Włodzimierz Kasprzak

Neural networks for blind separation with unknown number of sources

Blind source separation problems have recently drawn a lot of attention in unsupervised neural learning. In the current approaches, the number of sources is typically assumed to be known in advance, but this does not usually hold in practical applications. In this paper, various neural network architectures and associated adaptive learning algorithms are discussed for handling the cases where the number of sources is unknown. These techniques include estimation of the number of sources, redundancy removal among the outputs of the networks, and extraction of the sources one at a time. Validity and performance of the described approaches are demonstrated by extensive computer simulations for natural image and magnetoencephalographic (MEG) data. ( 1999 Elsevier Science B.V. All rights reserved.

Self-adaptive neural networks for blind separation of sources

Novel on-line learning algorithms with self adaptive learning rates (parameters) for blind separation of signals are proposed. The main motivation for development of new learning rules is to improve convergence speed and to reduce cross-talk, especially for non-stationary signals. Furthermore, we have discovered that under some conditions the proposed neural network models with associated learning algorithms exhibit a random switch of attention, i.e. they have the ability of chaotic or random switching or cross-over of output signals in such way that a specified separated signal may appear at various outputs at different time windows. Validity, performance and dynamic properties of the proposed learning algorithms are investigated by computer simulation experiments.

On Neural Blind Separation with Noise Suppression and Redundancy Reduction

Noise is an unavoidable factor in real sensor signals. We study how additive and convolutive noise can be reduced or even eliminated in the blind source separation (BSS) problem. Particular attention is paid to cases in which the number of sensors is larger than the number of sources. We propose various methods and associated adaptive learning algorithms for such an extended BSS problem. Performance and validity of the proposed approaches are demonstrated by extensive computer simulations.

Hidden image separation from incomplete image mixtures by independent component analysis

It is known that the independent component analysis (ICA) (also called blind source separation) can be applied only if the number of received signals (sensors) is at least equal to the number of mixed sources, contained in the sensor signals. In this paper an application of the ICA is proposed for hidden (secured) image transmission by communication channels. We assume that only a single image mixture is transmitted. A friendly receiver contains the remaining original sources and therefore it can separate the hidden image of lowest energy. The influence of two nonlossless signal reduction stages, compression by principal component analysis and signal quantization, onto the separation ability is tested. Constraints of the mixing process are discussed that make impossible the hidden image separation without the key images.

Performance Evaluation of Binary Descriptors of Local Features

The article is devoted to the evaluation of performance of image features with binary descriptors for the purpose of their utilization in recognition of objects by service robots. In the conducted experiments we used the dataset and followed the methodology proposed by Mikolajczyk and Schmid. The performance analysis takes into account the discriminative power of a combination of keypoint detector and feature descriptor, as well as time consumption.

Control and programming of a multi‐robot‐based reconfigurable fixture

Purpose – Machining fixtures must fit exactly the work piece to support it appropriately. Even slight change in the design of the work piece renders the costly fixture useless. Substitution of traditional fixtures by a programmable multi‐robot system supporting the work pieces requires a specific control system and a specific programming method enabling its quick reconfiguration. The purpose of this paper is to develop a novel approach to task planning (programming) of the reconfigurable fixture system.Design/methodology/approach – The multi‐robot control system has been designed following a formal approach based on the definition of the system structure in terms of agents and transition function definition of their behaviour. Thus, a modular system resulted, enabling software parameterisation. This facilitated the introduction of changes brought about by testing different variants of the mechanical structure of the system. A novel approach to task planning (programming) of the reconfigurable fixture syst...

Multimodal segmentation of dense depth maps and associated color information

An integrated segmentation approach for color images and depth maps is proposed. The 3D pointclouds are characterized by normal vectors and then grouped into planar, concave or convex faces. The empty regions in the depth map are filled by segments of the associated color image. In the experimental part two types of depth maps are analysed: generated by the MS-Kinect sensor or by a stereo-pair of cameras.

Adaptive approach to blind source separation with cancellation of additive and convolutional noise

In this paper an adaptive approach to the cancellation of additive, convolutional noise from many-source mixtures with simultaneous blind source separation is proposed. Associated neural network learning algorithms are developed on the basis of the decorrelation principle and energy minimization of the output signals. The reference noise is transformed into convolutional noise by employing an adaptive FIR filter in each channel. Several models of NN learning processes are considered. In the basic approach the noisy signals are separated simultaneously with additive noise cancellation. The simplified model employs separate learning steps for noise cancellation and source separation. Multi-layer neural networks improve the quality of the results. The results of comparative tests of the proposed methods are provided.

Integrated motion and geometry-based obstacle detection in image sequences of traffic scenes

In this paper a robust method for visual motion estimation under ego-motion is developed. The possible application of this method is image sequence analysis of road traffic or airport runway/taxiway scenes, where the camera is located in a moving vehicle. The method combines an application independent estimation of visual motion with specific methods for instantaneous detection of the vanishing point in the image plane and of the over-road location of the camera. The stationary background is separated from the obstacles while detecting the ego-motion corrected visual motion of on-road objects.

A hierarchical CSP search for path planning of cooperating self-reconfigurable mobile fixtures

The paper presents the application of artificial intelligence tools for the path planning of complex multi-agent robotic systems. In particular, a solution is proposed to the planning problem for the conjoint operation of two or more mobile robotic fixtures used for the manufacturing of large workpieces, like those used in the aerospace industry. Such fixturing systems have been recently designed and tested, raising hopes to better satisfy the dynamic conditions of modern manufacturing, with its increasing emphasis on flexibility, adaptability, and automation. The proposed planning method is novel in two fundamental aspects. First, it interprets planning as a constraint satisfaction problem (CSP), rather than as a constrained optimisation, an approach ubiquitous in the path and motion planning literature. Secondly, the formulated CSP is solved by a hierarchy of incremental state space search algorithms which differ in some way from the existing state of the art. This hierarchy includes levels related to the robot and workpiece arrangement parameters and to three components of mobile fixture agents: a supporting head, a mobile base, and a parallel manipulator, respectively. Due to the use of CSP search, the planner constitutes a largely application-independent framework, on the basis of which specific industrial implementations can be defined by supplying the relevant physical, geometrical, and time-related constraints.