Градиент яркости в задаче распознавания полутоновых изображений на основе статистического подхода
Proceedings of the 2015 IEEE International Conference on Computer Vision
The Shape Boltzmann Machine (SBM) and its multilabel version MSBM have been recently introduced as deep generative models that capture the variations of an object shape. While being more flexible MSBM requires datasets with labeled parts of the objects for training. In the paper we present an algorithm for training MSBM using binary masks of objects and the seeds which approximately correspond to the locations of objects parts. The latter can be obtained from part-based detectors in an unsupervised manner. We derive a latent variable model and an EM-like training procedure for adjusting the weights of MSBM using a deep learning framework. We show that the model trained by our method outperforms SBM in the tasks related to binary shapes and is very close to the original MSBM in terms of quality of multilabel shapes.
AIST'2014 is an international data science conference on Analysis of Images, Social Networks, and Texts. Traditionally, the conference is held annually in Yekaterinburg, Russia. The conference is intended for computer scientists and practitioners whose research interests involve Internet mathematics and other related fields of data science.
LIST OF TOPICS (NON EXHAUSTIVE)Applications of Data Mining and Machine Learning techniques to Analysis of images and video Natural Language Processing Social Network Analysis Recommender systems and collaborative technologies Geoinformation systems Game analytics Information Retrieval Core Data Mining and Machine Learning techniques Sematic Web and Ontologies Data Mining in social sciences and economics Computational econometrics Experimental Economics Educational Data Mining
This volume presents new results in the study and optimization of information transmission models in telecommunication networks using different approaches, mainly based on theiries of queueing systems and queueing networks .
The paper provides a number of proposed draft operational guidelines for technology measurement and includes a number of tentative technology definitions to be used for statistical purposes, principles for identification and classification of potentially growing technology areas, suggestions on the survey strategies and indicators. These are the key components of an internationally harmonized framework for collecting and interpreting technology data that would need to be further developed through a broader consultation process. A summary of definitions of technology already available in OECD manuals and the stocktaking results are provided in the Annex section.