Object Oriented Motion Estimation in Color Image Sequences

This paper describes a color region-based approach to motion estimation in color image sequences. The system is intended for robotic and vehicle guidance applications where the task is to detect and track moving objects in the scene. It belongs to the class of feature-based matching techniques and uses color regions, resulting from a prior color segmentation, as the matching primitives. In contrast to other regionbased approaches it takes into account the unavoidable variations in the segmentation by the extension of the matching model to multi matches

Fast and Robust Segmentation of Natural Color Scenes

This paper describes our entire color segmentation system, called CSC (Color Structure Code), in detail. In section 2 we introduce the hexagonal, hierarchical island structure on which our method is based. Section 3 describes the actual segmentation method. In Section 4 the new color similarity measure is presented. Section 5 discusses the complexity of our approach. The system is very fast and thus applicable in real world problems. Finally we present some results and conclusions in section 6. 2 Hexagonal, hierarchical island structur

Detection And Tracking Of Moving Objects In Color Outdoor Scenes

We present a close to real-time system (4 frames per second) capable to detect and track moving objects in natural color scenes. Our approach is based on the idea of symbolic matching. Complete segments are used for matching instead of matching single pixels or features. Thus, first of all each image has to be segmented. The segmented regions are described by features and matched on a symbolic level from frame to frame. The use of color significantly improves the stability of the segmentation and matching phase. In the matching phase we explicitly take into account that no segmentation algorithm can guarantee that regions will be segmented uniformly stable from frame to frame. This approach has several advantages over traditional differential techniques or feature point matching algorithms: e.g. better performance in the presence of noise, detection of large motion between frames, dense motion vector fields and accurate estimation of motion boundaries. 1 Introduction One of the most i..

Detection And Tracking Of Moving Objects

We present a close to real-time system (4 frames per second) capable to detect and track moving objects in natural color scenes. Our approach is based on the idea of symbolic matching. Complete segments are used for matching instead of matching single pixels or features. Thus, first of all each image has to be segmented. The segmented regions are described by features and matched on a symbolic level from frame to frame. The use of color significantly improves the stability of the segmentation and matching phase. In the matching phase we explicitly take into account that no segmentation algorithm can guarantee that regions will be segmented uniformly stable from frame to frame. This approach has several advantages over traditional differential techniques or feature point matching algorithms: e.g. better performance in the presence of noise, detection of large motion between frames, dense motion vector fields and accurate estimation of motion boundaries

A Parallel System for Real-Time Traffic Sign Recognition

We present a system for the real-time recognition of traffic signs from a moving car on European highways. The traffic sign recognition system (TSR) was developed within the European PROMETHEUS project in cooperation with Daimler-Benz and is installed in an autonomous car. Our TSR is also intended to serve as a driver assistance tool. The TSR is based on a fast color image analysis. This analysis involves different methods, such as an inherently parallel color image segmentation, a data-driven decision graph with fuzzy techniques, and classical pattern recognition. Due to the good quality of the color segmentation and the fault-tolerant evaluation the system is highly robust against the difficult conditions in natural outdoor scenes. To meet real-time constraints the TSR has been implemented on a high speed parallel image processing system with MPC 601 processors. A prototype of the TSR runs in a usual car and reaches a recognition rate of 98 %

Farbe und Symmetrie für die datengetriebene Generierung prägnanter Fokuspunkte

Heidemann G, Nattkemper TW, Ritter H. Farbe und Symmetrie für die datengetriebene Generierung prägnanter Fokuspunkte. In: Rehrmann V, ed. 4. Workshop Farbbildverarbeitung. Koblenzer Schriften zur Informatik. Vol 9. Koblenz: Fölbach; 1998: 65-71

Real-Time Detection of Arbitrary Objects in Alternating Industrial Environments

Consider the following problem: How to detect objects of undetermined and unequal shape, size and color in front of an alternating but repeating background in real-time? In addition, the objects may slightly overlap each other but have to be considered as separate objects. Also, the repeating background may change in time because of variations of the illumination or staining of the environment. This problem arises from industrial applications and requires a very robust and fast solution. Some hundreds of objects have to be detected and analyzed for overlapping within a second. The presented solution has already been used successfully in several automatic sorting techniques for recyclable materials, where the objects to be sorted are placed on a very fast conveyor belt and a perfect distribution of the objects cannot be guaranteed. The solution is based on the following principles:

Traffic Sign Recognition Based on Color Image Evaluation

This paper originates from a cooperation of Daimler-Benz AG, the University of Paderborn, and our group, within the European PROMETHEUS project. Common aim is the development of a traffic sign recognition (TSR) system. The Paderborn group provides gray-level image evaluation, DaimlerBenz AG pictogram identification, and our group color image evaluation. Our approach to TSR is based on a fast and stable color segmentation, the CSC (Color Structure Code). For the segmentation we use a newly developed hierarchical region growing method. This technique combines the advantages of local (simplicity and quickness) and global region growing methods (robustness and accuracy) and avoids chaining mismatches. The result of this segmentation is a hierarchical data structure. In the evaluation phase geometrical primitives (as circles, ellipses, triangles, rectangles, etc.) can be quickly found in this data forest. Answering a few and simple questions (about inclusion of certain colored geometrical ..