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Author |
Arash Akbarinia; C. Alejandro Parraga |
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Title |
Biologically plausible boundary detection |
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Conference Article |
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Year |
2016 |
Publication |
27th British Machine Vision Conference |
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Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on two benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. |
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York; UK; September 2016 |
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BMVC |
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NEUROBIT; 600.068; 600.072 |
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Admin @ si @ AkP2016a |
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2867 |
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Author |
Arash Akbarinia; C. Alejandro Parraga |
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Title |
Dynamically Adjusted Surround Contrast Enhances Boundary Detection, European Conference on Visual Perception |
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Conference Article |
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2016 |
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European Conference on Visual Perception |
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Barcelona; Spain; August 2016 |
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ECVP |
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NEUROBIT |
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no |
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Admin @ si @ AkP2016b |
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2900 |
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Author |
Arash Akbarinia; C. Alejandro Parraga |
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Title |
Colour Constancy Beyond the Classical Receptive Field |
Type |
Journal Article |
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Year |
2018 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
40 |
Issue |
9 |
Pages |
2081 - 2094 |
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The problem of removing illuminant variations to preserve the colours of objects (colour constancy) has already been solved by the human brain using mechanisms that rely largely on centre-surround computations of local contrast. In this paper we adopt some of these biological solutions described by long known physiological findings into a simple, fully automatic, functional model (termed Adaptive Surround Modulation or ASM). In ASM, the size of a visual neuron's receptive field (RF) as well as the relationship with its surround varies according to the local contrast within the stimulus, which in turn determines the nature of the centre-surround normalisation of cortical neurons higher up in the processing chain. We modelled colour constancy by means of two overlapping asymmetric Gaussian kernels whose sizes are adapted based on the contrast of the surround pixels, resembling the change of RF size. We simulated the contrast-dependent surround modulation by weighting the contribution of each Gaussian according to the centre-surround contrast. In the end, we obtained an estimation of the illuminant from the set of the most activated RFs' outputs. Our results on three single-illuminant and one multi-illuminant benchmark datasets show that ASM is highly competitive against the state-of-the-art and it even outperforms learning-based algorithms in one case. Moreover, the robustness of our model is more tangible if we consider that our results were obtained using the same parameters for all datasets, that is, mimicking how the human visual system operates. These results might provide an insight on how dynamical adaptation mechanisms contribute to make object's colours appear constant to us. |
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NEUROBIT; 600.068; 600.072 |
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Call Number |
Admin @ si @ AkP2018a |
Serial |
2990 |
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Author |
Arash Akbarinia; C. Alejandro Parraga |
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Title |
Feedback and Surround Modulated Boundary Detection |
Type |
Journal Article |
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Year |
2018 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
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Volume |
126 |
Issue |
12 |
Pages |
1367–1380 |
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Keywords |
Boundary detection; Surround modulation; Biologically-inspired vision |
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Abstract |
Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of receptive field surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on three benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods. |
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NEUROBIT; 600.068; 600.072 |
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no |
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Admin @ si @ AkP2018b |
Serial |
2991 |
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Author |
Arash Akbarinia |
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Title |
Computational Model of Visual Perception: From Colour to Form |
Type |
Book Whole |
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Year |
2017 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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The original idea of this project was to study the role of colour in the challenging task of object recognition. We started by extending previous research on colour naming showing that it is feasible to capture colour terms through parsimonious ellipsoids. Although, the results of our model exceeded state-of-the-art in two benchmark datasets, we realised that the two phenomena of metameric lights and colour constancy must be addressed prior to any further colour processing. Our investigation of metameric pairs reached the conclusion that they are infrequent in real world scenarios. Contrary to that, the illumination of a scene often changes dramatically. We addressed this issue by proposing a colour constancy model inspired by the dynamical centre-surround adaptation of neurons in the visual cortex. This was implemented through two overlapping asymmetric Gaussians whose variances and heights are adjusted according to the local contrast of pixels. We complemented this model with a generic contrast-variant pooling mechanism that inversely connect the percentage of pooled signal to the local contrast of a region. The results of our experiments on four benchmark datasets were indeed promising: the proposed model, although simple, outperformed even learning-based approaches in many cases. Encouraged by the success of our contrast-variant surround modulation, we extended this approach to detect boundaries of objects. We proposed an edge detection model based on the first derivative of the Gaussian kernel. We incorporated four types of surround: full, far, iso- and orthogonal-orientation. Furthermore, we accounted for the pooling mechanism at higher cortical areas and the shape feedback sent to lower areas. Our results in three benchmark datasets showed significant improvement over non-learning algorithms.
To summarise, we demonstrated that biologically-inspired models offer promising solutions to computer vision problems, such as, colour naming, colour constancy and edge detection. We believe that the greatest contribution of this Ph.D dissertation is modelling the concept of dynamic surround modulation that shows the significance of contrast-variant surround integration. The models proposed here are grounded on only a portion of what we know about the human visual system. Therefore, it is only natural to complement them accordingly in future works. |
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Address |
October 2017 |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
C. Alejandro Parraga |
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978-84-945373-4-9 |
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NEUROBIT |
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no |
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Admin @ si @ Akb2017 |
Serial |
3019 |
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Author |
Antonio Lopez; W. Niessen; Joan Serrat; K. Nikolay; B. Ter Haar Romeny; Juan J. Villanueva; M. Viergerver |
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Title |
New improvements in the multiscale analysis of trabecular bone patterns |
Type |
Book Chapter |
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Year |
2000 |
Publication |
Pattern Recognition and Applications |
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251-260 |
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IOS Press |
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ADAS |
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no |
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Admin @ si @ |
Serial |
3418 |
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Author |
Antonio Lopez; W. Niessen; Joan Serrat; K. Nicolay; Bart M. Ter Haar Romeny; Juan J. Villanueva; M. Viergever |
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Title |
New improvements in the multiscale analysis of trabecular bone patterns. |
Type |
Miscellaneous |
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Year |
1999 |
Publication |
Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes (SNRFAI’99), pags. 497–504 |
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Bilbao |
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ADAS |
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ADAS @ adas @ LNS1999 |
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17 |
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Author |
Antonio Lopez; W. Niessen; Joan Serrat; K. Nicolay; Bart M. Ter Haar Romeny; Juan J. Villanueva; M. Viergever |
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Title |
New improvements in the multiscale analysis of trabecular bone patterns. |
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Miscellaneous |
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2000 |
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Pattern Recognition and Applications, IOS Press, 251–260. |
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ADAS |
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ADAS @ adas @ LNS2000 |
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332 |
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Author |
Antonio Lopez; Ricardo Toledo; Joan Serrat; Juan J. Villanueva |
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Title |
Extraction of vessel centerlines from 2D coronary angiographies |
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Miscellaneous |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. pgs. 489–496, volume I |
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Bilbao |
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ADAS |
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ADAS @ adas @ LTS1999 |
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14 |
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Author |
Antonio Lopez; Joan Serrat; J. Saludes; Cristina Cañero; Felipe Lumbreras; T. Graf |
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Title |
Ridgeness for Detecting Lane Markings |
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Miscellaneous |
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2005 |
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2nd International Workshop on Intelligent Transportation Systems (WIT2005), Conference Proceedings (Sponsored by the IEEE Communication Society, Germany Chapter) |
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lane markings |
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Hamburg (Germany) |
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ADAS |
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no |
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ADAS @ adas @ LSS2005 |
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548 |
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Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras; T. Graf |
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Title |
Robust lane markings detection and road geometry computation |
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Journal Article |
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2010 |
Publication |
International Journal of Automotive Technology |
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IJAT |
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11 |
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3 |
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395–407 |
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lane markings |
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Detection of lane markings based on a camera sensor can be a low-cost solution to lane departure and curve-over-speed warnings. A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue because of cast shadows, worn and occluded markings, variable ambient lighting conditions, for example. We focus on increasing detection reliability in two ways. First, we employed an image feature other than the commonly used edges: ridges, which we claim addresses this problem better. Second, we adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane lines to the image features, based on both ridgeness and ridge orientation. In addition, the model was fitted for the left and right lane lines simultaneously to enforce a consistent result. Four measures of interest for driver assistance applications were directly computed from the fitted parametric model at each frame: lane width, lane curvature, and vehicle yaw angle and lateral offset with regard the lane medial axis. We qualitatively assessed our method in video sequences captured on several road types and under very different lighting conditions. We also quantitatively assessed it on synthetic but realistic video sequences for which road geometry and vehicle trajectory ground truth are known. |
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The Korean Society of Automotive Engineers |
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1229-9138 |
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ADAS |
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ADAS @ adas @ LSC2010 |
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1300 |
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Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras |
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Title |
Robust Lane Lines Detection and Quantitative Assessment |
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Conference Article |
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2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis |
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4477 |
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274–281 |
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lane markings |
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Girona (Spain) |
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J. Marti et al |
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LNCS |
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IbPRIA |
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ADAS |
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ADAS @ adas @ LSC2007 |
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881 |
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Antonio Lopez; Joan Serrat |
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Title |
Tracing crease curves by solving a system of differential equations. |
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1996 |
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ADAS @ adas @ LoS1996 |
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84 |
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Author |
Antonio Lopez; Joan Serrat |
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Title |
Image Analysis through Surface Geometric Descriptors |
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Miscellaneous |
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1995 |
Publication |
VI National Simposium on Pattern Recognition and image Analysis. |
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Cordoba |
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ADAS @ adas @ LoS1995 |
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133 |
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Author |
Antonio Lopez; Joan Serrat |
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Title |
Ridge/Valley-like structures: Creases, separatrices and drainage patterns |
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Report |
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1997 |
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CVC Technical Report #21 |
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no |
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ADAS @ adas @ LoS1997 |
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524 |
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