원문정보
초록
영어
With the fast development of industry and computer science technology, the image color recognition has been a hot topic. The prior research focus more on sensor and hardware based approaches which are not intelligent or convenient. In this paper, we present a novel image segmentation combined color recognition algorithm through boundary detection and deep neural network. The deep learning algorithm can largely increase the accuracy of classification whereas cut down the processing time consumed, we adopt the deep neural network and support vector machine to extract image features both in RGB and YUV color spaces. Boundary detection in sudden change, by contrast, is more global in nature, such as texture, so need to integrate the whole information of the image. Under the guidance, we modify the current segmentation methods with boundary detection technique to serve as the pre-processing step before classifying colors. Experimental results on synthetic and real images show that the new algorithm is effective and efficient, and is relatively independent of this type of noise. Further analysis is also conducted in the final section.
목차
1. Introduction
2. The Color Image Segmentation
2.1. The Color Space Model
2.2. The Color Model Based Image Segmentation
3. Deep Neural Network Based Boundary Detection
3.1. Unsupervised Feature Learning for Detection
3.2. The Supervised Prediction
4. Color Recognition with Segmentation and Boundary Detection Prior
4.1. The Feature Extraction Algorithm
4.2. The Combined Color Recognition and Classification Algorithm
5. Experimental Analysis
5.1. The Simulation Set-up and Environment
5.2. The Experiment for the Segmentation Part
5.3. The Experiment for the Color Recognition Part
6. Conclusion and Summary
References