Automatic and semi-automatic magnetic resonance angiography (MRA) segmentation techniques can potentially save radiologists large amounts of time required for manual segmentation and can facilitate further data analysis. Our proposed MRA segmentation method uses a mathematical modeling technique which is well-suited to the complicated curve-like structure of blood vessels. Specifically, we defi...

We present a systematic investigation of applying weakly supervised co-training approaches to improve parsing performance for parsing Mandarin broadcast news (BN) and broadcast conversation (BC) transcripts, by iteratively retraining two competitive Chinese parsers from a small set of treebanked data and a large set of unlabeled data. We compare co-training to self-training, and our results sho...

This paper presents a three-dimensional level set-based image segmentation method. Instead of the typical image features, like intensity or edge information, the method uses texture feature analysis in order to be more applicable to image sets with distinctive patterns. The current implementation makes use of a set of Grey Level Co-occurrence Matrix texture features that are generated and selec...

Automatic and semi-automatic magnetic resonance angiog-raphy (MRA) segmentation techniques can potentially save radiologists large amounts of time required for manual segmentation and can facilitate further data analysis. The proposed MRA segmentation method uses a mathematical modeling technique which is well-suited to the complicated curve-like structure of blood vessels. We deene the segment...

We address the problem of object co-segmentation in images. Object co-segmentation aims to segment common objects in images and has promising applications in AI agents. We solve it by proposing a co-occurrence map, which measures how likely an image region belongs to an object and also appears in other images. The co-occurrence map of an image is calculated by combining two parts: objectness sc...

The goal of this thesis is to design and build a system which automatically classifies an image of a flower for hundreds of flower species. The classification should be done within a reasonable time so that it is usable for a real-time computer vision application. The classification performance of the system is improved if only the flower region (foreground) of the image is considered, and the ...

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...

This document analyses the bakeoff results from NetEase Co. in the SIGHAN5 Word Segmentation Task and Named Entity Recognition Task. The NetEase WS system is designed to facilitate research in natural language processing and information retrieval. It supports Chinese and English word segmentation, Chinese named entity recognition, Chinese part of speech tagging and phrase conglutination. Evalua...

In this paper, we propose an unsupervised video object cosegmentation framework based on the primary object proposals to extract the common foreground object(s) from a given video set. In addition to the objectness attributes and motion coherence our framework exploits the temporal consistency of the object-like regions between adjacent frames to enrich the set of original object proposals. We ...

Image co-segmentation is a challenging computer vision task that aims to segment all pixels of the common objects in an image set. In real-world cases, however, the common objects often vary greatly in poses, locations and scales, making their global shapes highly inconsistent across images and difficult to be segmented. To address this problem, this paper proposes a novel co-segmentation appro...