Do you want to publish a course? Click here

Fractal Compression of Stereo Images Using Block Matching and SGM Algorithms to Obtain the Disparity Map

الضغــط التجزيئــي للصور المجسمة باستخدام خوارزميتي مطابقة الكتل و SGM لتحصيل خريطة التفاوت

1134   0   14   0 ( 0 )
 Publication date 2017
and research's language is العربية
 Created by Shamra Editor




Ask ChatGPT about the research

Considering the increasing importance of stereo image compression and Fractal geometry becoming one of the most important fields of modern science ,we applied fractal image compression based on quadtree portioning method and global search algorithm , on a group of stereo image pairs . As the stereo image consists of two planar images , left and right .Both the left image ( reference image ) and the disparity map between left and right images , were compressed using fractal compression . We applied both block matching algorithm and Semi Global Method (SGM)to obtain the disparity map. The left image and the depth map were reconstructed using fractal decompression while the right image (target image ) was reconstructed using the reconstructed left image , disparity map and the error image between the original right image and the reconstructed right image that was build from the left image and the disparity map . The results were evaluated using quality objective measures which are MSE (Mean Square Error ) and PSNR (Peak Signal to Noise Ratio) and efficiency objective measures which are CR(Compression Ratio) and compression time . The results were compared with JPEG compression of stereo pairs based on Discrete Cosine Transform DCT and JPEG2000 compression of stereo pairs on stereo image based on Discrete Wavelet Transform DWT .

References used
MENDIBURU, B.Fundamentals Of Stereoscopiic Imaging. Digital Cinema Summit, Nab Las Vegas, April 18, 2009 ,1-3
PIRODDI, R .Stereoscopic 3d Technologies, Algorithm Engineer. Snell Ltd, April 2010,1-2
CHENG, Q ;CANG ,W . Designing a communication system for IVAS Stereo Video Coding Based on H.264 , Electrical Engineering , Blekinge Institute of Technology ,May 2010,4-7
rate research

Read More

Image compression is one of the most important branches of digital image processing. It reduces the size of the captured images and minimizes the storage space on the drivers to speed up the transferring and transmission. In this paper we will pre sent a new approach for compressing stereo images based on three algorithms; the first one is comparing the two images that perform the stereoscopic view by noticing the great similarities between them and encoding the difference between the two images instead of encoding the whole image. The second one is reducing the redundancy between the Pixels using a 2D Digital Curvelet Transformation so we can utilize the great ability to represent the curves in the image with minimum number of coefficients. Then quantize them and remove undesirable coefficient. The low number of coefficient contains most of image data. Last one is using Huffman Encoding and take advantage of the lossless property so we can encode image and reduce the size of data without getting any image distortion or lose any part of this image. The performance of the proposed algorithm evaluated using Compression Ratio standard which is the number of the image bits after compression to the number of the original image bits before compression. Also, Peak Signal to Noise Ratio standard (PSNR) which represent the similarity between the restored image and the original image. In final, the Mean Square Error standard (MSE) which represent the error between the restored image and original image. In conclusion, the main objective here is to get the lowest rate for image compression ratio with the highest value for the image quality PSNR at the lowest value of the errors MSE.
This Research suggests a new mechanism that aims to increase the effectiveness of surveillance systems by extracting the moving objects coming from surveillance camera in order to identify them and propose a new mechanism for indexing and storing i n database and classified them according to the basic characteristics and strong indicators and retrieval when needed in less possible time. The basic idea lies in the combination of the basic characteristics of the goal (color, edges and texture) which ensures the best performance in extracting the basic target features and depend on it as indexes, then nonlinear transfers has been done on the edges of the target in order to get a picture bearing the minutest details, then conducted adverse transfers on the edges of the target during the process retrieved from the database. Finally, we propose a new mechanism for indexing all images tabase to Retrieval them in best accuracy and less time, and a program had been achieved to realize this idea.
Sentence Compression (SC), which aims to shorten sentences while retaining important words that express the essential meanings, has been studied for many years in many languages, especially in English. However, improvements on Chinese SC task are sti ll quite few due to several difficulties: scarce of parallel corpora, different segmentation granularity of Chinese sentences, and imperfect performance of syntactic analyses. Furthermore, entire neural Chinese SC models have been under-investigated so far. In this work, we construct an SC dataset of Chinese colloquial sentences from a real-life question answering system in the telecommunication domain, and then, we propose a neural Chinese SC model enhanced with a Self-Organizing Map (SOM-NCSCM), to gain a valuable insight from the data and improve the performance of the whole neural Chinese SC model in a valid manner. Experimental results show that our SOM-NCSCM can significantly benefit from the deep investigation of similarity among data, and achieve a promising F1 score of 89.655 and BLEU4 score of 70.116, which also provides a baseline for further research on Chinese SC task.
We present a COVID-19 news dashboard which visualizes sentiment in pandemic news coverage in different languages across Europe. The dashboard shows analyses for positive/neutral/negative sentiment and moral sentiment for news articles across countrie s and languages. First we extract news articles from news-crawl. Then we use a pre-trained multilingual BERT model for sentiment analysis of news article headlines and a dictionary and word vectors -based method for moral sentiment analysis of news articles. The resulting dashboard gives a unified overview of news events on COVID-19 news overall sentiment, and the region and language of publication from the period starting from the beginning of January 2020 to the end of January 2021.
This study aimed to evaluate the possibility to make knowledge map in Tishreen University through showing the administrative and human infrastructure. The researcher depended on the survey method where he distributed the questionnaire on 303 individu als of central administration in Tishreen University. The outcome was lack in the administrative and human infrastructure in the university, as it does not depend on accounting, administrative and human information systems that make easy and quick access to data and information. The researcher presented some of the recommendations including the need to work on a plan to make knowledge map by modernization of databases and to take the administrative and legal measures, which are necessary for electronic development.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا