Automatic affective dimension recognition from naturalistic facial expressions based on wavelet filtering and PLS regression

Automatic affective dimension recognition from facial expression continuously in naturalistic contexts is a very challenging research topic but very important in human-computer interaction. In this paper, an automatic recognition system was proposed to predict the affective dimensions such as Arousal, Valence and Dominance continuously in naturalistic facial expression videos. Firstly, visual and vocal features are extracted from image frames and audio segments in facial expression videos. Secondly, a wavelet transform based digital filtering method is applied to remove the irrelevant noise information in the feature space. Thirdly, Partial Least Squares regression is used to predict the affective dimensions from both video and audio modalities. Finally, two modalities are combined to boost overall performance in the decision fusion process. The proposed method is tested in the fourth international Audio/Visual Emotion Recognition Challenge (AVEC2014) dataset and compared to other state-of-the-art methods in the affect recognition sub-challenge with a good performance

Linear and Non-Linear Multimodal Fusion for Continuous Affect Estimation in-the-Wild

Automatic continuous affect recognition from multiple modality in the wild is arguably one of the most challenging research areas in affective computing. In addressing this regression problem, the advantages of the each modality, such as audio, video and text, have been frequently explored but in an isolated way. Little attention has been paid so far to quantify the relationship within these modalities. Motivated to leverage the individual advantages of each modality, this study investigates behavioral modeling of continuous affect estimation, in multimodal fusion approaches, using Linear Regression, Exponent Weighted Decision Fusion and Multi-Gene Genetic Programming. The capabilities of each fusion approach are illustrated by applying it to the formulation of affect estimation generated from multiple modality using classical Support Vector Regression. The proposed fusion methods were applied in the public Sentiment Analysis in the Wild (SEWA) multimodal dataset and the experimental results indicate that employing proper fusion can deliver a significant performance improvement for all affect estimation. The results further show that the proposed systems is competitive or outperform the other state-of-the-art approaches

1980 Missouri commercial strawberry spray schedule

"MP 266, 1/80, 1.5M

1980 Missouri commercial peach spray schedule

"MP 265, 1/80, 2.5M

Missouri commercial strawberry spray schedule, 1984

Double-sided ; 3 hole punches at top ; folded in half ; white ; 43 cm"1/84 1.5M""These recommendations are intended to serve as guidelines for commercial strawberry growers in Missouri. The pesticides and application rates listed for any given pest problem are based on their effectiveness, economy, safety and general integration into control programs for other pests present at or about the same time. The choice of which chemicals to use, when to use them, and how they are applied must be made by the individual grower relative to his own experience, equipment, and special problems associated with his fields. The effective and efficient use of all pesticides requires careful selection of the most appropriate material and the rate required, critical timing of the application(s), and uniform, thorough coverage of the plants."--first paragraphA.E. Gaus (Horticulture), E.W. Palm (Plant Pathology), W.S. Craig (Entomology), J.F. Moore (Plant Pathology), H. Townsend (Entomology

Decoupling Temporal Dynamics for Naturalistic Affect Recognition in a Two-Stage Regression Framework

Automatic continuous affect recognition from multiple modalities is one of the most active research areas in affective computing. In addressing this regression problem, the advantages of a model, such as Support Vector Regression (SVR), or a model that can capture temporal dependencies within a predefined time window, such as Time Delay Neural Network (TDNN), Long Short-Term Memory (LSTM) or Kalman Filter (KF), have been frequently explored, but in an isolated way. The motivation is towards decoupling temporal information from its features at the semantic level, in order to exploit the slow-changing emotional property at decision level. This paper explores and proposes 2-stage regression framework where SVR, that has been regarded as the baseline approach on affective recognition task, is concatenated together with subsequent models. Extensive experiments have been carried out on a naturalistic emotion dataset, using eight modalities present in RECOLA database. The results shows the proposed framework can capture temporal information at the prediction level, and outperform state-of-theart approaches in continuous affective recognition

Missouri commercial strawberry spray schedule, 1987

Double-sided ; 3 hole punches at top ; folded in half ; green ; 43 cm"1/87 1.5M""These recommendations are intended to serve as guidelines for commercial strawberry growers in Missouri. The pesticides and application rates listed for any given pest problem are based on their effectiveness, economy, safety and general integration into control programs for other pests present at or about the same time. The choice of which chemicals to use, when to use them, and how they are applied must be made by the individual grower relative to his own experience, equipment, and special problems associated with his fields. The effective and efficient use of all pesticides requires careful selection of the most appropriate material and the rate required, critical timing of the application(s), and uniform, thorough coverage of the plants."--first paragraphA.E. Gaus (Horticulture), E.W. Palm (Plant Pathology), J.W. Johnson (Entomology), J.F. Moore (Plant Pathology), H. Townsend (Entomology