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This paper presents a medical imaging system able to assist practitioner to analyze colposcopic images. The goal is to automatically make all images to a common frame. Image registration has been used to ensure pixel correspondence to the same tissue location throughout the whole temporal sequence. Best approaches are based on using local information, but they are very sensitive to light change and...
This work presents a colposcopic image registration system able to help physicians for cervical cancer diagnosis. The goal is to make registration between the cervical tissue throughout the whole temporal sequence. Recent digital images processing works, suggested using feature points to compute the tissue displacement. These methods achieve good results, because they are fast and do not need any...
Recent works on Content Based Image Retrieval rely on Bag of Visual Words to index images. Analogically to the Bag of Words approach used in text retrieval, this model allows describing an image as a bag of elementary local features called visual words. As a result, an image is represented by a vector of weights, where each weight corresponds to the importance of a visual word in the image. The choice...
This work presents a digital imaging system able to assist physicians to track cervical cancer. The goal is to automatically extract the region where the cervical cancer starts to occur. This region called Acetowite Epithelium Zone (AEZ) is highlighted by physicians using a diluate acetic acid on the surface of the cervix. Recent digital images processing works suggested to extract this region with...
Like many problems in image analysis, motion estimation is an ill-posed one, since the available data do not always sufficiently constrain the solution. It is therefore necessary to regularize the solution by imposing a smoothness constraint. One of the main difficulties while estimating motion is to preserve the discontinuities of the motion field. In this paper, we address this problem by integrating...
A template-based technique for automatic recognition of birdsong syllables is presented. This technique combines time delay neural networks (TDNNs) with an autoregressive (AR) version of the backpropagation algorithm in order to improve the accuracy of bird species identification. The proposed neural network structure (AR-TDNN) has the advantage of dealing with a pattern classification of syllable...
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