Object Recognition Based on Maximally Stable Extremal Region and Scale-Invariant Feature Transform

Abstract

For the defect in describing affine and blur invariable of scale-invariant feature transform (SIFT) at large viewpoint variation, a new object recognition method is proposed in this paper IntroductionIn the object recognition with complicated background or occlusion, local feature is better than global feature in stability, repeatability and authenticability and it has been widely applied in image matching, machine vision and other fields in recent years. This paper mainly makes in-depth research to the detection and description of local region feature.Scale-invariant feature transform (SIFT) [1] algorithm has excellent scale invariance and rotation invariance in feature point extraction in linear scale space and the main direction of local gradient distribution, but it has no affine invariance. Compared with blotch feature, the region detection methods proposed in recent years are applicable to the feature region detection of various shapes and it can preserve excellent invariance even when the view-angle changes greatly. Literature [2] has made comparative analysis in such methods as SIFT, HarrisAffine, Hessian-Affine and maximally stable extremal region (MSER) [3] region detection which is proposed by Matas and the result shows that MSER has the best detection effects in recognizing gray-level consistency region with strong boundaries to be recognized, view-angle changes and light variation; that when the image scale changes, MSER follows only after Hessian-Affine and that when the image is fuzzy, MSER is the most non-ideal in performance. The research result of Literature [4] shows that SIFT has better description effect in plane objects, but MSER has excellent description effect in most natural scenes.In the local feature description, plenty of local feature descriptors have been proposed in recent years and their performances are significantly different in different applications; however, there is no universal description algorithm. Literature [5] and [6] analyze the performance of the local feature descriptors which are proposed in the past years from different perspectives and the analytical result demonstrates that the SIFT descriptor based on one-order histogram has the best scale invariance and MROGH [7] has the best performance in light variation. Huang and others have come up with the local feature descriptor based on the distribution of the histograms of second-order gradients (HSOG) and it excels in describing the geometrical features related to curvature; however, it is low in the recognition efficiency of second-order histogram. Therefore, this paper integrates MSER detection and SIFT description and uses the improved MSER to detect the local objective local feature region and SIFT to construct feature descriptor for object recognition.

Deteksi Citra dan Posisi Barcode Menggunakan Metode Oriented Fast and Rotated Brief (ORB) dan Maximally Stable Extremal Regions (MSER)

“…Berbeda dari deteksi sudut di bagian terakhir, MSER menggunakan konsep yang mirip DAS untuk mendapatkan daerah yang stabil secara lokal. Algoritma DAS dalam pengolahan gambar adalah terutama digunakan dalam segmentasi gambar dan fokus pada "tingkat air" gambar tingkat abu abu ketika daereah bergabung, tetapi daerah wilayah tidak stabil sementara MSER fokus pada daerah "permukaan air" ketika daerah daerah tersebut stabil [5].…”

A Vision System Based on Time-of-Flight and RGB Cameras Applied to Robotic Tree Peony Fruit Harvesting

“…The Normalized Direct Linear Transformation (NDLT) algorithm is used to match two images after feature points matching is completed (Guo, 2009). Compared to the DLT algorithm (Guo, 2009;Abdel-Aziz et al, 2015), the NDLT algorithm has an invariance property of similarity transformation while having high calculation accuracy. The algorithm needs to perform the orthogonal transformation of the matched feature points before DLT to calculate the projection matrix and adjust the images.…”