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Segmentation and classification of cells in biological data are important problems in bio-medical image analysis. This paper outlines a novel probabilistic approach to simultaneously classify and segment multiple cells of different classes in a multi-variate setting. Superpixels are extracted from the input vector-valued image, and a 2D hidden Markov model (HMM) is set up on the superpixel graph....
In this paper we propose a two-dimensional hidden Markov model (HMM)-based framework for solving the cell tracing problem in a biological image sequence. Given label initialization in the first frame, we model the problem as pixel labeling for every consequent frame. Common Markov random field-based frameworks for this task require a fixed set of labels S = {1,2,···, L}, while in our framework the...
Gesture and motion evaluation provide an interface for a variety of human-computer interaction (HCI)applications. In particular, using human hand motions as a natural interface tool has motivated an active research area to conduct studies on modeling, analyzing and recognizing various hand motions. Recently, human-computer interaction has been a focus of research in vision-based gesture recognition...
In this paper, we use image processing techniques on the speech spectrogram to perform speech phoneme segmentation. The proposed method relies solely on visual cues on the spectrogram, without the need for language-specific training data. The results are evaluated on the TIMIT corpus, and compared to other unsupervised speech segmentation techniques, with comparable results obtained. We also fuse...
Due to the quality of paper and long-time preservation, the ink on one side of the historical documents often seeps through and appears on the other side. In this paper, a new blind ink bleed-through removal method is proposed to deal with the scanned historical document images. The scanned historical document image generally consists of three components: foreground, bleed-through and background....
A new approach to multi-resolution modeling of images is introduced and applied to the task of semi-unsupervised texture segmentation using Gaussian Markov random fields (GMRFs). It is shown that traditional GMRF modeling of multi-resolution coefficients is incapable of accounting for the non-Gaussian statistics which often characterize the multi-resolution coefficients. On the other hand, the marginal...
We consider the problem of semi-supervised segmentation of textured images. In this paper, we propose a new Bayesian framework by modeling two-dimensional textured images as the concatenation of two one-dimensional hidden Markov autoregressive models for the lines and the columns, respectively. A new segmentation algorithm, which is similar to turbo decoding in the context of error-correcting codes,...
Many applications are concerned by human action recognition notably in multimedia and more particularly for video retrieval and archival. Usual approaches focus on probabilistic methods and assume a still camera. In this paper, a method based on the Transferable Belief Model fusion process and considering a moving camera is proposed. In this framework, the affine camera motion estimation and temporal...
Color model express colors in a prescribed way, according to a certain specification. The color of image pixels could be represented in distinct color spaces which takes into consideration different properties. This paper presents the study of different color space models for land cover classification. The work is focused around generating the pseudo color image using fully polarimetric SAR data and...
Brain tumors are created by abnormal and uncontrolled cell division inside the brain. The segmentation of brain tumors which is carried out manually from MRI is a crucial and time consuming task. The accuracy of detecting brain tumor location and size takes the most important role in the successful diagnosis and treatment of tumors. So the detection of brain tumor needs to be fast and accurate. Brain...
World dynamic situation are full of random changes which include multiple factors like audio, video and image. So cause of concurrent incident and simultaneously device performance requirement become typical for information retrieval. That make segmentation approach a method of combining feature transformation with clustering algorithm which is proposed for adequate retrieval of image, that could...
Tracking trajectory of three-dimensional trees is a difficult problem in computer animation and virtual reality. It requires not only high sense of reality for the morphology of trees and tree moving, but also adequate real-time. In this paper, we present a simulation method based on video data driven. Firstly, split out the main branches and leaves of trees from video images by using hybrid method...
Machine simulation of human reading has been a subject of intensive research for almost four decades. Automatic Urdu character recognition remains a challenging task due to its cursive nature despite the fact that the latest improvements in recognition methods and systems for Latin script are very promising. This work introduces a robust approach based on statistical models that provide solution for...
During imitation learning or learning by demon-stration/observation, a crucial element of conception involves segmenting the continuous flow of motion into simpler units ÂĂŞ- motion primitives -ÂĂŞ by identifying the boundaries of an action. Secondly, in realistic environment the robot must be able to learn the observed motion patterns incrementally in a stable adaptive manner. In this paper, we propose...
It has been demonstrated that a finite mixture model (FMM) with Gaussian distribution is a powerful tool in modeling probability density function of image data, with wide applications in computer vision and image analysis. We propose a simple-yet-effective way to enhance robustness of finite mixture models (FMM) by incorporating local spatial constraints. It is natural to make an assumption that the...
We present in this paper a state of the latest advances in the field of offline handwritten signature verification. We describe the main approaches that have been proposed in the recent decades. Besides, we introduce the database of static signatures published in the literature as well as international competitions organized in the domain. Also, we present our contribution in the field.
In this paper, we explore the use of advanced statistical models for unsupervised segmentation of challenging eye images. A previous work has shown the superiority of Triplet Markov Field (TMF) over HMF for segmenting challenging eye region but TMF implementation is computationally very expensive. To enable faster processing while preserving performance, we investigate in this paper Hidden Markov...
Segmentation of line, word and character are one of the critical phases of optical character recognition (OCR). Due to the imperfection in segmentation, most of the recognition system produce poor recognition rate. In this paper we are discussing some novel approach for line, word and character segmentation of printed Manipuri document. Few works has been done for optical character recognition on...
In this paper we present a novel method for describing the EEG as a sequence of topographies, based on the notion of microstates. We use Hidden Markov Models (HMM) to model the temporal evolution of the topography of the average Event Related Potential (ERP) and we calculate the Fisher score of the sequence by taking the gradient of the trained model parameters given the sequence. In this context,...
Reading text from natural images is a hard computer vision task. We present a method for applying deep convolutional neural networks to recognize numbers in natural scene images. In this paper, we proposed a noval method to eliminating the need of explicit segmentation when deal with multi-digit number recognition in natural scene images. Convolution Neural Network(CNN) requires fixed dimensional...
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