Exemplar-based image inpainting, as proposed by Criminisi et al. (IEEE Trans Image Process 13(9):1200–1212, 2004), fills missing regions by using a similar exemplar. However, when the missing region is a unique texture patch, an incorrect texture is filled in the missing region because a similar exemplar of damaged patch could not be found. A new image inpainting method based on an eight-direction or arbitrary direction symmetrical exemplar is proposed, suitable for damaged images containing local symmetry. The following three steps are the keys of this method. (1) According to certain similarity criteria, the symmetrical exemplars of damaged regions in eight directions or arbitrary directions are found. (2) The most similar symmetrical exemplar is selected from eight-direction or arbitrary-direction symmetrical exemplars. (3) Finally, the damaged region is filled using the most similar symmetrical exemplar. It is shown that the results of image inpainting are good when missing image regions have similar symmetry. Image inpainting is a single, efficient method. In addition, a new evaluation method of image restoration results based on similar exemplars is proposed for the inpainting effect, which is closely related to the repair algorithm. Therefore, the methods can more objectively measure the inpainting effect.

Accurate layout analysis without subsequent text-line segmentation remains an ongoing challenge, especially when facing the Kangyur, a kind of historical Tibetan document featuring considerable touching components and mottled background. Aiming at identifying different regions in document images, layout analysis is indispensable for subsequent procedures such as character recognition. However, there was only a little research being carried out to perform line-level layout analysis which failed to deal with the Kangyur. To obtain the optimal results, a fine-grained sub-line level layout analysis approach is presented. Firstly, we introduced an accelerated method to build the dataset which is dynamic and reliable. Secondly, enhancement had been made to the SOLOv2 according to the characteristics of the Kangyur. Then, we fed the enhanced SOLOv2 with the prepared annotation file during the training phase. Once the network is trained, instances of the text line, sentence, and titles can be segmented and identified during the inference stage. The experimental results show that the proposed method delivers a decent 72.7% average precision on our dataset. In general, this preliminary research provides insights into the fine-grained sub-line level layout analysis and testifies the SOLOv2-based approaches. We also believe that the proposed methods can be adopted on other language documents with various layouts.

Tibetan is a low-resource language, and Tibetan culture carried by historical Tibetan documents is an important part of Chinese civilization. The study of historical Tibetan documents is of great significance to the protection of Tibetan culture and the promotion of Chinese culture. Character segmentation is an important step in image analysis and recognition of historical Tibetan documents. However, the following three challenges prevent solving problems of character segmentation in historical Tibetan documents: 1) the text lines have different degrees of tilt and twist; 2) there are many complex situations such as overlapping, crossing, touching and breaking character strokes; and 3) these documents are written by different people with different stroke styles. To resolve these problems, we propose a character segmentation method based on key feature information for historical Tibetan documents. The proposed method consists of three parts: 1) projection and syllable point location information are used to shorten the text lines of historical Tibetan documents and establish a character block database; 2) the local baseline of the character block is detected by using the location information of syllable points or combined with horizontal projection and straight line detection, and the character block is divided into two areas above and below the baseline, and different segmentation methods are adopted; and 3) in view of the large difference in stroke styles, three stroke attribution distances are used to complete the attribution. The experimental results show that the method proposed in this paper can effectively solve the problem of character segmentation of historical Tibetan documents and achieve a better character segmentation effect, which also provides a reference for the relevant document character segmentation.

A geofence is a virtual boundary delineated around an area of interest that can be created with a variety of different techniques, such as Wi-Fi, cellular mobile, RFID and GPS. In order to develop a solution that is able to prevent elderly persons with dementia from experiencing elopement or boundary transgression that is frequently related to getting lost or other adverse events if without timely assistive services, in this paper, we propose a data mining based approach to construct a personalized safe geofence by mining individuals' historical GPS trajectories. Specifically, we first model each individuals' outdoor regular movement as a graph based on the fact that most of elders follow a simple life style in routine by visiting relatively fixed places. With this graph model, we can model each elder's outdoor safe geofence as an arbitrary polygon that is defined by the outmost vertexes of the graph model. We then propose a data mining method to construct personalized safe geofence by mining an individual's historical GPS trajectories based on the constructed geofence model, which is separated into three main stages: partitioning GPS trajectories into line segments, extracting characteristic points from a set of the line segments, and instantiating the safe geofence model with extracted characteristic points. A dataset consisting of 3 individuals' GPS trajectory data has been used to evaluate our proposed method. The qualitative results have shown that our method is workable to construct personalized safe geofence by mining individuals' GPS trajectories. Our proposed method has great potential to be used for preventing elders with dementia from wandering to out of the safe area.