Abstract: Speaking of road maintenance, the preventive maintenance strategy is preferable for most governments. Many governments possess special vehicles that can accurately detect and classify many types of road distresses. By running these vehicles frequently, small road distresses will be detected before growing into the big ones. However, because running these huge and expensive vehicles is not easy, in practical, it usually ends up with infrequent road inspection regardless of having automatic road inspection vehicles. In this paper, we focus on investigating and developing an automatic and nondestructive visual inspection system whose setup and usage are designed by considering the context of drivers, driving styles, and road conditions in Bangkok, the capital city of Thailand. Our proposal includes a workflow diagram of a vision-based road inspection system that is capable of detecting, classifying, tracking, measuring, and pricing road distresses. As for the proof-of-concept, our current system focuses on detecting one specific type of road distresses called pothole, using only one onboard in-car camera. Experimental results reveal that the context of Bangkok introduces many nontrivial challenges for vision-based analysis systems where maintaining both accuracy and ease of use altogether may not be easy. PubDate: Sun, 03 Jun 2018 06:53:45 +000
Abstract: Saliency detection has attracted significant attention in the field of computer vision technology over years. At present, more than 100 saliency detection models have been proposed. In this paper, a relatively more detailed classification is proposed. Furthermore, we selected 25 models and evaluated their performance using four public image datasets. We also discussed common problems, such as the influence to model performance by prior information and multiple objects. Finally, we offered future research directions. PubDate: Sun, 03 Jun 2018 00:00:00 +000
Abstract: The aim of this paper is to evaluate user satisfaction based on System Usability Scale (SUS) questionnaire on Augmented Reality (AR) application for productive vocabulary using speech recognition. There is still lack of research focus on user satisfaction towards the use of AR-based app using speech recognition for vocabulary learning in early education. The first objective of this paper is to develop an AR application for children using speech recognition to enhance productive vocabulary learning that integrates visual script (orthography) and audio (phonology). The second objective is to evaluate and identify the user satisfaction in enhancing productive vocabulary methods by combining AR technology and speech recognition to the parents and teachers by doing a mixed method testing such as questionnaire and interview. To achieve this, an interview session was conducted with the experts and SUS questionnaire was given to the teachers and parents of the student to evaluate user satisfaction. The results show that the research hypotheses of this study were successfully achieved. It revealed that the teachers and students were satisfied with the application based on SUS score (SUS score > 68). Since the total SUS score is 80.3, which is above average, it shows that they were satisfied with the overall application. Apart from that, the mean of the usability study based on questionnaire also shows that the users have found that this application is usable in terms of learnability study and that this application is easy to use. PubDate: Sun, 03 Jun 2018 00:00:00 +000
Abstract: Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network. PubDate: Thu, 24 May 2018 00:00:00 +000
Abstract: The interaction paradigm has changed with the emerging technology, mobile augmented reality, and spatial awareness mobile device. The traditional way for designing a kitchen can cause mistake in measurement and user’s imagination is different from kitchen advisor’s sketch design due to limitation of human imagination. Using mobile augmented reality technology, the user can overlay the virtual kitchen outcome that could fit with the actual kitchen environment. Interaction technique is required in order to allow the user to change the characteristic of the virtual kitchen to suite their need. Thus, this paper is intended to propose the tangible and spatial awareness interaction techniques in the kitchen design authoring tool. A scoping review related to the previous research on tangible and spatial awareness interaction is presented in this paper. The proposed techniques were based on the review on the existing interaction technique and the interview section with the Malaysian kitchen designer by understanding the kitchen design elements and flow. The proposed techniques will be further improved by going through the heuristic evaluation with the augmented reality expert. PubDate: Mon, 21 May 2018 00:00:00 +000
Abstract: In person reidentification distance metric learning suffers a great challenge from impostor persons. Mostly, distance metrics are learned by maximizing the similarity between positive pair against impostors that lie on different transform modals. In addition, these impostors are obtained from Gallery view for query sample only, while the Gallery sample is totally ignored. In real world, a given pair of query and Gallery experience different changes in pose, viewpoint, and lighting. Thus, impostors only from Gallery view can not optimally maximize their similarity. Therefore, to resolve these issues we have proposed an impostor resilient multimodal metric (IRM3). IRM3 is learned for each modal transform in the image space and uses impostors from both Probe and Gallery views to effectively restrict large number of impostors. Learned IRM3 is then evaluated on three benchmark datasets, VIPeR, CUHK01, and CUHK03, and shows significant improvement in performance compared to many previous approaches. PubDate: Thu, 03 May 2018 00:00:00 +000
Abstract: A novel method for camera calibration is proposed based on an analysis of lens distortion in camera imaging. In the method, a line through the centre of concentric circles is used as a template in which orthogonal directions can be determined from an angle of circumference that corresponds to a diameter. By using three lines through the centre of concentric circles, based on the invariance of the cross-ratio, an image at the centre of the concentric circles can be used to obtain the vanishing point. The intrinsic parameters of the camera can be computed based on the constraints of the orthogonal vanishing points and the imaged absolute conic. The lens distortion causes points in the template to have a position offset. In the proposed method, we optimize the positions of the distortion points such that they gradually approach those of the ideal points. The simulated and real-world experiments demonstrate that the proposed method is efficient and feasible. PubDate: Sun, 22 Apr 2018 00:00:00 +000
Abstract: As smartphones, tablet computers, and other mobile devices have continued to dominate our digital world ecosystem, there are many industries using mobile or wearable devices to perform Augmented Reality (AR) functions in their workplaces in order to increase productivity and decrease unnecessary workloads. Mobile-based AR can basically be divided into three main types: phone-based AR, wearable AR, and projector-based AR. Among these, projector-based AR or Spatial Augmented Reality (SAR) is the most immature and least recognized type of AR for end users. This is because there are a small number of commercial products providing projector-based AR functionalities in a mobile manner. Also, prices of mobile projectors are still relatively high. Moreover, there are still many technical problems regarding projector-based AR that have been left unsolved. Nevertheless, it is projector-based AR that has potential to solve a fundamental problem shared by most mobile-based AR systems. Also the always-visible nature of projector-based AR is one good answer for solving current user experience issues of phone-based AR and wearable AR systems. Hence, in this paper, we analyze what are the user experience issues and technical issues regarding common mobile-based AR systems, recently widespread phone-based AR systems, and rising wearable AR systems. Then for each issue, we propose and explain a new solution of how using projector-based AR can solve the problems and/or help enhance its user experiences. Our proposed framework includes hardware designs and architectures as well as a software computing paradigm towards mobile projector-based AR systems. The proposed design is evaluated by three experts using qualitative and semiquantitative research approaches. PubDate: Wed, 18 Apr 2018 00:00:00 +000
Abstract: Recognizing human actions in videos is an active topic with broad commercial potentials. Most of the existing action recognition methods are supposed to have the same camera view during both training and testing. And thus performances of these single-view approaches may be severely influenced by the camera movement and variation of viewpoints. In this paper, we address the above problem by utilizing videos simultaneously recorded from multiple views. To this end, we propose a learning framework based on multitask random forest to exploit a discriminative mid-level representation for videos from multiple cameras. In the first step, subvolumes of continuous human-centered figures are extracted from original videos. In the next step, spatiotemporal cuboids sampled from these subvolumes are characterized by multiple low-level descriptors. Then a set of multitask random forests are built upon multiview cuboids sampled at adjacent positions and construct an integrated mid-level representation for multiview subvolumes of one action. Finally, a random forest classifier is employed to predict the action category in terms of the learned representation. Experiments conducted on the multiview IXMAS action dataset illustrate that the proposed method can effectively recognize human actions depicted in multiview videos. PubDate: Tue, 20 Mar 2018 07:45:43 +000
Abstract: With the rise in biometric-based identity authentication, facial recognition software has already stimulated interesting research. However, facial recognition has also been subjected to criticism due to security concerns. The main attack methods include photo, video, and three-dimensional model attacks. In this paper, we propose a multifeature fusion scheme that combines dynamic and static joint analysis to detect fake face attacks. Since the texture differences between the real and the fake faces can be easily detected, LBP (local binary patter) texture operators and optical flow algorithms are often merged. Basic LBP methods are also modified by considering the nearest neighbour binary computing method instead of the fixed centre pixel method; the traditional optical flow algorithm is also modified by applying the multifusion feature superposition method, which reduces the noise of the image. In the pyramid model, image processing is performed in each layer by using block calculations that form multiple block images. The features of the image are obtained via two fused algorithms (MOLF), which are then trained and tested separately by an SVM classifier. Experimental results show that this method can improve detection accuracy while also reducing computational complexity. In this paper, we use the CASIA, PRINT-ATTACK, and REPLAY-ATTACK database to compare the various LBP algorithms that incorporate optical flow and fusion algorithms. PubDate: Thu, 08 Mar 2018 09:24:12 +000
Abstract: Extracting and recognizing complex human movements from unconstrained online/offline video sequence is a challenging task in computer vision. This paper proposes the classification of Indian classical dance actions using a powerful artificial intelligence tool: convolutional neural networks (CNN). In this work, human action recognition on Indian classical dance videos is performed on recordings from both offline (controlled recording) and online (live performances, YouTube) data. The offline data is created with ten different subjects performing 200 familiar dance mudras/poses from different Indian classical dance forms under various background environments. The online dance data is collected from YouTube for ten different subjects. Each dance pose is occupied for 60 frames or images in a video in both the cases. CNN training is performed with 8 different sample sizes, each consisting of multiple sets of subjects. The remaining 2 samples are used for testing the trained CNN. Different CNN architectures were designed and tested with our data to obtain a better accuracy in recognition. We achieved a 93.33% recognition rate compared to other classifier models reported on the same dataset. PubDate: Mon, 22 Jan 2018 00:00:00 +000
Abstract: Automatic colorization is generally classified into two groups: propagation-based methods and reference-based methods. In reference-based automatic colorization methods, color image(s) are used as reference(s) to reconstruct original color of a gray target image. The most important task here is to find the best matching pairs for all pixels between reference and target images in order to transfer color information from reference to target pixels. A lot of attractive local feature-based image matching methods have already been developed for the last two decades. Unfortunately, as far as we know, there are no optimal matching methods for automatic colorization because the requirements for pixel matching in automatic colorization are wholly different from those for traditional image matching. To design an efficient matching algorithm for automatic colorization, clustering pixel with low computational cost and generating descriptive feature vector are the most important challenges to be solved. In this paper, we present a novel method to address these two problems. In particular, our work concentrates on solving the second problem (designing a descriptive feature vector); namely, we will discuss how to learn a descriptive texture feature using scaled sparse texture feature combining with a nonlinear transformation to construct an optimal feature descriptor. Our experimental results show our proposed method outperforms the state-of-the-art methods in terms of robustness for color reconstruction for automatic colorization applications. PubDate: Wed, 10 Jan 2018 07:50:41 +000
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