In this paper, we investigate vision-based navigation using the self-organizing hierarchical optimal subspace learning and inference framework (SHOSLIF) that incorporates states and a visual attention mechanism. With states to keep the history information and regarding the incoming video input as an observation vector, the vision-based navigation is formulated as an observation-driven Markov model (ODMM). The ODMM can be realized through recursive partitioning regression. A stochastic recursive partition tree (SRPT), which maps an preprocessed current input raw image and the previous state into the current state and the next control signal, is used for efficient recursive partitioning regression. The SRPT learns incrementally: each learning sample is learned or rejected "on-the-fly." The purposed scheme has been successfully applied to indoor navigation.

Local weights in phase unwrapping play an important role in guiding the flow of the phase integration. Unwrapping algorithms can proceed in a path-following or a non-path-following fashion. In path-following phase unwrapping algorithms, local weights guide the selection of unwrapping paths. In nonpath-following algorithms, the introduction of predetermined local weights is necessary to accommodate phase inconsistencies in practice. In current unwrapping algorithms, these local weights are extracted either from the quality information (correlation in interferometric synthetic aperture radar and modulation in phase shifting interferometry) or from the information contained in the wrapped phase difference alone. In other words, the crucial issue is how a phase unwrapping algorithm should select these location-associated weights in order to prevent the propagation of phase errors. This paper presents a quantitative scheme for selecting local weights for phase unwrapping opt the basis of both these types of information (the quality map and the wrapped phase). Real objects as well as synthetic ones have been investigated for various unwrapping methods in experiments. Integrated 3D free-form object models demonstrate the validity of the phasor-weighted phase unwrapping approach.

Vision-based navigation is investigated using SHOSLIF that incorporates states and a visual attention mechanism. The problem is formulated as an observation-driven Markov model (ODMM) which is realized through recursive partitioning regression. A stochastic recursive partition tree (SRPT), which maps a preprocessed current input raw image and the previous state into the current state and the next control signal is used for efficient recursive partitioning regression. The SRPT learns incrementally: each learning sample is rejected or learned "on-the-fly". The proposed scheme has been successfully applied to indoor navigation.

In this paper, we explore the issue of incremental learning for autonomous navigation of a mobile robot. The autonomous navigation problem is regarded as a content-based retrieval problem where the robot learns the navigation experience using a hierarchical recursive partition tree (RPT). During real navigation, each time a new image is grabbed to retrieve the learned tree. The associated control signals of the retrieved are used to control the new action of the robot. Use of RPT can achieve efficient retrieval. In the proposed incremental learning scheme, a new image with the associated control signals is learned or rejected according to whether its retrieved output control signals are within tolerance of the desired control signals of the input query image. We use the eigen-subspace method for feature extraction in our incremental learning. The proposed algorithm has a real-time implementation for both learning and performance phases. Experimental results are shown to confirm the effectiveness of proposed method.

With the current rapid growth in multimedia technology, there is an imminent need for efficient techniques to search and query large image databases. Because of their unique and peculiar needs, image databases cannot be treated in a similar fashion to other types of digital libraries. The contextual dependencies present in images, and the complex nature of two-dimensional image data make the representation issues more difficult for image databases. An invariant representation of an image is still an open research issue. For these reasons, it is difficult to find a universal content-based retrieval technique. Current approaches based on shape, texture, and color for indexing image databases have met with limited success. Further, these techniques have not been adequately tested in the presence of noise and distortions. A given application domain offers stronger constraints for improving the retrieval performance. Fingerprint databases are characterized by their large size as well as noisy and distorted query images. Distortions are very common in fingerprint images due to elasticity of the skin. In this paper, a method of indexing large fingerprint image databases is presented. The approach integrates a number of domain-specific high-level features such as pattern class and ridge density at higher levels of the search. At the lowest level, it incorporates elastic structural feature-based matching for indexing the database. With a multilevel indexing approach, we have been able to reduce the search space. The search engine has also been implemented on Splash 2-a field programmable gate array (FPGA)-based array processor to obtain near-ASIC level speed of matching. Our approach has been tested on a locally collected test data and on NIST-9, a large fingerprint database available in the public domain.

An image taken from a typical camera loses its intensity and contrast around the periphery due to optical attenuation. A model is derived to characterize this effect quantitatively. This model is derived for a commonly used thick lens using the sine condition, and thus, is more general than those from the Gauss geometrical optics. Based on this model, the authors developed an algorithm to calibrate the periphery attenuation. Some experimental results are presented.>

Presents a new framework for detection of dynamic obstacles in the unstructured outdoor road environment by integrating binocular color image sequences. In the authors' system, color image segmentation, stereo obstacle detection, visual egomotion estimation, and moving object analysis, are all built-in task-oriented modules and hence are efficient and robust. Most of these functions can be performed in real-time. They are activated and integrated adaptively in the manner of neural parallel distributed processing (PDP). Experimental results are given to validate the authors' philosophy of so-called purposive vision, retinal mapping, adaptive integration and parallel distributed processing.>

Based on flat ground assumption, a correlation method is developed to estimate the orientation of a vehicle relative to its running environment. A weight function is introduced to account for figure variation caused by 3D dynamic scene and perspective effects caused by the camera system. The estimated parameters can be qualitatively used in visual navigation. Results indicate that robust estimation can be achieved in real time by using the powerful image processing system PIPE (Pipelined Image Processing Engine).>

A novel method for outdoor natural color image segmentation for road following is presented. From the results of experiments it can be seen that the hue, saturation, and intensity (HSI) system should be used rather than the red, green, blue (RGB) system, and segmentation can be done in S-I space with good performance. An automatic adaptive threshold selection method is developed. The preliminary results indicate the effectiveness and efficiency of this method.>