曲率尺度空间与链码方向统计的角点检测

针对曲率尺度空间角点检测中,由于选择的尺度不同,会造成角点的漏检测,以及检测到错误角点的问题。提出一种基于曲率尺度空间与链码方向统计的角点检测方法。首先在较低的曲率尺度空间上检测出候选角点集;再通过自适应阈值及链码方向统计的方法删除错误角点。该方法采用较低的曲率尺度可检测出更多的角点,降低了角点漏检测率;通过计算椭圆角点自适应阈值可删除椭圆角点;采用Freeman链码方向统计可剔除伪角点;从而提高角点检测精度。通过实验充分验证了本文提出的角点检测算法比其他角点检测算法具有的高效性和准确性。     

Unsupervised segmentation of noisy and inhomogeneous images using global region statistics with non-convex regularization

Improving the segmentation of magnetic resonance (MR) images remains challenging because of the presence of noise and inhomogeneous intensity. In this paper, we present an unsupervised, multiphase segmentation model based on a Bayesian framework for both MR image segmentation and bias field correction in the presence of noise. In our model, global region statistics are utilized as segmentation criteria in order to classify regions with similar mean intensities but different variances. Additionally, we propose an edge indicator function based on a guided filter (instead of a Gaussian filter) that can preserve the underlying edges of the image obscured by noise. The proposed edge indicator function is integrated with non-convex regularization to overcome the influence of noise, resulting in more accurate segmentation. Furthermore, the proposed model utilizes a Markov random field to model the spatial correlation between neighboring pixels, which increases the robustness of the model under high-noise conditions. Experimental results demonstrate significant advantages in terms of both segmentation accuracy and bias field correction for inhomogeneous images in the presence of noise.     

Microcalcification detection using fuzzy logic and scale space approaches

Breast cancer is one of the leading causes of women mortality in the world. Since the causes are unknown, breast cancer cannot be prevented. It is difficult for radiologists to provide both accurate and uniform evaluation over the enormous number of mammograms generated in widespread screening. Computer-aided mammography diagnosis is an important and challenging task. Microcalcifications and masses are the early signs of breast carcinomas and their detection is one of the key issues for breast cancer control. In this study, a novel approach to microcalcification detection based on fuzzy logic and scale space techniques is presented. First, we employ fuzzy entropy principal and fuzzy set theory to fuzzify the images. Then, we enhance the fuzzified image. Finally, scale-space and Laplacian-of-Gaussian filter techniques are used to detect the sizes and locations of microcalcifications. A free-response operating characteristic curve is used to evaluate the performance. The major advantage of the proposed method is its ability to detect microcalcifications even in the mammograms of very dense breasts. A data set of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications is studied. Experimental results demonstrate that the microcalcifications can be accurately and efficiently detected using the proposed approach. It can produce lower false positives and false negatives than the existing methods.     

Unsupervised person re-identification by hierarchical cluster and domain transfer

Multimedia Tools and Applications - Person re-identification (re-ID) has recently been tremendously boosted due to the advancement of deep convolutional neural networks. Unfortunately, the majority...     

Unsupervised clustering in Hough space for identification ofpartially occluded objects

An automated approach for template-free identification of partially occluded objects is presented. The contour of each relevant object in the analyzed scene is modeled with an approximating polygon whose edges are then projected into the Hough space. A structurally adaptive self-organizing map neural network generates clusters of collinear and/or parallel edges, which are used as the basis for identifying the partially occluded objects within each polygonal approximation. Results on a number of cases under different conditions are provided     

New scale invariant template matching technique using hyper space image representation

A new approach for the template image matching is being presented. The method first converts the image into edges, then, the vital information of these edges has been presented as a set of vectors in a four dimensional hyper-space. A modified Radon Transform has been proposed to facilitate this vectorization process. All the above processing is being done offline for the main image of the area. The template image has also been vectorized in a same fashion in real time which is to be matched with the main image. A vector matching algorithm has been proposed to deliver match location with a very low computational cost. It works for a wide range of template scaling and noise conditions which were not there in the previous algorithms found in the literature.     

Reasoning about edges in scale space

Explores the role of reasoning in early vision processing. In particular, the problem of detecting edges is addressed. The authors do not try to develop another edge detector, but rather, they study an edge detector rigorously to understand its behavior well enough to formulate a reasoning process that allow appliance of the detector judiciously to recover useful information. They present a multiscale reasoning algorithm for edge recovery: reasoning about edges in scale space (RESS). The knowledge in RESS is acquired from the theory of edge behavior in scale space and represented by a number of procedures. RESS recovers desired edge curves through a number of reasoning processes on zero crossing images at various scales. The knowledge of edge behavior in scale space enables RESS to select proper scale parameters, recover missing edges, eliminate noise or false edges, and correct the locations of edges. A brief evaluation of RESS is performed by comparing it with two well-known multistage edge detection algorithms     

Unsupervised feature selection and general pattern discovery using Self-Organizing Maps for gaining insights into the nature of seismic wavefields

This study presents an unsupervised feature selection and learning approach for the discovery and intuitive imaging of significant temporal patterns in seismic single-station or network recordings. For this purpose, the data are parametrized by real-valued feature vectors for short time windows using standard analysis tools for seismic data, such as frequency-wavenumber, polarization, and spectral analysis. We use Self-Organizing Maps (SOMs) for a data-driven feature selection, visualization and clustering procedure, which is in particular suitable for high-dimensional data sets. Our feature selection method is based on significance testing using the Wald–Wolfowitz runs test for individual features and on correlation hunting with SOMs in feature subsets. Using synthetics composed of Rayleigh and Love waves and real-world data, we show the robustness and the improved discriminative power of that approach compared to feature subsets manually selected from individual wavefield parametrization methods. Furthermore, the capability of the clustering and visualization techniques to investigate the discrimination of wave phases is shown by means of synthetic waveforms and regional earthquake recordings.     

Unsupervised manifold alignment using soft-assign technique

In this paper, we propose a robust unsupervised algorithm for automatic alignment of two manifolds in different datasets with possibly different dimensionalities. The significant contribution is that the proposed alignment algorithm is performed automatically without any assumptions on the correspondences between the two manifolds. For such purpose, we first automatically extract local feature histograms at each point of the manifolds and establish an initial similarity between the two datasets by matching their histogram-based features. Based on such similarity, an embedding space is estimated where the distance between the two manifolds is minimized while maximally retaining the original structure of the manifolds. The elegance of this idea is that such complicated problem is formulated as a generalized eigenvalue problem, which can be easily solved. The alignment process is achieved by iteratively increasing the sparsity of correspondence matrix until the two manifolds are correctly aligned and consequently one can reveal their joint structure. We demonstrate the effectiveness of our algorithm on different datasets by aligning protein structures, 3D face models and facial images of different subjects under pose and lighting variations. Finally, we also compare with a state-of-the-art algorithm and the results show the superiority of the proposed manifold alignment in terms of vision effect and numerical accuracy.     

Unsupervised feature selection using feature similarity

In this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast. A new feature similarity measure, called maximum information compression index, is introduced. The algorithm is generic in nature and has the capability of multiscale representation of data sets. The superiority of the algorithm, in terms of speed and performance, is established extensively over various real-life data sets of different sizes and dimensions. It is also demonstrated how redundancy and information loss in feature selection can be quantified with an entropy measure     

Unsupervised context detection using wireless signals

The sensing context plays an important role in many pervasive and mobile computing applications. Continuing from previous work [D. Phung, B. Adams, S. Venkatesh, Computable social patterns from sparse sensor data, in: Proceedings of First International Workshop on Location Web, World Wide Web Conference (WWW), New York, NY, USA, 2008, ACM 69–72.], we present an unsupervised framework for extracting user context in indoor environments with existing wireless infrastructures. Our novel approach casts context detection into an incremental, unsupervised clustering setting. Using WiFi observations consisting of access point identification and signal strengths freely available in office or public spaces, we adapt a density-based clustering technique to recover basic forms of user contexts that include user motion state and significant places the user visits from time to time. High-level user context, termed rhythms, comprising sequences of significant places are derived from the above low-level context by employing probabilistic clustering techniques, latent Dirichlet allocation and its n-gram temporal extension. These user contexts can enable a wide range of context-ware application services. Experimental results with real data in comparison with existing methods are presented to validate the proposed approach. Our motion classification algorithm operates in real-time, and achieves a 10% improvement over an existing method; significant locations are detected with over 90% accuracy and near perfect cluster purity. Richer indoor context and meaningful rhythms, such as typical daily routines or meeting patterns, are also inferred automatically from collected raw WiFi signals.     

Unsupervised texture segmentation using Gabor filters

This paper presents a texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain, and a systematic filter selection scheme is proposed, which is based on reconstruction of the input image from the filtered images. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of “energy” in a window around each pixel. A square-error clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial information in the clustering process is proposed. A relative index is used to estimate the “true” number of texture categories.     

The scale space aspect graph

Currently the aspect graph is computed from the theoretical standpoint of perfect resolution in object shape, the viewpoint and the projected image. This means that the aspect graph may include details that an observer could never see in practice. Introducing the notion of scale into the aspect graph framework provides a mechanism for selecting a level of detail that is “large enough” to merit explicit representation. This effectively allows control over the number of nodes retained in the aspect graph. This paper introduces the concept of the scale space aspect graph, defines three different interpretations of the scale dimension, and presents a detailed example for a simple class of objects, with scale defined in terms of the spatial extent of features in the image     

Signal matching through scale space

Given a collection of similar signals that have been deformed with respect to each other, the general signal-matching problem is to recover the deformation. We formulate the problem as the minimization of an energy measure that combines a smoothness term and a similarity term. The minimization reduces to a dynamic system governed by a set of coupled, first-order differential equations. The dynamic system finds an optimal solution at a coarse scale and then tracks it continuously to a fine scale. Among the major themes in recent work on visual signal matching have been the notions of matching as constrained optimization, of variational surface reconstruction, and of coarse-to-fine matching. Our solution captures these in a precise, succinct, and unified form. Results are presented for one-dimensional signals, a motion sequence, and a stereo pair.     

Generalized optical flow in the scale space

Scale space is a natural way to handle multi-scale problems. Yang and Ma have considered the correspondence between scales, and proposed optical flow in the scale space. In this paper, we generalized Yang and Ma’s work to generic images. We first generalize the Horn–Schunck algorithm to multi-dimensional multi-channel image sequence. Since the global smoothness constraint for regularization is no longer suitable in general cases, we introduce localized smoothness regularization. In scale space optical flow, points in original image trends to aggregate at a large scale, so we introduce aggregation density as an additional smoothness coefficient. At last, we apply the proposed methods to color images and 3D images.     

Directional analysis of images in scale space

The authors propose a computational technique for the directional analysis of piecewise linear patterns in images based on the notion of zero crossings in gradient images. A given image is preprocessed by a sequence of filters that are second derivatives of 2-D Gaussian functions with different scales. This gives a set of zero-crossing maps (the scale space) from which a stability map is generated. Significant linear patterns are detected from measurements on the stability map. Information regarding orientation of the linear patterns in the image and the area covered by the patterns in specific directions is then computed. The performance of the method is illustrated through applications to a simple test image made up of straight bar patterns as well as to scanning electron microscope images of collagen fibrils in rabbit ligaments. The method has significant applications in quantitative analysis of ligament healing and in comparison of treatment methods for ligament injuries