Visen, N.S., Paliwal, J., Jayas, D.S. and White, N.D.G. 2004. Image analysis of bulk grain samples using neural networks. Canadian Biosystems Engineering/Le génie des biosystèmes au Canada 46: 7.117.15. Algorithms were developed to acquire and process color images of bulk grain samples of five grain types, namely barley, oats, rye, wheat, and durum wheat. The developed algorithms were used to e...

We propose an algorithm for segmenting natural images based on texture and color information, which leverages the co-sparse analysis model for image segmentation within a convex multilabel optimization framework. As a key ingredient of this method, we introduce a novel textural similarity measure, which builds upon the co-sparse representation of image patches. We propose a Bayesian approach to...

A method for the classification of individual aerosol particles into one of two main groups, namely, fly ash and soil dust, is presented. It is based on the calculation of the fractal dimensions of the images of the particles obtained with the computer-controlled scanning electron microscopy. The risk of an improper determination of the fractal dimension is pointed out. It is shown that spheric...

Cutaneous malignant melanoma is one of the most frequent types of cancer in the world but if a malignant lesion is detected early, it can be cured without complication. Automated skin lesion analysis attempts to accomplish early detection of malignancy using digital dermoscopic images. We address two challenging applications in automated analysis of dermoscopic skin lesion images: lesion segmen...

The SAR and SAS images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. If the background of an image is uneven, the fixed thresholding technique is not suitable to segment an image using adaptive thresholding method. In this paper a new Adaptive thresholding method is proposed to reduce the speckle noise, preserving the structural...

In this paper, we deal with the problem of extracting meaningful textural features leading to good segmentations on satellite images of natural environments. Standard texture features using graylevel co-occurrence matrices have been widely applied on remote sensed images but they impose limitations (due to finite window sizes) as poor spatial localization. We have generalized the definition of ...

Subsurface structural features of biological tissue are visualized using polarized light images. The technique of Pearson correlation coefficient analysis is used to reduce blurring of these features by unpolarized backscattered light and to visualize the regions of high statistical similarities within the noisy tissue images. It is shown that under certain conditions, such correlation coeffici...

Text localization is the first step when automatically reading text in images. Since existing methods often fail when applied to unconstrained images, in this paper we propose a more robust approach exploiting different kind of information. In particular, we first extract textural features, a combination of a stroke filter with a super-pixel segmentation, and then search for connected component...

—In this paper, we propose a scheme for segmentation of multitexture images. The methodology involves extraction of texture features using an overcomplete wavelet decomposition scheme called discrete M-band wavelet packet frame (DMbWPF). This is followed by the selection of important features using a neuro-fuzzy algorithm under unsupervised learning. A computationally efficient search procedure...

DNA damage had been suggested to contribute to the pathogenesis of insulin dependent diabetes mellitus (IDDM). In this work we present a method for detection and discrimination of such DNA changes by means of analysis of light microscope images of anisotropically stained leukocytes nuclei. Several features of the images have been evaluated, including integrated optical density, degree of polari...