Connected component labeling is a fundamental operation in binary image processing. A plethora of algorithms have been proposed for this low-level operation with the early ones dating back to the 1960s. However, very few of these algorithms were designed to handle color images. In this paper, we present a simple algorithm for labeling connected components in color images using an approximately linear-time seed fill algorithm. Experiments on a large set of photographic and synthetic images demonstrate that the proposed algorithm provides fast and accurate labeling without requiring excessive stack space.
Gray-level quantization (reduction) is an important operation in image processing and analysis. The Lloyd-
Max algorithm (LMA) is a classic scalar quantization algorithm that can be used for gray-level reduction with
minimal mean squared distortion. However, the algorithm is known to be very sensitive to the choice of initial
centers. In this paper, we introduce an adaptive and deterministic algorithm to initialize the LMA for gray-level
quantization. Experiments on a diverse set of publicly available test images demonstrate that the presented
method outperforms the commonly used uniform initialization method.
Wireless Capsule Endoscopy (WCE) is a relatively new technology (FDA approved in 2002) allowing doctors to view
most of the small intestine. WCE transmits more than 50,000 video frames per examination and the visual inspection of
the resulting video is a highly time-consuming task even for the experienced gastroenterologist. Typically, a medical
clinician spends one or two hours to analyze a WCE video. To reduce the assessment time, it is critical to develop a
technique to automatically discriminate digestive organs and shots each of which consists of the same or similar shots. In
this paper a multi-level WCE video segmentation methodology is presented to reduce the examination time.
We present an order-statistics-based vector filter for the removal of impulsive noise from color images. The filter preserves the edges and fine image details by switching between the identity (no filtering) operation and the vector median filter operation based on the robust univariate median operator. Experiments on a diverse set of images and comparisons with state of the art filters shows that the proposed filter combines simplicity, flexibility, excellent filtering quality, and low computational requirements.
In this paper, we present a new graph-based query language and its query processing for a Graph-based Video
Database Management System (GVDBMS). Although extensive researches have proposed various query languages
for video databases, most of them have the limitation in handling general-purpose video queries. Each
method can handle specific data model, query type or application. In order to develop a general-purpose video
query language, we first produce Spatio-Temporal Region Graph (STRG) for each video, which represents spatial
and temporal information of video objects. An STRG data model is generated from the STRG by exploiting
object-oriented model. Based on the STRG data model, we propose a new graph-based query language named
STRG-QL, which supports various types of video query. To process the proposed STRG-QL, we introduce a
rule-based query optimization that considers the characteristics of video data, i.e., the hierarchical correlations
among video segments. The results of our extensive experimental study show that the proposed STRG-QL is
promising in terms of accuracy and cost.
A comprehensive survey of 48 filters for impulsive noise removal from color images is presented. The filters are formulated using a uniform notation and categorized into 8 families. The performance of these filters is compared on a large set of images that cover a variety of domains using three effectiveness and one efficiency criteria. In order to ensure a fair efficiency comparison, a fast and accurate approximation for the inverse cosine function is introduced. In addition, commonly used distance measures (Minkowski, angular, and directional-distance) are analyzed and evaluated. Finally, suggestions are provided on how to choose a filter given certain requirements.
As a result of advances in skin imaging technology and the development of suitable image processing
techniques during the last decade, there has been a significant increase of interest in the computer-aided
diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure,
since the accuracy of the subsequent steps crucially depends on it. In this paper, a fast and unsupervised
approach to border detection in dermoscopy images of pigmented skin lesions based on the Statistical
Region Merging algorithm is presented. The method is tested on a set of 90 dermoscopy images. The
border detection error is quantified by a metric in which a set of dermatologist-determined borders is
used as the ground-truth. The proposed method is compared to six state-of-the-art automated methods
(optimized histogram thresholding, orientation-sensitive fuzzy c-means, gradient vector flow snakes,
dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method)
and borders determined by a second dermatologist. The results demonstrate that the presented method
achieves both fast and accurate border detection in dermoscopy images.
As a result of the advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of skin cancer. Dermoscopy is a non-invasive skin imaging technique which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the useful features in dermoscopic diagnosis is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film) which is mostly associated with invasive melanoma. In this preliminary study, a machine learning approach to the detection of blue-white veil areas in dermoscopy images is presented. The method involves pixel classification based on relative and absolute color features using a decision tree classifier. Promising results were obtained on a set of 224 dermoscopy images.