Mode-seeking Algorithm Research Articles

Study of Clustering Algorithms in Object Tracking and Image Segmentation

Mean Shift is a kind of clustering algorithm, which is mostly used for target tracking, image segmentation, etc. In order to solve the problem that image information is not effectively utilized because of unclear traffic video images and random jitter between image sequences, this paper has studied how to achieve stability of traffic video images and proposed an improved Mean Shift algorithm about how to conduct object centroid registration in compensation for the deviations in space localization and, on this basis, how to select kernel window width to eliminate the errors in scale positioning. This algorithm gets the tracking effect and computation analysis of improved Mean Shift from the perspective of applications, removes or relieves the impact motion has on imaging, improves the quality of the video image information obtained, automatically adjusts the size of window according to the scale changes of moving object in the image, and effectively enhances the stability and real time of object tracking. Besides, in the postprocessing stage, the superpixel based on the Mean Shift algorithm is applied to further optimize the segmentation result; it is a popular mode-seeking clustering algorithm, which makes Mean Shift method ideal on low-dimensional applications such as image segmentation. Finally, we show promising results for remote sensing image segmentation.

Analysis of Deep Learning Techniques for Maasai Boma Mapping in Tanzania

Underdeveloped countries in sub-Saharan Africa often contain cultural subpopulations that are underserved in regard to health and education. This perpetuates the health challenges of the country as a whole, and it is therefore of interest to be able to automatically map the subpopulation for the health services delivery. International nonprofit health organizations have often taken the lead in these efforts, providing humanitarian aid (e.g., clean water and food) as well as health care. This is necessary, as the ethnic subpopulations are not well integrated into the society and the existing health care systems. In this study, we explicitly explore the Maasailand of Tanzania, to evaluate the use of deep neural networks (DNN) to aid in the automatic visual analysis of remote sensing data to geolocate Maasai boma structures. We investigate the performance of four state-of-the-art DNN as classifiers of boma presence within high-resolution imagery; all showing over 95% F1 score performance. Additionally, we scan over 3900 km <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> of high-resolution imagery, combining a ProxylessNAS with broad area aggregation and mapping techniques and demonstrate the discovery of hundreds of boma, many that were not discovered by human analysts performing visual scans. The trained ProxylessNAS model generates a classified vector response field (CVRF). The CVRF is aggregated by a mode-seeking algorithm to detect potential locations of boma structures within the study area. The model detected numerous human false negatives (HFNs) and achieved 94.022% TPR and 95.395% F1 score using an aggregation aperture of 250 m within a 76.620 square kilometers area of interest.

An automatic identification of lung tumor by using CNN network and fuzzy-clustering algorithm

The most important view of image segmentation is a process of analyzing image in to individual pixel values for each boundary. All the more definitely, segmentation is the strategy of doling out a mark to each and each pixel in a photograph to such an extent that pixels with the equivalent name share positive traits. This article implements an image segmentation method based on Mean-Shift Clustering Algorithm with a Gaussian Mixture Models. Mean shift is a non-parametric feature-space analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm. Application domains include cluster analysis in computer vision and image processing. The proposed method is used to evaluated in terms of accuracy, precision and actual solution. For this analysis produced accurate image with minimum time period. This new technique is used to analyze the varies lung nodules by means of lowering the noise the usage of computerized segmentation algorithm in specific region.

Aircraft Skin Rivet Detection Based on 3D Point Cloud via Multiple Structures Fitting

Rivet detection is usually the first step for almost all surface and rivet inspection methods in aircraft skins. With 3D laser scanners, one can rapidly obtain the precise 3D information, i.e. point cloud, of the surface and rivets. Subsequently, rivet detection can be converted to a multiple-structure fitting problem from 3D point clouds. However, robust structure fitting from scanned 3D point cloud remains an open problem due to its challenging nature, such as noise and outliers, irregular sampling density and missing scanning. To reduce the fitting variability, this paper presents an automated density-aware multiple-structure fitting algorithm to perform rivet detection based on a 3D point cloud. The key observation is that the local density of points belonging to the rivet contour is relatively higher. We hereby formulate rivet detection as a multiple structure fitting problem with a density-based significance measure. By considering the local distribution characteristics, we first perform adaptive density enhancement on the basic local density. Subsequently, we detect the potential circle hypotheses and thereby extract rivet contours. By performing the mode-seeking algorithm on hypergraphs, all the circle structures can be obtained simultaneously. Overall, the proposed extraction algorithm is able to efficiently and effectively detect rivets from the raw scanned point clouds. We also demonstrate that the proposed algorithm achieves significant superiority over several state-of-the-art model fitting methods on the real scanned point cloud via experimental results. Moreover, we give the application of our algorithm on rivet flush inspection, showing that our method can assist in the rapid measurement of riveting quality.

Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting

In this paper, we propose a simple and effective geometric model fitting method to fit and segment multi-structure data even in the presence of severe outliers. We cast the task of geometric model fitting as a representative mode-seeking problem on hypergraphs. Specifically, a hypergraph is first constructed, where the vertices represent model hypotheses and the hyperedges denote data points. The hypergraph involves higher-order similarities (instead of pairwise similarities used on a simple graph), and it can characterize complex relationships between model hypotheses and data points. In addition, we develop a hypergraph reduction technique to remove "insignificant" vertices while retaining as many "significant" vertices as possible in the hypergraph. Based on the simplified hypergraph, we then propose a novel mode-seeking algorithm to search for representative modes within reasonable time. Finally, the proposed mode-seeking algorithm detects modes according to two key elements, i.e., the weighting scores of vertices and the similarity analysis between vertices. Overall, the proposed fitting method is able to efficiently and effectively estimate the number and the parameters of model instances in the data simultaneously. Experimental results demonstrate that the proposed method achieves significant superiority over several state-of-the-art model fitting methods on both synthetic data and real images.

A Fully-Unsupervised Possibilistic C-Means Clustering Algorithm

In 1993, Krishnapuram and Keller first proposed possibilistic C-means (PCM) clustering by relaxing the constraint in fuzzy C-means of which memberships for a data point across classes sum to 1. The PCM algorithm tends to produce coincident clusters that can be a merit of PCM as a good mode-seeking algorithm, and so various extensions of PCM had been proposed in the literature. However, the performance of PCM and its extensions heavily depends on initializations and parameters selection with a number of clusters to be given a priori . In this paper, we propose a novel PCM algorithm, termed a fully unsupervised PCM (FU-PCM), without any initialization and parameter selection that can automatically find a good number of clusters. We start by constructing a generalized framework for PCM clustering that can be a generalization of most existing PCM algorithms. Based on the generalized PCM framework, we propose the new type FU-PCM so that the proposed FU-PCM algorithm is free of parameter selection and initializations without a given number of clusters. That is, the FU-PCM becomes a FU-PCM clustering algorithm. Comparisons between the proposed FU-PCM and other existing methods are made. The computational complexity of the FU-PCM algorithm is also analyzed. Some numerical data and real data sets are used to show these good aspects of FU-PCM. Experimental results and comparisons actually demonstrate the proposed FU-PCM is an effective parameter-free clustering algorithm that can also automatically find the optimal number of clusters.

Robust clustering using a kNN mode seeking ensemble

In this paper we present a new algorithm for parameter-free clustering by mode seeking. Mode seeking, especially in the form of the mean shift algorithm, is a widely used strategy for clustering data, but at the same time prone to poor performance if the parameters are not chosen correctly. We propose to form a clustering ensemble consisting of repeated and bootstrapped runs of the recent kNN mode seeking algorithm, an algorithm which is faster than ordinary mean shift and more suited for high dimensional data. This creates a robust mode seeking clustering algorithm with respect to the choice of parameters and high dimensional input spaces, while at the same inheriting all other strengths of mode seeking in general. We demonstrate promising results on a number of synthetic and real data sets.

Robust Fusion of Diffusion MRI Data for Template Construction

Construction of brain templates is generally carried out using a two-step procedure involving registering a population of images to a common space and then fusing the aligned images to form a template. In practice, image registration is not perfect and simple averaging of the images will blur structures and cause artifacts. In diffusion MRI, this is further complicated by intra-voxel inter-subject differences in fiber orientation, fiber configuration, anisotropy, and diffusivity. In this paper, we propose a method to improve the construction of diffusion MRI templates in light of inter-subject differences. Our method involves a novel q-space (i.e., wavevector space) patch matching mechanism that is incorporated in a mean shift algorithm to seek the most probable signal at each point in q-space. Our method relies on the fact that the mean shift algorithm is a mode seeking algorithm that converges to the mode of a distribution and is hence robust to outliers. Our method is therefore in effect seeking the most probable signal profile at each voxel given a distribution of signal profiles. Experimental results show that our method yields diffusion MRI templates with cleaner fiber orientations and less artifacts caused by inter-subject differences in fiber orientation.

Support Vector Data Descriptions and $k$ -Means Clustering: One Class?

We present ClusterSVDD, a methodology that unifies support vector data descriptions (SVDDs) and $k$ -means clustering into a single formulation. This allows both methods to benefit from one another, i.e., by adding flexibility using multiple spheres for SVDDs and increasing anomaly resistance and flexibility through kernels to $k$ -means. In particular, our approach leads to a new interpretation of $k$ -means as a regularized mode seeking algorithm. The unifying formulation further allows for deriving new algorithms by transferring knowledge from one-class learning settings to clustering settings and vice versa. As a showcase, we derive a clustering method for structured data based on a one-class learning scenario. Additionally, our formulation can be solved via a particularly simple optimization scheme. We evaluate our approach empirically to highlight some of the proposed benefits on artificially generated data, as well as on real-world problems, and provide a Python software package comprising various implementations of primal and dual SVDD as well as our proposed ClusterSVDD.

Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion.

Construction of brain atlases is generally carried out using a two-step procedure involving registering a population of images to a common space and then fusing the aligned images to form an atlas. In practice, image registration is not perfect and simple averaging of the images will blur structures and cause artifacts. In diffusion MRI, this is further complicated by the possibility of within-voxel fiber misalignment due to natural inter-subject orientation dispersion. In this paper, we propose a method to improve the construction of diffusion atlases in light of inter-subject fiber dispersion. Our method involves a novel q-space (i.e., wavevector space) patch matching mechanism that is incorporated in a mean shift algorithm to seek the most probable signal at each point in q-space. Our method relies on the fact that the mean shift algorithm is a mode seeking algorithm that converges to the mode of a distribution and is hence robustness to outliers. Our method is therefore in effect seeking the most probable signal profile at each voxel given a distribution of profiles. Experimental results confirm that our method yields cleaner fiber orientation distribution functions with less artifacts caused by dispersion.

An Efficient SIFT-Based Mode-Seeking Algorithm for Sub-Pixel Registration of Remotely Sensed Images

Several image registration methods, based on the scaled-invariant feature transform (SIFT) technique, have appeared recently in the remote sensing literature. All of these methods attempt to overcome problems encountered by SIFT in multimodal remotely sensed imagery, in terms of the quality of its feature correspondences. The deterministic method presented in this letter exploits the fact that each SIFT feature is associated with a scale, orientation, and position to perform mode seeking (in transformation space) to eliminate outlying corresponding key points (i.e, features) and improve the overall match obtained. We also present an exhaustive empirical study on a variety of test cases, which demonstrates that our method is highly accurate and rather fast. The algorithm is capable of automatically detecting whether it succeeded or failed.

모드 탐색 알고리즘을 이용한 측정치 기반의 전파 환경 시각화 기법

In this paper, we propose an algorithm to visualize radio wave environment based on the measured Received Signal Strength Indication(RSSI) and 3D geographic information. We estimate the source position using the circumcenter of the triangle and visualize the radio wave environment using the empirical propagation models. A mode seeking algorithm(mean-shift clustering) is used to seek the peak points and the center of gravity is utilized to reduce the estimation errors. Our approach finds its applications in the radio wave monitoring systems for the efficient utilization of radio resources.

Generalized Transport Mean Shift algorithm for ubiquitous intelligence

Much research has been conducted recently relating to ubiquitous intelligent computing. Ubiquitous intelligence-enabled techniques, such as clustering and image segmentation, have focused on the development of intelligence methodologies. In this paper, a simultaneous mode-seeking and clustering algorithm called the Generalized Transport Mean Shift (GTMS) was introduced. The data points were designated as the ‘transporter–trailer’ characteristic. The important concept of transportation was used to solve the problem of redundant computations of mode-seeking algorithms. The time complexity of the GTMS algorithm is much lower than that of the Mean Shift (MS) algorithm. This means it is able to be used in a problem that has a very high data point, in particular, the segmentation of images containing the green vegetation. The proposed algorithm was tested on clustering and image-segmentation problems. The experimental results showed that the GTMS algorithm improves upon the existing algorithms in terms of both accuracy and time consumption. The GTMS algorithm’s highest speed is also 333.98 times faster than that of the standard MS algorithm. The redundancy computation can be reduced by omitting more than 90% of the data points at the third iteration of the mode-seeking process. This is because GTMS algorithm mainly reduces the data in the mode-seeking process. Thus, use of the GTMS algorithm would allow for the building of an intelligent portable device for surveying green vegetables in a ubiquitous environment.

A Robust Automatic Merging Possibilistic Clustering Method

Krishnapuram and Keller [IEEE Trans. Fuzzy Syst., vol. 1, no. 2, pp. 98-110, May 1993] proposed a possibilistic c-means (PCM) clustering by relaxing the constraint in fuzzy c-means (FCMs) that the memberships of a data point across classes sum to 1. The PCM algorithm has a tendency to produce coincident clusters. This can be a merit of PCM as a good mode-seeking algorithm if initials and parameters are suitably chosen. However, the performance of PCM heavily depends on initializations and parameter selection. In this paper, we propose a mechanism of robust automatic merging. We then create an automatic merging possibilistic clustering method (AM-PCM), where the proposed algorithm does not only solve these parameter-selection and initialization problems but also obtains an optimal cluster number. The proposed AM-PCM algorithm first uses all data points as initial cluster centers and then automatically merges these surrounding points around each cluster mode such that it can self-organize data groups according to the original data structure. The AM-PCM can exhibit the robustness to parameter, noise, cluster number, different volumes, and initializations. The computational complexity of AM-PCM is also analyzed. Comparisons between AM-PCM and other clustering methods are made. Some numerical data and real datasets are used to show these good aspects of AM-PCM. Experimental results and comparisons actually demonstrate that the proposed AM-PCM is an effective and parameter-free robust clustering algorithm.

Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods

Terrestrial laser scanning is becoming a common surveying technique to measure quickly and accurately dense point clouds in 3-D. It simplifies measurement tasks on site. However, the massive volume of 3-D point measurements presents a challenge not only because of acquisition time and management of huge volumes of data, but also because of processing limitations on PCs. Raw laser scanner point clouds require a great deal of processing before final products can be derived. Thus, segmentation becomes an essential step whenever grouping of points with common attributes is required, and it is necessary for applications requiring the labelling of point clouds, surface extraction and classification into homogeneous areas. Segmentation algorithms can be classified as surface growing algorithms or clustering algorithms. This paper presents an unsupervised robust clustering approach based on fuzzy methods. Fuzzy parameters are analysed to adapt the unsupervised clustering methods to segmentation of laser scanner data. Both the Fuzzy C-Means (FCM) algorithm and the Possibilistic C-Means (PCM) mode-seeking algorithm are reviewed and used in combination with a similarity-driven cluster merging method. They constitute the kernel of the unsupervised fuzzy clustering method presented herein. It is applied to three point clouds acquired with different terrestrial laser scanners and scenarios: the first is an artificial (synthetic) data set that simulates a structure with different planar blocks; the second a composition of three metric ceramic gauge blocks (Grade 0, flatness tolerance ± 0.1 μm) recorded with a Konica Minolta Vivid 9i optical triangulation digitizer; the last is an outdoor data set that comes up to a modern architectural building collected from the centre of an open square. The amplitude-modulated-continuous-wave (AMCW) terrestrial laser scanner system, the Faro 880, was used for the acquisition of the latter data set. Experimental analyses of the results from the proposed unsupervised planar segmentation process are shown to be promising.

A Variation on a Nonparametric Clustering Method

A single modification to a mode-seeking clustering algorithm proposed by Koontz, Narendra, and Fukunaga is shown to generate a novel clustering and to provide an indication of cluster stability. The modified method should provide better clusterings for ``uniform, touching'' clusters than the original, although the original should work better than the modified method for ``touching Gaussian'' clusters. Suitable ranges for the clustering parameters of both methods are investigated. Since the modification requires changing only one line of the original algorithm, two clusterings can be obtained for the price of one coding.