keypoint (e.g. tracking, multi-view stereo or visual SLAM).
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| 9. |
- Lindblad, Joakim, et al.
(författare)
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Microscopy image enhancement for cost-effective cervical cancer screening
- 2015
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Ingår i: Image Analysis. - Cham : Springer. - 9783319196640 ; , s. 440-451
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Konferensbidrag (refereegranskat)abstract
- We propose a simple and fast method for microscopy imageenhancement and quantitatively evaluate its performance on a databasecontaining cell images obtained from microscope setups of several levelsof quality. The method utilizes an efficiently and accurately estimated rel-ative modulation transfer function to generate images of higher quality,starting from those of lower quality, by filtering in the Fourier domain.We evaluate the method visually and based on correlation coefficientand normalized mutual information. We conclude that enhanced imagesexhibit high similarity, both visually and in terms of information con-tent, with acquired high quality images. This is an important result forthe development of a cost-effective screening system for cervical cancer.
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