Titlebook: Intelligent Computing Theories and Methodologies; 11th International C De-Shuang Huang,Kang-Hyun Jo,Abir Hussain Conference proceedings 201

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Multiple Convolutional Neural Network for Feature Extraction,is still difficult. One of the neural network, Convolutional Neural Network (CNN) has gained great success in image recognition and it builds features by automatic learning. More importantly, CNN can operate directly on the gray image, so it can be directly used for processing classification of the

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Hybrid Deep Learning for Plant Leaves Classification,oduced, which included AutoEncoder(AE) and convolutional neural network (CNN). This neural network is applied for extracting the features of the plant leaves. In this paper, we proved that hybrid deep learning can extract better features for classification task. We apply the hybrid deep learning to

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Distance Sensor Fusion for Obstacle Detection at Night Based on Kinect Sensors,ers connected via Gigabit Ethernet. The cameras are mounted on the back side of the vehicle at different altitudes and tilt angles allowing detection of objects behind the vehicle and defects of the road surface. The safety system triggers an alarm if an object appears in dangerous proximity to the

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Plant Leaf Recognition Based on Contourlet Transform and Support Vector Machine,so essential for environmental protection and helping farmers increase the production of food. As a fundamental task in botanical study, plant leaf recognition has been a hot research topic in these years. In this paper, we propose a new method based on contourlet transform and Support Vector Machin

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Implementation of Plant Leaf Recognition System on ARM Tablet Based on Local Ternary Pattern,se, particularly in near-uniform image regions has stimulated many transformations of LBP to improve the ability of feature description. The Local Ternary Pattern (LTP) extends the conventional LBP to ternary codes and makes a significant improvement. LTP is more resistant to noise, but no longer st

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A BRMF-Based Model for Missing-Data Estimation of Image Sequence,is proposed by combining Bayesian robust matrix factorization (BRMF) and particle swarm optimization (PSO). Experimental results on several widely used image sequences demonstrate the effectiveness and feasibility of the proposed algorithm.

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Image Splicing Detection Based on Markov Features in QDCT Domain,n digital forensics. Gray images are used directly, or color images are converted to gray images before processing in previous image splicing detection algorithms. However, most natural images are color images. In order to make use of the color information in images, a classification algorithm is pu