Titlebook: Image Analysis and Recognition; Third International Aurélio Campilho,Mohamed Kamel Conference proceedings 2006 Springer-Verlag Berlin Heid

根深蒂固

書目名稱Image Analysis and Recognition影響因子(影響力)




書目名稱Image Analysis and Recognition影響因子(影響力)學(xué)科排名




書目名稱Image Analysis and Recognition網(wǎng)絡(luò)公開度




書目名稱Image Analysis and Recognition網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Image Analysis and Recognition被引頻次




書目名稱Image Analysis and Recognition被引頻次學(xué)科排名




書目名稱Image Analysis and Recognition年度引用




書目名稱Image Analysis and Recognition年度引用學(xué)科排名




書目名稱Image Analysis and Recognition讀者反饋




書目名稱Image Analysis and Recognition讀者反饋學(xué)科排名

斜坡

送秋波

Alternative Approaches and Algorithms for Classificatione several advantages over the previous ones via the introduction of a new concept, Centers of Masses for classes, and new cost functions which enforce clustering of different classes more explicitly compared to the previous approaches.

gregarious

forbid

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/i/image/461403.jpg

手術(shù)刀

Using Local Integral Invariants for Object Recognition in Complex ScenesIntegral invariants capture the local structure of the neighborhood around the points where they are computed. This makes them very well suited for constructing highly-discriminative local descriptors. The features are by definition invariant to Euclidean motion. We show how to extend the local feat

Instantaneous

CLAN

Object Categorization Using Kernels Combining Graphs and Histograms of Gradients shape or appearance. In this paper, we aim at performing object recognition by mixing kernels obtained from different cues. Our method is based on two complementary descriptions of an object. First, we describe its shape thanks to labeled graphs. This graph is obtained from morphological skeleton,

Graphite

Alternative Approaches and Algorithms for Classificatione several advantages over the previous ones via the introduction of a new concept, Centers of Masses for classes, and new cost functions which enforce clustering of different classes more explicitly compared to the previous approaches.

Chameleon

A Pool of Classifiers by SLP: A Multi-class Caseial training of the perceptrons, one may obtain a pool of different classification algorithms. Means to improve training speed and reduce generalization error are studied. Training dynamics is illustrated by solving artificial multi-class pattern recognition task and important real world problem: de