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This paper addresses the problem of remote sensing image multi-scale classification by: (i) showing that using multiple scales does improve classification results, but not all scales have the same importance; (ii) showing that image descriptors do not offer the same contribution at all scales, as commonly thought, and some of them are very correlated; (iii) introducing a simple approach to automatically...
Semantic image segmentation assigns a predefined class label to each pixel. This paper proposes a unified framework by using region bank to solve this task. Images are hierarchically segmented leading to region banks. Local features and high-level descriptors are extracted on each region of the bank. Discriminative classifiers are learned based on the histograms of feature descriptors computed from...
Superpixel segmentation has become a popular preprocessing step in computer vision with a great variety of existing algorithms. Almost all algorithms claim to compute compact superpixels, but no one showed how to measure compactness and no one investigated the implications. In this paper, we propose a novel metric to measure superpixel compactness. With this metric, we show that there is a trade-off...
Teaching boards are omnipresent in classrooms throughout the world. Tableau is a software environment for processing images from teaching-boards acquired using portable digital cameras and cell-phones, being the first software environment able to process non-white boards. The aim of the Tableau environment goes far beyond processing and compressing teaching-board images, it also targets at content...
Accurate vessel segmentation is the first step in retinal image analysis for medical diagnosis. In this paper we propose a novel method to segment vessel network in fundus image. Vessel centerlines are first extracted by using a set of directional line detectors. Next an Iterative Geodesic Time Transform (ItGTT) is designed to segment the entire vessel network. The idea of the ItGTT is to use centerline...
We propose a method to learn and classify pixels in document images, e.g., to separate text from illustrations or other predefined classes. We extract texture information using a bank of Gabor filters, and learn a hierarchical clustering model that can be used as a K-Nearest Neighbours (KNN) classifier. The model has advantages over other local document image classification methods, making it efficient...
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,...
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...
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...
Optic cup is the primary image indicator clinically used for identifying glaucoma. To automatically localize the optic cup in fundus images, an effective and efficient superpixel classification based approach is proposed in this work, which maintains both advantages of existing pixel and window based approaches. This method provides three major contributions. First, it proposes processing of the fundus...
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...
Although atlas-based methods simplify the segmentation process by making it more automated, such methods are often very sensitive to the computationally expensive image registration step. Also, existing methods based on a parametric deformation model may fail when the transformation between the atlas and target images can not be properly described with this model. This paper presents a novel and efficient...
This paper focuses on producing fast and accurate co-segmentation to a pair of images that is scalable and able to apply multimodal features. We present a general solution for this purpose and specifically propose a noniterative and fully unsupervised method using pointwise color and regional covariance features for image co-segmentation. The scalability and generality of our method mainly attribute...
One of the most critical limitations of KinectTM-based interfaces is the need for persistence in order to interact with virtual objects. Indeed, a user must keep her arm still for a not-so-short span of time while pointing at an object with which she wishes to interact. The most natural way to overcome this limitation and improve interface reactivity is to employ a vision module able to recognize...
Quantitative analysis of optical coherence tomography volumes is an important tool for both clinicians and researchers. Until now, most work has focused on segmentation of the intraretinal cell layers, but the segmentation of pathological datasets remains challenging. We propose the application of random forest to detect the locations of drusen in the retinal pigment epithelium. This is an important...
Gridding, which has a large impact on the identification of differentially expressed genes, is the first and key step for microarray image analysis. Most gridding methods are semi-automatic or require parameter preset. In this paper, an improved method was proposed for rapid and accurate gridding compared to the mathematical morphology based method. First, the image quality was enhanced by using the...
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 order to track and estimate the pose of known rigid objects with high accuracy in unconstrained environment with light disturbance, scale changes and occlusion, we propose to combine 3D particle filter (PF) framework with algebraic pose optimization in a closed loop. A new PF observation model based on line similarity in 3D space is devised and the output of 3D PF tracking, namely line correspondences...
We present a novel facial expression recognition framework using audio-visual information analysis. In particular, we design a single good image representation of the image sequence by weighted sum of registered face images where the weights are derived using auditory features. We use a still image based technique for the expression recognition task. We performed experiments using eNTERFACE'05 audio-visual...
Iris recognition from at-a-distance face images has high applications in wide range of applications such as remote surveillance and for civilian identification. This paper presents a completely automated joint iris and periocular recognition approach from the face images acquired at-a-distance. Each of the acquired face images are used to detect and segment periocular images which are then employed...
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