Jin Huazhong

Work place: School of Computer Science Hubei University of Technology, Wuhan, China

E-mail: galaxy0522@gmail.com


Research Interests: Image Processing, Computer Vision, Computer systems and computational processes


Jin Huazhong, male, is an associate professor at the school of computer science, Hubei university of technology. He received Ph.D. degree in Photogrammetry from Wuhan University, Wuhan, China in 2011. Her research interests include computer vision, digital image processing and mobile mapping. He has published more than twenty papers in the area of image processing and computer vision.

Author Articles
A New Automatic Selection Method of Optimal Segmentation Scale for High Resolution Remote Sensing Image

By Jin Huazhong Zhiwei Ye Zhengbing Hu

DOI: https://doi.org/10.5815/ijigsp.2017.03.02, Pub. Date: 8 Mar. 2017

Multi-scale segmentation is one of the most important methods for object-oriented classification. The selection of the optimal scale segmentation parameters has become difficult and hot in current research certainly. This paper takes aerial images and IKONOS images as the experimental objects and proposes an automatic selection method of optimal segmentation scale for high resolution remote sensing image based on multi-scale MRF model. This method introduces the region feature into the object, and obtains the hierarchical structure of the image from the bottom up through the message propagation between the objects. Finally, the optimal segmentation scale is obtained automatically by computing the marginal probabilities of the objects in each scale image. Experimental results show that this method can effectively avoid the subjectivity and sidedness of the segmentation process, and improve the accuracy and efficiency of high resolution segmentation. 

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Pavement Crack Detection Using Spectral Clustering Method

By Jin Huazhong Zhiwei Ye Su Jun

DOI: https://doi.org/10.5815/ijigsp.2015.02.08, Pub. Date: 8 Jan. 2015

Pavement crack detection plays an important role in pavement maintaining and management, nowadays, which could be performed through remote image analysis. Thus, edges of pavement crack should be extracted in advance; in general, traditional edge detection methods don’t consider phase information and the spatial relationship between the adjacent image areas to extract the edges. To overcome the deficiency of the traditional approaches, this paper proposes a pavement crack detection algorithm based on spectral clustering method. Firstly, a measure of similarity between pairs of pixels is taken into account through orientation energy. Then, spatial relationship is needed to find regions where similarity between pixels in a given region is high and similarity between pixels in different regions is low. After that, crack edge detection is completed with spectral clustering method. The presented method has been run on some real life images of pavement crack, experimental results display that the crack detection method of this paper could obtain ideal result.

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