A whitening transformation for two-color blood cell images

An image enhancement technique is described for the preprocessing of stained white blood cell images which have been digitized through two different color filters from either end of the visible spectrum. Typically, corresponding picture elements (or pixels) from blood cell images digitized in this manner exhibit slight changes in grey-level due to the color filtering, but remain strongly correlated in optical density with each other. Also, color and density information are interrelated in the pixels of both of the filtered images. The technique described is a whitening transformation on the bivariate distribution of image pixels, this results in two uncorrelated axes, one relating to density and the other relating to color. The spatial effect on the two original images is to produce two separate, transformed, “color” and “density” images.     

An image segmentation method using histograms and the human characteristics of HSI color space for a scene image

Image segmentation is an important subject for image recognition. Here, we propose a new image segmentation method for scene images. The proposed segmentation method classifies images into several segments based on the human visual sense and achromatic color. We calculate the histograms of the image for each component of the hue, saturation, and intensity (HSI) color space, and obtain three results of image segmentation from each histogram. We consider achromatic colors in order to decrease the number of regions. We compare the results of the proposed method with those of the k-means methods for the effectiveness of the proposed method. This work was presented, in part, at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

On the use of the overlapping area matrix for image segmentation evaluation: A survey and new performance measures

The development of common and reasonable criteria for evaluating and comparing the performance of segmentation algorithms has always been a concern for researchers in the area. As it is discussed in the paper, some of the measures proposed are not adequate for general images (i.e. images of any sort of scene, without any assumption about the features of the scene objects or the illumination distribution) because they assume a certain distribution of pixel gray-level or colour values for the interior of the regions. This paper reviews performance measures not performing such an assumption and proposes a set of new performance measures in the same line, called the percentage of correctly grouped pixels (CG), the percentage of over-segmentation (OS) and the percentage of under-segmentation (US). Apart from accounting for misclassified pixels, the proposed set of new measures are intended to compute the level of fragmentation of reference regions into output regions and vice versa. A comparison involving similar measures is provided at the end of the paper.     

Design considerations for image segmentation quality assessment measures

Factors to consider when designing quality assessment measures for image segmentation are discussed. Quality assessment requires one manually generated segmentation (for reference) plus computer-generated segmentations corresponding to different image segmentation algorithms or algorithm parameter settings. Since true pixel class assignments are seldom available, one must typically rely on a trained human analyst to produce a reference by using a mouse to draw boundaries of perceived regions on a digital image background. Different algorithms and parameter settings can be compared by ranking computed disparities between maps of computer-generated region boundaries and region boundaries from a common reference.Proximity-based association between two boundary pixels is discussed in the context of association distance. Motivated by the concept of phase-modulated signals, a penalty factor on the degree of association is then introduced as some non-negative power (phase modulation order) of the cosine of disparity in phase (boundary direction) between two boundary pixels. Families of matching measures between maps of region boundaries are defined as functions of associations between many pairs of boundary pixels. The measures are characterized as one-way (reflecting relationships in one direction between region boundaries from two segmentations) vs. two-way (reflecting relationships in both directions). Measures of inconsistency between perceived and computed matches of computer and manually generated region boundaries are developed and exercised so that effects of association distance, phase modulation, and choice of matching measure on image segmentation quality assessment can be quantified. It is quantitatively established that consistency can be significantly improved by using two-way measures in conjunction with high-order phase modulation and moderate association distances.     

Graph-based tools for microscopic cellular image segmentation

We propose a framework of graph-based tools for the segmentation of microscopic cellular images. This framework is based on an object oriented analysis of imaging problems in pathology. Our graph tools rely on a general formulation of discrete functional regularization on weighted graphs of arbitrary topology. It leads to a set of useful tools which can be combined together to address various image segmentation problems in pathology. To provide fast image segmentation algorithms, we also propose an image simplification based on graphs as a pre processing step. The abilities of this set of image processing discrete tools are illustrated through automatic and interactive segmentation schemes for color cytological and histological images segmentation problems.     

Mahalanobis distance based on fuzzy clustering algorithm for image segmentation

Conventional Fuzzy C-means (FCM) algorithm uses Euclidean distance to describe the dissimilarity between data and cluster prototypes. Since the Euclidean distance based dissimilarity measure only characterizes the mean information of a cluster, it is sensitive to noise and cluster divergence. In this paper, we propose a novel fuzzy clustering algorithm for image segmentation, in which the Mahalanobis distance is utilized to define the dissimilarity measure. We add a new regularization term to the objective function of the proposed algorithm, reflecting the covariance of the cluster. We experimentally demonstrate the effectiveness of the proposed algorithm on a generated 2D dataset and a subset of Berkeley benchmark images.     

Fuzzy spectral clustering with robust spatial information for image segmentation

In recent years, spectral clustering has become one of the most popular clustering algorithms in areas of pattern analysis and recognition. This algorithm uses the eigenvalues and eigenvectors of a normalized similarity matrix to partition the data, and is simple to implement. However, when the image is corrupted by noise, spectral clustering cannot obtain satisfying segmentation performance. In order to overcome the noise sensitivity of the standard spectral clustering algorithm, a novel fuzzy spectral clustering algorithm with robust spatial information for image segmentation (FSC_RS) is proposed in this paper. Firstly, a non-local-weighted sum image of the original image is generated by utilizing the pixels with a similar configuration of each pixel. Then a robust gray-based fuzzy similarity measure is defined by using the fuzzy membership values among gray values in the new generated image. Thus, the similarity matrix obtained by this measure is only dependent on the number of the gray-levels and can be easily stored. Finally, the spectral graph partitioning method can be applied to this similarity matrix to group the gray values of the new generated image and then the corresponding pixels in the image are reclassified to obtain the final segmentation result. Some segmentation experiments on synthetic and real images show that the proposed method outperforms traditional spectral clustering methods and spatial fuzzy clustering in efficiency and robustness.     

Color image segmentation in HSI space for automotive applications

This article presents a method for classifying color points for automotive applications in the Hue Saturation Intensity (HSI) Space based on the distances between their projections onto the SI plane. Firstly the HSI Space is analyzed in detail. Secondly the projection of image points from a typical automotive scene onto the SI plane is shown. The minimal classes relevant for driver assistance applications are derived. The requirements for the classification of the points into those classes are obtained. Several weighting functions are proposed and a fast form of an euclidean metric is investigated in detail. In order to improve the sensitivity of the weighting function, dynamic coefficients are introduced. It is shown how to compute them automatically in order to get optimal results for the classification. Finally some results of applying the metric to the sample images are shown and the conclusions are drawn.

Bounded irregular pyramid: a new structure for color image segmentation

This paper presents a new segmentation technique for color images. It relies on building an irregular pyramid into a regular one, presenting only nodes associated to homogeneous color regions. Hence, the size of the irregular pyramid is bounded. Segmentation is performed by rearranging the set of links among pyramid nodes. Unlike other hierarchical methods based on relinking procedures, our algorithm does not operate in an iterative way and it preserves region connectivity.     

Parallel watershed transformation algorithms for image segmentation

The watershed transformation is a mid-level operation used in morphological image segmentation. Techniques applied on large images, which must often complete fast, are usually computationally expensive and complex entailing efficient parallel algorithms. Two distributed approaches of the watershed transformation are introduced in this paper. The algorithms survey in a Single Program Multiple Data (SPMD) model both local and global connectivity properties of the morphological gradient of a gray-scale image to label connected components. The sequentiality of the serial algorithm is broken in the parallel versions by exploiting the ordering relation between two neighboring pixels successively incorporated in the same region. Thus, a path is traced, for every unlabeled pixel, down to its region of inclusion (whose label is then propagated backwards); in the second algorithm, regions grow independently around their seeds. In both cases only pixels which satisfy the ordering relation are incorporated in any region. This way, not only different regions are explored in a parallel fashion, but also different parts of the same region, when the latter extends to neighboring subdomains, are treated likewise. Running time and relative speedup evaluated on a Cray T3D parallel computer are used to appreciate the performance of both algorithms.     

A universal texture segmentation and representation scheme based on ant colony optimization for iris image processing

This paper proposes a novel scheme for texture segmentation and representation based on Ant Colony Optimization (ACO). Texture segmentation and texture characteristic expression are two important areas in image pattern recognition. Nevertheless, until now, how to find an effective way for accomplishing these tasks is still a major challenge in practical applications such as iris image processing. We propose a framework for ACO based image processing methods. Considering the specific characteristics of various tasks, such a framework possesses the flexibility of only defining different criteria for ant behavior correspondingly. By defining different kinds of direction probability and movement difficulty for artificial ants, an ACO based image segmentation algorithm and a texture representation method are then presented for automatic iris image processing. Experimental results demonstrated that the ACO based image processing methods are competitive and quite promising, with excellent effectiveness and practicability especially for images with complex local texture situations.     

Modeling errors for dimensional inspection using active vision

In active vision inspection, errors in dimensional measurements are often due to displacement of the sensor and image digitization effects. Using a model of error in linear measurements based on a normally distributed sensor displacement, uniform image digitization and geometric approximation, the accuracy of measurements from a particular sensor setting can be assessed. In comparison with experimental measurements, this error model demonstrates a high predictive ability. Related experiments demonstrate a procedure for assessing the accuracy of sensor settings providing insight into what sensor settings lead to lower measurement variances and thus higher measurement reliability.     

Shared Sampling for Real‐Time Alpha Matting

Image matting aims at extracting foreground elements from an image by means of color and opacity (alpha) estimation. While a lot of progress has been made in recent years on improving the accuracy of matting techniques, one common problem persisted: the low speed of matte computation. We present the first real‐time matting technique for natural images and videos. Our technique is based on the observation that, for small neighborhoods, pixels tend to share similar attributes. Therefore, independently treating each pixel in the unknown regions of a trimap results in a lot of redundant work. We show how this computation can be significantly and safely reduced by means of a careful selection of pairs of background and foreground samples. Our technique achieves speedups of up to two orders of magnitude compared to previous ones, while producing high‐quality alpha mattes. The quality of our results has been verified through an independent benchmark. The speed of our technique enables, for the first time, real‐time alpha matting of videos, and has the potential to enable a new class of exciting applications.     

A highly accurate and computationally efficient approach for unconstrained iris segmentation

Biometric research has experienced significant advances in recent years given the need for more stringent security requirements. More important is the need to overcome the rigid constraints necessitated by the practical implementation of sensible but effective security methods such as iris recognition. An inventive iris acquisition method with less constrained image taking conditions can impose minimal to no constraints on the iris verification and identification process as well as on the subject. Consequently, to provide acceptable measures of accuracy, it is critical for such an iris recognition system to be complemented by a robust iris segmentation approach to overcome various noise effects introduced through image capture under different recording environments and scenarios. This research introduces a robust and fast segmentation approach towards less constrained iris recognition using noisy images contained in the UBIRIS.v2 database (the second version of the UBIRIS noisy iris database). The proposed algorithm consists of five steps, which include: (1) detecting the approximate localization of the eye area of the noisy image captured at the visible wavelength using the extracted sclera area, (2) defining the outer iris boundary which is the boundary between iris and sclera, (3) detecting the upper and lower eyelids, (4) conducting the verification and correction for outer iris boundary detection and (5) detecting the pupil area and eyelashes and providing means for verification of the reliability of the segmentation results. The results demonstrate that the accuracy is estimated as 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at ?97%$97\%$ in a “Noisy Iris Challenge Evaluation (NICE.I)” in an international competition that involved 97 participants worldwide, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time.     

Efficient Multi‐image Correspondences for On‐line Light Field Video Processing

Light field videos express the entire visual information of an animated scene, but their shear size typically makes capture, processing and display an off‐line process, i. e., time between initial capture and final display is far from real‐time. In this paper we propose a solution for one of the key bottlenecks in such a processing pipeline, which is a reliable depth reconstruction possibly for many views. This is enabled by a novel correspondence algorithm converting the video streams from a sparse array of off‐the‐shelf cameras into an array of animated depth maps. The algorithm is based on a generalization of the classic multi‐resolution Lucas‐Kanade correspondence algorithm from a pair of images to an entire array. Special inter‐image confidence consolidation allows recovery from unreliable matching in some locations and some views. It can be implemented efficiently in massively parallel hardware, allowing for interactive computations. The resulting depth quality as well as the computation performance compares favorably to other state‐of‐the art light field‐to‐depth approaches, as well as stereo matching techniques. Another outcome of this work is a data set of light field videos that are captured with multiple variants of sparse camera arrays.     

Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization,Mahalanobis distance and post-segmentation correction

In this paper, we propose an improvement method for image segmentation using the fuzzy c-means clustering algorithm (FCM). This algorithm is widely experimented in the field of image segmentation with very successful results. In this work, we suggest further improving these results by acting at three different levels. The first is related to the fuzzy c-means algorithm itself by improving the initialization step using a metaheuristic optimization. The second level is concerned with the integration of the spatial gray-level information of the image in the clustering segmentation process and the use of Mahalanobis distance to reduce the influence of the geometrical shape of the different classes. The final level corresponds to refining the segmentation results by correcting the errors of clustering by reallocating the potentially misclassified pixels. The proposed method, named improved spatial fuzzy c-means IFCMS, was evaluated on several test images including both synthetic images and simulated brain MRI images from the McConnell Brain Imaging Center (BrainWeb) database. This method is compared to the most used FCM-based algorithms of the literature. The results demonstrate the efficiency of the ideas presented.