| 1. |
- Jiang, Fangyuan, et al.
(författare)
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A Minimal Solution to Relative Pose with Unknown Focal Length and Radial Distortion
- 2015
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Ingår i: [Host publication title missing]. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 9004, s. 443-456
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Konferensbidrag (refereegranskat)abstract
- In this paper, we study the minimal problem of estimating the essential matrix between two cameras with constant but unknown focal length and radial distortion. This problem is of both theoretical and practical interest and it has not been solved previously. We have derived a fast and stable polynomial solver based on Gr{\"o}bner basis method. This solver enables simultaneous auto-calibration of focal length and radial distortion for cameras. For experiments, the numerical stability of the solver is demonstrated on synthetic data. We also evaluate on real images using either RANSAC or kernel voting. Compared with the standard minimal solver, which does not model the radial distortion, our proposed solver both finds a larger set of geometrically correct correspondences on distorted images and gives an accurate estimate of the radial distortion and focal length.
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| 2. |
- Jiang, Fangyuan, et al.
(författare)
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Improved Object Detection and Pose Using Part-Based Models
- 2013
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Ingår i: Lecture Notes in Computer Science (Image Analysis : 18th Scandinavian Conference, SCIA 2013, Espoo, Finland, June 17-20, 2013. Proceedings). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783642388859 - 9783642388866 ; 7944, s. 396-407
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Konferensbidrag (refereegranskat)abstract
- Automated object detection is perhaps the most central task of computer vision and arguably the most difficult one. This paper extends previous work on part-based models by using accurate geometric models both in the learning phase and at detection. In the learning phase manual annotations are used to reduce perspective distortion before learning the part-based models. That training is performed on rectified images, leads to models which are more specific, reducing the risk of false positives. At the same time a set of representative object poses are learnt. These are used at detection to remove perspective distortion. The method is evaluated on the bus category of the Pascal dataset with promising results.
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| 3. |
- Jiang, Fangyuan, et al.
(författare)
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On the Minimal Problems of Low-Rank Matrix Factorization
- 2015
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Ingår i: Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. - 9781467369633 ; , s. 2549-2557
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Konferensbidrag (refereegranskat)abstract
- Low-rank matrix factorization is an essential problem in many areas including computer vision, with applications in e.g. affine structure-from-motion, photometric stereo, and non-rigid structure from motion. However, very little attention has been drawn to minimal cases for this problem or to using the minimal configuration of observations to find the solution. Minimal problems are useful when either outliers are present or the observation matrix is sparse. In this paper, we first give some theoretical insights on how to generate all the minimal problems of a given size using Laman graph theory. We then propose a new parametrization and a building-block scheme to solve these minimal problems by extending the solution from a small sized minimal problem. We test our solvers on synthetic data as well as real data with outliers or a large portion of missing data and show that our method can handle the cases when other iterative methods, based on convex relaxation, fail.
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| 4. |
- Jiang, Fangyuan, et al.
(författare)
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Time Delay Estimation for TDOA Self-Calibration using Truncated Nuclear Norm Regularization
- 2013
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Ingår i: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. - 1520-6149. ; , s. 3885-3889
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Konferensbidrag (refereegranskat)abstract
- Measurements with unknown time delays are common in different applications such as microphone array, radio antenna array calibration, where the sources (e.g. sounds) are transmitted in unknown time instants. In this paper, we present a method for estimating unknown time delays from Time-Difference-of-Arrival (TDOA) measurements. We propose a novel rank constraint on a matrix depending on the measurements and the unknown time delays. The time delays are recovered by solving a truncated nuclear norm minimization problem using alternating direction method of multipliers (ADMM). We show in synthetic experiments that the proposed method recovers the time delays with good accuracy for noisy and missing data.
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| 5. |
- Medved, Dennis, et al.
(författare)
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Combining Text Semantics and Image Geometry to Improve Scene Interpretation
- 2014
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Ingår i: Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods. - 9789897580185 ; , s. 479-486
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Inthispaper,wedescribeanovelsystemthatidentifiesrelationsbetweentheobjectsextractedfromanimage. We started from the idea that in addition to the geometric and visual properties of the image objects, we could exploit lexical and semantic information from the text accompanying the image. As experimental set up, we gathered a corpus of images from Wikipedia as well as their associated articles. We extracted two types of objects: human beings and horses and we considered three relations that could hold between them: Ride, Lead, or None. We used geometric features as a baseline to identify the relations between the entities and we describe the improvements brought by the addition of bag-of-wordf eatures and predicate–arguments tructures we derived from the text. The best semantic model resulted in a relative error reduction of more than 18% over the baseline.
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| 6. |
- Medved, Dennis, et al.
(författare)
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Improving the Detection of Relations Between Objects in an Image Using Textual Semantics
- 2015
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Ingår i: Pattern Recognition Applications and Methods /Lecture Notes in Computer Science. - Cham : Springer International Publishing. - 9783319255293 - 9783319255309 ; 9443, s. 133-145
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Konferensbidrag (refereegranskat)abstract
- In this article, we describe a system that classifies relations between entities extracted from an image. We started from the idea that we could utilize lexical and semantic information from text associated with the image, such as captions or surrounding text, rather than just the geometric and visual characteristics of the entities found in the image. We collected a corpus of images from Wikipedia together with their corresponding articles. In our experimental setup, we extracted two kinds of entities from the images, human beings and horses, and we defined three relations that could exist between them: Ride, Lead,or None. We used geometric features as a baseline to identify the relations between the entities and we describe the improvements brought by the addition of bag-of-word features and predicate–argument structures that we extracted from the text. The best semantic model resulted in a relative error reduction of more than 18 % over the baseline
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| 7. |
- Tegen, Agnes, et al.
(författare)
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Image Segmentation and Labeling Using Free-Form Semantic Annotation
- 2014
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Ingår i: [Host publication title missing]. - 1051-4651. ; , s. 2281-2286
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Konferensbidrag (refereegranskat)abstract
- In this paper we investigate the problem of segmenting images using the information in text annotations. In contrast to the general image understanding problem, this type of annotation guided segmentation is less ill-posed in the sense that for the output there is higher consensus among human annotations. In the paper we present a system based on a combined visual and semantic pipeline. In the visual pipeline, a list of tentative figure-ground segmentations is first proposed. Each such segmentation is classified into a set of visual categories. In the natural language processing pipeline, the text is parsed and chunked into objects. Each chunk is then compared with the visual categories and the relative distance is computed using the word-net structure. The final choice of segments and their correspondence to the chunked objects are then obtained using combinatorial optimization. The output is compared to manually annotated ground-truth images. The results are promising and there are several interesting avenues for continued research.
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