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- 저자 : Canlin Li,Baohua Jin,Wenjie Cao (검색결과 6 건)
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Design and Implementation of iOS-based Mobile Application about Awakening by CNR
Canlin Li,Baohua Jin,Wenjie Cao 보안공학연구지원센터 2014 International Journal of u- and e- Service, Scienc Vol.7 No.6
With the growing popularity of the iPhone, there is a constantly increasing demand of users for mobile applications of iPhone, in which the alarm clock application is contained. But existing alarm clock application is not favored by many users because of functional singleness. This paper presents an iOS-based mobile application on alarm clock with online radio support, and designs and implements its framework by virtue of Objective-C and SQLite in the Xcode 4.5.2 development environment, based on the popular three-tier MVC software design structure. After the application is installed and deployed on iPhone, it is available to users by providing some functions such as auto-playing radio, setting radio alarm clock, turning off the alarm by shaking iPhone, sleeping timer by radio, reserving radio program, binding and sharing microblog. This proposed iOS-based mobile application provides users with more choices and allows the user to enjoy a more colorful leisure time. The practical test from some people shows that the proposed application is very popular with users, which also illustrates its practicability and effectiveness.
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Measuring Image Similarity Based on Shape Context
Canlin Li,Shenyi Qian 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.3
Measuring image similarity is important for a number of image processing applications. The goal of research in objective image similarity assessment is to develop quantitative measures that can automatically predict perceived image similarity. In this paper, we propose a new objective approach of measuring image similarity based on shape context. We take the geometric structures of objects into account during measuring the image similarity by virtue of shape context which is a robust and compact, yet highly discriminative descriptor. Firstly we find visual salient regions of images by virtue of a regional contrast based saliency extraction algorithm and employ shape context to describe the shape of visual salient region. Then we detect shape deformations of visual salient regions between two images through estimating shape context distances, and accordingly compute the image similarity values. Real data have been used to test the proposed approach and very good results have been achieved, validating it.
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EDMFEN: Edge detection-based multi-scale feature enhancement Network for low-light image enhancement
Canlin Li,Shun Song,Pengcheng Gao,Wei Huang,Lihua Bi 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.4
To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the state-of-the-art LLIE methods.
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Content-aware Image Retargeting Based on Visual Effect Assessment
Lihua Bi,Canlin Li 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.5
Content-aware image retargeting has drawn much attention in image and vision research in recent years. However, existing methods are very difficult to ensure that the result images from retargeting achieve good visual effect on the whole, since these methods mainly focus on spatial image information. In this paper, we propose a new approach on content-aware image retargeting based on visual effect assessment. We establish an evaluation mechanism of the visual effects of retargeted images which is based on a priori statistical knowledge through studying the user's evaluation, and build the computable model of visual effect assessment of retargeted image with the help of mathematical description from Dynamic Bayesian Networks. After finishing content-aware processing and construct a three-level model of visual saliency contents for the original image, we retarget the original image into the target image by virtue of deforming image, and integrate computable model of visual effect assessment into retargeting process, so as to guide the retargeting. Finally, by steadily adjusting the size of the intermediate results from deforming image, we make this size be eventually equal to the size of the target image of retargeting, under the constraint that the result image should acquire good visual effect through optimizing the parameters of visual effect assessment. Real data have been used to test the proposed approach and very good results have been achieved, validating it.
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