Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

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Data clustering: a review

https://surfer.nmr.mgh.harvard.edu/ftp/articles/dale99-recon1.pdf

Cortical surface-based analysis. I. Segmentation and surface reconstruction

Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.

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FSIM: A Feature Similarity Index for Image Quality Assessment

This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction.

Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast.

Details of the new feature detectors and of the new noise reduction method are described, along with test results.

SUSAN—A New Approach to Low Level Image Processing

The goal of this paper is to present a critical survey of existing literature on human and machine recognition of faces. Machine recognition of faces has several applications, ranging from static matching of controlled photographs as in mug shots matching and credit card verification to surveillance video images. Such applications have different constraints in terms of complexity of processing requirements and thus present a wide range of different technical challenges. Over the last 20 years researchers in psychophysics, neural sciences and engineering, image processing analysis and computer vision have investigated a number of issues related to face recognition by humans and machines. Ongoing research activities have been given a renewed emphasis over the last five years. Existing techniques and systems have been tested on different sets of images of varying complexities. But very little synergism exists between studies in psychophysics and the engineering literature. Most importantly, there exists no evaluation or benchmarking studies using large databases with the image quality that arises in commercial and law enforcement applications In this paper, we first present different applications of face recognition in commercial and law enforcement sectors. This is followed by a brief overview of the literature on face recognition in the psychophysics community. We then present a detailed overview of move than 20 years of research done in the engineering community. Techniques for segmentation/location of the face, feature extraction and recognition are reviewed. Global transform and feature based methods using statistical, structural and neural classifiers are summarized. >

Human and machine recognition of faces: a survey

This text is intended to provide a balanced introduction to machine vision. Basic concepts are introduced with only essential mathematical elements. The details to allow implementation and use of vision algorithm in practical application are provided, and engineering aspects of techniques are emphasized. This text intentionally omits theories of machine vision that do not have sufficient practical applications at the time.

Machine vision

Computing oriented texture fields

Image flow is the velocity field in the image plane caused by the motion of the observer, objects in the scene, or apparent motion, and can contain discontinuities due to object occlusion in the scene. An algorithm that can estimate the image flow velocity field when there are discontinuities due to occlusions is described. The constraint line clustering algorithm uses a statistical test to estimate the image flow velocity field in the presence of step discontinuities in the image irradiance or velocity field. Particular emphasis is placed on motion estimation and segmentation in situations such as random dot patterns where motion is the only cue to segmentation. Experimental results on a demanding synthetic test case and a real image are presented. A smoothing algorithm for improving the velocity field estimate is also described. The smoothing algorithm constructs a smooth estimate of the velocity field by approximating a surface between step discontinuities. It is noted that the velocity field estimate can be improved using surface reconstruction between velocity field boundaries. >

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Image flow segmentation and estimation by constraint line clustering

A two-stage algorithm for visual surface reconstruction from scattered data while preserving discontinuities is presented The first stage consists of a robust local approximation algorithm (the moving least median of squares (MLMS) of error) to clean the data and create a grid from the original scattered data points This process is discontinuity preserving The second stage introduces a weighted bicubic spline (WBS) as a surface descriptor The WBS has a factor in the regularizing term that adapts the behavior of the spline across discontinuities The weighted bicubic approximating spline can approximate data with step discontinuities with no discernible distortion in the approximating surface The combination of robust surface fitting and WBSs removes outliers and reduces Gaussian noise Either stage by itself would not effectively remove both kinds of noise Experimental results with the two-stage algorithm are presented >

A two-stage algorithm for discontinuity-preserving surface reconstruction

We address the problem of building a map of the environment utilizing sensory depth information obtained from multiple viewpoints The desired representation of the environment is in the form of a finite-resolution three-dimensional grid of voxels Each voxel within the grid is assigned a binary value corresponding to its occupancy state We present an approach for multi-sensory depth information assimilation based on the Dempster-Shafer theory for evidential reasoning This approach provides a mechanism to explicitly model ignorance which is desirable when dealing with an unknown environment We present results obtained from this approach on a laboratory stereo sequence and contrast these with results obtained from the traditional Bayesian approach >

Brian G. Schunck

Papers

Machine vision

Computing oriented texture fields

Image flow segmentation and estimation by constraint line clustering

A two-stage algorithm for discontinuity-preserving surface reconstruction

Evidential reasoning for building environment maps