@conference {88, title = {The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences}, booktitle = {14th edition of the Ibero-American Conference on Artificial Intelligence}, year = {2014}, month = {11/2014}, publisher = {Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence LNCS/LNAI series}, organization = {Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence LNCS/LNAI series}, address = {Santiago de Chile}, abstract = {

Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitatively describes independences in the distribution, which can be exploited to factorize the distribution into a set of compact functions. A key application for learning structures from data is to automatically discover knowledge. In practice, structure learning algorithms focused on "knowledge discovery" present a limitation: they use a coarse-grained representation of the structure. As a result, this representation cannot describe context-specific independences. Very recently, an algorithm called CSPC was designed to overcome this limitation, but it has a high computational complexity. This work tries to mitigate this downside presenting CSGS, an algorithm that uses the Grow-Shrink strategy for reducing unnecessary computations. On an empirical evaluation, the structures learned by CSGS achieve competitive accuracies and lower computational complexity with respect to those obtained by CSPC.

}, isbn = {978-3-319-12027-0}, url = {http://www.springer.com/computer/ai/book/978-3-319-12026-3}, author = {Edera, Alejandro and Strappa, Yanela and Bromberg, Facundo} } @article {233, title = {Grapevine buds detection and localization in 3D space based on Structure from Motion and 2D image classification}, journal = {Computers in Industry}, volume = {99C }, year = {2018}, month = {04/2018}, pages = {303-312}, chapter = {303}, abstract = {

In viticulture, there are several applications where 3D bud detection and localization in vineyards is a necessary task susceptible to automation: measurement of sunlight exposure, autonomous pruning, bud counting, type-of-bud classification, bud geometric characterization, internode length, and bud development stage.\ This paper presents a workflow to achieve quality 3D localizations of grapevine buds based on well-known computer vision and machine learning algorithms when provided with images captured in natural field conditions (i.e., natural sunlight and the addition of no artificial elements), during the winter season and using a mobile phone RGB camera. Our pipeline combines the Oriented FAST and Rotated BRIEF (ORB) for keypoint detection, a Fast Local Descriptor for Dense Matching (DAISY) for describing the keypoint, and the Fast Approximate Nearest Neighbor (FLANN) technique for matching keypoints, with the Structure from Motion multi-view scheme for generating consistent 3D point clouds. Next, it uses a 2D scanning window classifier based on Bag of Features and Support Vectors Machine for classification of 3D points in the cloud. Finally, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for 3D bud localization is applied. Our approach resulted in a maximum precision\ of 1.0\ (i.e., no false detections), a maximum recall\ of 0.45\ (i.e. 45\%\ of the buds detected), and a localization error within the range of 259-554\ pixels (corresponding to approximately 3\ bud diameters, or 1.5cm) when evaluated over the whole range of user-given parameters of workflow components.

}, keywords = {computer vision, Grapevine bud detection, Precision viticulture}, issn = {0166-3615}, doi = {10.1016/j.compind.2018.03.033}, url = {https://www.sciencedirect.com/science/article/pii/S0166361517304815}, author = {Diaz, Carlos Ariel and P{\'e}rez, Diego Sebasti{\'a}n and Miatello, Humberto and Bromberg, Facundo} } @article {169, title = {Guest Editorial: 10th Argentinean Symposium on Artificial Intelligence (ASAI 2009)}, journal = {Inteligencia Artificial.}, volume = {13}, year = {2009}, pages = {4}, chapter = {3}, issn = { 1137-3601}, doi = {0.4114/ia.v13i44.1040}, url = {http://www.redalyc.org/articulo.oa?id=92513154001}, author = {Berd{\'u}n, Luis and Bromberg, Facundo} }