Palmprint identification using sparse and dense hybrid representation

Multimedia Tools and Applications - Among various palmprint identification methods proposed in the literature, Sparse Representation for Classification (SRC) is very attractive, offering high...     

Applications,databases and open computer vision research from drone videos and images: a survey

Analyzing videos and images captured by unmanned aerial vehicles or aerial drones is an emerging application attracting significant attention from researchers in various areas of computer vision. Currently, the major challenge is the development of autonomous operations to complete missions and replace human operators. In this paper, based on the type of analyzing videos and images captured by drones in computer vision, we have reviewed these applications by categorizing them into three groups. The first group is related to remote sensing with challenges such as camera calibration, image matching, and aerial triangulation. The second group is related to drone-autonomous navigation, in which computer vision methods are designed to explore challenges such as flight control, visual localization and mapping, and target tracking and obstacle detection. The third group is dedicated to using images and videos captured by drones in various applications, such as surveillance, agriculture and forestry, animal detection, disaster detection, and face recognition. Since most of the computer vision methods related to the three categories have been designed for real-world conditions, providing real conditions based on drones is impossible. We aim to explore papers that provide a database for these purposes. In the first two groups, some survey papers presented are current. However, the surveys have not been aimed at exploring any databases. This paper presents a complete review of databases in the first two groups and works that used the databases to apply their methods. Vision-based intelligent applications and their databases are explored in the third group, and we discuss open problems and avenues for future research.

     

Why Would I Use This in My Home? A Model of Domestic Social Robot Acceptance

Many independent studies in social robotics and human–robot interaction have gained knowledge on various factors that affect people’s perceptions of and behaviors toward robots. However, only a few of those studies aimed to develop models of social robot acceptance integrating a wider range of such factors. With the rise of robotic technologies for everyday environments, such comprehensive research on relevant acceptance factors is increasingly necessary. This article presents a conceptual model of social robot acceptance with a strong theoretical base, which has been tested among the general Dutch population (n = 1,168) using structural equation modeling. The results show a strong role of normative believes that both directly and indirectly affect the anticipated acceptance of social robots for domestic purposes. Moreover, the data show that, at least at this stage of diffusion within society, people seem somewhat reluctant to accept social behaviors from robots. The current findings of our study and their implications serve to push the field of acceptable social robotics forward. For the societal acceptance of social robots, it is vital to include the opinions of future users at an early stage of development. This way future designs can be better adapted to the preferences of potential users.     

High-speed stability of a rigid rotor supported by aerostatic journal bearings with compound restrictors

Demands for higher rotational speed and accuracy for effective manufacture of small holes on printed circuit boards and very small precise parts have been increasing remarkably. Aerostatic journal bearings with compound restrictors have greater stiffness than those with conventional inherently compensated restrictors and are one of the most effective candidates to satisfy these demands. In this work, the instability of a rigid rotor supported by aerostatic journal bearings with compound restrictors was investigated numerically and experimentally. It was found that this type of aerostatic bearings showed a much higher threshold speed for instability compared with bearings with inherently compensated restrictors.     

Gait recognition based on modified phase-only correlation

Gait recognition is an emerging biometric technology aiming to identify people purely through the analysis of the way they walk. The technology has attracted interest as a method of identification because it is noncontact and does not require the subject’s cooperation. Clothing, carrying conditions and other intra-class variations, also referred to as “covariates,” affect the performance of gait recognition systems. This paper proposes a supervised feature extraction method, which is able to select relevant discriminative features for human recognition to mitigate the impact of covariates and hence improve the recognition performances. The proposed method is evaluated using the CASIA gait database (dataset B), and the experimental results suggest that our method yields 81.40 % of correct classification when compared against similar techniques which do not exceed 77.96 %.     

Exploring influencing variables for the acceptance of social robots

In order to introduce social robots successfully, we must first understand the underlying reasons whereupon potential users accept these robots to reside within their own homes. An extensive literature review has been conducted and provides an overview of variables influencing the acceptance of social robots categorized by utilitarian variables, hedonic variables, user characteristics, social normative beliefs and control beliefs. In a user study, in which 60 participants interacted with a social robot, both the robot itself and the interaction experience the users had with the robot were evaluated. The results indicate that especially the variables of usefulness, adaptability, enjoyment, sociability, companionship and perceived behavioral control are important evaluating the user acceptance of social robots. Hence, the present study contributes to human–robot interaction research by designating the variables that lead to social robot acceptance. Subsequently, this study may serve as a onset in developing an integral model which takes into consideration the relevant determinants of social robot acceptance.     

Novel geometric features for off-line writer identification

Writer identification is an important field in forensic document examination. Typically, a writer identification system consists of two main steps: feature extraction and matching and the performance depends significantly on the feature extraction step. In this paper, we propose a set of novel geometrical features that are able to characterize different writers. These features include direction, curvature, and tortuosity. We also propose an improvement of the edge-based directional and chain code-based features. The proposed methods are applicable to Arabic and English handwriting. We have also studied several methods for computing the distance between feature vectors when comparing two writers. Evaluation of the methods is performed using both the IAM handwriting database and the QUWI database for each individual feature reaching Top1 identification rates of 82 and 87 % in those two datasets, respectively. The accuracies achieved by Kernel Discriminant Analysis (KDA) are significantly higher than those observed before feature-level writer identification was implemented. The results demonstrate the effectiveness of the improved versions of both chain-code features and edge-based directional features.     

Gait recognition for person re-identification

The Journal of Supercomputing - Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes....     

Round-Robin sequential forward selection algorithm for prostate cancer classification and diagnosis using multispectral imagery

This paper proposes an automatic classification system for the use in prostate cancer diagnosis. The system aims to detect and classify prostatic tissue textures captured from microscopic samples taken from needle biopsies. Biopsies are usually analyzed by a trained pathologist with different grades of malignancy typically corresponding to different structural patterns as well as apparent textures. In the context of prostate cancer diagnosis, four major groups have to be accurately recognized: stroma, benign prostatic hyperplasia, prostatic intraepithelial neoplasia, and prostatic carcinoma. Recently, multispectral imagery has been proposed as a new image acquisition modality which unlike conventional RGB-based light microscopy allows the acquisition of a large number of spectral bands within the visible spectrum, resulting in a large feature vector size. Many features in the initial feature set are irrelevant to the classification task and are correlated with each other, resulting in an increase in the computational complexity and a reduction in the recognition rate. In this paper, a Round-Robin (RR) sequential forward selection RR-SFS is used to address these problems. RR is a technique for handling multi-class problems with binary classifiers by training one classifier for each pair of classes. The experimental results demonstrate this finding when compared with classical method based on the multiclass SFS and other ensemble methods such as bagging/boosting with decision tree (C4.5) classifier where it is shown that RR-SFS method achieves the best results with a classification accuracy of 99.9%.     

On improving Dynamic Time Warping for pattern matching

This paper introduces a new distance metric function to enhance the capability of the Dynamic Time Warping (DTW) for two dimension pattern matching. Moreover, the classical boundary condition is relaxed to further improve the performance of the DTW. An efficient approach for constructing the warping path is presented to reduce computational execution time in detecting eye and face regions in head–shoulder images with plain background. The performance of the enhanced DTW is compared against that of the classical DTW in detecting the faces and eyes of 15 subjects with various poses and facial expressions. The experimental results indicate that improvement in the detection rates of both eyes and faces is achieved using the enhanced DTW.