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Author |
Cristhian A. Aguilera; Angel D. Sappa; R. Toledo |
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Title |
LGHD: A feature descriptor for matching across non-linear intensity variations |
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Conference Article |
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Year |
2015 |
Publication |
IEEE International Conference on, Quebec City, QC, 2015 |
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Pages |
178 - 181 |
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Keywords |
Feature descriptor, multi-modal, multispectral, NIR, LWIR |
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Abstract |
This paper presents a new feature descriptor suitable to the task of matching features points between images with nonlinear intensity variations. This includes image pairs with significant illuminations changes, multi-modal image pairs and multi-spectral image pairs. The proposed method describes the neighbourhood of feature points combining frequency and spatial information using multi-scale and multi-oriented Log- Gabor filters. Experimental results show the validity of the proposed approach and also the improvements with respect to the state of the art. |
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IEEE |
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Quebec City, QC, Canada |
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English |
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English |
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2015 IEEE International Conference on Image Processing (ICIP) |
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Approved |
no |
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Call Number |
cidis @ cidis @ |
Serial |
40 |
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Author |
M. Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel D. Sappa; A. Tomé |
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Title |
Concurrent Learning of Visual Codebooks and Object Categories in Open- ended Domains |
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Conference Article |
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Year |
2015 |
Publication |
Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, Hamburg, Germany, 2015 |
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Pages |
2488 - 2495 |
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Keywords |
Birds, Training, Legged locomotion, Visualization, Histograms, Object recognition, Gaussian mixture model |
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Abstract |
In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline. |
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IEEE |
Place of Publication |
Hamburg, Germany |
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English |
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English |
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2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
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Approved |
no |
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Call Number |
cidis @ cidis @ |
Serial |
41 |
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Author |
Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias |
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Title |
Scene representations for autonomous driving: an approach based on polygonal primitives |
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Conference Article |
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Year |
2015 |
Publication |
Iberian Robotics Conference (ROBOT 2015), Lisbon, Portugal, 2015 |
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Volume |
417 |
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Pages |
503-515 |
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Keywords |
Scene reconstruction, Point cloud, Autonomous vehicles |
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Abstract |
In this paper, we present a novel methodology to compute a 3D scene representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
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Springer International Publishing Switzerland 2016 |
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English |
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English |
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Second Iberian Robotics Conference |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
45 |
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Permanent link to this record |