The analysis and interpretation of satellite images generally require the realization of a classification step. For this purpose, many methods over the year have been developed with good performances. But with the explosion of VHR images availability, these methods became more difficult to use. Recently, deep neural networks emerged as a method to address the VHR images classification which is a key point in remote sensing field. This work aims to evaluate the performance of fine-tuning pretrained convolutional neural networks (CNNs) on the classification of VHR imagery. The results are promising since they show better accuracy comparing to that of CNNs as features extractor.
Colorectal cancer is the third most common type of cancer. However, this disease can be prevented by detection and removal of precursor adenomatous polyps after the diagnosis given by experts on computer tomographic colonography (CTC). During CTC diagnosis, the radiologist looks for colon polyps and measures not only the size but also the malignancy. It is a common sense that to segment polyp volumes from their complicated growing environment is of much significance for accomplishing the CTC based early diagnosis task. Previously, the polyp volumes are mainly given from the manually or semi-automatically drawing by the radiologists. As a result, some deviations cannot be avoided since the polyps are usually small (6~9mm) and the radiologists’ experience and knowledge are varying from one to another. In order to achieve automatic polyp segmentation carried out by the machine, we proposed a new method based on the colon decomposition strategy. We evaluated our algorithm on both phantom and patient data. Experimental results demonstrate our approach is capable of segment the small polyps from their complicated growing background.
In this paper, we extend Isotropy-based LSB steganalysis for detecting the existence of hidden message and estimating
hidden message length when the embedding is performed using both of two distinct embedding paradigms in one or
more than one LSB. The extended method is based on the analysis of image isotropy, which is usually affected by secret
message embedding. The proposed method is general framework because it encompasses a more general case of LSB
steganalysis, namely when the embedding is performed in more LSBs and using both embedding paradigms. Compared
with our previous proposed weighted stego-image based method, the detection accuracy is high. Experimental results
and theoretical verification show that this framework is an effective framework of LSB steganalysis.
KEYWORDS: Principal component analysis, 3D modeling, Image registration, 3D image processing, Data modeling, Facial recognition systems, 3D scanning, Systems modeling, Eye models, Image processing
3D facial feature point localization is very important to registration. This paper proposes a localization method
that is capable of locating 3D facial feature points rapidly while achieving high localization and registration
accuracy. There are two contributions of this paper. The first is the introduction of the Cascade PCA which
allows the non-occluded and symmetric face models to be normalized quickly while spending more computation
on occluded face models. The second is the three face shape models which are used to verify the normalization
results produced by Cascade PCA, and localize dozens of feature points at the same time. Experimental results
prove the efficiency and accuracy of our method both in localization and registration.
With the development of the current networked society, personal identification based on biometrics has received more and more attention. Iris recognition has a satisfying performance due to its high reliability and non-invasion. In an iris recognition system, preprocessing, especially iris localization plays a very important role. The speed and performance of an iris recognition system is crucial and it is limited by the results of iris localization to a great extent. Iris localization includes finding the iris boundaries (inner and outer) and the eyelids (lower and upper). In this paper, we propose an iris localization algorithm based on texture segmentation. First, we use the information of low frequency of wavelet transform of the iris image for pupil segmentation and localize the iris with a differential integral operator. Then the upper eyelid edge is detected after eyelash is segmented. Finally, the lower eyelid is localized using parabolic curve fitting based on gray value segmentation. Extensive experimental results show that the algorithm has satisfying performance and good robustness.
KEYWORDS: 3D modeling, Clouds, Databases, Facial recognition systems, Data modeling, Nose, 3D image processing, Distance measurement, Process modeling, Detection and tracking algorithms
This paper proposes the automatic face recognition method based on the face representation with a 3D mesh, which precisely reflects the geometric features of the specific subject. The mesh model is generated by using nonlinear subdivision scheme and fitting with the 3D point cloud, and describes the deep information of human faces accurately. The effective method for matching two mesh models is developed to decide whether they are from the same person. We test our proposed algorithm on 3D_RMA database, and experimental results and comparisons with others' work show the effectiveness and competitive performance of the proposed method.
KEYWORDS: Facial recognition systems, Biometrics, Cameras, Detection and tracking algorithms, 3D metrology, Photography, 3D modeling, Databases, Video, Head
Biometrics is a rapidly developing technology that is to identify a person based on his or her physiological or behavioral characteristics. To ensure the correction of authentication, the biometric system must be able to detect and reject the use of a copy of a biometric instead of the live biometric. This function is usually termed "liveness detection". This paper describes a new method for live face detection. Using structure and movement information of live face, an effective live face detection algorithm is presented. Compared to existing approaches, which concentrate on the measurement of 3D depth information, this method is based on the analysis of Fourier spectra of a single face image or face image sequences. Experimental results show that the proposed method has an encouraging performance.
This paper presents an on-line handwriting authentication system for text-independent Chinese handwriting. The proposed strategy is implemented on the stroke level, and the writing strokes and interstrokes are separated stepwise. The writing features are extracted from the dynamics of substrokes and interstrokes, including the writing velocity, the pressure, and the angle between the pen and the writing surface. To alleviate the effect of writing character number on the performance of the algorithm, we adopt the feature vectors of selected dimensions. In live experiments the authentication result is promising.
In this paper, a novel algorithm is presented for writer identification from handwritings. Principal Component Analysis is applied to the gray-scale handwriting images to find a set of individual words which best characterize a person's handwriting style and have maximal difference from other people style. During identification, we only need to utilize a set of individual characteristic words for comparison, instead of comparing the whole handwriting text to identify the writers. So not only is a very high average identification performance of 97.5% obtained, but also a very fast identification speed is achieved in our method. In the experiment, 400 pages ofhandwriting texts, containing almost 16000 Chinese words written by 40 different writers are used to validate the performance ofthe method.
The unequally training set causes the low classification rate of a neural network recognizer. In order to equalize the training set, two methods are proposed in this paper. The first way controls the training parameters according to the property of training samples, i.e. adjusts the study rate with a fuzzy rule. The fuzzy rule is defined by the distribution of the training set and the important level of each kind of samples. The classification rate can be improved in this way and the fast convergence property can be achieved. The second means of equalizing the training set reduces the over- represented samples by fuzzy clustering and increases the deficient samples by interpolating. The BP neural network is used as recognizer here. From the results of the computer simulations, the two methods show to be effective when the training data are imbalance. The two ways improve the classification rate of neural network recognizer by equalizing the training set.
Under the illumination of high resolution radar wave, a target can not be considered as a single dot target, it must be an extended target composed of many scatter dots. This paper mainly studies the model and the echo character of millimeter high resolution ground target. Because of the moving between target and system, Doppler frequency is important and useful information. An extend target is composed of many scatter dots. Each scatter dot, because of different position and angle, generates different Doppler frequency. Each kind of target has different geometry form. There are great distinction of the number and the position of scatter dots among targets. An extended target echo is a complicated Doppler modulation signal. The distinction of Doppler modulation echo of different target is very great. This property is very usable for target recognition. First, the echo spectral is computed by Fourier transform. Second, we choose the total spectral energy and four segment spectral energy as characters. Finally, target recognition adopted BP neural network to get high recognition ratio.
This paper studies the relationship between high range resolution and signal bandwidth. In order to generate big bandwidth, a novel spread spectrum radar waveform is present in this paper. This paper derives the ambiguous function of intrapulse and interpulse FM radar waveform. The result proves that this kind of waveform stimulus is of big time duration and big bandwidth. This waveform trades off the drawback of interpulse stepped frequency waveform in range resolution. There is not range ambiguous problem in time domain. According to the distinguishing feature of this kind of waveform, a novel two-stage pulse compression system using surface acoustic wave (SAW) device is put forward. It aims to generate a intrapulse rectangular spectrum by means of SAW device. Then, the system can obtain spread rectangular spectrum using interpulse frequency shifting. After matched pulse compression processing, this waveform holds high resolution in range domain and in velocity domain. The system total compression ratio can be controlled by the intrapulse compression ratio and interpulse compression ratio.
The radial basis function network (RBFN) is analyzed and the fuzzy radial basis function network (FRBFN) which is more suitable for the radar target recognition is proposed in this paper. Here both of the two networks are used as classifiers. This FRBFN utilize fuzzy clustering method to determine the structure of the net. The generalization property of the two networks are discussed. It is shown from the theoretical analysis and experiment that the FRBFN has better generalization property. The Doppler echoes of the targets gotten from a current surveillance radar are used in the experiment. The experimental results shows that the classification rate of the FRBFN is higher than that of the RBFN. The network proposed in this paper is promising in the application of radar target recognition.
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