Image Segmentation based on Multi-region Multi-scale Local Binar Fitting and Kullback-Leibler Divergence

The inhomogeneity of intensity and the noise of image are the two major obstacles to accurate image segmentation by region-based level set models. To provide a more general solution to these challenges and address the difficulty of image segmentation methods to handle an arbitrary number of regions, we propose a region-based multi-phase level set method, which is based on the multi-scale local binary fitting (MLBF) and the Kullback–Leibler (KL) divergence, called KL–MMLBF. We first apply the multi-scale theory and multi-phase level set framework to the local binary fitting model to build the multi-region multi-scale local binary fitting (MMLBF). Then the energy term measured by KL divergence between regions to be segmented is incorporated into the energy function of MMLBF. KL–MMLBF utilizes the between-cluster distance and the adaptive kernel function selection strategy to formulate the energy function. Being more robust to the initial location of the contour than the classical segmentation models, KL–MMLBF can deal with blurry boundaries and noise problems. The results of experiments on synthetic and medical images have shown that KL–MMLBF can improve the effectiveness of segmentation while ensuring the accuracy by accelerating this minimization of this energy function and the model has achieved better segmentation results in terms of both accuracy and efficiency to analyze the multi-region image

Ear Identification by Fusion of Segmented Slice Regions using Invariant Features: An Experimental Manifold with Dual Fusion Approach

This paper proposes a robust ear identification system which is developed by fusing SIFT features of color segmented slice regions of an ear. The proposed ear identification method makes use of Gaussian mixture model (GMM) to build ear model with mixture of Gaussian using vector quantization algorithm and K-L divergence is applied to the GMM framework for recording the color similarity in the specified ranges by comparing color similarity between a pair of reference ear and probe ear. SIFT features are then detected and extracted from each color slice region as a part of invariant feature extraction. The extracted keypoints are then fused separately by the two fusion approaches, namely concatenation and the Dempster-Shafer theory. Finally, the fusion approaches generate two independent augmented feature vectors which are used for identification of individuals separately. The proposed identification technique is tested on IIT Kanpur ear database of 400 individuals and is found to achieve 98.25% accuracy for identification while top 5 matched criteria is set for each subject.Comment: 12 pages, 3 figure

A review of domain adaptation without target labels

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.Comment: 20 pages, 5 figure

AUTOMATIC 3D DEFORMED MIDSAGITTAL SURFACE LOCALIZATION BY CONSTRAINED MONTE CARLO OPTIMIZATION

AUTOMATIC 3D DEFORMED MIDSAGITTAL SURFACE LOCALIZATION BY CONSTRAINED MONTE CARLO OPTIMIZATIO

Variational methods for texture segmentation

In the last decades, image production has grown significantly. From digital photographs to the medical scans, including satellite images and video films, more and more data need to be processed. Consequently the number of applications based on digital images has increased, either for medicine, research for country planning or for entertainment business such as animation or video games. All these areas, although very different one to another, need the same image processing techniques. Among all these techniques, segmentation is probably one of the most studied because of its important role. Segmentation is the process of extracting meaningful objects from an image. This task, although easily achieved by the human visual system, is actually complex and still a true challenge for the image processing community despite several decades of research. The thesis work presented in this manuscript proposes solutions to the image segmentation problem in a well established mathematical framework, i.e. variational models. The image is defined in a continuous space and the segmentation problem is expressed through a functional or energy optimization. Depending on the object to be segmented, this energy definition can be difficult; in particular for objects with ambiguous borders or objects with textures. For the latter, the difficulty lies already in the definition of the term texture. The human eye can easily recognize a texture, but it is quite difficult to find words to define it, even more in mathematical terms. There is a deliberate vagueness in the definition of texture which explains the difficulty to conceptualize a model able to describe it. Often these textures can neither be described by homogeneous regions nor by sharp contours. This is why we are first interested in the extraction of texture features, that is to say, finding one representation that can discriminate a textured region from another. The first contribution of this thesis is the construction of a texture descriptor from the representation of the image similar to a surface in a volume. This descriptor belongs to the framework of non-supervised segmentation, since it will not require any user interaction. The second contribution is a solution for the segmentation problem based on active contour models and information theory tools. Third contribution is a semi-supervised segmentation model, i.e. where constraints provided by the user will be integrated in the segmentation framework. This processus is actually derived from the graph of image patches. This graph gives the connectivity measure between the different points of the image. The segmentation will be expressed by a graph partition and a variational model. This manuscript proposes to tackle the segmentation problem for textured images

Edges Detection Based On Renyi Entropy with Split/Merge

Most of the classical methods for edge detection are based on the first and second order derivatives of gray levels of the pixels of the original image. These processes give rise to the exponential increment of computational time, especially with large size of images, and therefore requires more time for processing. This paper shows the new algorithm based on both the Rényi entropy and the Shannon entropy together for edge detection using split and merge technique. The objective is to find the best edge representation and decrease the computation time. A set of experiments in the domain of edge detection are presented. The system yields edge detection performance comparable to the classic methods, such as Canny, LOG, and Sobel.  The experimental results show that the effect of this method is better to LOG, and Sobel methods. In addition, it is better to other three methods in CPU time. Another benefit comes from easy implementation of this method. Keywords: Rényi Entropy, Information content, Edge detection, Thresholdin