Semih Dinç

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Automated image analysis of microscopic images such as protein crystallization images and cellular images is one of the important research areas. If objects in a scene appear at different depths with respect to the camera's focal point, objects outside the depth of field usually appear blurred. Therefore, scientists capture a collection of images(More)
In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base(More)
In this paper, we investigate the performance of classification of protein crystallization images captured during protein crystal growth process. We group protein crystallization images into 3 categories: noncrystals, likely leads (conditions that may yield formation of crystals) and crystals. In this research, we only consider the subcategories of(More)
In general, a single thresholding technique is developed or enhanced to separate foreground objects from background for a domain of images. This idea may not generate satisfactory results for all images in a dataset, since different images may require different types of thresholding methods for proper binarization or segmentation. To overcome this(More)
Large number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these features make utilization of automated(More)
One of the difficulties for proper imaging in microscopic image analysis is defocusing. Microscopic images such as cellular images, protein images, etc. need properly focused image for image analysis. A small difference in focal depth affects the details of an object significantly. In this paper, we introduce a novel auto-focusing approach based on Harris(More)
Detection of surface defects on industrial products by machine vision technology is one of the main research topics. Surface scratchs, texture deformations and color differences are common problems at the industrial products. In this paper, a new method named learnable transform filters (LTF) are employed to detect surface defects. On learning stage, the(More)
In this paper, a new algorithm related with feature selection method mostly used in data mining, machine learning and pattern recognition areas is proposed. Classical Fukunaga-Koontz Transform is extended to a binary kernel classifier. We used cDNA microarrays to assess 11.000 gene expression profiles in 60 human cancer cell lines used in a drug discovery(More)