5904664
A Novel Approach of Face Detection based on Skin Color Segmentation and PCA
Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on skin color segmentation and principal components analysis(PCA) is proposed. Firstly, segmenting image using color model to filter candidate faces roughly; And then Eye-analogue segments at a given scale are discovered by finding regions which are darker than their neighborhoods to filter candidate faces farther; at the end, PCA method is used to extract the relevant information in human faces.
18578234
Texture-Based Image Retrieval by Edge Detection Matching GLCM
Content-Based Image Retrieval (CBIR) system is emerging as an important research area, users can search and retrieve images based on their properties such as shape, color and texture from the image database. Usually texture-based image retrieval just consider an original image of coarseness, contrast and roughness, actually there is much texture information in the edge image. This paper proposed a novel approach to retrieve images by texture characterization using a composition of edge information and co-occurrence matrix properties. The proposed method gives encouraging results when comparing its retrieval performance to that of the traditional co-occurrence matrices and Yaopsilas approach.
15233755
Leopard Image Retrieval using Region of Interest and Texture Feature
In content based image retrieval system, texture based natural image retrieval can not extract texture feature efficiently and accurately, because the nature image is very complex and most of method extract feature use the whole nature image. In this paper, a new method is proposed for leopard texture image retrieval. Usually, the habitat of leopard is grass or tree, there is large difference between leopard image background and foreground, so it is easy to separate the leopard region from background using Canny edge detection, and then extract region of interest of foreground. The similarity of two images will be determined by the similarities between pairs of region of interest. The experimental results on the leopard texture image database demonstrate that the proposed method is encouraging with an average successful retrieval rate of 95.66%
46405724
A Novel Approach Using PCA and SVM for Face Detection
Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on principal components analysis (PCA) and support vector machine (SVM) is proposed. It firsly filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, SVM classifier is used to detect face feature in the test image, SVM has great performance in classification task. PCA is used to reduce dimension of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database. The experimental results demonstrate that the proposed method is encouraging with a successful detection rate.
1312444
A Novel Approach Using Edge Detection Information for Texture Based Image Retrieval
Most texture-based image retrieval system just consider an original image of coarseness, contrast and roughness, actually there are many texture information in the edge image. In this paper, a method combining both edge information and gray level co-occurrence matrix properties is proposed to improve the retrieval performance. The proposed method gives encouraging results when comparing its retrieval performance to that of the Yao's method, in the same image database.
13418168
ROI Based Natural Image Retrieval Using Color and Texture Feature
Region of interest(ROI) plays an important role in image analysis. In this paper, an efficient approach for content based image retrieval combining both color and texture features using three ROIs is proposed. Firstly, segment image to three parts using K-means algorithm. Secondly, select three ROIs from the three parts and then extract color features and texture features of ROIs. The similarity of two images will be determined by the similarities between pairs of ROIs. The experimental results demonstrate that the proposed method is encouraging with a successful retrieval rate.
32758032
Adaptive motion selection for online hand-eye calibration
While a robot moves, online hand–eye calibration to determine the relative pose between the robot gripper/end-effector and the sensors mounted on it is very important in a vision-guided robot system. During online hand–eye calibration, it is impossible to perform motion planning to avoid degenerate motions and small rotations, which may lead to unreliable calibration results. This paper proposes an adaptive motion selection algorithm for online hand–eye calibration, featured by dynamic threshold determination for motion selection and getting reliable hand–eye calibration results. Simulation and real experiments demonstrate the effectiveness of our method.
6109448
Automatic detection and recognition of signs from natural scenes
In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.
13958128
Towards Re-engineering Legacy Systems for Assured Dynamic Adaptation
Increasingly, software must adapt its behavior in response to changes in the supporting computing, communication infrastructure, and in the surrounding physical environment. Since most existing software was not designed to adapt, research on technique to make legacy software dynamically adaptive has gained increasing interest. Assurance is crucial for adaptive software to fulfill its intended purpose. Correctness is even more critical if it is to be applied in high assurance systems. This paper proposes a model-driven approach to introduce dynamic adaptation to non-adaptive legacy systems while maintaining assurance properties. An aspect-oriented technique is applied to achieve separation of concerns in the implementation.
9860944
Semantic Information Extraction of Video Based on Ontology and Inference
In this paper, a new ontology-based composite concept detection method is proposed, which adopts Bayesian network to construct the ontology and uses the inference rules to perform the composite concept detection providing the concrete concepts in a phrase of video. Furthermore, the probability instead of binary value is gained through the inference pattern of Bayesian network, which can obtain more precise results. The main contribution of this paper is that semantic concept ontology is constructed using Bayesian network and the constructed ontology represents the hierarchical relationship between the concepts which can be used for inference. The method narrows the influence of "Semantic Gap" in some extent and achieves good performance in composite concept detection.
62240401
A model-based approach to adding autonomic capabilities to network fault management system
Adding autonomic capabilities to network management systems provides great promise in delivering high QoS while lowering operation and maintenance cost. In this paper, we present a model-based approach to adding autonomic capabilities to a fault management system for cellular networks. We propose the use of modeling techniques to specify software failures and their dispositions at the model level for the target system. This facilitates the deployment of a control loop for adding autonomic capabilities into the system architecture, which include self-monitoring, self-healing, and self-adjusting. Our case study on the intelligent network fault management system illustrates the proposed approach by adding and deploying these autonomic capabilities derived from self-model specifications, to mitigate the risk of specified failures and maintain the level of healthiness of the system, dynamically and effectively.