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Detecting and identifying Regions of Interest (ROIs) is an important task for navigation and retrieval services. In this paper, we focus on indoor scene images and detect object regions such as shop signs and merchandise. Our method is based on two approaches; 1) Indoor structure analysis from a single image by learning the types of scenes. 2) Detect ROIs by taking advantage of the relationship of...
2D Gel Electrophoresis image analysis is widely recognized as one of the most crucial processes following a proteomic experiment. Amongst its stages, detection and segmentation are the most challenging ones. The available software packages and techniques fail to detect and segment some of the real spots while they often detect a vast number of spurious spots. In this paper, an original approach to...
Scale invariance is a desirable property for many vision tasks such as image segmentation and classification. One way to achieve such invariance is to collect images containing objects of all scales and then train a classifie r. In practice, however, only a finite number of images at a finite number of scales can be collected, and this poses the problem of scale sampling. In this paper, we focus on...
An automated histology analysis is proposed for classification of local image patches of colon histopathology images into four principle classes: normal, cancer, adenomatous and inflamed classes. Shape features based on stroma, lumen and imperfectly segmented nuclei are combined with texture features for classification. The classification is analyzed under the three scenarios: normal vs. abnormal,...
Unsupervised image segmentation is an important and difficult technique in pattern recognition. In this paper, we propose an interesting region merging algorithm for segmentation of natural images. It consists of two steps: first forming initial over-segmentation by the Connected Coherence Tree Algorithm (CCTA), and then merging the primitive regions in terms of their similarity and feature in the...
The problem of simultaneously estimating affine deformations between multiple objects occur in many applications. Herein, a direct method is proposed which provides the result as a solution of a linear system of equations without establishing correspondences between the objects. The key idea is to construct enough linearly independent equations using covariant functions, and then finding the solution...
In this paper we propose a color-based approach for skin detection and interest garment selection aimed at an automatic segmentation of pieces of clothing. For both purposes, the color description is extracted by an iterative energy minimization approach and an automatic initialization strategy is proposed by learning geometric constraints and shape cues. Experiments confirms the good performance...
Color Editing plays an important role in image processing, which aims to change the original image color according to specific image color characteristics. We present a new interactive local color editing method. The user first draws strokes specifying the target region needed to transfer color, which can be segmented using K-means clustering. Then patch-based inpainting technology is applied to achieve...
Breast cancer grading of histological tissue samples by visual inspection is the standard clinical practice for the diagnosis and prognosis of cancer development. An important parameter for tumor prognosis is the number of mitotic cells present in histologically stained breast cancer tissue sections. We propose a hierarchical learning workflow for automated mitosis detection in breast cancer. From...
This paper proposes a new feature extraction technique for indexing and matching historical images. The complexity of these historical images troubles the existing indexing approaches due to their line patterns. Our indexing method relies on the global segmentation integrated with the knowledge of edge density to deal with the line patterns and eliminate over-segmented regions. Then the historical...
In this paper a new connectivity model is introduced which allows combined clustering and partitioning of structures without distortion, in contrast to mask connectivity. An algorithm to compute morphological attribute filters based on Max-Trees for this new form of connectivity is presented. It is shown that the new form of connectivity is effective in clustering diacritics together with the appropriate...
In this paper, we address the problem of representing objects using contours for the purpose of recognition. We propose a novel segmentation method for integrating a new contour matching energy into level set based segmentation schemes. The contour matching energy is represented by major components of Elliptic Fourier shape descriptors and serves as a shape prior to guide the curve evolution. The...
Classifying images of HEp-2 cells from indirect im-munofluorescence has important clinical applications. We have developed an automatic method based on random forests that classifies an HEp-2 cell image into one of six classes. The method is applied to the data set of the ICPR 2012 contest. The previously obtained best accuracy is 79.3% for this data set, whereas we obtain an accuracy of 97.4%. The...
In this paper, we propose a new saliency-seeded active contour based automatic natural object segmentation method. It is known that using saliency regions or pixels can easily get the approximately location of the desired object in the map. The salient object points are employed as the seeds of convex hull to generate the initial contour for our automatic object segmentation system. In contrast with...
Road segmentation is a critical application of satellite and aerial remote sensing. Traditional attempts to apply machine learning and computer vision have yielded good results but rely on specific characteristics, such as the contrast of paved roads to their surroundings or contextual clues. However, these methods still lack the sensitivity of human perception when identifying rural, non-paved, or...
This paper presents a new approach to image-thresholding-based segmentation. It considerably improves existing methods by efficiently modeling non-Gaussian and multi-modal class-conditional distributions. The proposed approach seamlessly: 1) extends the Otsu's method to arbitrary numbers of thresholds and 2) extends the Kittler and Illingworth minimum error thresholding to non-Gaussian and multi-modal...
Font can be used as a notion of similarity amongst multiple documents written in same script. We could automatically retrieve document images with specific font from a huge digital document repository. So Optical Font Recognition could be a useful pre-processing step in an automated questioned document analysis system for sorting documents with similar fonts. We propose a scheme to identify 10 different...
In this paper we present a novel method for automatic text-line parameter selection for stereo image pairs. The parameters are selected such that correspondence between the same content in a stereo pair is maximized. Automatic parameter selection has been carried out by establishing robust text-line correspondence which is also a contribution of the presented work. The proposed method is applied to...
In this work, we propose a novel multilingual word spotting framework based on Hidden Markov Models that works on corpus of multilingual handwritten documents and documents that contain more than one handwritten script. The system deals with large multilingual vocabularies without need for word or character segmentation. A keyword is represented by concatenating its character models. We propose and...
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