Watershed merging method for color images

Watershed transformation can be applied to color as well as to gray-scale images. A problem arises when dealing with color images. It is caused by the fact that pixels in such images are vectors that describe all color components whereas the watershed transformation requires a scalar height function as its input. There are multiple gradient magnitude definitions for color images that allow for the needed conversion. As in the case of gray-scale images, the image after watershed transformation is heavily over-segmented. One can blur the image before calculating the gradient magnitude, threshold the gradient image or merge the resulting watersheds. Unfortunately, the result is still over-segmented.A solution presented in this paper complements those mentioned above. It uses hierarchical cluster analysis methods for joining similar classes of the over-segmented image into a given number of clusters. After the image has been preprocessed and segmented, the over-segmentation is reduced by means of the cluster analysis. The attribute values for each watershed in each color component are calculated and clustering is performed. The resulting similarity hierarchy allows for the simple selection of the number of clusters in the final segmentation.Several clustering methods, including complete linkage and Ward's methods with different sets of components, have been tested. Selected results are presented

Watershed merging method for color images

Watershed transformation can be applied to color as well as to gray-scale images. A problem arises when dealing with color images. It is caused by the fact that pixels in such images are vectors that describe all color components whereas the watershed transformation requires a scalar height function as its input. There are multiple gradient magnitude definitions for color images that allow for the needed conversion. As in the case of gray-scale images, the image after watershed transformation is heavily over-segmented. One can blur the image before calculating the gradient magnitude, threshold the gradient image or merge the resulting watersheds. Unfortunately, the result is still over-segmented.A solution presented in this paper complements those mentioned above. It uses hierarchical cluster analysis methods for joining similar classes of the over-segmented image into a given number of clusters. After the image has been preprocessed and segmented, the over-segmentation is reduced by means of the cluster analysis. The attribute values for each watershed in each color component are calculated and clustering is performed. The resulting similarity hierarchy allows for the simple selection of the number of clusters in the final segmentation.Several clustering methods, including complete linkage and Ward's methods with different sets of components, have been tested. Selected results are presented

An Efficient Image Segmentation Approach through Enhanced Watershed Algorithm

Image segmentation is a significant task for image analysis which is at the middle layer of image engineering. The purpose of segmentation is to decompose the image into parts that are meaningful with respect to a particular application. The proposed system is to boost the morphological watershed method for degraded images. Proposed algorithm is based on merging morphological watershed result with enhanced edge detection result obtain on pre processing of degraded images. As a post processing step, to each of the segmented regions obtained, color histogram algorithm is applied, enhancing the overall performance of the watershed algorithm. Keywords – Segmentation, watershed, color histogra

Automatic Image Segmentation by Dynamic Region Merging

This paper addresses the automatic image segmentation problem in a region merging style. With an initially over-segmented image, in which the many regions (or super-pixels) with homogeneous color are detected, image segmentation is performed by iteratively merging the regions according to a statistical test. There are two essential issues in a region merging algorithm: order of merging and the stopping criterion. In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test (SPRT) and the maximum likelihood criterion. Starting from an over-segmented image, neighboring regions are progressively merged if there is an evidence for merging according to this predicate. We show that the merging order follows the principle of dynamic programming. This formulates image segmentation as an inference problem, where the final segmentation is established based on the observed image. We also prove that the produced segmentation satisfies certain global properties. In addition, a faster algorithm is developed to accelerate the region merging process, which maintains a nearest neighbor graph in each iteration. Experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI

Analisis Segmentasi Citra Ultrasound Menggunakan Watershed dan Region Merging pada Kasus Pengidentifikasian Usia Janin

ABSTRAKSI: Segmentasi citra adalah proses penting dalam pengolahan citra yang dapat digunakan dalam analisis citra medis untuk mendapatkan diagnosis yang efektif dan akurat. Dalam proses segmentasi, terdapat beberapa teknik yang dapat digunakan. Tugas akhir ini menerapkan metode Watershed dan Region Merging dalam mencari region dari janin pada suatu citra ultrasound dengan hasil akhir berupa identifikasi panjang dan usia janin. Analisis dari hasil proses ini dilakukan melalui penilaian kuantatif dan kualitatif. Penilaian secara kuantatif, yaitu dengan menghitung nilai akurasi segmentasi, dengan cara menghitung nilai jarak kedekatan antara citra USG uji dengan citra USG dokter yang telah ditandai sebelumnya, dan perhitungan hasil identifikasi panjang dan usia berdasarkan data USG yang diperoleh dari dokter. Sedangkan penilaian secara kualitatif, yaitu dengan Mean Opinion Score. Dari hasil percobaan yang dilakukan, metode Watershed merupakan salah satu cara yang tepat dalam mensegmentasi suatu citra ultrasound. Kekurangan dari metode watersheds adalah adanya oversegmentation, hal ini dapat ditanggulangi dengan melakukan kuantisasi warna dan multiscale morphological gradient sebagai tahap pre-segmentation, dan Region Merging sebagai post-segmentation-nya.Kata Kunci : watershed, region merging, segmentasi, ultrasound, multiscaleABSTRACT: use in medical images analysis to getting an effective and accurate diagnosis. In segmentation process, there are several techniques that can be used. This final project used Watershed and Region Merging method to find fetus region on ultrasound images with fetus length and age for the last result. This process analysis had done by using quantitative and qualitative test. Therefore, Quantitative test is done by counting the similarity distance between the ultrasound image from segmentation with ultrasound image from doctor. And Mean Opinion Score for the qualitative test. Watershed is one of the right methods to segment ultrasound images. In spite of that, the weakness in Watershed are the over segmented result. These problems can be solve by using the color quantization and multiscale morphological gradient for the pre-segmentation and Region Merging for the post-segmentation.Keyword: watershed, region merging, segmentation, ultrasound, multiscal

A simple and efficient face detection algorithm for video database applications

The objective of this work is to provide a simple and yet efficient tool to detect human faces in video sequences. This information can be very useful for many applications such as video indexing and video browsing. In particular the paper focuses on the significant improvements made to our face detection algorithm presented by Albiol, Bouman and Delp (see IEEE Int. Conference on Image Processing, Kobe, Japan, 1999). Specifically, a novel approach to retrieve skin-like homogeneous regions is presented, which is later used to retrieve face images. Good results have been obtained for a large variety of video sequences.Peer ReviewedPostprint (published version

Local Variation as a Statistical Hypothesis Test

The goal of image oversegmentation is to divide an image into several pieces, each of which should ideally be part of an object. One of the simplest and yet most effective oversegmentation algorithms is known as local variation (LV) (Felzenszwalb and Huttenlocher 2004). In this work, we study this algorithm and show that algorithms similar to LV can be devised by applying different statistical models and decisions, thus providing further theoretical justification and a well-founded explanation for the unexpected high performance of the LV approach. Some of these algorithms are based on statistics of natural images and on a hypothesis testing decision; we denote these algorithms probabilistic local variation (pLV). The best pLV algorithm, which relies on censored estimation, presents state-of-the-art results while keeping the same computational complexity of the LV algorithm