His primary areas of investigation include Artificial intelligence, Gesture recognition, Computer vision, Machine learning and Gesture. His Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition. His research in Pattern recognition intersects with topics in Embedding, Categorization and Statistical model.
His research investigates the connection between Gesture recognition and topics such as Field that intersect with issues in Human–computer interaction. His work in the fields of Machine learning, such as Support vector machine, intersects with other areas such as Tree structure. His Gesture research includes themes of Dynamic time warping, Speech recognition, Facial expression and Lexicon.
Sergio Escalera focuses on Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Segmentation. Deep learning, RGB color model, Facial recognition system, Classifier and Gesture recognition are subfields of Artificial intelligence in which his conducts study. His Pattern recognition research includes elements of Random forest and Invariant.
His study involves Pixel, Pose and Object, a branch of Computer vision. The Machine learning study combines topics in areas such as Field and Categorization. Sergio Escalera has included themes like Speech recognition and Dynamic time warping in his Gesture study.
Sergio Escalera mainly focuses on Artificial intelligence, Pattern recognition, Deep learning, Facial recognition system and Computer vision. His work deals with themes such as Context and Machine learning, which intersect with Artificial intelligence. His Pattern recognition research is multidisciplinary, relying on both RGB color model and Feature.
His Deep learning research integrates issues from Contextual image classification, Sign language and Feature extraction. In his work, Disgust is strongly intertwined with Facial expression, which is a subfield of Facial recognition system. In general Computer vision, his work in Noise reduction and Inpainting is often linked to Sequence linking many areas of study.
Artificial intelligence, Pattern recognition, Deep learning, Convolutional neural network and Feature extraction are his primary areas of study. His Artificial intelligence study frequently links to adjacent areas such as Computer vision. His Pattern recognition research is multidisciplinary, incorporating elements of Graphical model and Gesture.
His biological study spans a wide range of topics, including Activity recognition, Recurrent neural network, Pose and Focus. His Convolutional neural network study combines topics in areas such as Frame, Embedding, Information loss, Boosting and One-hot. The various areas that Sergio Escalera examines in his Feature extraction study include Visualization, Emotion classification and Feature vector.
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On the Decoding Process in Ternary Error-Correcting Output Codes
S. Escalera;O. Pujol;P. Radeva.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)
Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification
X. Baro;S. Escalera;J. Vitria;O. Pujol.
IEEE Transactions on Intelligent Transportation Systems (2009)
Chalearn looking at people challenge 2014: Dataset and results
Sergio Escalera;Xavier Baró;Jordi Gonzàlez;Miguel Ángel Bautista.
european conference on computer vision (2014)
Featureweighting in dynamic timewarping for gesture recognition in depth data
Miguel Reyes;Gabriel Dominguez;Sergio Escalera.
international conference on computer vision (2011)
Multi-modal gesture recognition challenge 2013: dataset and results
Sergio Escalera;Jordi Gonzàlez;Xavier Baró;Miguel Reyes.
international conference on multimodal interfaces (2013)
RGB-D-based human motion recognition with deep learning: A survey
Pichao Wang;Wanqing Li;Philip O Ogunbona;Jun Wan.
Computer Vision and Image Understanding (2018)
ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition
Jun Wan;Stan Z. Li;Yibing Zhao;Shuai Zhou.
computer vision and pattern recognition (2016)
ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results
Sergio Escalera;Junior Fabian;Pablo Pardo;Xavier Baro.
international conference on computer vision (2015)
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
Shanxin Yuan;Guillermo Garcia-Hernando;Bjorn Stenger;Gyeongsik Moon.
computer vision and pattern recognition (2018)
A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences
Maryam Asadi-Aghbolaghi;Albert Clapes;Marco Bellantonio;Hugo Jair Escalante.
ieee international conference on automatic face gesture recognition (2017)
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