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This paper proposes a new approach of monocular ceiling vision based simultaneous localization and mapping (SLAM) by utilizing an improved Square Root Unscented Kalman Filter (SRUKF). With a monocular camera mounted on the top of a mobile robot and looking upward to the ceiling, the robot only needs to process salient features, which greatly reduce the computational complexity and have a high accuracy...
To begin with, this paper introduces a topic map oriented e-learning platform--Yotta. We compare the Yotta with the traditional e-learning platform Moodle from the aspects of learning resources organization, design of learning activities, learning behavior characteristics of learners, etc. Secondly, we provide two new criteria which can reflect e-learners` learning behavior using topic maps: the coverage...
Correct mass diagnosis in mammogram can reduce the unnecessary biopsy without increasing false negatives. In this paper, we investigated the usage of random forest classifier for the classification of masses with geometry and texture features. Before extracting features, the mass regions need to be extracted. Based on the initial contour guided by radiologist, level set segmentation is used to deform...
In this paper, an automatically method for mass detection was introduced, which combines multiple layers concentric (MLC) and narrow band region-based active contour (NBAC) technique. We used an improved level set method to segment the mass for contour refinement, after the boundary of a mass is found, texture features from Gray Level Cooccurrence Matrix (GLCM) are extracted from the surrounding area...
In this paper, we investigate mass classification using an improved local binary pattern operator. In the proposed classification algorithm, the improved local binary pattern operator is used to extract the features of masses and is used to determine whether the mass is benign or malignant. For classifier, support vector machine is adopted. 309 images from the DDSM database were used and the experimental...
Crop planting area is significant to agricultural structure adjustment and regional food balance policy. Based on field investigation and spectral property analysis of corn in HJ1-CCD images, different threshold configurations are set to build decision trees and corn planting area is precisely extracted utilizing multi-temporal HJ-1 CCD images in Zhecheng county, Henan Province of China. Both statistical...
Current e-learning systems are primarily resource oriented, rather than cognition oriented. To reduce learners' cognitive overload in e-learning, we proposed a novel e-learning system Yotta tackling the problem of knowledge acquisition, knowledge presentation, and knowledge resources management. The granularity for knowledge acquisition in Yotta is based on knowledge units that are the smallest integral...
Color and shape descriptions of an image are the most widely used visual features in content-based image retrieval systems. Feature vectors for shape and color can be combined to improve the performance of the content-based image retrieval systems. In this paper, a novel image retrieval method integrating HSV color quantization and curve let transform is proposed. By analyzing properties of HSV(Hue,...
A new ontology-based image retrieval framework which brings in SIFT features is presented in this paper. Firstly, it brings SIFT features into the image ontology to build an ontology library which describes the image element together with shape, color, texture and other low-level features. And then, calculate the similarity of SIFT features, low-level features, concept of ontology and semantic features...
In this paper, we investigate the classification of masses with texture features. We propose an improved level set method to find the boundary of a mass, based on the initial contour provided by radiologists. After the boundary of a mass is found, texture features from Gray Level Co-occurrence Matrix (GLCM) are extracted from the surrounding area of the boundary of the mass. The extracted texture...
In this paper, a review of man-made object detection algorithms is presented based on various fractal features which are derived from the blanket covering method. These fractal features include fractal dimension (D), fractal model fitting error (FE), D-dimension area (K), multi-scale fractal feature related with D (MFFD), and multi-scale fractal feature related with K (MFFK). To choose the optimal...
A new man-made target tracking algorithm integrating features from (Forward Looking InfraRed) image sequence is presented based on particle filter. Firstly, a multi-scale fractal feature is used to enhance targets in FLIR images. Secondly, the gray space feature is defined by Bhattacharyya distance between intensity histograms of the reference target and a sample target from MFF (Multi-scale Fractal...
Skeletons as important shape features of an object are useful for shape description. Unfortunately, methods obtaining skeletons of a grayscale volume are lacking due to no clear boundary between object and background. In this paper, we present a new segmentation-free skeletonization method on grayscale volumes based on marching cubes, topological thinning and a novel pruning routine. Our method is...
The 3D clothing fitting on a body model is an important research topic in the garment computer aided design (GCAD). During the fitting process, the match between the clothing and body models is still a problem for researchers. In this paper, we provide a 3D clothing fitting method based on the feature point match. We firstly use a new cubic-order weighted fitting patch to estimate the geometric properties...
Knowledge element relation recognition is to mine intrinsic and hidden relations, i.e., preorder, analogy and illustration from knowledge element set, which can be used in knowledge organization and knowledge navigation system. This paper focuses on what information is employed to recognize knowledge element relations. First, a formal definition of knowledge element and the types of relation are given...
In this paper, we propose a novel algorithm for face feature extraction, namely the cascade two-dimensional locality preserving projections (C2DLPP), which directly extracts the proper features from image matrices based on locality preserving criterion. Experiments on ORL and PIE face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of proposed...
In this paper, we propose a novel algorithm for face feature extraction, namely the bilateral two-dimensional principal component analysis (B2DPCA), which directly extracts the proper features from image matrices. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vector so the image matrix does not need to be transformed into a vector prior to feature extraction. Experiments on...
A novel face feature extraction method based on Bilateral Two-dimensional Principal Component Analysis (B2DPCA) and Kernel Discriminant Analysis (KDA) was presented in this paper. In this method, B2DPCA method directly extracts the proper features from image matrices at first, then the KDA was performed on the features to enhance discriminant power. As opposed to PCA, B2DPCA is based on 2D image matrices...
Aiming at deficiencies of existing knowledge resources management systems, we designed a new collaborative knowledge construction system for massive knowledge resources. By collaborative knowledge building in the following three phases: acquisition of knowledge factors, generation of the local extended topic maps and integration of global extended topic map, we realized a conjunction of concept level...
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