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Object deformation and occlusion are ubiquitous problems for visual tracking. Though many efforts have been made to handle object deformation and occlusion, most existing tracking algorithms fail in case of large deformation and severe occlusion. In this paper, we propose a graph learning-based tracking framework to handle both challenges. For each consecutive frame pair, we construct a weighted graph,...
We present an application of a Multiple Instance Learning (MIL) approach to image classification. In particular we focus on a recent MIL method for binary classification where the objective is to discriminate between positive and negative sets of points. Such sets are called bags and the points inside the bags are called instances. In the case of two classes of instances (positive and negative), a...
In this paper, we present an efficient approach to investigate data of EEG-based Brain-Machine Interface (BMI) using a bagging Support Vector Machines (SVMs) for collected data classification from a P3-speller paradigm. The combination of SVMs allows to handle the problem of EEG data variability between the different sessions of the acquisition process. This variability is caused by temporal non-stationarity...
In this paper, a system to aid the visually impaired by providing contextual information of the surroundings using 360° view camera combined with deep learning is proposed. The system uses a 360° view camera with a mobile device to capture surrounding scene information and provide contextual information to the user in the form of audio. The scene information from the spherical camera feed is classified...
The process through which children learn about the world and develop perceptual, cognitive and motor skills relies heavily on object exploration in their physical world. New types of assistive technology that enable children with impairments to interact with their environment have emerged in recent years, and they could be beneficial for children's cognitive and perceptual skills development. Many...
Image classification is a method that distinguishes the different categories of targets based on the different features of image. The current problem usually is that the feature modeling of target has a great influence on recognition robustness. In order to solve this problem, a correlation-based method is presented to optimize the bag-of-visual-word (BOVW) model by reducing the dictionary size. The...
UAVs (Unmanned Aerial Vehicles) have been widely used in power line inspections, but low autonomous cruise capacity of UAVs requires strict condition for operators and site while landing during UAV power line inspections. This paper presents an autonomous landing control technique for UAVs when charging at the electric towers based on vision positioning method. The proposed system consists of three...
The inherent dependencies between visual elements and aural elements are crucial for affective video content analyses, yet have not been successfully exploited. Therefore, we propose a multimodal deep regression Bayesian network (MMDRBN) to capture the dependencies between visual elements and aural elements for affective video content analyses. The regression Bayesian network (RBN) is a directed graphical...
Many existing person re-identification (PRID) methods typically attempt to train a faithful global metric offline to cover the enormous visual appearance variations, so as to directly use it online on various probes for identity match- ing. However, their need for a huge set of positive training pairs is very demanding in practice. In contrast to these methods, this paper advocates a different paradigm:...
The present study describes the collaborative writing problem of Hana nikki, a novel published in Yasunari Kawabata’s name but suspected to be written by Tsuneko Nakazato. The aim of the present study is to visualize variances between the writing styles of the author (Yasunari Kawabata) and the potential co-author (Tsuneko Nakazato). We performed the rolling-SVM method on the pre-established...
Although shadows in images have a constructive role providing a natural view of features of the scene, they also have a destructive role in image processing by hiding significant information. Improving the quality of 3D textured models for serious games and augmented reality applications via shadow detection and removal remains challenging due to the complexity of an image scene. This paper proposes...
Recent statistics show that more than 10 million people in the world suffer amputation. Most of these people also have depression because of losing their hand, arm and leg movements. With current technology it is possible to give these people hands, arms and legs. Our aim is to give these people a chance to live. In this study we have designed a robotic hand in order to grasp objects. Grid based feature...
This paper aims to develop an effective flower classification approach using the technology of feature extraction. With this regard, a fused descriptor based on Pyramid Histogram of Visual Words (PHOW) is used to extract the color, texture and contour information of flower image. Secondly, Dictionary Learning and Locality-constrained Linear Coding (LLC) are operated on PHOW feature and then images...
Gaze analysis in dynamic environments has remained an unresolved problem due to the complexities that pertain to the detection and tracking of objects in the visual environment. This study provides a solution to the problem for face-to-face communication, in which the visual objects in the environment are faces. The application that has been developed for this purpose is able to detect and track faces...
To improve the accuracy of surface defect detection, an approach of defect inspection based on visual saliency map and Support Vector Machine(SVM) is proposed. Monochrome fabric defect images are taken as examples in this paper. By analyzing the visual saliency maps of these images, the global associated value and the background associated value are extracted as the two features. After being normalized,...
Evaluating aesthetic value of digital photographs is a challenging task, mainly due to numerous factors that need to be taken into account and subjective manner of this process. In this paper, we propose to approach this problem using deep convolutional neural networks. Using a dataset of over 1.7 million photos collected from Flickr, we train and evaluate a deep learning model whose goal is to classify...
This paper presents a new discriminative learning framework to associate the relationship between the objects and the words in an image and perform template matching scheme for complex association patterns. The problem is first formulated as a bipartite graph matching problem. Thereafter, structural support vector machine (SVM) is employed to obtain the optimal compatibility function to encode the...
Image classification algorithms using state-of-the art methods have grabbed much attention in computer vision area. In-domain classification assumes the testing data to be in the same domain as of the training data. Cross-Domain classification is a paradigm where testing data is from a different but related domain to the training data. We use Speeded-Up Robust Features (SURF) for feature extraction,...
In this paper, we introduce a digital edition of the Altan Tobchi, a Mongolian historical manuscript written in traditional Mongolian script. The Text Encoding Initiative guidelines were adopted to encode the named entities, commentaries, transcriptions, and interpretations of ancient Mongolian words. Named entities such as personal names and place names were extracted from digitized text by employing...
Recognition of vehicle types in real life traffic scenarios is a challenging task due to the diversity of vehicles and uncontrolled environments. Efficient methods and feature representations are needed to cope with these challenges. In this paper, we address the vehicle type classification problem in real life traffic scenarios and propose a multimodal method that uses efficient representations of...
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