IEEE Trans Neural Netw Learn Syst - Region-Based Object Recognition by Color Segmentation Using a Simplified PCNN.

Tópicos

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Resumo

In this paper, we propose a region-based object recognition (RBOR) method to identify objects from complex real-world scenes. First, the proposed method performs color image segmentation by a simplified pulse-coupled neural network (SPCNN) for the object model image and test image, and then conducts a region-based matching between them. Hence, we name it as RBOR with SPCNN (SPCNN-RBOR). Hereinto, the values of SPCNN parameters are automatically set by our previously proposed method in terms of each object model. In order to reduce various light intensity effects and take advantage of SPCNN high resolution on low intensities for achieving optimized color segmentation, a transformation integrating normalized Red Green Blue (RGB) with opponent color spaces is introduced. A novel image segmentation strategy is suggested to group the pixels firing synchronously throughout all the transformed channels of an image. Based on the segmentation results, a series of adaptive thresholds, which is adjustable according to the specific object model is employed to remove outlier region blobs, form potential clusters, and refine the clusters in test images. The proposed SPCNN-RBOR method overcomes the drawback of feature-based methods that inevitably includes background information into local invariant feature descriptors when keypoints locate near object boundaries. A large number of experiments have proved that the proposed SPCNN-RBOR method is robust for diverse complex variations, even under partial occlusion and highly cluttered environments. In addition, the SPCNN-RBOR method works well in not only identifying textured objects, but also in less-textured ones, which significantly outperforms the current feature-based methods.

Resumo Limpo

paper propos regionbas object recognit rbor method identifi object complex realworld scene first propos method perform color imag segment simplifi pulsecoupl neural network spcnn object model imag test imag conduct regionbas match henc name rbor spcnn spcnnrbor hereinto valu spcnn paramet automat set previous propos method term object model order reduc various light intens effect take advantag spcnn high resolut low intens achiev optim color segment transform integr normal red green blue rgb oppon color space introduc novel imag segment strategi suggest group pixel fire synchron throughout transform channel imag base segment result seri adapt threshold adjust accord specif object model employ remov outlier region blob form potenti cluster refin cluster test imag propos spcnnrbor method overcom drawback featurebas method inevit includ background inform local invari featur descriptor keypoint locat near object boundari larg number experi prove propos spcnnrbor method robust divers complex variat even partial occlus high clutter environ addit spcnnrbor method work well identifi textur object also lesstextur one signific outperform current featurebas method

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