39861836
A Novel Method for Eye Features Extraction
Extraction of the eye features, like the pupil center and radius, eye corners and eyelid contours from the frontal face images often provides us useful cues in many important applications, such as face recognition, facial expression recognition and 3D face modeling from 2D images. Assuming the rough eye window is known, the color information, Gabor features are extracted from the eye window and then these two features and their mutual relationship have been incorporated into the design of our algorithm. Firstly, after the pupil center is detected in H channel of HSV color space, the pupil radius is estimated and refined. Secondly, eye corners are localized using eye-corner filter based on Gabor feature space. Finally, eyelid curves are fitted by spline function. The new algorithm has been tested on our SJTU database and some other pictures taken from the web. The experimental results show that our algorithm has high accuracy and is robust under different conditions.
45767095
A Color Image Segmentation Algorithm by Using Region and Edge Information
A novel segmentation algorithm for natural color image is proposed. Fibonacci Lattice-based Sampling is used to get the symbols of image so as to make each pixel’s label containing color information rather than only as a class marker. Next, Region map is formed based on Fibonacci Lattice symbols to depict homogeneous regions. On the other hand, by applying fuzzy homogeneity algorithm on the image, we filter it to acquire Edge map. To strengthen the ability of discrimination, both the weighted maps are combined to form Region-Edge map. Based on above processes, growing-merging method is used to segment the image. Finally, experiments show very promising results.
28794181
JSEG Based Color Separation of Tongue Image in Traditional Chinese Medicine
The process of color separation of tongue image in traditional Chinese medicine (TCM) is decomposed into two steps: region partition and color classification. In the first step, a partition method based on JSEG is proposed to obtain homogenous regions in the tongue. In the second step, a priori template and some standard color patches are designed to assist the classification according to a priori knowledge from the TCM experts, where the nearest neighbour classifier is applied for color classification. The experimental results show that the proposed approach is greatly promising to computerized tongue diagnosis.
32026454
Color-texture image segmentation by integrating directional operators into JSEG method
We presented a new color-texture image segmentation method that combined directional operators and the JSEG (J measure based SEGmentation) algorithm. In particular, two major contributions were set forth in this paper. (1) The two measures defined in JSEG and HSEG (H measure based SEGmentation) were discussed respectively and then we found the over-segmentation problem of JSEG could be attributed partly to the absence of color discontinuity in the J measure. Moreover, we have also proven in essence the H measure is isotropic operators for edge detection in images. (2) A new measure was proposed by integrating directional operators into the J measure, on which our segmentation method was based. The new segmentation method took account of both textural homogeneity and color discontinuity in local regions. Performance improvement due to the proposed modification was demonstrated on a variety of real color images.
15418757
Fingerprint classification by directional fields
Fingerprint classification provides an important fingerprint index and can reduce fingerprint matching time in a large database. A good classification algorithm can give an accurate index that is able to search a fingerprint database more effectively. We present a fingerprint classification algorithm that is based on directional fields. We compute directional fields of fingerprint images and detect singular points (cores). Then, we extract features that we define from fingerprint images. We also use k-means classifier and 3-nearest neighbor to classify features and distinguish which fingerprint is Arch, Left Loop, Right Loop, or Whorl. Experimental results show a significant improvement in fingerprint classification performance. Moreover, the time required for the classification algorithm is reduced.
6831574
Situation-Aware Caching Strategies in Highly Varying Mobile Networks
Some emerging applications in mobile ad hoc networks (MANETs) and mobile sensor networks (MSNs) have varying mobility patterns that entails different routing strategies at different times to maintain high performance. This requires the routing protocol to adapt to different situations for better overall routing performance. We propose a model-based approach to enable such situation-awareness for the Dynamic Source Routing (DSR) protocol in such challenging environments. Our model captures the access behavior of route cache (hit, miss, and false hit) and is simple enough to be used in real-world settings. We also present a feedback-based architecture that uses the model outputs to cope with mobility changes by adjusting caching strategy on-the-fly. We validate our model against ns-2 simulations for a variety of scenarios, including a real-world mobility. Our results show that the model can aptly drive adaptive routing that leads to consistent performance improvement over DSR for the scenarios considered.
16424311
Model-based fault detection in powerline networking
Powerline networking is increasingly becoming an important component of home networking systems. Its reliability is however still a problem. We consider the popular X10 powerline communication protocol. Our main contribution is a model-based fault detection system that achieves completeness of coverage for X10 faults. We develop experimentally a finite state automaton that models all legal sequences of X10 commands. The task of detecting every violation of this model is complicated by the presence of hidden state and unobservable illegal transitions. This problem is addressed by deducing the model state indirectly from the sequence of X10 commands that is observed on the powerline. To this end, we formulate the model state deduction task in terms of the observability of the model, a concept which arises in discrete-event dynamic systems. Based on the observability property of our X10 model, the detection of model violations is performed in our current implementation via regular expressions on observable X10 command sequences. Our final contribution is a preliminary diagnosis procedure for identifying X10 faults.
15514608
Application of Relief-F feature filtering algorithm to selecting informative genes for cancer classification using microarray data
Numerous recent studies have shown that microarray gene expression data is useful for cancer classification. Classification based on microarray data is very different from previous classification problems in that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy. It is thus important to first apply feature selection methods prior to classification. In the machine learning field, one of the most successful feature filtering algorithms is the Relief-F algorithm. In this work, we empirically evaluate its performance on three published cancer classification data sets. We use the linear SVM and the k-NN as classifiers in the experiments, and compare the performance of Relief-F with other feature filtering methods, including Information Gain, Gain Ratio, and /spl chi//sup 2/-statistic. Using the leave-one-out cross validation, experimental results show that the performance of Relief-F is comparable with other methods.
28971506
Statistical identification of encrypted Web browsing traffic
Encryption is often proposed as a tool for protecting the privacy of World Wide Web browsing. However, encryption-particularly as typically implemented in, or in concert with popular Web browsers-does not hide all information about the encrypted plaintext. Specifically, HTTP object count and sizes are often revealed (or at least incompletely concealed). We investigate the identifiability of World Wide Web traffic based on this unconcealed information in a large sample of Web pages, and show that it suffices to identify a significant fraction of them quite reliably. We also suggest some possible countermeasures against the exposure of this kind of information and experimentally evaluate their effectiveness.