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In this paper, we suggest an accessible and effective approach to the urban road state estimation. The main technical contribution of the proposed method is a novel feature extraction on the basis of the multi-resolution, along with support vector machine for classification. Experimental tests have been carried out to validate our proposed approach which can estimate road state in the sample of a...
The problem of judging direction of the road is a tough problem in navigation system. This paper presents a image-based approach to address the direction problem in the road using Support Vector Machine(SVM) and Bayesian rules. Established localization or orientation algorithm is infeasible in the direction problem due to its time and space complexity. SVM is employed as an effective model for classification...
The aim of this work is to propose a fusion procedure based on lidar and camera to solve the pedestrian detection problem in autonomous driving. Current pedestrian detection algorithms have focused on improving the discriminability of 2D features that capture the pedestrian appearance, and on using various classifier architectures. However, less focus on exploiting the 3D structure of object has limited...
We address the problem of unstructured road detection. This paper tries to build a database named OffRoadScene, which addresses the need for experimental data to quantitatively evaluate the performance of different unstructured road detection algorithms. OffRoadScene is comprised of two level of databases. In the first level, each frame document consists of not only image information, but also information...
The absorption spectra from 0.2THz to 1.6THz of four kinds of similar Chinese herbs, including huangyujin, lvyujin, guiyujin and wenyujin, have been investigated by terahertz time-domain spectroscopy (THz-TDS). Furthermore, by using support vector machines (SVM) method, the linear kernel function, the polynomial kernel function, and the radial basis kernal function are employed for separating four...
This work presents a study of a new adaptive sampling strategy for the construction of explicit boundaries using Support Vector Machines (SVMs), referred to as Explicit Design Space Decomposition (EDSD). The new adaptive sampling strategy called local minimum point is based on choosing the sample on the current SVM boundary with the minimum distance to the most important support for the current SVM...
This paper develops a cascade of linear SVM classifiers for fast object detection. The learning problem of every node in the cascade structure is described as a new quadratic programming problem in the frame of SVM, which makes every linear classifier achieve very high detection rate but only moderate false positive rate. The real experiment shows that this method enjoys good generalization capacity...
Surface reconstruction based on Support Vector Machine (SVM) is a hot topic in the field of 3D surface construction. SVM based method for surface reconstruction can reduce the noise in the sampled data as well as repair the holes. However, the regress speed of SVM is too slow to reconstruct surface quickly from cloud points data set which has a lot of points. In this paper, a feature-preserved nonuniform...
To reduce the learning time of reinforcement learning (RL), hybrid algorithms that combine reinforcement learning with various supervised learning methods have attracted many research interests. However, the global convergence and optimality become one of the main problems for hybrid reinforcement learning algorithms. In this paper, the convergence of a hybrid RL algorithm, which is combined with...
Human has the learning ability to control various systems. In this paper, a support vector machine (SVM) based learning controller is presented to approximate human control decisions and outputs when the car is driven. The experiments show that this controller has a great ability of generalization
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