960 research outputs found
Information-theoretic active contour model for microscopy image segmentation using texture
High throughput technologies have increased the need for automated image analysis in a wide variety of microscopy techniques. Geometric active contour models provide a solution to automated image segmentation by incorporating statistical information in the detection of object boundaries. A statistical active contour may be defined by taking into account the optimisation of an information-theoretic measure between object and background. We focus on a product-type measure of divergence known as Cauchy-Schwartz distance which has numerical advantages over ratio-type measures. By using accurate shape derivation techniques, we define a new geometric active contour model for image segmentation combining Cauchy-Schwartz distance and Gabor energy texture filters. We demonstrate the versatility of this approach on images from the Brodatz dataset and phase-contrast microscopy images of cells
A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval
The computer-aided diagnosis (CAD) system can provide a reference basis for
the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs)
can not only extract visual elements such as colors and shapes but also
semantic features. As such they have made great improvements in many tasks of
dermoscopy images. The imaging of dermoscopy has no principal orientation,
indicating that there are a large number of skin lesion rotations in the
datasets. However, CNNs lack rotation invariance, which is bound to affect the
robustness of CNNs against rotations. To tackle this issue, we propose a
rotation meanout (RM) network to extract rotation-invariant features from
dermoscopy images. In RM, each set of rotated feature maps corresponds to a set
of outputs of the weight-sharing convolutions and they are fused using meanout
strategy to obtain the final feature maps. Through theoretical derivation, the
proposed RM network is rotation-equivariant and can extract rotation-invariant
features when followed by the global average pooling (GAP) operation. The
extracted rotation-invariant features can better represent the original data in
classification and retrieval tasks for dermoscopy images. The RM is a general
operation, which does not change the network structure or increase any
parameter, and can be flexibly embedded in any part of CNNs. Extensive
experiments are conducted on a dermoscopy image dataset. The results show our
method outperforms other anti-rotation methods and achieves great improvements
in dermoscopy image classification and retrieval tasks, indicating the
potential of rotation invariance in the field of dermoscopy images
Reliability measure for shape-from-focus
This is the author’s version of a work that was accepted for publication in Journal Image and Vision Computing . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal Image and Vision Computing , 31, 10 (2013) DOI: 10.1016/j.imavis.2013.07.005Shape-from-focus (SFF) is a passive technique widely used in image processing for obtaining depth-maps. This technique is
attractive since it only requires a single monocular camera with focus control, thus avoiding correspondence problems typically
found in stereo, as well as more expensive capturing devices. However, one of its main drawbacks is its poor performance when
the change in the focus level is difficult to detect. Most research in SFF has focused on improving the accuracy of the depth
estimation. Less attention has been paid to the problem of providing quality measures in order to predict the performance of SFF
without prior knowledge of the recovered scene. This paper proposes a reliability measure aimed at assessing the quality of the
depth-map obtained using SFF. The proposed reliability measure (the R-measure) analyses the shape of the focus measure function
and estimates the likelihood of obtaining an accurate depth estimation without any previous knowledge of the recovered scene. The
proposed R-measure is then applied for determining the image regions where SFF will not perform correctly in order to discard
them. Experiments with both synthetic and real scenes are presented
Fuzzy automata system with application to target recognition based on image processing
AbstractIn order to get better image processing and target recognition, this paper presents a fuzzy automata system to target recognition. The system first performs image processing, and then accomplishes the target recognition. The system consists of four parts: image preprocessing, feature extraction, target matching and experiment. Compared with existing approaches, this paper uses both global features and local features of the target image, and carries out target recognition by using a fuzzy automata system. Simulation results show that the correct recognition rate based on the fuzzy automata system for target recognition is higher at 94.59%, an improvement on an average of 29.24%, compared to other existing approaches. Finally, some directions for future research are described
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images
We present a novel kernel regression framework for smoothing scalar surface
data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel
constructed from the eigenfunctions, we formulate a new bivariate kernel
regression framework as a weighted eigenfunction expansion with the heat kernel
as the weights. The new kernel regression is mathematically equivalent to
isotropic heat diffusion, kernel smoothing and recently popular diffusion
wavelets. Unlike many previous partial differential equation based approaches
involving diffusion, our approach represents the solution of diffusion
analytically, reducing numerical inaccuracy and slow convergence. The numerical
implementation is validated on a unit sphere using spherical harmonics. As an
illustration, we have applied the method in characterizing the localized growth
pattern of mandible surfaces obtained in CT images from subjects between ages 0
and 20 years by regressing the length of displacement vectors with respect to
the template surface.Comment: Accepted in Medical Image Analysi
Enhancement of Imagery in Poor Visibility Condition by Using GUI
Our focus in this work will be primarily in examples of enhancements in poor weather condition. GUI will be made in order for better user interference. These tools classify the overall brightness, contrast, and sharpness of an image based upon its regional statistics. Wavelet transform is the most exciting development in the last decade. The method focuses on wavelet-based image resolution enhancement and suitable for processing the image/video resolution enhancement. The Software tool used is MATLAB
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