R Pires

Implementation and analysis of an optimized rainfalling watershed algorithm

In this paper we discuss a new implementation of a floating point based rainfalling watershed algorithm. First, we analyze and compare our proposed algorithm and its implementation with two implementations based on the well-known discrete Vincent- Soille flooding watershed algorithms. Next, we show that by carefully designing and optimizing our algorithm a memory (bandwidth) efficient and high speed implementation can be realized. We report on timing and memory usage results for different compiler settings, computer systems and algorithmic parameters. Our optimized implementation turns out to be significantly faster than the two Vincent-Soille based implementations with which we compare. Finally, we include some segmentation results to illustrate that visually acceptable and almost identical segmentation results can always be obtained for all algorithms being compared. And, we also explain how, in combination with other pre- or post- processing techniques, the problem of oversegmentation (a typical problem of all raw watershed algorithms) can be (partially) overcome. All these properties make that our proposed implementation is an excellent candidate for use in various practical applications where high speed performance and/or efficient memory usage is needed.

Line extraction with the use of an automatic gradient threshold technique and the Hough transform

We present a method to detect roads and building boundaries on high resolution aerial photographs. These image features are found through the use of a line detection algorithm that is based on the three following steps: first, pre-smoothing, to remove noise and possibly irrelevant small scale details; this is followed by an automatic threshold of the gradient magnitude, thus generating a binary edge map; and in order to detect straight lines, this edge map is then used as the input of the Hough transform. We describe these steps with particular emphasis on the gradient threshold and show typical results obtainable with this method.

Activity driven nonlinear diffusion for color image watershed segmentation

Nonlinear diffusion processes and watershed algorithms have been well studied for gray-scale image segmentation. In this paper we extend the use of these techniques to color or multichannel images. First, we formulate a general definition for a nonlinear diffusion process using the concept of an activity image that can be calculated for several image components. Then, we explain how the final activity image, obtained as a result of the nonlinear diffusion process, is fed through a watershed algorithm, yielding the segmentation of the image. The qualitative performance of the algorithm is illustrated with results for both gray-scale and color photographic images. Finally, we discuss the segmentation results obtained using a few well-known color spaces and demonstrate that a color principal component analysis gives the best results.

A fast dynamic border linking algorithm for region merging

In this paper we present our region merging algorithm that is built with special attention on speed but still includes all the necessary functionality to use a wide range of both region based and border based dissimilarity criteria. The algorithm includes a novel method to dynamically link the common borders between two segments during the region merging. The main part of the paper will concentrate on the efficient data structures and functions that are needed to obtain a fast region merging algorithm. Also, all the special situations that can occur in the segment topology are completely covered. We give a detailed report on the execution times of the algorithm and show some of the segmentation results we obtained.

Watershed segmentation and region merging

The aim of this paper is to present a methodology to generate a partition of an image and a hierarchical region merging scheme to improve the meaningfulness of the segmentation, by reducing excessive object fragmentation. The segmentation method is based on the watershed transform applied to the image gradient magnitude. Prior to the actual segmentation, the image is smoothed to decrease the amount of detail detected by the watershed transform. To further improve the segmentation result, we use an iterative region merging process that uses a graph to represent the image partitions. In this process the most similar pair of adjacent regions is sequentially merged according to a predefined similarity metric. We investigate the use of a combined region merging criterion that takes into account both the intensity similarity and the contrast at the boundary of two adjacent regions. Results obtained illustrate the good combined performance of this segmentation and merging methods and the usefulness of the combined similarity function.

Activity Driven Non-linear Diffusion for Colour Image Segmentation

Traditionally, non-linear diffusion processes and watershed segmentation have been well studied for greyscale image segmentation. In this paper we extend their use to colour images. First, we formulate a general definition for a non-linear diffusion process using the concept of an activity image that can be calculated for several image components. Then, we explain how the cleaned activity image is fed through a watershed algorithm yielding the colour image segmentation. Finally, the qualitative performance is illustrated with results for real colour images.