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In this paper, we present a robust image segmentation technique based on the Geodesic Convex Active Contour (GCAC) and the Chan-Vese (CV) model. The proposed algorithm overcomes the drawbacks of existing interactive image segmentation techniques which are heavily dependent upon the initial user input. Here, we propose to start with a Geodesic based contour before using the Chan-Vese model. Contrary...
In this paper, a new method for segmentation and splitting of touching vaginal bacteria based on super pixel method is proposed. Feature fusion is integrated with kernel-based support vector machine (SVM) for bacteria segmentation. After segmentation by super pixel, the touching bacteria regions are further separated according to the defined effective distance. Finally, the separated bacteria are...
The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine...
Interest on anomaly detection for hyperspectral images is increasingly growing the last decades due to the diversity of applications that aims for detecting small distinctive objects dispersed in a large geographic zone, without any prior knowledge about the scene. In addition to the absence of prior knowledge, many problems are particularly challenging for the anomaly detection such as the differentiation...
In this paper we present a way to calculate the fusion of multi-focus images based on the linear combination of a pair of images taken by a digital camera with different levels of focus. For the linear combination, a linear function with spatial coherence is optimized to maximize the sharpness of the merged image. By the complexity and dispersity of the linear system of equations arises, the solution...
This paper presents a comparative study of Support Vector Machines (SVMs) which is classified based on melanoma imaging technique. After the preprocessing and segmentation of a set of distinct 35 images, the extracted features were Asymmetry, Border, Color, Diameter,(ABCD) Entropy and Correlations respectively. Further the resultant data was fed into five different SVM classifiers namely linear, poly,...
In this work, we propose a novel approach for MRI-based generation of pseudo-CT images in whole-body PET/MRI based on Hofmann's pattern recognition and atlas registration approach. The major improvement emanates from sorting registered atlas images based on voxelwise local normalized cross-correlation and choosing the most similar atlas image for Gaussian process regression (GPR) analysis. Furthermore,...
Accurate segmentation of breast on MR images is an essential and crucial step for computer-aided breast disease diagnosis and surgical planning. In this paper, an effective approach is proposed for segmenting the breast image into different regions, each corresponding to a different tissue. The segmentation work flow comprises two key steps. Firstly, we use the threshold-based method and morphological...
Fuzzy clustering techniques, especially Fuzzy C-Means clustering method (FCM), is a popular algorithm widely used in the images segmentation. However, as the conventional FCM doesn't optimize data in feature space and doesn't involve any spatial information, it is sensitive to the noise. In the paper, we presented a novel FCM clustering algorithm based on kernel spatial information to segment the...
Face detection is one of the most important parts of biometrics and face analysis science. Numerous methods and algorithms have been developed in recent years; however, there is a sensible gap between the current detection rate and the ideal one yet. In this paper, a novel multi-stage face detection method is proposed which can remarkably detect faces in different images with different illumination...
This paper proposes a language-independent method for segmenting text lines from handwritten document images. Our method is based on the seam carving, which has been already used for text line segmentation, but in order to tolerate multi-skewed text lines even in the same document image, we propose a constrained seam carving method, which can constrain energy to be passed along the connected components...
In this paper an object-based method for multispectral image segmentation and classification is proposed. Normally, in remote sensing a scene is represented by pixel-based features. It is possible to reduce data redundancy by a segmentfeature extraction process, where the segment-features, rather than the pixel-features, are used for multispectral scene representation and classification. Object-based...
We propose a new Kernel-based Atlas Image Selection computed in the Embedding Representation space (termed KAISER) aiming to support labeling of brain tissue on 3D magnetic resonance (MR) images. KAISER approach provides efficient feature extraction from MR volumes based on an introduced inter-slice kernel (ISK). Thus, using the ISK matrix eigendecomposition, the inherent structure of data distribution...
This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast...
Surgical treatment is suggested for seizure control in medically intractable epilepsy patients. Detailed pre-surgical evaluation and lateralization using Magnetic Resonance Images (MRI) is expected to result in a successful surgical outcome. In this study, an optimized pattern recognition approach is proposed for lateralization of mesial Temporal Lobe Epilepsy (mTLE) patients using asymmetry of imaging...
Despite pattern recognition methods for human behavioral analysis has flourished in the last decade, animal behavioral analysis has been almost neglected. Those few approaches are mostly focused on preserving livestock economic value while attention on the welfare of companion animals, like dogs, is now emerging as a social need. In this work, following the analogy with human behavior recognition,...
This paper proposes a novel local contrast pattern (LCP) to drive the histogram-based Chan-Vese (CV) model for texture image segmentation. The local contrast pattern has two maps, differential contrast map and orientation map, which are well suited to describe texture structure, especially the texture orientation information. In order to enable the extraction of accurate local texture features, a...
This paper proposes a human detection method that combines range image segmentation and human detection based on image local features. The method uses a stereo vision system called Subtraction Stereo, which extracts a range image of foreground regions. An extracted range image is segmented for each object by Mean Shift Clustering. Human detection based on local features is applied to each segment...
This paper presents a novel structural approach to quantitatively characterising nuclear chromatin texture in light microscope images of Pap smears. The approach is based on segmenting the chromatin into blob-like primitives and characterising their properties and arrangement. The segmentation approach makes use of multiple focal planes. It comprises two basic steps: (i) mean-shift filtering in the...
In this paper we address the task of learning how to segment a particular class of objects, by means of a training set of images and their segmentations. In particular we propose a method to overcome the extremely high training time of a previously proposed solution to this problem, Kernelized Structural Support Vector Machines. We employ a one-class SVM working with joint kernels to robustly learn...
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