Liu, Dong Wang, Yongtao Tang, Zhi Li, Luyuan Gao, LiangcaiĬomic page image understanding aims to analyse the layout of the comic page images by detecting the storyboards and identifying the reading order automatically. The segmentation and edge detection result is one closed boundary per actual region in the image.Īutomatic comic page image understanding based on edge segment analysis A common edge detectors that work on (MRF) segmented image are used and the results are compared. The edge map is obtained using a merge process based on averaged intensity mean values.
After all pixels of the segmented regions are processed, a map of primitive region with edges is generated.
The segmentation results are improved by using watershed algorithm. In MRF model,gray level l, at pixel location i, in an image X, depends on the gray levels of neighboring pixels.
This help as priority knowledge to know the possibility of the region segmentation by the next step (MRF), which gives an image that has all the edges and regions information. DIS calculation is used for each pixel to define all the edges (weak or strong) in the image. The gradient values are calculated and then the watershed technique is used. Then the region process is modeled by MRF to obtain an image that contains different intensity regions. An initial segmented result is obtained based on K-means clustering technique and the minimum distance. It first applies edge detection technique to obtain a Difference In Strength (DIS) map. Institute of Scientific and Technical Information of China (English)Ī method that incorporates edge detection technique, Markov Random field (MRF), watershed segmentation and merging techniques was presented for performing image segmentation and edge detection tasks. IMAGE ANALYSIS BASED ON EDGE DETECTION TECHNIQUES Our best model achieves >90% overall accuracy on the ISPRS Vaihingen benchmark. We show that boundary detection significantly improves semantic segmentation with CNNs in an end-to-end training scheme. Second, we also include boundary detection in FCN-type models and set up a high-end classifier ensemble. First, we construct a comparatively simple, memory-efficient model by adding boundary detection to the SEGNET encoder-decoder architecture. Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge detection, thus making class boundaries explicit in the model. However, this success comes at a cost, since the associated loss of effective spatial resolution washes out high-frequency details and leads to blurry object boundaries. A major reason for their success is that deep networks learn to accumulate contextual information over very large receptive fields. Semantic segmentation is a fundamental remote sensing task, and most state-of-the-art methods rely on DCNNs as their workhorse. We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic segmentation with built-in awareness of semantically meaningful boundaries. Both an indoor and outdoor scene are used for an experiment to demonstrate and discuss the effectiveness and robustness of the proposed segmentation method.Ĭlassification with an edge: Improving semantic image segmentation with boundary detection Moreover, the proposed segmentation does not require estimation of the normal at each point, which limits the errors in normal estimation propagating to segmentation. The proposed method efficiently exploits the gridded scan pattern utilized during acquisition of TLS data from most sensors and takes advantage of parallel programming to process approximately 1 million points per second. Then a modified region growing algorithm groups the points lying on the same smooth surface. First, by computing the projected incidence angles and performing the normal variation analysis, the silhouette edges and intersection edges are separated from the smooth surfaces. This paper presents a novel method to rapidly segment TLS data based on edge detection and region growing. Segmentation is a common procedure of post-processing to group the point cloud into a number of clusters to simplify the data for the sequential modelling and analysis needed for most applications. Terrestrial Laser Scanning (TLS) utilizes light detection and ranging (lidar) to effectively and efficiently acquire point cloud data for a wide variety of applications. Fast Edge Detection and Segmentation of Terrestrial Laser Scans Through Normal Variation Analysis