Academic literature on the topic 'Local visual feature'
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Journal articles on the topic "Local visual feature"
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Jia, Xi Bin, and Mei Xia Zheng. "Video Based Visual Speech Feature Model Construction." Applied Mechanics and Materials 182-183 (June 2012): 1367–71. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1367.
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This paper aims to give a solutions for the construction of chinese visual speech feature model based on HMM. We propose and discuss three kind representation model of the visual speech which are lip geometrical features, lip motion features and lip texture features. The model combines the advantages of the local LBP and global DCT texture information together, which shows better performance than the single feature. Equally the model combines the advantages of the local LBP and geometrical information together is better than single feature. By computing the recognition rate of the visemes from the model, the paper shows the HMM which describing the dynamic of speech, coupled with the combined feature for describing the global and local texture is the best model.
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Wang, Yin-Tien, Chen-Tung Chi, and Ying-Chieh Feng. "Robot mapping using local invariant feature detectors." Engineering Computations 31, no. 2 (February 25, 2014): 297–316. http://dx.doi.org/10.1108/ec-01-2013-0024.
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Purpose – To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner detector is a common method utilized to detect visual landmarks for constructing a map of the environment. However, due to the scale-variant characteristic of corner detection, extensive computational cost is needed to recover the scale and orientation of corner features in SLAM tasks. The purpose of this paper is to build the map using a local invariant feature detector, namely speeded-up robust features (SURF), to detect scale- and orientation-invariant features as well as provide a robust representation of visual landmarks for SLAM. Design/methodology/approach – SURF are scale- and orientation-invariant features which have higher repeatability than that obtained by other detection methods. Furthermore, SURF algorithms have better processing speed than other scale-invariant detection method. The procedures of detection, description and matching of regular SURF algorithms are modified in this paper in order to provide a robust representation of visual landmarks in SLAM. The sparse representation is also used to describe the environmental map and to reduce the computational complexity in state estimation using extended Kalman filter (EKF). Furthermore, the effective procedures of data association and map management for SURF features in SLAM are also designed to improve the accuracy of robot state estimation. Findings – Experimental works were carried out on an actual system with binocular vision sensors to prove the feasibility and effectiveness of the proposed algorithms. EKF SLAM with the modified SURF algorithms was applied in the experiments including the evaluation of accurate state estimation as well as the implementation of large-area SLAM. The performance of the modified SURF algorithms was compared with those obtained by regular SURF algorithms. The results show that the SURF with less-dimensional descriptors is the most suitable representation of visual landmarks. Meanwhile, the integrated system is successfully validated to fulfill the capabilities of visual SLAM system. Originality/value – The contribution of this paper is the novel approach to overcome the problem of recovering the scale and orientation of visual landmarks in SLAM tasks. This research also extends the usability of local invariant feature detectors in SLAM tasks by utilizing its robust representation of visual landmarks. Furthermore, data association and map management designed for SURF-based mapping in this paper also give another perspective for improving the robustness of SLAM systems.
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Sun, Huadong, Xu Zhang, Xiaowei Han, Xuesong Jin, and Zhijie Zhao. "Commodity Image Classification Based on Improved Bag-of-Visual-Words Model." Complexity 2021 (March 17, 2021): 1–10. http://dx.doi.org/10.1155/2021/5556899.
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With the increasing scale of e-commerce, the complexity of image content makes commodity image classification face great challenges. Image feature extraction often determines the quality of the final classification results. At present, the image feature extraction part mainly includes the underlying visual feature and the intermediate semantic feature. The intermediate semantics of the image acts as a bridge between the underlying features and the advanced semantics of the image, which can make up for the semantic gap to a certain extent and has strong robustness. As a typical intermediate semantic representation method, the bag-of-visual-words (BoVW) model has received extensive attention in image classification. However, the traditional BoVW model loses the location information of local features, and its local feature descriptors mainly focus on the texture shape information of local regions but lack the expression of color information. Therefore, in this paper, the improved bag-of-visual-words model is presented, which contains three aspects of improvement: (1) multiscale local region extraction; (2) local feature description by speeded up robust features (SURF) and color vector angle histogram (CVAH); and (3) diagonal concentric rectangular pattern. Experimental results show that the three aspects of improvement to the BoVW model are complementary, while compared with the traditional BoVW and the BoVW adopting SURF + SPM, the classification accuracy of the improved BoVW is increased by 3.60% and 2.33%, respectively.
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Manandhar, Dipu, Kim-Hui Yap, Zhenwei Miao, and Lap-Pui Chau. "Lattice-Support repetitive local feature detection for visual search." Pattern Recognition Letters 98 (October 2017): 123–29. http://dx.doi.org/10.1016/j.patrec.2017.09.021.
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Yang, Hong-Ying, Yong-Wei Li, Wei-Yi Li, Xiang-Yang Wang, and Fang-Yu Yang. "Content-based image retrieval using local visual attention feature." Journal of Visual Communication and Image Representation 25, no. 6 (August 2014): 1308–23. http://dx.doi.org/10.1016/j.jvcir.2014.05.003.
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Dong, Baoyu, and Guang Ren. "A New Scene Classification Method Based on Local Gabor Features." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/109718.
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A new scene classification method is proposed based on the combination of local Gabor features with a spatial pyramid matching model. First, new local Gabor feature descriptors are extracted from dense sampling patches of scene images. These local feature descriptors are embedded into a bag-of-visual-words (BOVW) model, which is combined with a spatial pyramid matching framework. The new local Gabor feature descriptors have sufficient discrimination abilities for dense regions of scene images. Then the efficient feature vectors of scene images can be obtained byK-means clustering method and visual word statistics. Second, in order to decrease classification time and improve accuracy, an improved kernel principal component analysis (KPCA) method is applied to reduce the dimensionality of pyramid histogram of visual words (PHOW). The principal components with the bigger interclass separability are retained in feature vectors, which are used for scene classification by the linear support vector machine (SVM) method. The proposed method is evaluated on three commonly used scene datasets. Experimental results demonstrate the effectiveness of the method.
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Gao, Yuhang, and Long Zhao. "Coarse TRVO: A Robust Visual Odometry with Detector-Free Local Feature." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 5 (September 20, 2022): 731–39. http://dx.doi.org/10.20965/jaciii.2022.p0731.
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The visual SLAM system requires precise localization. To obtain consistent feature matching results, visual features acquired by neural networks are being increasingly used to replace traditional manual features in situations with weak texture, motion blur, or repeated patterns. However, to improve the level of accuracy, most deep learning enhanced SLAM systems, which have a decreased efficiency. In this paper, we propose Coarse TRVO, a visual odometry system that uses deep learning for feature matching. The deep learning network uses a CNN and transformer structures to provide dense high-quality end-to-end matches for a pair of images, even under indistinctive settings with low-texture regions or repeating patterns occupying the majority of the field of view. Meanwhile, we made the proposed model compatible with NVIDIA TensorRT runtime to boost the performance of the algorithm. After obtaining the matching point pairs, the camera pose is solved in an optimized way by minimizing the re-projection error of the feature points. Experiments based on multiple data sets and real environments show that Coarse TRVO achieves a higher robustness and relative positioning accuracy in comparison with the current mainstream visual SLAM system.
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N. Sultani, Zainab, and Ban N. Dhannoon. "Modified Bag of Visual Words Model for Image Classification." Al-Nahrain Journal of Science 24, no. 2 (June 1, 2021): 78–86. http://dx.doi.org/10.22401/anjs.24.2.11.
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Image classification is acknowledged as one of the most critical and challenging tasks in computer vision. The bag of visual words (BoVW) model has proven to be very efficient for image classification tasks since it can effectively represent distinctive image features in vector space. In this paper, BoVW using Scale-Invariant Feature Transform (SIFT) and Oriented Fast and Rotated BRIEF(ORB) descriptors are adapted for image classification. We propose a novel image classification system using image local feature information obtained from both SIFT and ORB local feature descriptors. As a result, the constructed SO-BoVW model presents highly discriminative features, enhancing the classification performance. Experiments on Caltech-101 and flowers dataset prove the effectiveness of the proposed method.
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Aw, Y. K., Robyn Owens, and John Ross. "An analysis of local energy and phase congruency models in visual feature detection." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 40, no. 1 (July 1998): 97–122. http://dx.doi.org/10.1017/s0334270000012406.
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AbstractA variety of approaches have been developed for the detection of features such as edges, lines, and corners in images. Many techniques presuppose the feature type, such as a step edge, and use the differential properties of the luminance function to detect the location of such features. The local energy model provides an alternative approach, detecting a variety of feature types in a single pass by analysing order in the phase components of the Fourier transform of the image. The local energy model is usually implemented by calculating the envelope of the analytic signal associated with the image function. Here we analyse the accuracy of such an implementation, and show that in certain cases the feature location is only approximately given by the local energy model. Orientation selectivity is another aspect of the local energy model, and we show that a feature is only correctly located at a peak of the local energy function when local energy has a zero gradient in two orthogonal directions at the peak point.
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Han, Xian-Hua, and Yen-Wei Chen. "Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms." International Journal of Biomedical Imaging 2011 (2011): 1–7. http://dx.doi.org/10.1155/2011/241396.
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We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram of some predefined vocabulary words from image captions is used. Then, we combine the different features using normalized kernel functions for SVM classification. Furthermore, for some easy misclassified modality pairs such as CT and MR or PET and NM modalities, a local classifier is used for distinguishing samples in the pair modality to improve performance. The proposed strategy is evaluated with the provided modality dataset by ImageCLEF 2010.
Dissertations / Theses on the topic "Local visual feature"
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Andreasson, Henrik. "Local visual feature based localisation and mapping by mobile robots." Doctoral thesis, Örebro : Örebro University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-2444.
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Manivannan, Siyamalan. "Visual feature learning with application to medical image classification." Thesis, University of Dundee, 2015. https://discovery.dundee.ac.uk/en/studentTheses/10e26212-e836-4ccd-9b12-a576458de5eb.
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Various hand-crafted features have been explored for medical image classification, which include SIFT and Local Binary Patterns (LBP). However, hand-crafted features may not be optimally discriminative for classifying images from particular domains (e.g. colonoscopy), as not necessarily tuned to the domain’s characteristics. In this work, I give emphasis on learning highly discriminative local features and image representations to achieve the best possible classification performance for medical images, particularly for colonoscopy and histology (cell) images. I propose approaches to learn local features using unsupervised and weakly-supervised methods, and an approach to improve the feature encoding methods such as bag-of-words. Unlike the existing work, the proposed weakly-supervised approach uses image-level labels to learn the local features. Requiring image-labels instead of region-level labels makes annotations less expensive, and closer to the data normally available from normal clinical practice, hence more feasible in practice. In this thesis, first, I propose a generalised version of the LBP descriptor called the Generalised Local Ternary Patterns (gLTP), which is inspired by the success of LBP and its variants for colonoscopy image classification. gLTP is robust to both noise and illumination changes, and I demonstrate its competitive performance compared to the best performing LBP-based descriptors on two different datasets (colonoscopy and histology). However LBP-based descriptors (including gLTP) lose information due to the binarisation step involved in their construction. Therefore, I then propose a descriptor called the Extended Multi-Resolution Local Patterns (xMRLP), which is real-valued and reduces information loss. I propose unsupervised and weakly-supervised learning approaches to learn the set of parameters in xMRLP. I show that the learned descriptors give competitive or better performance compared to other descriptors such as root-SIFT and Random Projections. Finally, I propose an approach to improve feature encoding methods. The approach captures inter-cluster features, providing context information in the feature as well as in the image spaces, in addition to the intra-cluster features often captured by conventional feature encoding approaches. The proposed approaches have been evaluated on three datasets, 2-class colonoscopy (2, 100 images), 3-class colonoscopy (2, 800 images) and histology (public dataset, containing 13, 596 images). Some experiments on radiology images (IRMA dataset, public) also were given. I show state-of-the-art or superior classification performance on colonoscopy and histology datasets.
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Emir, Erdem. "A Comparative Performance Evaluation Of Scale Invariant Interest Point Detectors For Infrared And Visual Images." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12610159/index.pdf.
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In this thesis, the performance of four state-of-the-art feature detectors along with SIFT and SURF descriptors in matching object features of mid-wave infrared, long-wave infrared and visual-band images is evaluated across viewpoints and changing distance conditions. The utilized feature detectors are Scale Invariant Feature Transform (SIFT), multiscale Harris-Laplace, multiscale Hessian-Laplace and Speeded Up Robust Features (SURF) detectors, all of which are invariant to image scale and rotation. Features on different blackbodies, human face and vehicle images are extracted and performance of reliable matching is explored between different views of these objects each in their own category. All of these feature detectors provide good matching performance results in infrared-band images compared with visual-band images. The comparison of matching performance for mid-wave and long-wave infrared images is also explored in this study and it is observed that long-wave infrared images provide good matching performance for objects at lower temperatures, whereas mid-wave infrared-band images provide good matching performance for objects at higher temperatures. The matching performance of SURF detector and descriptor for human face images in long-wave infrared-band is found to be outperforming than other detectors and descriptors.
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Ferro, Demetrio. "Effects of attention on visual processing between cortical layers and cortical areas V1 and V4." Doctoral thesis, Università degli studi di Trento, 2019. http://hdl.handle.net/11572/246290.
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Visual attention improves sensory processing, as well as perceptual readout and behavior. Over the last decades, many proposals have been put forth to explain how attention affects visual neural processing. These include the modulation of neural firing rates and synchrony, neural tuning properties, and rhythmic, subthreshold activity. Despite the wealth of knowledge provided by previous studies, the way attention shapes interactions between cortical layers within and between visual sensory areas is only just emerging. To investigate this, we studied neural signals from macaque V1 and V4 visual areas, while monkeys performed a covert, feature-based spatial attention task. The data were simultaneously recorded from laminar electrodes disposed normal to cortical surface in both areas (16 contacts, 150 μm inter-contact spacing). Stimuli presentation was based on the overlap of the receptive fields (RFs) of V1 and V4. Channel depths alignment was referenced to laminar layer IV, based on spatial current source density and temporal latency analyses. Our analyses mainly focused on the study of Local Field Potential (LFP) signals, for which we applied local (bipolar) re-referencing offline. We investigated the effects of attention on LFP spectral power and laminar interactions between LFP signals at different depths, both at the local level within V1 and V4, and at the inter-areal level across V1 and V4. Inspired by current progress from literature, we were interested in the characterization of frequency-specific laminar interactions, which we investigated both in terms of rhythmic synchronization by computing spectral coherence, and in terms of directed causal influence, by computing Granger causalities (GCs). The spectral power of LFPs in different frequency bands showed relatively small differences along cortical depths both in V1 and in V4. However, we found attentional effects on LFP spectral power consistent with previous literature. For V1 LFPs, attention to stimuli in RF location mainly resulted in a shift of the low-gamma (∼30-50 Hz) spectral power peak towards (∼3-4 Hz) higher frequencies and increases in power for frequency bands above low-gamma peak frequencies, as well as decreases in power below these frequencies. For V4 LFPs, attention towards stimuli in RF locations caused a decrease in power for frequencies < 20 Hz and a broad band increase for frequencies > 20 Hz. Attention affected spectral coherence within V1 and within V4 layers in similar way as the spectral power modulation described above. Spectral coherence between V1 and V4 channel pairs was increased by attention mainly in the beta band (∼ 15-30 Hz) and the low-gamma range (∼ 30-50 Hz). Attention affected GC interactions in a layer and frequency dependent manner in complex ways, not always compliant with predictions made by the canonical models of laminar feed-forward and feed-back interactions. Within V1, attention increased feed-forward efficacy across almost all low-frequency bands (∼ 2-50 Hz). Within V4, attention mostly increased GCs in the low and high gamma frequency in a 'downwards' direction within the column, i.e. from supragranular to granular and to infragranular layers. Increases were also evident in an ‘upwards’ direction from granular to supragranular layers. For inter-areal GCs, the dominant changes were an increase in the gamma frequency range from V1 granular and infragranular layers to V4 supragranular and granular layers, as well as an increase from V4 supragranular layers to all V1 layers.
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Zhu, Chao. "Effective and efficient visual description based on local binary patterns and gradient distribution for object recognition." Phd thesis, Ecole Centrale de Lyon, 2012. http://tel.archives-ouvertes.fr/tel-00755644.
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Cette thèse est consacrée au problème de la reconnaissance visuelle des objets basé sur l'ordinateur, qui est devenue un sujet de recherche très populaire et important ces dernières années grâce à ses nombreuses applications comme l'indexation et la recherche d'image et de vidéo , le contrôle d'accès de sécurité, la surveillance vidéo, etc. Malgré beaucoup d'efforts et de progrès qui ont été fait pendant les dernières années, il reste un problème ouvert et est encore considéré comme l'un des problèmes les plus difficiles dans la communauté de vision par ordinateur, principalement en raison des similarités entre les classes et des variations intra-classe comme occlusion, clutter de fond, les changements de point de vue, pose, l'échelle et l'éclairage. Les approches populaires d'aujourd'hui pour la reconnaissance des objets sont basé sur les descripteurs et les classiffieurs, ce qui généralement extrait des descripteurs visuelles dans les images et les vidéos d'abord, et puis effectue la classification en utilisant des algorithmes d'apprentissage automatique sur la base des caractéristiques extraites. Ainsi, il est important de concevoir une bonne description visuelle, qui devrait être à la fois discriminatoire et efficace à calcul, tout en possédant certaines propriétés de robustesse contre les variations mentionnées précédemment. Dans ce contexte, l'objectif de cette thèse est de proposer des contributions novatrices pour la tâche de la reconnaissance visuelle des objets, en particulier de présenter plusieurs nouveaux descripteurs visuelles qui représentent effectivement et efficacement le contenu visuel d'image et de vidéo pour la reconnaissance des objets. Les descripteurs proposés ont l'intention de capturer l'information visuelle sous aspects différents. Tout d'abord, nous proposons six caractéristiques LBP couleurs de multi-échelle pour traiter les défauts principaux du LBP original, c'est-à-dire, le déffcit d'information de couleur et la sensibilité aux variations des conditions d'éclairage non-monotoniques. En étendant le LBP original à la forme de multi-échelle dans les différents espaces de couleur, les caractéristiques proposées non seulement ont plus de puissance discriminante par l'obtention de plus d'information locale, mais possèdent également certaines propriétés d'invariance aux différentes variations des conditions d'éclairage. En plus, leurs performances sont encore améliorées en appliquant une stratégie de l'image division grossière à fine pour calculer les caractéristiques proposées dans les blocs d'image afin de coder l'information spatiale des structures de texture. Les caractéristiques proposées capturent la distribution mondiale de l'information de texture dans les images. Deuxièmement, nous proposons une nouvelle méthode pour réduire la dimensionnalité du LBP appelée la combinaison orthogonale de LBP (OC-LBP). Elle est adoptée pour construire un nouveau descripteur local basé sur la distribution en suivant une manière similaire à SIFT. Notre objectif est de construire un descripteur local plus efficace en remplaçant l'information de gradient coûteux par des patterns de texture locales dans le régime du SIFT. Comme l'extension de notre première contribution, nous étendons également le descripteur OC-LBP aux différents espaces de couleur et proposons six descripteurs OC-LBP couleurs pour améliorer la puissance discriminante et la propriété d'invariance photométrique du descripteur basé sur l'intensité. Les descripteurs proposés capturent la distribution locale de l'information de texture dans les images. Troisièmement, nous introduisons DAISY, un nouveau descripteur local rapide basé sur la distribution de gradient, dans le domaine de la reconnaissance visuelle des objets. [...]
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Abid, Muhammad Rizwan. "Visual Recognition of a Dynamic Arm Gesture Language for Human-Robot and Inter-Robot Communication." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32800.
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This thesis presents a novel Dynamic Gesture Language Recognition (DGLR) system for human-robot and inter-robot communication.
We developed and implemented an experimental setup consisting of a humanoid robot/android able to recognize and execute in real time all the arm gestures of the Dynamic Gesture Language (DGL) in similar way as humans do.
Our DGLR system comprises two main subsystems: an image processing (IP) module and a linguistic recognition system (LRS) module. The IP module enables recognizing individual DGL gestures. In this module, we use the bag-of-features (BOFs) and a local part model approach for dynamic gesture recognition from images. Dynamic gesture classification is conducted using the BOFs and nonlinear support-vector-machine (SVM) methods. The multiscale local part model preserves the temporal context.
The IP module was tested using two databases, one consisting of images of a human performing a series of dynamic arm gestures under different environmental conditions and a second database consisting of images of an android performing the same series of arm gestures.
The linguistic recognition system (LRS) module uses a novel formal grammar approach to accept DGL-wise valid sequences of dynamic gestures and reject invalid ones. LRS consists of two subsystems: one using a Linear Formal Grammar (LFG) to derive the valid sequence of dynamic gestures and another using a Stochastic Linear Formal Grammar (SLFG) to occasionally recover gestures that were unrecognized by the IP module. Experimental results have shown that the DGLR system had a slightly better overall performance when recognizing gestures made by a human subject (98.92% recognition rate) than those made by the android (97.42% recognition rate).
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Ventura, Royo Carles. "Visual object analysis using regions and local features." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/398407.
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The first part of this dissertation focuses on an analysis of the spatial context in semantic image segmentation. First, we review how spatial context has been tackled in the literature by local features and spatial aggregation techniques. From a discussion about whether the context is beneficial or not for object recognition, we extend a Figure-Border-Ground segmentation for local feature aggregation with ground truth annotations to a more realistic scenario where object proposals techniques are used instead. Whereas the Figure and Ground regions represent the object and the surround respectively, the Border is a region around the object contour, which is found to be the region with the richest contextual information for object recognition. Furthermore, we propose a new contour-based spatial aggregation technique of the local features within the object region by a division of the region into four subregions. Both contributions have been tested on a semantic segmentation benchmark with a combination of free and non-free context local features that allows the models automatically learn whether the context is beneficial or not for each semantic category.
The second part of this dissertation addresses the semantic segmentation for a set of closely-related images from an uncalibrated multiview scenario. State-of-the-art semantic segmentation algorithms fail on correctly segmenting the objects from some viewpoints when the techniques are independently applied to each viewpoint image. The lack of large annotations available for multiview segmentation do not allow to obtain a proper model that is robust to viewpoint changes. In this second part, we exploit the spatial correlation that exists between the different viewpoints images to obtain a more robust semantic segmentation. First, we review the state-of-the-art co-clustering, co-segmentation and video segmentation techniques that aim to segment the set of images in a generic way, i.e. without considering semantics. Then, a new architecture that considers motion information nd provides a multiresolution segmentation is proposed for the co-clustering framework nd outperforms state-of-the-art techniques for generic multiview segmentation. Finally, the proposed multiview segmentation is combined with the semantic segmentation results giving a method for automatic resolution selection and a coherent semantic multiview segmentation.La primera part de la tesi es focalitza en l'anàlisi del context espacial en la segmentació semàntica d'imatges. En primer lloc, revisem com s'ha tractat el context espacial en la literatura per mitjà de descriptors locals i tècniques d'agregació espacial. A partir de la discussió sobre si el context és beneficial o no per al reconeixement d'objectes, extenem una segmentació en objecte, contorn i fons per a l'agregació espacial de descriptors locals amb annotacions a un escenari més realístic on s'utilitzen hipòtesis de localitzacions d'objectes enlloc d'annotacions. Mentres que les regions corresponen a objecte i fons representes aquestes àrees respectives de la imatge, el contorn és una regió al voltant de l'objecte, la qual ha resultat ser la regió més rica amb informació contextual per al reconeixement d'objectes. A més a més, proposem una nova tècnica d'agregació espacial dels descriptors locals de l'interior de l'objecte amb una divisió d'aquesta regió en 4 subregions. Ambdues contribucions han estat verificades en un benchmark de segmentació semàntica amb la combinació de descriptors locals dependents i independents del context que permet que els models automàticament aprenguin si el context és beneficiós o no per a cada categoria semàntica. La segona part de la tesi aborda el problema de segmentació semàntica per a un conjunt d'imatges relacionades en un escenari multi-vista sense calibració. Els algorismes de l'estat de l'art en segmentació semàntica fallen en segmentar correctament els objects dels diferents punts de vista quan les tècniques són aplicades de forma independent a cadascun dels punts de vista. La manca d'un nombre elevat d'annotacions disponibles per a segmentació multi-vista no permet obtenir un model que sigui robust als canvis de vista. En aquesta segona part, explotem la correlació espacial existent entre els diferents punts de vista per obtenir una segmentació semàntica més robusta. En primer lloc, revisem les tècniques de l'estat de l'art en co-agrupament, co-segmentació i segmentació de vídeo que tenen per objectiu segmentar el conjunt d'imatges de forma genèrica, és a dir, sense considerar la semàntica. A continuació, proposem una nova arquitectura de co-agrupament que considera informació de moviment i proveeix una segmentació amb múltiples resolucions i millora les tècniques de l'estat de l'art en segmentació genèrica multi-vista. Finalment, la segmentació multivista proposada és combinada amb els resultats de la segmentació semàntica donant lloc a un mètode per a una selecció automàtica de la resolució i una segmentació semàntica multi-vista coherent.
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Bai, Hequn. "Mobile 3D Visual Search based on Local Stereo Image Features." Thesis, KTH, Ljud- och bildbehandling, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-102603.
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Many recent applications using local image features focus on 2D image recognition. Such applications can not distinguish between real objects and photos of objects. In this project, we present a 3D object recognition method using stereo images. Using the 3D information of the objects obtained from stereo images, objects with similar image description but different 3D shapes can be distinguished, such as real objects and photos of objects. Besides, the feature matching performance is improved compared with the method using only local image features. Knowing the fact that local image features may consume higher bitrates than transmitting the compressed images itself, we evaluate the performance of a recently proposed low-bitrate local image feature descriptor CHoG in 3D object reconstruction and recognition, and propose a difference compression method based on the quantized CHoG descriptor, which further reduces bitrates.
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Le, Viet Phuong. "Logo detection, recognition and spotting in context by matching local visual features." Thesis, La Rochelle, 2015. http://www.theses.fr/2015LAROS029/document.
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Cette thèse présente un framework pour le logo spotting appliqué à repérer les logos à partir de l’image des documents en se concentrant sur la catégorisation de documents et les problèmes de récupération de documents. Nous présentons également trois méthodes de matching par point clé : le point clé simple avec le plus proche voisin, le matching par règle des deux voisins les plus proches et le matching par deux descripteurs locaux à différents étapes de matching. Les deux derniers procédés sont des améliorations de la première méthode. En outre, utiliser la méthode de classification basée sur la densité pour regrouper les correspondances dans le framework proposé peut aider non seulement à segmenter la région candidate du logo mais également à rejeter les correspondances incorrectes comme des valeurs aberrantes. En outre, afin de maximiser la performance et de localiser les logos, un algorithme à deux étages a été proposé pour la vérification géométrique basée sur l’homographie avec RANSAC. Comme les approches fondées sur le point clé supposent des approches coûteuses, nous avons également investi dans l’optimisation de notre framework. Les problèmes de séparation de texte/graphique sont étudiés. Nous proposons une méthode de segmentation de texte et non-texte dans les images de documents basée sur un ensemble de fonctionnalités puissantes de composants connectés. Nous avons appliqué les techniques de réduction de dimensionnalité pour réduire le vecteur de descripteurs locaux de grande dimension et rapprocher les algorithmes de recherche du voisin le plus proche pour optimiser le framework. En outre, nous avons également mené des expériences pour un système de récupération de documents sur les documents texte et non-texte segmentés et l'algorithme ANN. Les résultats montrent que le temps de calcul du système diminue brusquement de 56% tandis que la précision diminue légèrement de près de 2,5%. Dans l'ensemble, nous avons proposé une approche efficace et efficiente pour résoudre le problème de spotting des logos dans les images de documents. Nous avons conçu notre approche pour être flexible pour des futures améliorations. Nous croyons que notre travail peut être considéré comme une étape sur la voie pour résoudre le problème de l’analyse complète et la compréhension des images de documentsThis thesis presents a logo spotting framework applied to spotting logo images on document images and focused on document categorization and document retrieval problems. We also present three key-point matching methods: simple key-point matching with nearest neighbor, matching by 2-nearest neighbor matching rule method and matching by two local descriptors at different matching stages. The last two matching methods are improvements of the first method. In addition, using a density-based clustering method to group the matches in our proposed spotting framework can help not only segment the candidate logo region but also reject the incorrect matches as outliers. Moreover, to maximize the performance and to locate logos, an algorithm with two stages is proposed for geometric verification based on homography with RANSAC. Since key-point-based approaches assume costly approaches, we have also invested to optimize our proposed framework. The problems of text/graphics separation are studied. We propose a method for segmenting text and non-text in document images based on a set of powerful connected component features. We applied dimensionality reduction techniques to reduce the high dimensional vector of local descriptors and approximate nearest neighbor search algorithms to optimize our proposed framework. In addition, we have also conducted experiments for a document retrieval system on the text and non-text segmented documents and ANN algorithm. The results show that the computation time of the system decreases sharply by 56% while its accuracy decreases slightly by nearly 2.5%. Overall, we have proposed an effective and efficient approach for solving the problem of logo spotting in document images. We have designed our approach to be flexible for future improvements by us and by other researchers. We believe that our work could be considered as a step in the direction of solving the problem of complete analysis and understanding of document images
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Asbach, Mark [Verfasser]. "Modeling for part-based visual object detection based on local features / Mark Asbach." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2012. http://d-nb.info/1021938211/34.
Full textBooks on the topic "Local visual feature"
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O’Neal, M. Angela. Postpartum Visual Disturbance. Edited by Angela O’Neal. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190609917.003.0017.
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Preeclampsia (PE) is a multi-organ system disorder defined as hypertension with blood pressures greater than 140/90 on two occasions and proteinuria of more than 300 mg/24 hours. Eclampsia is defined as when seizures occur in a woman with preeclampsia. The pathophysiology of preeclampsia/eclampsia is felt to be related to incomplete penetration of the cytotrophoblasts of the placenta into the myometrium, leading to local ischemia, propagation of ischemic factors causing hypertension, resulting in endothelial dysfunction. The clinical features are related to which end organ is involved: in the kidney, proteinuria; in the liver, coagulopathy; and in the brain, posterior white matter dysfunction. The involvement of the parietal and occipital lobes explains the associated neurological features of confusion and visual changes. MRI reflects the white matter changes associated with eclampsia in posterior reversible encephalopathy syndrome (PRES). Eclampsia is treated with blood pressure control and magnesium to treat the seizures.
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Wade, Nicholas J. Hidden Images. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199794607.003.0113.
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It is relatively easy to hide pictorial images, but this is of little value if they remain hidden. Presenting hidden images for visual purposes is a modern preoccupation, and some of the perceptual processes involved in them are described in this chapter. Pictorial images can be concealed in terms of detection or recognition. In both cases there is interplay between the global features of the concealed image and the local elements that carry it. Gestalt grouping principles can hinder as well as help recognition. Examples of images (mostly faces) hidden in geometrical designs and text as well as orientation are shown. Rather than being pictorial puzzles alone, hidden images can reveal aspects of visual processing. This chapter explores these concepts and related ideas such as perceptual portraits and pictorial puzzles.
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Forshaw, Joseph, and William Cooper. Pigeons and Doves in Australia. CSIRO Publishing, 2015. http://dx.doi.org/10.1071/9781486304042.
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Possibly the most successful urban birds, pigeons and doves in the Order Columbiformes are one of the most easily recognised groups. They are an ancient and very successful group with an almost worldwide distribution and are most strongly represented in tropical and subtropical regions, including Australia. In most species simple plumage patterns feature mainly grey and brown with black, white or dull reddish markings, but the highly colourful fruit-doves include some of the most beautiful of all birds.
From dense rainforests of north Queensland, where brilliantly plumaged Superb Fruit-Doves Ptilinopus superbus are heard more easily than seen, to cold, windswept heathlands of Tasmania, where Brush Bronzewings Phaps elegans are locally common, most regions of Australia are frequented by one or more species. For more than a century after arrival of the First Fleet, interest in these birds focused on the eating qualities of larger species. In addition to contributing to declines of local populations in some parts of Australia, excessive hunting brought about the extinction of two species on Lord Howe Island and another species on Norfolk Island.
In Pigeons and Doves in Australia, Joseph Forshaw and William Cooper have summarised our current knowledge of all species, including those occurring on Christmas, Norfolk and Lord Howe Islands, and with superb artwork have given readers a visual appreciation of the birds in their natural habitats. Historical accounts of extinct species are also included. Detailed information on management practices for all species is presented, ensuring that Pigeons and Doves in Australia will become the standard reference work on these birds for ornithologists and aviculturists.
Winner of a 2015 Whitley Awards Certificate of Commendation for Illustrated Text.
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Ilan, Jonathan, and Gregory J. Snyder. Graffiti. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199935383.013.144.
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Graffiti writing is often intensely policed despite being a relatively low-harm crime. Graffiti can be read by members of the public as a visual indicator of lawlessness and thus induce a certain amount of alarm. While there are a range of different kinds of graffiti, the most ubiquitous is that practiced by subcultural “writers.” Research indicates that there is an order and logic to writing. Writers often cultivate skills, experiences, and dispositions that imbue them with particular value in the postindustrial economy, offering them heightened career prospects. Graffiti and street art are increasingly featured as part of mainstream commercial aesthetics, even as unsanctioned writing continues to be met with zero-tolerance policing and tough situational crime prevention measures. Ultimately, writing can be managed in a more subculturally sensitive manner that better balances the needs and visions of different kinds of urban residents in local contexts.
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Eitan, Zohar, Renee Timmers, and Mordechai Adler. Cross-modal correspondences and affect in a Schubert song. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199351411.003.0006.
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Light, distance and motion are prominent features in Heine’s ‘Am fernen Horizonten’. A city is veiled in dusk, the sun rises from the earth and the boatman rows with sad strokes. Using empirical findings on cross-modal and affective associations with sounds, we examine Schubert’s interpretation and illustration of these metaphorical dimensions in ‘Die Stadt’. Focusing on local variations in tempo and dynamics, we analyse how the emotional and cross-modal connotations of the song are modified in three performances, provindinginsight into the interrelationship between cross-modal and affective connotations of musical sound. Such interrelationships may suggest complex and often equivocal musical meanings. For example, emotional ‘distance’ is associated with physical distance, as modulated by loudness; visual brightness, as modulated by pitch and timbre, can be painful when unveiling a ‘dark’ memory. Thus, our analysis indicates how musical structures and contours may suggest and interact with perceptual and metaphorical shape in multiple dimensions.
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Stamenkoviç, Marko, ed. Resistance. 2nd ed. punctum books, 2021. http://dx.doi.org/10.53288/0384.1.00.
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esistance features a selection of overtly non-conformist positions in the contemporary visual art scene of Albania vis-à-vis the most recent social, political, and economic turmoils in the Western Balkans – a region marked by the dark side of political governances that have remained “democratic” in their outward appearance (especially toward the European Union), while dramatically leaning toward autocratic regimes in the eyes of their own citizens. Regardless of their citizens’ primary interests, and despite some positive signals surfacing in the international media, almost every attempt to establish lasting conditions for democratic governance in the Western Balkans has been shrouded in the veil of profit-driven political scandals, personal greed for more and more power over the people’s rights, and the extinction of public property in pursuit of social elite’s corporate and private interests. Additionally, and more specifically related to Tirana, artists and citizens have, over the years, been involved in various types of revolt, expressing their disagreements with the ongoing destruction of public property in the name of “modernization and development”: a movement led by local political powers through financially and strategically motivated processes of architectural cannibalism – not only at the expense of erasing Albanian cultural heritage or long-term residents’ habitats, but also at the expense of taking human lives under the pretext of “urbanization.” The most obvious instance of this economy of destruction was the complex of buildings linked to the National Theater of Albania in downtown Tirana that has served as a symbolic and material place of citizens’ resistance: for more than two years, together with local artists, they have been opposing the government’s plans to demolish the old complex in order to build a new one – until this finally happened in Spring 2020, in the midst of the ongoing COVID19 pandemic. Rooted in the atmosphere of the National Theater Protests in Tirana, RESISTANCE was conceived in Summer 2019 by ZETA Center for Contemporary Art as the International Artists-in-Residence Program, in cooperation with three partner organizations from Kosovo, Serbia and North Macedonia (Stacion – Center for Contemporary Art in Prishtina; Ilija & Mangelos Foundation in Novi Sad; and Faculty of Things That Can’t Be Learned in Bitola) and supported by Swiss Cultural Fund in Albania, a project of the Swiss Agency for Development and Cooperation. Gradually, the project expanded into an exhibition (Heterotopias of Resistance, curated by Blerta Hoçia and featuring works by Lori Lako, Fatlum Doçi, Edona Kryeziu, Nina Galiç, Darko Vukiç, Nikola Slavevski, and Natasha Nedelkova) and a series of interviews and panel discussions (with contributions by Lindita Komani, Edmond Budina, Ervin Goci, Ergin Zaloshnja, Pleurad Xhafa, Gentian Shkurti, Stefano Romano, Luçjan Bedeni, HAVEIT, Leonard Qylafi, Jonida Gashi, and Fatmira Nikolli). The results of both have been collected and presented in the format of a publication that, besides serving as an indispensable reading material concerning visual arts and politics in contemporary Albania, especially to those abroad, functions by itself as a form of resistance against contagious cultural policies in weak post-socialist “democracies” in Southeastern Europe.
Book chapters on the topic "Local visual feature"
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Gruchalla, Kenny, Mark Rast, Elizabeth Bradley, and Pablo Mininni. "Segmentation and Visualization of Multivariate Features Using Feature-Local Distributions." In Advances in Visual Computing, 619–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24028-7_57.
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Razali, Mohd Norhisham, Noridayu Manshor, Alfian Abdul Halin, Razali Yaakob, and Norwati Mustapha. "Food Category Recognition Using SURF and MSER Local Feature Representation." In Advances in Visual Informatics, 212–23. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70010-6_20.
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Shi, Xun, Neil D. B. Bruce, and John K. Tsotsos. "Biologically Motivated Local Contextual Modulation Improves Low-Level Visual Feature Representations." In Lecture Notes in Computer Science, 79–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31295-3_10.
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Turcsany, Diana, and Andrzej Bargiela. "Learning Local Receptive Fields in Deep Belief Networks for Visual Feature Detection." In Neural Information Processing, 462–70. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12637-1_58.
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Xu, Xin, and Jie Wang. "Extended Non-local Feature for Visual Saliency Detection in Low Contrast Images." In Lecture Notes in Computer Science, 580–92. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11018-5_46.
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Lu, Huimin, Hui Zhang, and Zhiqiang Zheng. "A Novel Real-Time Local Visual Feature for Omnidirectional Vision Based on FAST and LBP." In RoboCup 2010: Robot Soccer World Cup XIV, 291–302. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20217-9_25.
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Khellat-Kihel, Souad, Zhenan Sun, and Massimo Tistarelli. "An Hybrid Attention-Based System for the Prediction of Facial Attributes." In Lecture Notes in Computer Science, 116–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_9.
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AbstractRecent research on face analysis has demonstrated the richness of information embedded in feature vectors extracted from a deep convolutional neural network. Even though deep learning achieved a very high performance on several challenging visual tasks, such as determining the identity, age, gender and race, it still lacks a well grounded theory which allows to properly understand the processes taking place inside the network layers. Therefore, most of the underlying processes are unknown and not easy to control. On the other hand, the human visual system follows a well understood process in analyzing a scene or an object, such as a face. The direction of the eye gaze is repeatedly directed, through purposively planned saccadic movements, towards salient regions to capture several details. In this paper we propose to capitalize on the knowledge of the saccadic human visual processes to design a system to predict facial attributes embedding a biologically-inspired network architecture, the HMAX. The architecture is tailored to predict attributes with different textural information and conveying different semantic meaning, such as attributes related and unrelated to the subject’s identity. Salient points on the face are extracted from the outputs of the S2 layer of the HMAX architecture and fed to a local texture characterization module based on LBP (Local Binary Pattern). The resulting feature vector is used to perform a binary classification on a set of pre-defined visual attributes. The devised system allows to distill a very informative, yet robust, representation of the imaged faces, allowing to obtain high performance but with a much simpler architecture as compared to a deep convolutional neural network. Several experiments performed on publicly available, challenging, large datasets demonstrate the validity of the proposed approach.
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Kampel, Martin, and Maia Zaharieva. "Recognizing Ancient Coins Based on Local Features." In Advances in Visual Computing, 11–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89639-5_2.
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Zohrizadeh, Fariba, Mohsen Kheirandishfard, Kamran Ghasedidizaji, and Farhad Kamangar. "Reliability-Based Local Features Aggregation for Image Segmentation." In Advances in Visual Computing, 193–202. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50835-1_18.
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Alex, Ann Theja, Vijayan K. Asari, and Alex Mathew. "Local Alignment of Gradient Features for Face Sketch Recognition." In Advances in Visual Computing, 378–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33191-6_37.
Full textConference papers on the topic "Local visual feature"
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Han, Yuping, Yajing Xu, Shishuo Liu, Sheng Gao, and Si Li. "Visual Relationship Detection Based on Local Feature and Context Feature." In 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC). IEEE, 2018. http://dx.doi.org/10.1109/icnidc.2018.8525683.
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Xu, Jingtao, Qiaohong Li, Peng Ye, Haiqing Du, and Yong Liu. "Local feature aggregation for blind image quality assessment." In 2015 Visual Communications and Image Processing (VCIP). IEEE, 2015. http://dx.doi.org/10.1109/vcip.2015.7457832.
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El-Gaaly, Tarek, Marwan Torki, and Ahmed Elgammal. "Spatial-Visual Label Propagation for Local Feature Classification." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.589.
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Zhou, Wengang, Houqiang Li, and Qi Tian. "Scalable local feature matching without visual codebook training." In the 7th International Conference. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2808492.2808575.
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Anh, La Tuan, and Jae-Bok Song. "Object tracking and visual servoing using features computed from local feature descriptor." In 2010 International Conference on Control, Automation and Systems (ICCAS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccas.2010.5669666.
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Bucak, Serhat, Ankur Saxena, Abhishek Nagar, Felix Fernandes, and Kong-Posh Bhat. "Mid-level feature based local descriptor selection for image search." In 2013 Visual Communications and Image Processing (VCIP). IEEE, 2013. http://dx.doi.org/10.1109/vcip.2013.6706455.
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Liu, Zhaoliang, Ling-Yu Duan, Jie Chen, and Tiejun Huang. "Depth-based local feature selection for mobile visual search." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7532362.
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Chandrasekhar, Vijay, David M. Chen, Andy Lin, Gabriel Takacs, Sam S. Tsai, Ngai-Man Cheung, Yuriy Reznik, Radek Grzeszczuk, and Bernd Girod. "Comparison of local feature descriptors for mobile visual search." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5649937.
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Miao, Jinyu, Haosong Yue, Zhong Liu, Xingming Wu, Zaojun Fang, and Guilin Yang. "Real-time Local Feature with Global Visual Information Enhancement." In 2022 IEEE 17th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2022. http://dx.doi.org/10.1109/iciea54703.2022.10006314.
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Lee, Xing Zhao, Hao Wang, Jiangtao Kong, Chi Su, Junliang Xing, and Sheng Mei Shen. "Global and Local Deep Feature Representation Fusion for Vehicle Re-Identification." In 2019 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2019. http://dx.doi.org/10.1109/vcip47243.2019.8965856.
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