Browsing by Keywords "Image classification"
Now showing items 1-13 of 13
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Alignment of uncalibrated images for multi-view classification
(IEEE, 2011)Efficient solutions for the classification of multi-view images can be built on graph-based algorithms when little information is known about the scene or cameras. Such methods typically require a pair-wise similarity ... -
Classification of histopathological cancer stem cell images in h&e stained liver tissues
(Bilkent University, 2016-03)Microscopic images are an essential part of cancer diagnosis process in modern medicine. However, diagnosing tissues under microscope is a time-consuming task for pathologists. There is also a signi cant variation in ... -
Graph walks for classification of histopathological images
(IEEE, 2013)This paper reports a new structural approach for automated classification of histopathological tissue images. It has two main contributions: First, unlike previous structural approaches that use a single graph for representing ... -
Image classification with energy efficient hadamard neural networks
(Bilkent University, 2018-01)Deep learning has made significant improvements at many image processing tasks in recent years, such as image classification, object recognition and object detection. Convolutional neural networks (CNN), which is a popular ... -
Image mining using directional spatial constraints
(Institute of Electrical and Electronics Engineers, 2010-01)Spatial information plays a fundamental role in building high-level content models for supporting analysts' interpretations and automating geospatial intelligence. We describe a framework for modeling directional spatial ... -
Land cover classification with multi-sensor fusion of partly missing data
(American Society for Photogrammetry and Remote Sensing, 2009-05)We describe a system that uses decision tree-based tools for seamless acquisition of knowledge for classification of remotely sensed imagery. We concentrate on three important problems in this process: information fusion, ... -
Microscopic image classification via WT-based covariance descriptors using Kullback-Leibler distance
(IEEE, 2012)In this paper, we present a novel method for classification of cancer cell line images using complex wavelet-based region covariance matrix descriptors. Microscopic images containing irregular carcinoma cell patterns are ... -
Multi-instance multi-label learning for whole slide breast histopathology
(International Society for Optical Engineering SPIE, 2016)Digitization of full biopsy slides using the whole slide imaging technology has provided new opportunities for understanding the diagnostic process of pathologists and developing more accurate computer aided diagnosis ... -
Multi-resolution segmentation and shape analysis for remote sensing image classification
(2005)We present an approach for classification of remotely sensed imagery using spatial information extracted from multi-resolution approximations. The wavelet transform is used to obtain multiple representations of an image ... -
A multiplication-free framework for signal processing and applications in biomedical image analysis
(IEEE, 2013)A new framework for signal processing is introduced based on a novel vector product definition that permits a multiplier-free implementation. First a new product of two real numbers is defined as the sum of their absolute ... -
Scene classification using bag-of-regions representations
(2007)This paper describes our work on classification of outdoor scenes. First, images are partitioned into regions using one-class classification and patch-based clustering algorithms where one-class classifiers model the regions ... -
Target detection and classification in SAR images using region covariance and co-difference
(2009)In this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed. The new approach is based on region covariance (RC) method which involves the computation of a ... -
Two-tier tissue decomposition for histopathological image representation and classification
(Institute of Electrical and Electronics Engineers, 2015)In digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly ...
