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Machine learning methods are main stream algorithms applied in short term load forecasting. However, typical machine learning methods consisting of Artificial Neural Network (ANN) and Support Vector Regression (SVR) have deficiencies hard to overcome, such as easy to be trapped in local optimization (for ANN) or hard to decide kernel parameter and penalty parameter (for SVR). On the other hand, grey...
Advances have been made continuously in detection networks such as SPPnet and Fast R-CNN. Recently the novel region proposal method RPN shares full-image convolutional features with the detection network and enables a state-of-the-art object detection network Faster R-CNN. In this work we apply Faster R-CNN to train a detection network on our digital image database of books and implement automatic...
The content-based image recognition is a research focus in the field of computer vision. Machine learning especially deep learning has a great potential in the field of image recognition. This paper adopts the support vector machine algorithm and deep learning method convolutional neural network to recognize books in the digital image library and compares their performance. Experiments show that both...
Machine learning methods are the main stream algorithms applied in short term load forecasting. However, typical machine learning methods consisting of Artificial Neural Network (ANN) and Support Vector Regression (SVR) have deficiencies hard to overcome, such as easy to be trapped in local optimization (for ANN) or hard to decide kernel parameter and penalty parameter (for SVR). On the other hand,...
Pose estimation is the most important step of nature interactive between human and machine, and body part recognition is the core of pose estimation. This paper describes an improved random forests method to recognize each part of the human body. What is different from the traditional random forest structure is that the algorithm proposed in this paper provides a feature Pre - selection for examples...
In this paper, gesture recognition algorithm with kinect sensor is proposed. the depth cue is used to locate the hand area. Based on the histograms of oriented gradient (HOG) and adaboost learning methods, the static hand algorithm is designed to recognize the predefine gesture in the hand Area. by tracking the hand trajectory by kinect, hmms is used to train and classify dynamic gesture. an intelligent...
This paper proposes a hand detection methodbased on statistical learning training way. Using Microsoft's Kinect sensor, to get the depth information. Through the analysis of the characetristics of hands, put out a kind of new features for statistical learning which approximate with Harr-like feature. The new feature is good at describing complex hand shape degeneration. With the help of Adaboost statistical...
In this paper, we propose and implement a novel method for recognition static hand gestures using depth data from Kinect sensor of Microsoft. Compared to the entire human body, the hand is a smaller object with more complex articulations and more easily affected by segmentation errors. So it is a very challenging problem to recognize hand gestures. Our approach involves choosing HOG feature with both...
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