Eurasip Journal on Image and Video Processing
About: Eurasip Journal on Image and Video Processing is an academic journal published by Springer Nature. The journal publishes majorly in the area(s): Pattern recognition (psychology) & Image processing. It has an ISSN identifier of 1687-5176. It is also open access. Over the lifetime, 913 publications have been published receiving 20409 citations. The journal is also known as: European Association for Signal Processing Journal on Image and Video Processing (Online).
Topics: Pattern recognition (psychology), Image processing, Feature (computer vision), Video tracking, Image segmentation
Papers published on a yearly basis
TL;DR: This work introduces two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time.
Abstract: Simultaneous tracking of multiple persons in real-world environments is an active research field and several approaches have been proposed, based on a variety of features and algorithms. Recently, there has been a growing interest in organizing systematic evaluations to compare the various techniques. Unfortunately, the lack of common metrics for measuring the performance of multiple object trackers still makes it hard to compare their results. In this work, we introduce two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time. These metrics have been extensively used in two large-scale international evaluations, the 2006 and 2007 CLEAR evaluations, to measure and compare the performance of multiple object trackers for a wide variety of tracking tasks. Selected performance results are presented and the advantages and drawbacks of the presented metrics are discussed based on the experience gained during the evaluations.
TL;DR: A quality metric for the assessment of stereopairs is proposed using the fusion of 2D quality metrics and of the depth information and is evaluated using the SAMVIQ methodology for subjective assessment.
Abstract: Several metrics have been proposed in literature to assess the perceptual quality of two-dimensional images. However, no similar effort has been devoted to quality assessment of stereoscopic images. Therefore, in this paper, we review the different issues related to 3D visualization, and we propose a quality metric for the assessment of stereopairs using the fusion of 2D quality metrics and of the depth information. The proposed metric is evaluated using the SAMVIQ methodology for subjective assessment. Specifically, distortions deriving from coding are taken into account and the quality degradation of the stereopair is estimated by means of subjective tests.
TL;DR: High-quality intermediate views for an existing 9-view auto-stereoscopic display as well as other stereo- and multiscopic displays are presented, which prove the suitability of the approach for advanced 3DV systems.
Abstract: Interest in 3D video applications and systems is growing rapidly and technology is maturating. It is expected that multiview autostereoscopic displays will play an important role in home user environments, since they support multiuser 3D sensation and motion parallax impression. The tremendous data rate cannot be handled efficiently by representation and coding formats such as MVC or MPEG-C Part 3. Multiview video plus depth (MVD) is a new format that efficiently supports such advanced 3DV systems, but this requires high-quality intermediate view synthesis. For this, a new approach is presented that separates unreliable image regions along depth discontinuities from reliable image regions, which are treated separately and fused to the final interpolated view. In contrast to previous layered approaches, our algorithm uses two boundary layers and one reliable layer, performs image-based 3D warping only, and was generically implemented, that is, does not necessarily rely on 3D graphics support. Furthermore, different hole-filling and filtering methods are added to provide high-quality intermediate views. As a result, high-quality intermediate views for an existing 9-view auto-stereoscopic display as well as other stereo- and multiscopic displays are presented, which prove the suitability of our approach for advanced 3DV systems.
TL;DR: This paper attempts to make the first formal review of state-of-art of vision-based defect detection and classification of steel surfaces as they are produced from steel mills using vision- based techniques.
Abstract: Steel is the material of choice for a large number and very diverse industrial applications. Surface qualities along with other properties are the most important quality parameters, particularly for flat-rolled steel products. Traditional manual surface inspection procedures are awfully inadequate to ensure guaranteed quality-free surface. To ensure stringent requirements of customers, automated vision-based steel surface inspection techniques have been found to be very effective and popular during the last two decades. Considering its importance, this paper attempts to make the first formal review of state-of-art of vision-based defect detection and classification of steel surfaces as they are produced from steel mills. It is observed that majority of research work has been undertaken for cold steel strip surfaces which is most sensitive to customers' requirements. Work on surface defect detection of hot strips and bars/rods has also shown signs of increase during the last 10 years. The review covers overall aspects of automatic steel surface defect detection and classification systems using vision-based techniques. Attentions have also been drawn to reported success rates along with issues related to real-time operational aspects.
TL;DR: The results show that the approach to detect face spoofing using the spatiotemporal extensions of the highly popular local binary pattern operator performs better than state-of-the-art techniques following the provided evaluation protocols of each database.
Abstract: User authentication is an important step to protect information, and in this context, face biometrics is potentially advantageous. Face biometrics is natural, intuitive, easy to use, and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using cheap low-tech equipment. This paper introduces a novel and appealing approach to detect face spoofing using the spatiotemporal (dynamic texture) extensions of the highly popular local binary pattern operator. The key idea of the approach is to learn and detect the structure and the dynamics of the facial micro-textures that characterise real faces but not fake ones. We evaluated the approach with two publicly available databases (Replay-Attack Database and CASIA Face Anti-Spoofing Database). The results show that our approach performs better than state-of-the-art techniques following the provided evaluation protocols of each database.