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The invention of wireless capsule endoscopy greatly helps physician to view small intestine images without causing much pain to patients. It becomes very popular around the world for its usability and performance. However, physician requires a long time (around 45 minutes) to examine a capsule endoscopy video generated from each examination. In this paper, we propose a new image processing method...
This paper presents a textural feature descriptor that can be effectively utilized for grading Hepatocellular carcinoma (HCC) histopathological images. The proposed feature descriptor observes the local and spatial characteristics of the texture by utilizing multifractal computation, and it is incorporated with a bag-of-feature (BOF)-based classification model to classify a set of images. We compare...
This study presents a recursive Kernel Density Estimation model (r-KDE) based method for the segmentation of dynamic scenes. In the algorithm, local maximum in the density functions is approximated recursively via mean shift method firstly. Via the proposed thresholding scheme, components and parameters in the mixture Gaussian distributions can be determined adaptively. The coarse foreground is obtained...
In this paper, we present a novel local-global salient region detection method. We first obtain the smoothed image via gradient minimization, resulting in more homogeneous background. Then, we partition the smoothed image into a set of regions and compute the region saliency by measuring the dissimilarity and spatial distance. Furthermore, we adopt the global color distribution, including the color...
In a pattern recognition sequence consisting of alternating steps of interactive labeling, classifier training, and automated labeling (e.g., CAVIAR systems), the choice of sample size at each step affects the overall amount of human interaction necessary to label all the samples correctly. The appropriate splits depend on the error rate of the classifier as a function of the size of the training...
We present a Bag-of-Visual-and-Depth-Words (BoVDW) model for gesture recognition, an extension of the Bag-of-Visual-Words (BoVW) model, that benefits from the multimodal fusion of visual and depth features. State-of-the-art RGB and depth features, including a new proposed depth descriptor, are analysed and combined in a late fusion fashion. The method is integrated in a continuous gesture recognition...
To date the methods to create accuracy dense realistic 3D models of outdoors by using laser scanners are highly dependent on the on-site conditions in the very moment of the 3D data collection. Thus, researchers put in a lot of effort on eliminating colour incoherencies (sunny/shady, bright/dark, non sensed areas, etc.) or modelling the light of the scene to obtain free-illumination models. This paper...
Document clustering has become inevitable for applications that aim to extract information from huge corpuses. Such applications face two main challenges; one is the efficient representation of the documents, along with using an efficient similarity measure, and the second is dealing with the dynamic nature of the corpus. In this paper, an efficient document clustering model is introduced for incrementally...
Hand posture recognition (HPR) plays an important role in human-computer interaction (HCI) since it is one of the most common and natural ways of communication among human beings. Different fingers often represent different meanings which will attract more attentions in HPR research. Based on finger geometric feature and its classification, we develop a HPR system that can tell its posture on possible...
Spatial pyramid matching (SPM) component is part of most state-of-art image classification methods. SPM encodes spatial distribution of image features, in an un-supervised fashion, by partitioning an image into regions at multiple scales and concatenating feature vectors for these regions. In this paper we propose to replace the unsupervised SPM procedure with a supervised two-stage feature selection...
In this paper, we propose a 3D model retrieval system using sketch queries. The 3D model is described by a single characteristic view with the biggest exposure opportunity. The sketch query and the characteristic view are divided into several parts. Each part is quantized into a Fourier descriptor, and the spatial arrangements of these parts are measured by the graph spectra. Our contributions are...
Kanungo noise model is widely used to test the robustness of different binary document image analysis methods towards noise. This model only works with binary images while most document images are in grayscale. Because binarizing a document image might degrade its contents and lead to a loss of information, more and more researchers are currently focusing on segmentation-free methods (Angelika et...
This paper describes an efficient acceleration of GAT (Global Affine Transformation) correlation as a powerful technique of distortion-tolerant image matching. The key ideas are twofold: efficient calculation of optimal affine parameters that maximize the normalized cross-correlation value between an input image and a template via separation of variables in the original GAT computational model and...
Given a single image of a scene rectangle of an unknown aspect ratio and size, we present a method to reconstruct the projective structure and to find camera parameters including focal length, position, and orientation. First, we solve the special case when the center of a scene rectangle is projected to the image center. We formulate this problem with coupled line cameras and present the analytic...
Recently, fusion of low- and high-dimensional approaches shows its success in the generic human motion tracking. However, how to choose the trackers adaptively according to the motion types is still a challenging problem. This paper presents a trackers sampling approach for generic human motion tracking using both low- and high-dimensional trackers. Gaussian Process Dynamical Model(GPDM) is trained...
Selectivity and invariance are thought to be important ingredients in biological or artificial visual systems. A fundamental problem is, however, to know what the visual system should be selective to and what to be invariant to. Building a statistical model of images, we learn here a three-layer feature extraction system where the selectivity and invariance emerges from the properties of the images.
Text classification (TC) has long been an important research topic in information retrieval (IR) related areas. Conventional language model (LM)-based TC is solely based on matching the words in the documents and classes by using a naïve Bayes classifier (NBC). In the literature, both the term association model (TA), which further considers word-to-word information, and the relevance model (RM), which...
We extend the PCT (Pseudo Census Transform)-based appearance model [3] to ranking-based appearance model for face alignment. The PCT-based weak ranking function is learned using RankSVM, and the ranking appearance model (RAM) is constructed in a boosting manner. Experiments show that the PCT-based RAM is more robust and generalize better than the PCT-based boosted appearance model (BAM). The PCT-RAM...
Mixture models are frequently used to classify data. They are likelihood based models, and the maximum likelihood estimates of parameters are often obtained using the expectation maximization (EM) algorithm. However, multimodality of the likelihood surface means that poorly chosen starting points for optimisation may lead to only a local maximum, not a global maximum. In this paper, different methods...
In this paper, we propose a novel unsupervised online learning trajectory analysis method based on weighted directed graph. Each trajectory can be represented as a sequence of key points. In the training stage, unsupervised expectation-maximization algorithm (EM) is applied for training data to cluster key points. Each class is a Gaussian distribution. It is considered as a node of the graph. According...
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