Fog Concentration Grade Judgment for Meter Reading Based on SVM Robot reading energy meter devices automatically is a basic task for smart grid. Unfortunately, the predicted accuracy is influenced by environment dramatically. In order to solve the problem, we propose a method which can metric fog concentration in environment before meter reading. Moreover, based on the characteristics of image and fog influence, joint features and SVM are used to discriminate the concentration level of foggy image. Specially, Contrast feature and DFT feature are extracted as joint features to describe the foggy image and SVM is used to study and train the joint feature. Finally the fog concentration level is discriminated naturally. The experimental results demonstrate the effectiveness of proposed method in term of judging the meter image with five level.

Haze weather recognition based on multiple features and Random Forest A single image based haze weather recognition is the fundamental operation of the applications of outdoor computer vision. Currently, the recognition results are remains undesirable and most existing methods are only for the fixed scene. In this paper, we propose multiple features and Random Forest based haze weather classification method for any scenario to improve the detection accuracy. First, through systematically investigation, multiple features are extracted and properly processed. Then, these features are combined into high dimension vectors and the Random Forest is adopted to lean an adaptive classifier for haze recognition. In the experiment, an outdoor image set which contains around 4000 images is collected. Form the experimental results is can be seen that the proposed method achieves 97.4% recognition accuracy of the haze weather on the collected dataset.