Publications by Johan Wiklund

The major goal of the COSPAL project is to develop an artificial cognitive system architecture, with the ability to autonomously extend its capabilities. Exploratory learning is one strategy that allows an extension of competences as provided by the environment of the system. Whereas classical learning methods aim at best for a parametric generalization, i.e., concluding from a number of samples of a problem class to the problem class itself, exploration aims at applying acquired competences to a new problem class, and to apply generalization on a conceptual level, resulting in new models. Incremental or online learning is a crucial requirement to perform exploratory learning. In the COSPAL project, we mainly investigate reinforcement-type learning methods for exploratory learning, and in this paper we focus on the organization of cognitive systems for efficient operation. Learning is used over the entire system. It is organized in the form of four nested loops, where the outermost loop reflects the user-reinforcement-feedback loop, the intermediate two loops switch between different solution modes at symbolic respectively sub-symbolic level, and the innermost loop performs the acquired competences in terms of perception-action cycles. We present a system diagram which explains this process in more detail. We discuss the learning strategy in terms of learning scenarios provided by the user. This interaction between user (teacher) and system is a major difference to classical robotics systems, where the system designer places his world model into the system. We believe that this is the key to extendable robust system behavior and successful interaction of humans and artificial cognitive systems. We furthermore address the issue of bootstrapping the system, and, in particular, the visual recognition module. We give some more in-depth details about our recognition method and how feedback from higher levels is implemented. The described system is however work in progress and no final results are available yet. The available preliminary results that we have achieved so far, clearly point towards a successful proof of the architecture concept.

A runtime system for implementation of image processing operations is presented. It is designed for working in a flexible and distributed environment related to the software architecture of a newly developed UAV system. The software architecture can be characterized at a coarse scale as a layered system, with a deliberative layer at the top, a reactive layer in the middle, and a processing layer at the bottom. At a finer scale each of the three levels is decomposed into sets of modules which communicate using CORBA, allowing system development and deployment on the UAV to be made in a highly flexible way. Image processing takes place in a dedicated module located in the process layer, and is the main focus of the paper. This module has been designed as a runtime system for data flow graphs, allowing various processing operations to be created online and on demand by the higher levels of the system. The runtime system is implemented in Java, which allows development and deployment to be made on a wide range of hardware/software configurations. Optimizations for particular hardware platforms have been made using Java's native interface.

The management of environmental and industrial disasters, search and rescue operations, surveillance of natural scenarios, environmental monitoring, and many other field robotics applications require high mobility and the need to reach locations that are difficult to access with ground vehicles. In many cases, the use of aerial vehicles is the best way to approach the objective to get information or to deploy instrumentation. Unmanned air vehicles (UAVs) have significantly increased their flight performance and autonomous onboard processing capabilities in the last ten years. But a single aerial vehicle equipped with a large array of different sensors of various modalities is limited at any time to a single viewpoint. A team of aerial vehicles, however, can simultaneously collect information from multiple locations and exploit the information derived from multiple disparate points. Furthermore, having a team with multiple heterogeneous aerial vehicles offers additional advantages due to the possibility of beneficial complementarities of the vehicles.

The problem of detection of orientation in finite dimensional Euclidean spaces is solved in the least squares sense. In particular, the theory is developed for the case when such orientation computations are necessary at all local neighborhoods of the n-dimensional Euclidean space. Detection of orientation is shown to correspond to fitting an axis or a plane to the Fourier transform of an n-dimensional structure. The solution of this problem is related to the solution of a well-known matrix eigenvalue problem. Moreover, it is shown that the necessary computations can be performed in the spatial domain without actually doing a Fourier transformation. Along with the orientation estimate, a certainty measure, based on the error of the fit, is proposed. Two applications in image analysis are considered: texture segmentation and optical flow. An implementation for 2-D (texture features) as well as 3-D (optical flow) is presented. In the case of 2-D, the method exploits the properties of the complex number field to by-pass the eigenvalue analysis, improving the speed and the numerical stability of the method. The theory is verified by experiments which confirm accurate orientation estimates and reliable certainty measures in the presence of noise. The comparative results indicate that the proposed theory produces algorithms computing robust texture features as well as optical flow. The computations are highly parallelizable and can be used in realtime image analysis since they utilize only elementary functions in a closed form (up to dimension 4) and Cartesian separable convolutions.

Tensor valued data are frequently used in medical imaging. For a  3-dimensional second order tensor such data imply at least six  degrees of freedom for each voxel. The operators ability to perceive  this information is of outmost importance and in many cases a  limiting factor for the interpretation of the data. In this paper we  propose a decomposition of such tensor fields using the Tflash  tensor glyphs that intuitively conveys important tensor features to  a human observer. A matlab implementation for visualization of  single tensors are described in detail and a VTK/ITK implementation  for visualization of tensor fields have been developed as a Medical  Studio component.

This Chapter presents a vision-based method for unmanned aerial vehicle (UAV) motion estimation that uses as input an image motion field obtained from matches of point-like features. The Chapter enhances visionbased techniques developed for single UAV localization and demonstrates how they can be modified to deal with the problem of multi-UAV relative position estimation. The proposed approach is built upon the assumption that if different UAVs identify, using their cameras, common objects in a scene, the relative pose displacement between the UAVs can be computed from these correspondences under certain assumptions. However, although point-like features are suitable for local UAV motion estimation, finding matches between images collected using different cameras is a difficult task that may be overcome using blob features. Results justify the proposed approach.

This chapter gives an introductory treatment of operations andrepresentations for low-level features in multi-dimensional spaces. Animportant issue is how to combine contributions from several filtersto provide robust statements in accordance with certain low-levelmodels. This chapter gives an introduction to the problems ofunambiguous mappings in multi-dimensional spaces.

The Application Visualization System software from Advanced Visual Systems Inc is an interactive visualization environment for scientists, engineers and technical professionals. This report contains a short overview of the AVS software packages and a discussion about its general performance. The software package has actively been used at the Computer Vision Laboratory, Linköping University, during the last three years. The AVS package has been used in many applications. Examples are generating images from a virtual environment, simulation of a controllable robot with a stereo camera head and visualization of multidimensional data structures. Lately we also have used AVS for handling communication between different processes which may be distributed on different machines. AVS was primarily developed as a tool for visualization of complex data sets. However, another important aspect of the software is that it can be used as an advanced workbench for controlling networks of Unix processes (including external ones on different machine types) using simple visual programming.

Convolution kernels are a commonly used tool in computer vision. These kernels are often specified by an ideal frequency response and the actual filter coefficients are obtained by minimizing some weighted distance with respect to the ideal filter. State-of-the-art approaches usually replace the continuous frequency response by a discrete Fourier spectrum with a multitude of samples compared to the kernel size, depending on the smoothness of the ideal filter and the weight function. The number of samples in the Fourier domain grows exponentially with the dimensionality and becomes a bottleneck concerning memory requirements.

In this paper we propose a method that avoids the discretization of the frequency space and makes filter optimization feasible in higher dimensions than the standard approach. The result is no longer depending on the choice of the sampling grid and it remains exact even if the weighting function is singular in the origin. The resulting improper integrals are efficiently computed using Gauss-Jacobi quadrature.

This paper explores the possibility to use a single low-resolution FIR camera for detection of pedestrians in the near zone in front of a vehicle. A low resolution sensor reduces the cost of the system, as well as the amount of data that needs to be processed in each frame.

We present a system that makes use of hot-spots and image positions of a near constant bearing to detect potential pedestrians. These detections provide seeds for an energy minimization algorithm that fits a pedestrian model to the detection. Since false alarms are hard to tolerate, the pedestrian model is then tracked, and the distance-to-collision (DTC) is measured by integrating size change measurements at sub-pixel accuracy, and the car velocity. The system should only engage braking for detections on a collision course, with a reliably measured DTC.

Preliminary experiments on a number of recorded near collision sequences indicate that our method may be useful for ranges up to about 10m using an 80x60 sensor, and somewhat more using a 160x120 sensor. We also analyze the robustness of the evaluated algorithm with respect to dead pixels, a potential problem for low-resolution sensors.

This paper presents the new project Efficient Convolution Operators for Image Processing of Volumes and Volume Sequences . The project is carried out in collaboration with Context Vision AB.

By using sequential filtering on 3D and 4D data, the number of nonzero filter coefficients for a desired filter set can be significantly reduced. A sequential convolution structure in combination with a convolver designed for sparse filters is a powerful tool for filtering of multidimensional signals.

The project mainly concerns the design of filter networks, that approximate a desired filter set, while keeping the computational load as low as possible. This is clearly an optimization problem, but it can be formulated in several different ways due to the complexity.

The project is in an initial state and the paper focuses on experiences from prior work and discuss possible approaches for the future progress.

The purpose of this paper is to provide a broad overview of the WITAS Unmanned Aerial Vehicle Project. The WITAS UAV project is an ambitious, long-term basic research project with the goal of developing technologies and functionalities necessary for the successful deployment of a fully autonomous UAV operating over diverse geographical terrain containing road and traffic networks. Theproject is multi-disciplinary in nature, requiring many different research competences, and covering a broad spectrum of basic research issues, many of which relate to current topics in artificial intelligence. A number of topics considered are knowledge representation issues, active vision systems and their integration with deliberative/reactive architectures, helicopter modeling and control, ground operator dialogue systems, actual physical platforms, and a number of simulation techniques.

The WITAS Unmanned Aerial Vehicle Project is a long term basic research project located at Linköping University (LIU), Sweden. The project is multi-disciplinary in nature and involves cooperation with different departments at LIU, and a number of other universities in Europe, the USA, and South America. In addition to academic cooperation, the project involves collaboration with a number of private companies supplying products and expertise related to simulation tools and models, and the hardware and sensory platforms used for actual flight experimentation with the UAV. Currently, the project is in its second phase with an intended duration from 2000-2003.

This paper will begin with a brief overview of the project, but will focus primarily on the computer vision related issues associated with interpreting the operational environment which consists of traffic and road networks and vehicular patterns associated with these networks.

This paper presents a general approach for obtaining optimal filters as well as filter sequences. A filter is termed optimal when it minimizes a chosen distance measure with respect to an ideal filter. The method allows specification of the metric via simultaneous weighting functions in multiple domains, e.g. the spatio-temporal space and the Fourier space. It is shown how convolution kernels for efficient spatio-temporal filtering can be implemented in practical situations. The method is based on applying a set of jointly optimized filter kernels in sequence. The optimization of sequential filters is performed using a novel recursive optimization technique. A number of optimization examples are given that demonstrate the role of key parameters such as: number of kernel coefficients, number of filters in sequence, spatio-temporal and Fourier space metrics. In multidimensional filtering applications the method potentially outperforms both standard convolution and FFT based approaches by two-digit numbers.

WITAS will be engaged in goal-directed basic research in the area of intelligent autonomous vehicles and other autonomous systems. In this paper an overview of the project is given together with a presentation of our research interests in the project. The current status of our part in the project is also given.

A methodology for spatio-temporal filtering of image sequences is under development at Computer Vision Laboratory, Linköping University. In later years scale analysis has been found to be a necessary tool in image analysis of stationary images. It is our belief that a combination of spatio-temporal filtering and scale analysis is required to get satisfactory results on image sequences. A growing need and the availability of more powerful computers are the most important reasons for this development. The objectives and proposed methods are discussed in relation to known properties of mammal vision.

There are a number of different algorithms for object motion estimation in image sequences. Almost every algorithm is based on one of three different mathematical methods. An overview of these methods is given, together with some published application examples on object tracking.

A method for tracking of multiple moving objects has been developed on a GOP 300 image processing system. This method works on image sequences with a stationary background, and can be divided into the following steps:

1. Find the positions for all objects that have moved.
2. Predict the new positions for all known objects.
3. Match these two sets of points.
4. Produce the required output.

These steps are repeated for every sample of the sequence. As an output from every processed sample in a test sequence, both a resulting image and a record in a datafile have been generated. The resulting image is a copy of the actual sample with the active object identities overlayed at the corresponding positions. The resulting images have been stored on a video tape.

One major goal of the COSPAL project is to develop an artificial cognitive system architecture with the capability of exploratory learning. Exploratory learning is a strategy that allows to apply generalization on a conceptual level, resulting in an extension of competences. Whereas classical learning methods aim at best possible generalization, i.e., concluding from a number of samples of a problem class to the problem class itself, exploration aims at applying acquired competences to a new problem class. Incremental or online learning is an inherent requirement to perform exploratory learning.

Exploratory learning requires new theoretic tools and new algorithms. In the COSPAL project, we mainly investigate reinforcement-type learning methods for exploratory learning and in this paper we focus on its algorithmic aspect. Learning is performed in terms of four nested loops, where the outermost loop reflects the user-reinforcement-feedback loop, the intermediate two loops switch between different solution modes at symbolic respectively sub-symbolic level, and the innermost loop performs the acquired competences in terms of perception-action cycles. We present a system diagram which explains this process in more detail.

We discuss the learning strategy in terms of learning scenarios provided by the user. This interaction between user (’teacher’) and system is a major difference to most existing systems where the system designer places his world model into the system. We believe that this is the key to extendable robust system behavior and successful interaction of humans and artificial cognitive systems.

We furthermore address the issue of bootstrapping the system, and, in particular, the visual recognition module.We give some more in-depth details about our recognition method and how feedback from higher levels is implemented. The described system is however work in progress and no final results are available yet. The available preliminary results that we have achieved so far, clearly point towards a successful proof of the architecture concept.

A recursive method to condense general multidimensional FIR-filters into a sequence of simple kernels with mainly one dimensional extent has been worked out. Convolver networks adopted for 2, 3 and 4D signals is presented and the performance is illustrated for spherically separable quadrature filters. The resulting filter responses are mapped to a non biased tensor representation where the local tensor constitutes a robust estimate of both the shape and the orientation (velocity) of the neighbourhood. A qualitative evaluation of this General Sequential Filter concept results in no detectable loss in accuracy when compared to conventional FIR (Finite Impulse Response) filters but the computational complexity is reduced several orders in magnitude. For the examples presented in this paper the attained speed-up is 5, 25 and 300 times for 2D, 3D and 4D data respectively The magnitude of the attained speed-up implies that complex spatio-temporal analysis can be performed using standard hardware, such as a powerful workstation, in close to real time. Due to the soft implementation of the convolver and the tree structure of the sequential filtering approach the processing is simple to reconfigure for the outer as well as the inner (vector length) dimensionality of the signal. The implementation was made in AVS (Application Visualization System) using modules written in C.