Dissertation > Excellent graduate degree dissertation topics show

Research on the Classifying Distorted Images Based on Random Forest

Author: LiXianJie
Tutor: ZhangQi
School: Dalian Maritime University
Course: Pattern Recognition and Intelligent Systems
Keywords: distorted images images classifying Boosting random forest
CLC: TP391.41
Type: Master's thesis
Year: 2013
Downloads: 54
Quote: 0
Read: Download Dissertation

Abstract


With the development of Internet and smart phone, the requirement of computer vision and image processing are increasing greatly. Images play an important role of information transmission in people’s life, increasing in the number of tens of thousands of every day. But images may get distorted when obtaining, compressing, processing, transfering and rebuilding. We all know the distorted images may lose some inportant information, so they cannot meet requirement for using. It’s important to classify the distorted images for image quality assessment and image processing with different processing algorithm.Different distorted images have different features. In order to classify the distorted images, in this paper, first we compute locally normalized luminances via local mean subtraction and divisive normalization, then utilize the pre-processing model and refer to the transformed luminances as mean subtracted contrast normalized (MSCN) coefficients, finally find that asymmetric generalized Gaussian distribution (AGGD) can be used to effectively capture a broader spectrum of distorted image statistics. We can get a total of36features of images-18at each scale, are used to identify distortions. In the part of classifying images,we use Boosting and random forest algorithm in OpenCV to contranst for experiment. The sets of experiment consist of six types of distorted images from TID2008database, which contain noise images (Gaussian noise/Impulse noise images), blur images (Gaussian blur/denoising images) and compression images (JPEG/JPEG2000compression).In the experiment we find random forest is better than Boosting in the classifying distorted images via adjusting parameters.

Related Dissertations

  1. An Approach for Identifying a Plant Resistance Gene Based on the Random Forest,Q943
  2. Body Part Recognition Based on Depth Images by Learning,TP391.41
  3. The Structural Model Learning Based on Local Features and the Application in Object Detection and Localization,TP391.41
  4. Shanghai Study on the Effects about Government Boost in the Development of Agricultural Insurance,F842.6
  5. Multi-target tracking algorithm,TN953
  6. Hash based on structured sparse spectrum image indexing algorithm,TP391.41
  7. Mobile robot based on 3D laser rangefinder object detection,TP242
  8. Study on Algorithms of Palmprint Recognition,TP391.41
  9. Effects of Qi-Boosting Toxin-Resolving Formula on the Structure of Cellular Adhesion System in the Implanted Tumors with Human NPC Cells Among Nude Mice,R739.63
  10. The Clinical Study on the Effect of Boosting Qi and Nourishing Yin, Transforming Stasis and Freeing the Collaterals to DML of Patients with DPN,R259
  11. Design of Analog-to-Digital Converter for the RFID Location System Based on AOA,TN792
  12. Research on Ensemble Technique for Multiple Classifiers,TP311.13
  13. Synonym Recognition Based on User Behaviors in E-commerce,TP391.1
  14. The Research on Classification Technology in Pedestrian Detection System of ITS,TP274.4
  15. The Application of Boosting in Text Classification,TP391.1
  16. Traffic monitoring system , target tracking and behavior recognition,TP277
  17. Research and Implementation of the system based on data mining technology guiding patients,TP311.13
  18. Segmentation and Analyze of Ultrasound Image,R445.1
  19. Research on Feature Selection and Model Optinization of Random Forest,TP181
  20. Research on the Effect of Feature Extraction Method on the Automated English Essay Scoring,TP391.1
  21. Estimation of Distribution Algorithm Based on Boosting,O211.67

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
© 2012 www.DissertationTopic.Net  Mobile