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	Generalised exponential-Gaussian distribution: a method for neural reaction time analysis 期刊论文 OAI收割 COGNITIVE NEURODYNAMICS, 2022, 页码: 17 作者: Marmolejo-Ramos, Fernando; Barrera-Causil, Carlos; Kuang, Shenbing; Fazlali, Zeinab; Wegener, Detlef \| 收藏 \| 浏览/下载：27/0 \| 提交时间：2022/07/18 Exponential Gaussian distribution Reaction times Neuronal response latency Cognitive neuroscience Generalised additive models for location Scale and shape
	Quantile rank maps: A new tool for understanding individual brain development 期刊论文 OAI收割 NEUROIMAGE, 2015, 卷号: 111, 期号: 1, 页码: 454-463 作者: Chen, Huaihou; Kelly, Clare; Castellanos, F. Xavier; He, Ye; Zuo, Xi-Nian 收藏 \| 浏览/下载：59/0 \| 提交时间：2015/01/23 Box-Cox transformation Generalized additive models for location scale and shape Quantile rank map MRI Resting-state fMRI Penalized B-splines
	A shape context based Hausdorff similarity measure in image matching 会议论文 OAI收割 5th International Symposium on Photoelectronic Detection and Imaging (ISPDI) - Infrared Imaging and Applications, Beijing, June 25-27, 2013 作者: Ma TL(马天磊); Liu YP(刘云鹏); Shi ZL(史泽林); Yin J(尹健) 收藏 \| 浏览/下载：36/0 \| 提交时间：2013/12/26 The traditional Hausdorff measure, which uses Euclidean distance metric (L2 norm) to define the distance between coordinates of any two points, has poor performance in the presence of the rotation and scale change although it is robust to the noise and occlusion. To address the problem, we define a novel similarity function including two parts in this paper. The first part is Hausdorff distance between shapes which is calculated by exploiting shape context that is rotation and scale invariant as the distance metric. The second part is the cost of matching between centroids. Unlike the traditional method, we use the centroid as reference point to obtain its shape context that embodies global information of the shape. Experiment results demonstrate that the function value between shapes is rotation and scale invariant and the matching accuracy of our algorithm is higher than that of previously proposed algorithm on the MEPG-7 database.
	Integrated intensity, orientation code and spatial information for robust tracking (EI CONFERENCE) 会议论文 OAI收割 2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007, May 23, 2007 - May 25, 2007, Harbin, China 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：26/0 \| 提交时间：2013/03/25 real-time tracking is an important topic in computer vision. Conventional single cue algorithms typically fail outside limited tracking conditions. Integration of multimodal visual cues with complementary failure modes allows tracking to continue despite losing individual cues. In this paper we combine intensity orientation codes and special information to form a new intensity-orientation codes-special (IOS) feature to represent the target. The intensity feature is not affected by the shape variance of object and has good stability. Orientation codes matching is robust for searching object in cluttered environments even in the cases of illumination fluctuations resulting from shadowing or highlighting etc The spatial locations of the pixels are used which allow us to take into account the spatial information which is lost in traditional histogram. Histograms of intensity orientation codes and spatial information are employed for represent the target Mean shift algorithm is a nonparametric density estimation method. The fast and optimal mode matching can be achieved by this method. In order to reduce the compute time we use the mean shift procedure to reach the target localization. Experiment results show that the new method can successfully cope with clutter partial occlusions illumination change and target variations such as scale and rotation. The computational complexity is very low. If the size of the target is 3628 pixels it only needs 12ms to complete the method. 2007 IEEE.
	A novel starting-point-independent wavelet coefficient shape matching (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Hu S.; Zhu M.; Wu C.; Song H.-J. 收藏 \| 浏览/下载：17/0 \| 提交时间：2013/03/25 In many computer vision tasks in order to improve the accuracy and robustness to the noise wavelet analysis is preferred for the natural multi-resolution property. However the wavelet representation suffers from the dependency of the starting point of the sampled contour. For overcoming the problem that the wavelet representation depends on the starting point of the sampled contour the Zernike moments are introduced and a novel Starting-Point-lndependent wavelet coefficient shape matching algorithm is presented. The proposed matching algorithm firstly gains the object contours and give the translation and scale invariant object shape representation. The object shape representation is converted to the dyadic wavelet representation by the wavelet transform. And then calculate the Zernike moments of wavelet representation in different scales. With respect to property of rotation invariant of Zernike moments consider the Zernike moments as the feature vector to calculate the dissimilarity between the object and template image which overcoming the problem of dependency of starting point. The experimental results have proved the proposed algorithm to be efficient precise and robust.