Search results

Segmentation is an important topic in computer vision and image processing. In this paper, we sketch a scheme for a multiscale segmentation algorithm and prove its validity on some real images. We propose an approach to the model based on MRF (Markov Random Field) as a systematic way for integrating constraints for robust image segmentation. To do that, robust features and their integration in the energy function, which directs the process, have been defined. In this approach, the image is first transformed to different scales to determine which one fits better to our purposes. Then, it is segmented into a set of disjoint regions, the adjacent graph (AG) is determined and a MRF model is defined on the corresponding AG. Robust features are incorporated to the energy function by means of clique functions and optimal segmentation is then achieved by finding a labeling configuration that minimizes the energy function using Simulated Annealing.

One of the main goals of vision systems is to recognize objects in real world to perform appropriate actions. This implies the ability of handling objects and, moreover, to know the relations between these objects and their environment in what we call scenes. Most of the time, navigation in unknown environments is difficult due to a lack of easily identifiable landmarks. Hence, in this work, some geometric features to identify objects are considered. Firstly, a Markov random field segmentation approach is implemented. Then, the key factor for the recognition is the calculation of the so‐called distance histograms, which relate the distances between the border points to the mass center for each object in a scene.

This work, first discusses the features to be analyzed in order to create a reliable database for a proper recognition of the objects in a scene. Then, a robust classification system is designed and finally some experiments are completed to show that the recognition system can be utilized in a real‐world operation.

The results of the experiments show that including this distance information improves significantly the final classification process.

This paper describes an object recognition scheme, where a set of histograms is included to the features vector. As is shown, the incorporation of this feature improves the robustness of the system and the recognition rate.

In this work, we propose an approach to the model based on Markov random field (MRF) as a systematic way for integrating constraints for robust image segmentation. To do that, robust features and their integration in the energy function, which directs the process, have been defined. The suitability of the method has been verified by comparing classic features with the robust ones. In this approach, the image is first segmented into a set of disjoint regions and the adjacent graph (AG) has been determined. This approach is applied by defining an MRF model on the corresponding AG. Robust features are incorporated to the energy function by means of clique functions, and optimal segmentation is then achieved by finding a labelling configuration, which minimizes the energy function using the simulated annealing.