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Histogram correlation based classifier fusion for object tracking

Histogram correlation based classifier fusion for object tra...

引用

IEEE Signal Processing and Communications Applications (SIU)

作者： İbrahim Saygın Topkaya Hakan Erdoğan Sabancı Üniversitesi Turkey

Mean shift is a popular method used in object tracking. The method, which relies on shifting the search area to the weight center of a generated “weight image” to track objects between consecutive frames, acquired a classifier based framework by using classifiers to generate the weight image. In this work, using multiple classifiers to generate the weight image and calculating contributions of the independent classifiers dynamically by using correlations between histograms of their weight images and histogram of a defined ideal weight image are presented.

关键词： Histograms pattern recognition Signal processing Conferences Computational modeling computer vision Adaptation model

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Scene classification using saliency based segmentation

Scene classification using saliency based segmentation

引用

IEEE Signal Processing and Communications Applications (SIU)

作者： Cemalettin Çiftçi Emrah Ergül Nafiz Arıca Bilgisayar Mühendisligi Bölümü Deniz Harp Okulu Turkey

For a better expression of images, we propose a Bag of Words approach which is position aware and uses saliency based segmentation to build vocabularies of their own segment. We also apply pLSA algorithm to find hidden topics in each scene image. The experiments show that the separate representation of the regions found by a rough segmentation increase the classification performance.

关键词： computer vision Conferences Signal processing pattern recognition Probabilistic logic Semantics Image segmentation

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Edge Saliency Map Detection with Texture Suppression

Edge Saliency Map Detection with Texture Suppression

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International Conference on Image and Graphics (ICIG)

作者： Huanhuan Ao Nenghai Yu

Edge is a basic feature in the field of computing vision. So to find edge saliency map is an indispensable operation for many applications on image processing. In this paper we present a fast algorithm to find edge saliency map for a natural image. The approach integrates three basic edge features: edge gradient value, edge segment length and edge density, and it works well to detect salient region boundaries and to suppress ill edges from texture. An edge saliency map can be used to image segmentation and boundaries detection. Experimental results demonstrate that our algorithm outperforms other edge saliency detection methods. Finally, our algorithm is applied on salient objects segmentation, compared with several state of-the-art salient region detection methods and the results show our work is valuable.

关键词： Image edge detection Image segmentation Visualization Feature extraction computer vision Object segmentation pattern recognition

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Signature Segmentation from Machine Printed Documents Using Conditional Random Field

Signature Segmentation from Machine Printed Documents Using ...

引用

International Conference on Document Analysis and recognition

作者： Ranju Mandal Partha Pratim Roy Umapada Pal Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Laboratoire dInformatique Université François Rabelais Tours France

ISBN: (纸本)9781457713507

Automatic separation of signatures from a document page involves difficult challenges due to the free-flow nature of handwriting, overlapping/touching of signature parts with printed text, noise, etc. In this paper, we have proposed a novel approach for the segmentation of signatures from machine printed signed documents. The algorithm first locates the signature block in the document using word level feature extraction. Next, the signature strokes that touch or overlap with the printed texts are separated. A stroke level classification is then performed using skeleton analysis to separate the overlapping strokes of printed text from the signature. Gradient based features and Support Vector Machine (SVM) are used in our scheme. Finally, a Conditional Random Field (CRF) model energy minimization concept based on approximated labeling by graph cut is applied to label the strokes as "signature" or "printed text" for accurate segmentation of signatures. Signature segmentation experiment is performed in "tobacco" dataset1 and we have obtained encouraging results.

关键词： Support vector machines Handwriting recognition Image segmentation Feature extraction Junctions Labeling Hidden Markov models

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Warp that smile on your face: Optimal and smooth deformations for face recognition

Warp that smile on your face: Optimal and smooth deformation...

引用

International Conference on Automatic Face and Gesture recognition

作者： Tobias Gass Leonid Pishchulin Philippe Dreuw Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Computer Vision Laboratory ETH Zurich Switzerland Computer Vision and Multimodal Computing MPI Informatics Saarbruecken Germany

In this work, we present novel warping algorithms for full 2D pixel-grid deformations for face recognition. Due to high variation in face appearance, face recognition is considered a very difficult task, especially if only a single reference image, for example a mug-shot, per face is available. Usually model-based approaches with additional training data are used to cope with several types of variation occurring in facial imaging. Image warping contrarily yields a distance measure which is invariant with regard to several types of variation. This allows for precise recognition even using only very few reference observations. Due to the computationally complex problem of optimal 2D warping, pseudo-2D warping-based approaches in the past represented strong approximations of the original problem, and were mainly successful on data with low variability or rectified images. We propose a novel 2D warping method which is globally optimal and makes no prior assumtions on the data variability besides two-dimensional smootheness constraints which both avoid local mirroring and gaps and significantly speed up the optimization. Furthermore, we show that occlusion handling is imperative to obtain smooth warpings in a variety of domains. We evaluate our novel algorithm on various well known databases, such as the AR-Face and CMU-PIE database, and provide a detailed comparison to existing warping approaches. We show that by using simple relative 2D constraints, strong local features and a kernel, which is robust w.r.t. occlusions, our computationally complex approaches outperform state-of-the-art results for recognizing faces under varying expressions, occlusions and poses. Most interestingly, we achieve higher accuracy using fewer training instances per class compared to methods learning a model of the 3D shape.

关键词： Pixel Face Face recognition Optimization Databases Approximation methods Hidden Markov models

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A Weighted Finite-State Transducer (WFST)-Based Language Model for Online Indic Script Handwriting recognition

A Weighted Finite-State Transducer (WFST)-Based Language Mod...

引用

International Conference on Document Analysis and recognition

作者： Suhan Chowdhury Utpal Garain Tanushyam Chattopadhyay Computer Vision & Pattern Recognition (CVPR) Unit Indian Statistical Institute Kolkata India Innovation Laboratory Kolkata Tata Consultancy Services Kolkata India

Though designing of classifies for Indic script handwriting recognition has been researched with enough attention, use of language model has so far received little exposure. This paper attempts to develop a weighted finite-state transducer (WFST) based language model for improving the current recognition accuracy. Both the recognition hypothesis (i.e. the segmentation lattice) and the lexicon are modeled as two WFSTs. Concatenation of these two FSTs accept a valid word(s) which is (are) present in the recognition lattice. A third FST called error FST is also introduced to retrieve certain words which were missing in the previous concatenation operation. The proposed model has been tested for online Bangla handwriting recognition though the underlying principle can equally be applied for recognition of offline or printed words. Experiment on a part of ISI-Bangla handwriting database shows that while the present classifiers (without using any language model) can recognize about 73% word, use of recognition and lexicon FSTs improve this result by about 9% giving an average word-level accuracy of 82%. Introduction of error FST further improves this accuracy to 93%. This remarkable improvement in word recognition accuracy by using FST-based language model would serve as a significant revelation for the research in handwriting recognition, in general and Indic script handwriting recognition, in particular.

关键词： Handwriting recognition Hidden Markov models Character recognition Optical character recognition software Accuracy Transducers Vocabulary

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Multi-color ULBP with wavelet transform in invariant pose face recognition

Multi-color ULBP with wavelet transform in invariant pose fa...

引用

IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

作者： Seyed Omid Shahdi S. A. R. Abu-Bakar Computer Vision Video and Image Processing (CvviP) Research Lab Faculty of Electrical Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia

Variation in pose is one of the main obstacles confronting researchers in the area of face recognition. In this paper, a novel method is proposed to explicitly tackle this problem. Multi-color uniform local binary pattern (ULBP) is introduced for extracting salient features along with wavelet transform. Learning scheme is adopted to obtain a mapping coefficient vector between face in a pose and frontal face. Then expected frontal face view vector could be generated by inserting the posed face. Instead of using the entire face, some of its important regions are taken into account. The proposed method relies only on single frontal face image as a gallery image. Results have demonstrated that the proposed method operates well even under the low-resolution conditions.

关键词： Face Vectors Face recognition Feature extraction Wavelet transforms Conferences

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Continuous emotion detection in response to music videos

Continuous emotion detection in response to music videos

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International Conference on Automatic Face and Gesture recognition

作者： Mohammad Soleymani Sander Koelstra Ioannis Patras Thierry Pun Computer Vision and Multimedia Laboratory University of Genova Switzerland School of Computer Science and Electronic Engineering Queen''s Mary University of London UK

Viewers' preference for multimedia selection depends highly on their emotional experience. In this paper, we present an emotion detection method for music videos using central and peripheral nervous system physiological signals as well as multimedia content analysis. A set of 40 music clips eliciting a broad range of emotions were first selected. After extracting the one minute long emotional highlight of each video, they were shown to 32 participants while their physiological responses were recorded. Participants self-reported their felt emotions after watching each clip by means of arousal, valence, dominance, and liking ratings. The physiological signals included electroencephalogram, galvanic skin response, respiration pattern, skin temperature, electromyograms and blood volume pulse using plethysmograph. Emotional features were extracted from the signals and the multimedia content. The emotional features were used to train a linear ridge regressor to detect emotions for each participant using a leave-one-out cross-validation strategy. The performance of the personalized emotion detection is shown to be significantly superior to a random regressor.

关键词： Videos Feature extraction Electroencephalography Skin Multimedia communication Heart rate variability Electrodes

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Multi-Level Kernel Machine for Scene Image Classification

Multi-Level Kernel Machine for Scene Image Classification

引用

International Conference on Computational Intelligence and Security

作者： Junlin Hu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781457720086

Recently, a new representation for recognizing instances and categories of scenes called spatial Principal component analysis of Census Transform histograms (PACT) has shown its excellent performance in the scene image classification task. PACT captures local structures of an image through the Census Transform (CT), meanwhile, large scale structures are captured by the strong correlation between neighboring CT values and the histogram. However, the original spatial PACT only simply concatenates all levels compact histograms together, and discards the difference between various levels. In order to improve this problem, we propose a multi-level kernel machine method, which computes a set of base kernels at each level of pyramid of PACT, and finds optimal weights for best fusing all these base kernels for scene recognition. Experiments on two popular benchmark datasets demonstrate that our proposed multi-level kernel machine method outperforms the spatial PACT on scene recognition. Besides, our method is easy to be implemented comparing with spatial PACT.

关键词： Kernel Computed tomography Transforms computer vision Histograms Conferences Support vector machines

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Neural network based motion segmentation for accelerometer applications

Neural network based motion segmentation for accelerometer a...

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IEEE Symposium on 3D User Interfaces (3DUI)

作者： Jong Gwan Lim Sang-Youn Kim Dong-Soo Kwon KAIST South Korea Korea Advanced Institute of Science and Technology Korea University of Technology and Education South Korea

Of several research issues related to motion interaction using inertia measurement units, faster motion segmentation without accuracy loss has recently been raised. Instead of using excessive filtering that produces time delay or tricky use of multiple thresholds that cause difficulty in parameter optimization, this poster demonstrates that time series prediction using neural networks significantly decreases time delay and guarantees rigid motion segmentation by detecting end points in accelerometer signals. According to a general pattern recognition procedure, feature selection is made by a filtering method and the optimal structure is determined by cross validation. Radial basis function networks and Multi-Layer Perceptrons (MLPs) are tested and the results are compared with the conventional methods to evaluate accuracy and time delay in a handwriting case in 3D space. This study confirms that MLP shows the best accuracy and shortens the time delay by 1/4~1/3 compared to the conventional methods.

关键词： Delay effects Acceleration Accelerometers Motion segmentation computer vision pattern recognition Accuracy

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