Quick Response Code, abbreviated as QR code, is a two dimensional matrix which is extensively used both in the automotive industry and the general commercial applications currently. Compared with traditional barcodes, QR code is prevalent for its enormous information capacity and efficient error correction mechanism. Moreover, the standard QR codes possess a high decode rate at the expense of the aesthetic appearance. With an intension to resolve the contradiction, we propose a novel aesthetic QR code generation method. Differing from previous works, which mainly rely on the error correction mechanism, we first enhance the contrast of the background image so that more modules can be eliminated after initial threshold based module elimination, while maintaining the readability and demonstrate visual information to customers simultaneously. User interaction can be further adopted to delete modules as customer required using error correction mechanism.
The facial beautification is very popular nowadays. There are many photographic apps supporting the facial beautification function. However, the automatic beautification of human faces in a video is still relatively rare. In this paper, we present an automatic facial beautification method for video post-processing software. Firstly we use OpenCV and Dlib to detect the human’s face. Secondly we use Gaussian blur and median filtering to whiten the facial area. And then we use linear interpolation to add the decoration to the cheek. Lastly we enhance the lip’s color based on digital differential analyzer (DDA) and scan line algorithm. The method has been developed as a plugin for After Effects (AE). Experiments show that our method can achieve good results with no obvious artifacts and it’s easy to operate.
Deep learning is a very hot topic currently in pattern recognition and artificial intelligence researches. Aiming at the practical problem that people usually don’t know correct classifications some rubbish should belong to, based on the powerful image classification ability of the deep learning method, we have designed a prototype system to help users to classify kinds of rubbish. Firstly the CaffeNet Model was adopted for our classification network training on the ImageNet dataset, and the trained network was deployed on a web server. Secondly an android app was developed for users to capture images of unclassified rubbish, upload images to the web server for analyzing backstage and retrieve the feedback, so that users can obtain the classification guide by an android device conveniently. Tests on our prototype system of rubbish classification show that: an image of one single type of rubbish with origin shape can be better used to judge its classification, while an image containing kinds of rubbish or rubbish with changed shape may fail to help users to decide rubbish’s classification. However, the system still shows promising auxiliary function for rubbish classification if the network training strategy can be optimized further.
QR (Quick Response) code is a kind of two dimensional barcode that was first developed in automotive industry. Nowadays, QR code has been widely used in commercial applications like product promotion, mobile payment, product information management, etc. Traditional QR codes in accordance with the international standard are reliable and fast to decode, but are lack of aesthetic appearance to demonstrate visual information to customers. In this work, we present a novel interactive method to generate aesthetic QR code. By given information to be encoded and an image to be decorated as full QR code background, our method accepts interactive user's strokes as hints to remove undesired parts of QR code modules based on the support of QR code error correction mechanism and background color thresholds. Compared to previous approaches, our method follows the intention of the QR code designer, thus can achieve more user pleasant result, while keeping high machine readability.
Online educational resources, such as MOOCs, is becoming increasingly popular, especially in higher education field. One most important media type for MOOCs is course video. Besides traditional bottom-position subtitle accompany to the videos, in recent years, researchers try to develop more advanced algorithms to generate speaker-following style subtitles. However, the effectiveness of such subtitle is still unclear. In this paper, we investigate the relationship between subtitle position and the learning effect after watching the video on tablet devices. Inspired with image based human eye tracking technique, this work combines the objective gaze estimation statistics with subjective user study to achieve a convincing conclusion -- speaker-following subtitles are more suitable for online educational videos.
Currently mobile apps for document scanning do not provide convenient operations to tackle large-size documents. In this paper, we present a one-click scanning approach for large-size documents using mobile phone camera. After capturing a continuous video of documents, our approach automatically extracts several key frames by optical flow analysis. Then based on key frames, a mobile GPU based image stitching method is adopted to generate a completed document image with high details. There are no extra manual intervention in the process and experimental results show that our app performs well, showing convenience and practicability for daily life.
Identification photo is a category of facial image that has strict requirements on image quality like size, illumination, user expression, dressing, etc. Traditionally, these photos are taken in professional studios. With the rapid popularity of mobile devices, how to conveniently take identification photo at any time and anywhere with such devices is an interesting problem. In this paper, we propose a novel semi-automatic identification photo generation approach. Given a user image, facial pose and expression are first normalized to meet the basic requirements. To correct uneven lighting condition in photo, an facial illumination normalization approach is adopted to further improve the image quality. Finally, foreground user is extracted and re-targeted to a specific photo size. Besides, background can also be changed as required. Preliminary experimental results show that the proposed method is efficient and effective in identification photo generation compared to commercial software based manual tunning.
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