To alleviate data sparsity in spoken Uyghur machine translation, we proposed a log-linear based morphological segmentation approach. Instead of learning model only from monolingual annotated corpus, this approach optimizes Uyghur segmentation for spoken translation based on both bilingual and monolingual corpus. Our approach relies on several features such as traditional conditional random fiel...

Topic Segmentation is the task of breaking documents into topically coherent multiparagraph subparts. In particular, Topic Segmentation is extensively used in Text Summarization to provide more coherent results by taking into account raw document structure. However, most methodologies are based on lexical repetition that show evident reliability problems or rely on harvesting linguistic resourc...

We study the problem of automatic recognition and segmentation of objects in indoor RGB-D scenes. We propose to formulate the object recognition and segmentation in RGBD data as a binary object-background segmentation, using an informative set of features and grouping cues for small regular superpixels. The main novelty of the proposed approach is the exploitation of the informative depth chann...

In this paper, we propose a new pipeline of word embedding for unsegmented languages, called segmentation-free word embedding, which does not require word segmentation as a preprocessing step. Unlike space-delimited languages, unsegmented languages, such as Chinese and Japanese, require word segmentation as a preprocessing step. However, word segmentation, that often requires manually annotated...

Segmenting a MRI images into homogeneous texture regions representing disparate tissue types is often a useful preprocessing step in the computer-assisted detection of breast cancer. That is why we proposed new algorithm to detect cancer in mammogram breast cancer images. In this paper we proposed segmentation using vector quantization technique. Here we used Linde Buzo-Gray algorithm (LBG) for...

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...

Introduction: Virtual bronchoscopy is a reliable and efficient diagnostic method for primary symptoms of lung cancer. The segmentation of airways from CT images is a critical step for numerous virtual bronchoscopy applications. Materials and Methods: To overcome the limitations of the fuzzy connectedness method, the proposed technique, called fuzzy connectivity - fuzzy C-mean (FC-FCM), utilized...

Image segmentation is a fundamental approach in the field of image processing and based on user’s application .This paper propose an original and simple segmentation strategy based on the EM approach that resolves many informatics problems about hyperspectral images which are observed by airborne sensors. In a first step, to simplify the input color textured image into a color image without tex...

This work explores the Multi-layer Perceptron’s inference capabilities to detect textured relationships of pixels belonging to a squared neighbourhood. Although hidden in the neuron connections, these relationships lend the neural network the necessary discriminant power to classify patterns. Results similar to those involving the combination co-occurrence matrices-MLP have been obtained for su...

The context here is image segmentation because it was in this domain that spectral clustering was introduced by Shi and Malik in 2000. Meila and Shi provide a random-walk interpretation of the spectral clustering algorithm, and then use a transition probability matrix to create a model which learns to segment images based on pixel intensity (which they call “edge strength”) and “co-circularity”...