In this paper,we propose a new algorithm to establish the data association between a camera and a 2-D Light Detection And Ranging sensor （LIDAR）.In contrast to the previous works,where data association is established by calibrating the intrinsic parameters of the camera and the extrinsic parameters of the camera and the LIDAR,we formulate the map between laser points and pixels as a 2-D homography.The line-point correspondence is employed to construct geometric constraint on the homography matrix.This enables checkerboard to be not essential and any object with straight boundary can be an effective target.The calculation of the 2-D homography matrix consists of a linear least-squares solution of a homogeneous system followed by a nonlinear minimization of the geometric error in the image plane.Since the measurement quality impacts on the accuracy of the result,we investigate the equivalent constraint and show that placing the calibration target nearby the 2-D LIDAR will provide sufficient constraints to calculate the 2-D homography matrix.Simulation and experimental results validate that the proposed algorithm is robust and accurate.Compared with the previous works,which require two calibration processes and special calibration targets such as checkerboard,our method is more flexible and easier to perform.
A two-stage state recognition method is proposed for asynchronous SSVEP(steady-state visual evoked potential) based brain-computer interface(SBCI) system.The two-stage method is composed of the idle state(IS) detection and control state(CS) discrimination modules.Based on blind source separation and continuous wavelet transform techniques,the proposed method integrates functions of multi-electrode spatial filtering and feature extraction.In IS detection module,a method using the ensemble IS feature is proposed.In CS discrimination module,the ensemble CS feature is designed as feature vector for control intent classification.Further,performance comparisons are investigated among our IS detection module and other existing ones.Also the experimental results validate the satisfactory performance of our CS discrimination module.
Artificial cognitive models and computational neuroscience methods have garnered great interest from both neurologist and leading analysts in recent years. Among the cognitive models, HMAX has been widely used in computer vision systems for its robustness shape and texture features inspired by the ventral stream of the human brain. This work presents a Color-HMAX （C-HMAX） model based on the HMAX model which imitates the color vision mechanism of the human brain that the HMAX model does not include. C-HMAX is then applied to the German Traffic Sign Recognition Benchmark （GTSRB） which has 43 categories and 51 840 sample traffic signs with an accuracy of 98.41%, higher than most other models including linear discriminant analysis and multi-scale convoiutional neural network.
Detection of pedestrians in images and video sequences is important for many applications but is very challenging due to the various silhouettes of pedestrians and partial occlusions. This paper describes a two-stage robust pedestrian detection approach. The first stage uses a full body detector applied to a single image to generate pedestrian candidates. In the second stage, each pedestrian candidate is verified with a detector ensemble consisting of part detectors. The full body detector is trained based on improved shapelet features, while the part detectors make use of Haar-like wavelets as features. All the detectors are trained by a boosting method. The responses of the part detectors are then combined using a detector ensemble. The verification process is formulated as a combinatoria~ optimization problem with a genetic a^gorithm for optimization. Then, the detection results are regarded as equivalent classes so that multiple detections of the same pedestrian are quickly merged together. Tests show that this approach has a detection rate of over 95% for 0.1% FPPW on the INRIA dataset, which is significantly better than that of the original shapelet feature based approach and the existing detector ensemble approach. This approach can robustly detect pedestrians in different situations.
