This paper presents a new method for face verification for vision applications. There are many approaches to detect and track a face in a sequence of images; however, the high computations of image algorithms, as well as, face detection and head tracking failures under unrestricted environments remain to be a difficult problem. We present a robust algorithm that improves face detection and tracking in video sequences by using geometrical facial information and a recurrent neural network verifier. Two types of neural networks are proposed for face detection verification. A new method, a three-face reference model (TFRM), and its advantages, such as, allowing for a better match for face verification, will be discussed in this paper.
In this paper we propose a new encryption technique for wireless sensor networks (3DSSec). Although RC4 is more susceptible to cryptanalysis attacks than RC5, the proposed encryption scheme has implemented RC4 in a way that boosts security when used in sensor networks and still allows a respectable performance/security trade off. The proposed encryption scheme is a good choice for Mica2 network sensors because it is based on an encryption algorithm that performs very well on Atmega128 platforms and has very modest memory demands. The encryption system seems a very good balance between performance and security given the limits of network sensors.
We present a crossbreed feature-based head tracking technique in natural and unspecified environment. Kalman filter is a famous estimation technique in many areas to predict the route of moving object. We tested and developed a Kalman filter to track unpredictable and fast moving objects. Depth information could generate robust tracking result that is little affected by background texture and color. However this is also limited by selected conditions like distance, accuracy of stereo camera, and object occlusion at same distance, etc. To overcome these restrictions, we combined multiple features together into single tracking system that does largely depend on depth feature. We consider multi people environment with rapid walking path.
In this paper, we describe an algorithm which can automatically recognize human gesture in a sequence of natural video image by utilizing two dimensional features extracted from bodily region of the images. In the algorithm, we first construct a gesture model space by analyzing the statistical information of sample images with principle component analysis method. And then, input images are compared to the model and individually symbolized to one part of the model space. In the last step, the symbolized images are recognized with HMM as one of model gestures. The feature of our method is to use a combination of partial and global information of two-dimensional abstract bodily motion, consequently it is very convenient to apply to real world situation and the recognition results are very robust.
This paper presents our approach to using a stereo camera to obtain 3-D image data to be used to improve existing lip boundary detection techniques. We show that depth information as provided by our approach can be used to significantly improve boundary detection systems. Our system detects the face and mouth area in the image by using color, geometric location, and additional depth information for the face. Initially, color and depth information can be used to localize the face. Then we can determine the lip region from the intensity information and the detected eye locations. The system has successfully been used to extract approximate lip regions using RGB color information of the mouth area. Merely using color information is not robust because the quality of the results may vary depending on light conditions, background, and the human race. To overcome this problem, we used a stereo camera to obtain 3-D facial images. 3-D data constructed from the depth information along with color information can provide more accurate lip boundary detection results as compared to color only based techniques.
The human visual system uses two-dimensional (2D) boundary information to recognize objects since the shape of the boundary usually contains the pertinent information about an object. Thus, representing a boundary concisely and consistently is necessary for object recognition. In this paper, we propose a consistent object representation method using mean field annealing (MFA) technique for computer vision applications. Since a curvature function computed on a preprocessed smooth boundary, which is obtained by the MFA approach is consistent, we can consistently detect corner points in this curvature function space. Furthermore, the MFA approach preserves the sharpness of corner points very well. Thus, we can detect corner points easier and better with this method than with other existing methods. Ideal corner points rarely exist for a real boundary. They are often rounded due to the smoothing effect of the preprocessing. In addition, a human recognizes both sharp corner points and slightly rounded segments as corner points. Thus, we use `corner sharpness,' which is qualitatively similar to a human's capability of detecting corner points, to increase the robustness of the proposed algorithm.
Matching of occluded objects is a difficult problem. Moreover, the problem is more difficult when scale-invariant matching is needed. A scale invariant representation is essential for this application. In this paper, we propose using the wavelet transform of a boundary to obtain a scale-invariant representation. We use the cubic B-spline as a smoothing function of the wavelet transform since the B-spline is analytically well defined and simple to implement. We implement the fast continuous wavelet transform by using a dyadic wavelet decomposition and dilated B-splines. As a result of using the wavelet transform, we obtain boundaries at various scales while using a small number of data points. The existing scale-space image approaches are not effective for occluded object matching since they use a normalized x-axis and too many data points. We propose a new scale-invariant representation similar to the scale-space image. The representation is generated by locating zero-crossings of the curvature function of boundaries at only the scales where the number of zero-crossings is changing. We scale the x-axis for each scale instead of using the same normalization for all scales. The proposed representation is scale-invariant and appropriate for scale-invariant matching with occlusion.
Surface reconstruction is a technique that is used for the interpolation of object information between contours. The majority of work done in the area of surface reconstruction has dealt primarily with medical image contours. Surface reconstruction has also been used to reduce the memory requirements in automobile and ship designs. However, this technology could be used for other types of applications. For instance, it could be used in airport security. A x-ray machine could be used to sample a suitcase along a particular axis and rotation. 2D objects inside the x-ray images could be extracted and a 3D object reconstructed from the extracted objects. This type of application requires a fast solution that takes into account shape information. In addition, the application must not require human interface and produce recognizable objects. This paper presents a surface reconstruction method that meets the above requirements for single parallel contour extracted from x-ray luggage images.
In a multicontext scene where several objects may be occluded or scenes may change rapidly, a single paradigm for computer vision may not be sufficient. The demand to adjust and learn new environment is therefore a challenging modeling problem in computer vision research. In response to this challenge we have developed a hybrid architecture which combines classical pattern recognition algorithms with fuzzy knowledge-base and Hopfield Neural Network. We also present elementary results obtained from this effort.
Automation is the driving force behind many technological advances. One of the major areas of automation research is machine vision. Machine vision centers on object recognition as a means of perceiving a real world environment. Addressing machine vision issues through the application of neural networks is the focus of much contemporary research. Neural networks model the computational units of the human brain, process information in parallel, and as such are extremely well suited for emulation of the human perception process. Thus a neural network approach is presented as a solution to the 3D object recognition problem. Specifically, a hybrid Hopfield network (HHN) previously used to solve 2D occluded object recognition problems is adapted to the 3D object recognition problem. Local and relational features are proposed for use in a HHN graph matching algorithm. Finally, 3D single and multiple input object recognition is realized.
Nowadays the method for the recognition of partially occluded objects has been needed increasingly. It can be used for airport security such as baggage inspection. Basically algorithm for airport security problem should be fast and exact to get solutions. That is, it should get global optimum as fast as possible. This is why we seek for Annealed Hopfield Network (AHN). Even if AHN is slower than Hybrid Hopfield Network (HHN), AHN provides nearly global solutions without initial restrictions and leads false matching less than HHN. Conclusively it is turned out that AHN is robust to identify occluded target objects with large tolerance of their features.
Despite human familiarity with the natural environment, the issue of vision--cognition and perception of objects is still a dominant variable in human performance assessment; especially in real-time, safety-oriented domains such as airport security. In this paper, we present some experimental results that seek to uncover how people interpret computer generated image data at different threshold modes. The results obtained are useful for training airport inspectors or pilots on how to recognize objects in different environments.
Speech recognition by machine has finally come of age in a practical sense. A major problem in speech recognition, however, stems from the large variance of different utterances for the same word. This paper proposes an efficient method of achieving high accuracy speaker- independent isolated-word recognition through the implementation of associative memories and neural networks. The basic architecture of such a process involves two-stages: speech analysis and recognition.
Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybrid Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a Neural Network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from X-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features.
Hopfield proposed two types of neural networks; Discrete Hopfield Network (DHN) and Continuous Hopfield Network (CHN). Those have been used for solving the well-known traveling salesman problem in a sense of optimization. DHN, a stochastic model is simple to implement and fast in computing. However, DHN uses binary value for states of neurons and results in an approximate solution. On the other hand, CHN gives a near-optimal solution, but it takes too much time to simulate a differential equation which represents a main characteristic of CHN. A matching problem using a graph matching technique can be cast into an optimization problem. In this paper, a new method for two-dimensional object recognition by using a Hopfield neural network is presented. A Hybrid Hopfield Network (HHN), which combines the merit of both the Continuous Hopfield Network and the Discrete Hopfield Network, is described and some of the advantages such as reliability and speed are shown in this paper. The main idea behind the new network is that stable states of neurons are analyzed and predicted based upon the theory of CHN after the convergence in DHN.