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Found 11 papers, 2 papers with code
Towards Practical 2D Grapevine Bud Detection with Fully Convolutional Networks
In Viticulture, visual inspection of the plant is a necessary task for measuring relevant variables.
Efficient comparison of independence structures of log-linear models
Our method relies only on the independence structure of the models, which is useful when the interest lies in obtaining knowledge from said structure, or when comparing the performance of structure learning algorithms, among other possible uses.
Blankets Joint Posterior score for learning Markov network structures
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields.
Image Classification of Grapevine Buds using Scale-Invariant Features Transform, Bag of Features and Support Vector Machines
This robustness on the position and size of the window demonstrates its viability for use as the classification stage in a scanning-window detection algorithms.
Estimacion de carga muscular mediante imagenes
En este trabajo elegimos un caso particular de medicion de variables biomecanicas: el problema de estimar el nivel discreto de carga muscular que estan ejerciendo los musculos de un brazo.
The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences
The structure qualitatively describes independences in the distribution, which can be exploited to factorize the distribution into a set of compact functions.
Computer Vision Approach for Low Cost, High Precision Measurement of Grapevine Trunk Diameter in Outdoor Conditions
This work presents a low cost, high precision method for the measurement of trunk diameter of grapevines based on Computer Vision techniques.
Efficient Markov Network Structure Discovery Using Independence Tests
We present two algorithms for learning the structure of a Markov network from data: GSMN* and GSIMN.
Learning Markov networks with context-specific independences
In this work we present CSPC, an independence-based algorithm for learning structures that encode context-specific independences, and encoding them in a log-linear model, instead of a graph.
Markov random fields factorization with context-specific independences
This is presented in our main contribution, the context-specific Hammersley-Clifford theorem, a generalization to CSIs of the Hammersley-Clifford theorem that applies for conditional independences.
The IBMAP approach for Markov networks structure learning
IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach.
