FIR 发表于 2025-3-30 11:11:31
Robust Fitting by Adaptive-Scale Residual Consensus parameters of a model, and ii) differentiate inliers from outliers. We propose a new estimator called Adaptive-Scale Residual Consensus (ASRC). ASRC scores a model based on both the residuals of inliers and the corresponding scale estimate determined by those inliers. ASRC is very robust to multiplResistance 发表于 2025-3-30 15:31:14
http://reply.papertrans.cn/24/2341/234005/234005_52.png尾巴 发表于 2025-3-30 20:25:10
An Adaptive Window Approach for Image Smoothing and Structures Preservingant modeling of the image with an adaptive choice of a window around each pixel. The adaptive smoothing technique associates with each pixel the weighted sum of data points within the window. We describe a statistical method for choosing the optimal window size, in a manner that varies at each pixelindifferent 发表于 2025-3-30 23:39:00
http://reply.papertrans.cn/24/2341/234005/234005_54.png贪婪的人 发表于 2025-3-31 01:28:25
Are Iterations and Curvature Useful for Tensor Voting? on tensor voting are presented. First the use of iterations is investigated, and second, a new method for integrating curvature information is evaluated. In opposition to other grouping methods, tensor voting claims the advantage to be non-iterative. Although non-iterative tensor voting methods pro荒唐 发表于 2025-3-31 06:30:17
http://reply.papertrans.cn/24/2341/234005/234005_56.png流动性 发表于 2025-3-31 11:38:14
http://reply.papertrans.cn/24/2341/234005/234005_57.pngCANDY 发表于 2025-3-31 16:27:27
Shape Matching and Recognition – Using Generative Models and Informative Featurese model allows for a class of transformations, such as affine and non-rigid transformations, and induces a similarity measure between shapes. The matching process is formulated in the EM algorithm. To have a fast algorithm and avoid local minima, we show how the EM algorithm can be approximated by uGeneralize 发表于 2025-3-31 20:25:27
http://reply.papertrans.cn/24/2341/234005/234005_59.png啜泣 发表于 2025-3-31 22:04:58
Recognizing Objects in Range Data Using Regional Point Descriptors successful in past research is the regional shape descriptor. In this paper, we introduce two new regional shape descriptors: 3D shape contexts and harmonic shape contexts. We evaluate the performance of these descriptors on the task of recognizing vehicles in range scans of scenes using a database