Limited by the difficulty of vehicle speed control when registering QR codes and the high maintenance cost of QR codes, the development of automated guided vehicle (AGV) navigation technology based on QR codes has encountered bottlenecks in the development of industrial logistics. The information fusion of the magnetic positioning method and the odometer can overcome the shortcomings of the AGV navigation technology based on the two-dimensional code. By laying magnetic nails on the ground, the magnetic positioning method provides the attitude of the closest magnetic nail relative to the automated guided vehicle. The experimental results show that the average path accuracy of the magnetic nail navigation based on the EKF algorithm proposed in this paper is 83±15 mm, and the average magnetic positioning accuracy is 11.7±0.59 mm.
KEYWORDS: Image segmentation, Principal component analysis, Detection and tracking algorithms, Binary data, Visualization, Color difference, Data modeling, Target detection, Information visualization, Image processing
The background information is full of essential clues which could be used to distinguish the foreground from images, especially when images contain multiple targets or complex backgrounds. In this paper, we formulate the saliency detection task as a labelling problem. We propose a novel saliency detection method via fusing a set of features based on background information. We firstly extract background features referred to as uniqueness feature, dense feature, and sparse feature. Specifically, uniqueness feature is defined using the color distinction and spatial distance based on the K-means algorithm; dense feature of the background segments is calculated by the PCA algorithm; sparse feature is computed based on the sparse encode algorithm. Then we fuse these background features under the CRF frame. Finally, we evaluate our proposed method on a new constructed dataset from THUS10000, SOD and ECSSD datasets to cover different scenarios. The experimental results show that our method can be well against the previous methods in terms of precision and recall.
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