Multiscale image representation and edge detection

Finding gradient maximum and local maximum based on multi-scale Canny operator edge detection is really the optimization for two dimension multi-element function.The maximal function gradient or local maximum which was derived by the traditional analytics is approximate and local.A new multi-scale edge detection algorithm is proposed by a genetic optimum searching algorithm.To upgrade the genetic algorithm convergence about edge detection,an improved GA+SA+TABU is used in order to overcome the defects of local searching in the general genetic algorithm and upgrade the whole resolution.Alternating optimization tactics are utilized by combining the general algorithm and heuristic searching methods.The experimental results show that the proposed algorithm applied to IR target image segmentation could result in copious details,single edge and exact location.

An image and surface representation based on regularization theory is introduced in this paper. This representation is based on a hybrid model derived from the physical membrane and plate models. The representation, called the /spl lambda//spl tau/-representation, has two dimensions; one dimension represents smoothness or scale while the other represents the continuity of the image or surface. It contains images/surfaces sampled both in scale space and the weighted Sobolev space of continuous functions. Thus, this new representation can be viewed as an extension of the well-known scale space representation. We have experimentally shown that the proposed hybrid model results in improved results compared to the two extreme constituent models, i.e., the membrane and the plate models. Based on this hybrid model, a generalized edge detector (GED) which encompasses most of the well-known edge detectors under a common framework is developed. The existing edge detectors can be obtained from the generalized edge detector by simply specifying the values of two parameters, one of which controls the shape of the filter (/spl tau/) and the other controls the scale of the filter (/spl lambda/). By sweeping the values of these two parameters continuously, one can generate an edge representation in the /spl lambda//spl tau/ space, which is very useful for developing a goal-directed edge detection scheme for a specific task. The proposed representation and the edge detector have been evaluated qualitatively and quantitatively on several different types of image data such as intensity, range, and stereo images.

Image representation as graphs can enhance the understanding of image semantics and facilitate multi-scale image representation. However, existing methods often overlook the significance of the relationship between elements at each scale or fail to encode the hierarchical relationship between graph elements. To cope with that, we introduce four novel approaches for graph construction from images. These approaches utilize hierarchical image segmentation techniques to generate segmentations at multiple scales, and one of them incorporates edges to encode the relationships at each scale. Leveraging these representations, we present two new models: the Hierarchical Graph Convolutional Network for Image Classification (HGCIC) and the Hierarchical Image Graph with Scale Importance (HIGSI). HGCIC uses an adaptive depth to capture significant features and patterns at different scales, while HIGSI employs a novel readout function that weighs the importance of each scale when generating a fixed-size graph representation. Experimental results with CIFAR-10 and STL-10 datasets show that the HIGSI model outperforms (or closely matches) state-of-the-art models. The model also utilizes smaller graphs, reaching the point of using graphs with 50% of the number of nodes compared to other approaches. Additionally, HIGSI outperforms models trained with only the base graph used to create the hierarchy, achieving up to 11.54% better performance while using fewer parameters.

Improved multi-scale wavelet in pantograph slide edge detection

We provide sufficient conditions of Pólya type which guarantee the positive definiteness of an isotropic 2 × 2-matrix-valued function in R and R3. Several isotropic bivariate covariance models have been proposed in literature, where all components of the covariance matrix are of the same parametric family, such as the bivariate Matérn model. Based on the Pólya type conditions, we introduce two novel bivariate parametric covariance models of this class, the powered exponential (or stable) covariance model and the generalized Cauchy covariance model. Both models allow for flexible smoothness, variance, scale, and cross-correlation parameters. The smoothness parameters are in (0,1]. Additionally, the bivariate generalized Cauchy model allows for distinct long range parameters. We also show that the univariate spherical model can be generalized to the bivariate case within the above class only in a trivial way.

As the high voltage circuit breaker works, the metal parts are prone to many types of mechanical defects, such as plastic deformation, metal loss, corrosion, and so on. The defects will inevitably decrease the performance, mechanical reliability, lifetime, and other critical parameters of the breaker. Therefore, the detection of mechanical defects is of great help to reduce the mechanical failure rate of circuit breakers. In the present paper, a novel method based on edge detection and deep learning is proposed to detect the mechanical defects of the high voltage circuit breaker. Firstly, the high resolution images of the circuit breaker are photographed, and the components in the images are segmented in the minimum range by the contour detection algorithm. After binarization and morphological processing of the segmented images, the edge is drawn with the improved edge detection algorithm. However, the components of the circuit breaker are complicated, which results that the edge features of the critical component are difficult to extract. Then, the specific contour of components is detected by the depth estimation and segmentation methods. Secondly, a deep learning platform base on Tensorflow is established, which is an optimization method based on Convolutional Neural Network. In order to enhance the feature extraction capacity, a convolution kernel is inputted into a sparse self-coding network to proceeds with the optimal pre-training. Based on the above method, the features of images are automatically learned, and each convolution carries for the equilibrium of image entropy of convolution kernel by introducing similarity constraint rule. Finally, 3 types of mechanical defects, including plastic deformation, metal loss, corrosion, images and non-defect images as input are exploited to validate the CNN with convolution kernel optimized. The experimental results show that the average accuracy of the approach is better than the traditional convolution neural network model and has good feature extraction ability and generalization ability. In conclusion, combined with the improved edge detection and deep learning algorithms, the detection of typical mechanical defects can be well performed. For the collected images of circuit breaker, so as to reduce the impact of noise on defect accuracy of mechanical, the proposed method provides a reference for the non-destructive mechanical defect detection of high voltage circuit breaker.

A thin-plate spline method for spatial warping was used to register multiple image sets duringthree-dimensional reconstruction ofhistological sections. In a neuroanatomical study, the samelabeling method was applied to several turtle brains. Each case produced a set of microscopicsections. Spatial warping was employed to map data sets from multiple cases onto a templatecoordinate system. This technique enabled us to produce an anatomical reconstruction of aneural network that controls limb movement.Keywords: spatial warping, thin-plate spline, registration, regularization theory, geometrictransformation, three-dimensional reconstruction, medical imaging. 1 Introduction Imaging has played an increasingly important role in contemporary biomedical research. A frequentproblem researchers face is how to compare images which are acquired from multiple subjects or differentmodalities. In neuroanatomical studies, the same labeling method is usually applied to several animals. Insome experiments, different labeling methods are given to same or different animals to obtain data under

In this article, we propose a fast method for detecting the horizon line in maritime scenarios by combining a multi-scale approach and region-of-interest detection. Recently, several methods that adopt a multi-scale approach have been proposed, because edge detection at a single is insufficient to detect all edges of various sizes. However, these methods suffer from high processing times, requiring tens of seconds to complete horizon detection. Moreover, the resolution of images captured from cameras mounted on vessels is increasing, which reduces processing speed. Using the region-of-interest is an efficient way of reducing the amount of processing information required. Thus, we explore a way to efficiently use the region-of-interest for horizon detection. The proposed method first detects the region-of-interest using a property of maritime scenes and then multi-scale edge detection is performed for edge extraction at each scale. The results are then combined to produce a single edge map. Then, Hough transform and a least-square method are sequentially used to estimate the horizon line accurately. We compared the performance of the proposed method with state-of-the-art methods using two publicly available databases, namely, Singapore Marine Dataset and buoy dataset. Experimental results show that the proposed method for region-of-interest detection reduces the processing time of horizon detection, and the accuracy with which the proposed method can identify the horizon is superior to that of state-of-the-art methods.

The Faber-Schauder wavelet transform is a simple multiscale transformation with many interesting properties in image processing. Some of these properties are: preservation of pixel ranges, arithmetic operations, non requirement of boundary processing, multiscale edge detection, elimination of the constant and the linear correlation, and the use of close neighboring information. In this study we describe this transformation and we propose a mixed scale visualization of the wavelet transform which makes it possible to show the transform result as an image. This visualization is used, with orientation information, to refine edge detection and image characterization by selecting regions with a high density of extrema wavelet coefficients.

Smoothing parameter or bandwidth plays a key role in nonparametric classification based on kernel density estimation. We consider choosing smoothing parameter in nonparametric classification, which optimize the Bayes risk. Hall and Kang (2005) clarified the theoretical properties of smoothing parameter in terms of minimizing Bayes risk and derived the optimal order of it. Bootstrap method was used in their exploring numerical properties. We compare cross-validation and bootstrap method numerically in terms of optimal order of bandwidth. Effects on misclassification rate are also examined. We confirm that bootstrap method is superior to cross-validation in both cases.

Sparse coding based noise reduction algorithms have been extensively applied on hyperspectral imagery (HSI) denoising. Dictionary learning schemes are strongly suggested for sparse reconstruction in many researches, aiming at a smaller error between the underlying clean image and the reconstruction result. In previous researches, the training samples (patches) are selected from either unrelated clean images or the noised image itself. The dictionaries learned form unrelated clean images can not perfectly represent the underlying clean target image, while the dictionaries learned form the noised image itself may be affected by the noise existing in training samples. In this paper, we propose a novel dictionary learning scheme that depends on a panchromatic image from the same or similar scene with HSI. Considering the fact that the noise level of a panchromatic image is always much lower than HSI, we take the patches from panchromatic image as training samples. Taking the multi-scale image representation into consideration, we construct the dictionary from different scales via Gaussian pyramid. The proposed dictionary shows its good denoising performance in our experiments.

The challenge in prostate cancer radiotherapy is to deliver the planned dose to the prostate, sparing as much as possible the neighboring organs, namely bladder and rectum. If a lower amount of dose, compared to the prescription, is delivered to the prostate, the risk of failure may increase. Likewise, if higher doses are delivered to the neighboring organs, undesirable side effects may occur. Accurate localization of prostate and organs at risk is therefore a bottleneck in radiotherapy. In recent Image Guided Radiotherapy (IGRT) procedures, an intra-operative Cone Beam CT (CBCT) is used at each session to align the prostate to the planned CT and to maximize the correct dose delivery. Tracking the prostate in these images may allow not only to achieve this goal but also to accurately measure the cumulated dose as the session goes. This work introduces a new method that automatically locates the prostate in CBCT images. The whole method lies in a Bayesian formulation where a multiscale image representation, the local jets, is used as a likelihood function, an the prior knowledge is learned from multiple examples by expert manual delineations. Compared with manual ground truth segmentations, the results showed a Jaccard similarity index of 0.84, and an accuracy of 98%in a set of 4 studies of four patients.

In this paper we present a new approach towards the extraction of affine image regions based on detecting shape-stable boundaries from a multi-scale image representation. We construct an affine morphological scale space (AMSS) representation [1], which performs anisotropic diffusion while preserving boundaries and being invariant to affine transformations. We extract the transition boundaries of the diffusivity velocity map and track their evolution at each level of the scale-space. We then determine the stability of the boundary shape through a minimization process over different scales. Unlike most state of the art detectors which use the Gaussian scale space for multi-scale image representation, our approach is intrinsically affine invariant. We evaluate our detector by measuring repeatability of regions in transformed images of the same scene and comparing it to the state-of-the-art region detectors [2].

A novel method for performing anatomical (MRI)-functional (PET or SPECT) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering based technique realized through a novel multi-scale system architecture. Firstly, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Secondly, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared to the state-of-the-art fusion methods.

We develop a statistical model to describe the spatially varying behavior of local neighborhoods of coefficients in a multiscale image representation. Neighborhoods are modeled as samples of a multivariate Gaussian density that are modulated and rotated according to the values of two hidden random variables, thus allowing the model to adapt to the local amplitude and orientation of the signal. A third hidden variable selects between this oriented process and a nonoriented scale mixture of Gaussians process, thus providing adaptability to the local orientedness of the signal. Based on this model, we develop an optimal Bayesian least squares estimator for denoising images and show through simulations that the resulting method exhibits significant improvement over previously published results obtained with Gaussian scale mixtures.