Many common foundations exist between neural networks and fuzzy inference systems in terms of their mathematical models and system structures. This paper explores such a rich synergy and uses it to form the basis for a unifying framework under which fuzzy logic processing and neural networks may be integrated to achieve more robust information processing. It in turn leads to a family of hierarchical fuzzy neural networks (FNNs) which incorporate an adaptive and modular design of neural networks into the basic fuzzy logic systems. Several important models which are critical to the development of the the hierarchical FNN family are studied. We demonstrate how existing unsupervised and supervised learning strategies can be an integral part of a fuzzy processing framework. In addition, hierarchical structures involving both expert modules and class modules are incorporated into the FNNs. Also presented are some promising application examples.

This paper proposes a face recognition system, based on probabilistic decision-based neural networks (PDBNN). With technological advance on microelectronic and vision system, high performance automatic techniques on biometric recognition are now becoming economically feasible. Among all the biometric identification methods, face recognition has attracted much attention in recent years because it has potential to be most nonintrusive and user-friendly. The PDBNN face recognition system consists of three modules: First, a face detector finds the location of a human face in an image. Then an eye localizer determines the positions of both eyes in order to generate meaningful feature vectors. The facial region proposed contains eyebrows, eyes, and nose, but excluding mouth (eye-glasses will be allowed). Lastly, the third module is a face recognizer. The PDBNN can be effectively applied to all the three modules. It adopts a hierarchical network structures with nonlinear basis functions and a competitive credit-assignment scheme. The paper demonstrates a successful application of PDBNN to face recognition applications on two public (FERET and ORL) and one in-house (SCR) databases. Regarding the performance, experimental results on three different databases such as recognition accuracies as well as false rejection and false acceptance rates are elaborated. As to the processing speed, the whole recognition process (including PDBNN processing for eye localization, feature extraction, and classification) consumes approximately one second on Sparc10, without using hardware accelerator or co-processor.

Detection of a (deformable) pattern or object is an important machine learning and computer vision problem. The task involves finding specific (but locally deformable) patterns in images, such as human faces and eyes/mouths. There are many important commercial applications. This paper presents a decision-based neural network for finding such patterns with specific applications to detecting human faces and locating eyes in the faces. The system built upon the proposal has been demonstrated to be applicable under reasonable variations of orientation and/or lighting, and with the possibility of eye glasses. This method has been shown to be very robust against a large variation of face features and eye shapes. The algorithm takes only 200 ms on a SUN Sparc20 workstation to find human faces in an image with 320/spl times/240 pixels. For a facial image with 320/spl times/240 pixels, the algorithm takes 500 ms to locate two eyes on a SUN Sparc20 workstation. Furthermore, the algorithm can be easily implemented via specialised hardware for real time performance. We have applied this technique to two applications (surveillance system, video browsing) and this paper provides experimental results. Although we have only shown its successful implementation on face detection and eye localization, the proposed technique is meant for more general applications of detection of any (locally deformable) object.

People usually make up a lot of the information content in videos. The abilities to answer queries and facilitate browsing related to people in videos are crucial. In a single video sequence, a particular person may appear multiple number of times. We propose a scheme to automatically detect the repeated occurrences of the same people to enable fast people related searching. In particular, we propose a video shot classification scheme using human faces, regardless of scale and background. Video shots are classified by clustering facial features extracted from these shots. Potential applications include video indexing and browsing. Employing unsupervised clustering algorithms, this scheme requires no human intervention. Experimental results on a 4-minute news sequence show that it achieves encouraging results.

The original learning rule of the decision based neural network (DBNN) is very much decision-boundary driven. When pattern classes are clearly separated, such learning usually provides very fast and yet satisfactory learning performance. Application examples including OCR and (finite) face/object recognition. Different tactics are needed when dealing with overlapping distribution and/or issues on false acceptance/rejection, which arises in applications such as face recognition and verification. For this, a probabilistic DBNN would be more appealing. This paper investigates several training rules augmenting probabilistic DBNN learning, based largely on the expectation maximization (EM) algorithm. The objective is to establish evidence that the probabilistic DBNN offers an effective tool for multi-sensor classification. Two approaches to multi-sensor classification are proposed and the (enhanced) performance studied. The first involves a hierarchical classification, where sensor information are cascaded in sequential processing stages. The second is multi-sensor fusion, where sensor information are laterally combined to yield improved classification. For the experimental studies, a hierarchical DBNN-based face recognition system is described. For a 38-person face database, the hierarchical classification significantly reduces the false acceptance (from 9.35% to 0%) and false rejection (from 7.29% to 2.25%), as compared to non-hierarchical face recognition. Another promising multiple-sensor classifier fusing face and palm biometric features is also proposed.

To achieve real time processing for volume rendering, it is necessary to employ parallel processing techniques. Therefore, a ray casting wavefront architecture is proposed. The architecture offers the following advantages. By a special memory organization, the processor array can perform fast rotation along x, y, z axes without incurring costly memory transformation. The projection phase and post-projection phase are assigned to different processors and a significant time-saving can be achieved by the pipelining technique. In the proposed architecture, the projection processors can "skip" the transparent region and step computation when further computation does not affect pixel values any more, e.g., the rays reach opaque surface. Finally, according to the VHDL simulation result, the speedup of the wavefront architecture over several SIMD designs (including CM2 and the Princeton Engine) is about 200% under normal condition.>