Bart Kroon

Biocontrol by Phenazine-1-carboxamide-Producing Pseudomonas chlororaphis PCL1391 of Tomato Root Rot Caused by Fusarium oxysporum f. sp. radicis-lycopersici

Seventy bacterial isolates from the rhizosphere of tomato were screened for antagonistic activity against the tomato foot and root rot-causing fungal pathogen Fusarium oxysporum f. sp. radicis-lycopersici. One isolate, strain PCL1391, appeared to be an efficient colonizer of tomato roots and an excellent biocontrol strain in an F. oxysporum/tomato test system. Strain PCL1391 was identified as Pseudomonas chlororaphis and further characterization showed that it produces a broad spectrum of antifungal factors (AFFs), including a hydrophobic compound, hydrogen cyanide, chitinase(s), and protease(s). Through mass spectrometry and nuclear magnetic resonance, the hydrophobic compound was identified as phenazine-1-carboxamide (PCN). We have studied the production and action of this AFF both in vitro and in vivo. Using a PCL1391 transposon mutant, with a lux reporter gene inserted in the phenazine biosynthetic operon (phz), we showed that this phenazine biosynthetic mutant was substantially decreased in both in vitro antifungal activity and biocontrol activity. Moreover, with the same mutant it was shown that the phz biosynthetic operon is expressed in the tomato rhizosphere. Comparison of the biocontrol activity of the PCN-producing strain PCL1391 with those of phenazine-1-carboxylic acid (PCA)producing strains P. fluorescens 2-79 and P. aureofaciens 30-84 showed that the PCN-producing strain is able to suppress disease in the tomato/F. oxysporum system, whereas the PCA-producing strains are not. Comparison of in vitro antifungal activity of PCN and PCA showed that the antifungal activity of PCN was at least 10 times higher at neutral pH, suggesting that this may contribute to the superior biocontrol performance of strain PCL1391 in the tomato/F. oxysporum system.

Eye localization for face matching: is it always useful and under what conditions?

In this paper we address the influence of eye localization accuracy on face matching performance in the case of low resolution image and video content. By means of a broad experimental evaluation involving several base-line eye localizers and face matching algorithms we investigated to which extent and under what conditions the eye localization accuracy will benefit the face matching performance, both in terms of effectiveness and efficiency. Our evaluation showed that (1) worst-case eye localization errors have a big impact on face matching performance, (2) in respect to that and under a minimum required accuracy, eye localization can boost performance of naive face matchers, (3) eye localization allows for more efficient face matching without degrading performance.

Eye localization in low and standard definition content with application to face matching

In this paper we address the problem of eye localization for the purpose of face matching in low and standard definition image and video content. In addition to an explorative study that aimed at discovering the effect of eye localization accuracy on face matching performance, we also present a probabilistic eye localization method based on well-known multi-scale local binary patterns (LBPs). These patterns provide a simple but powerful spatial description of texture, and are robust to the noise typical to low and standard definition content. The extensive evaluation involving multiple eye localizers and face matchers showed that the shape of the eye localizer error distribution has a big impact on face matching performance. Conditioned by the error distribution shape and the minimum required eye localization accuracy, eye localization can boost the performance of naive face matchers and allow for more efficient face matching without degrading its performance. The evaluation also showed that our proposed method has superior accuracy with respect to the state-of-the-art on eye localization, and that it fulfills the criteria for improving the face matching performance and efficiency mentioned above.

Comparison of face matching techniques under pose variation

The ability to match faces in video is a crucial component for many multimedia applications such as searching and recognizing people in semantic video browsing, surveillance and home video management systems. Unfortunately, most face matching methods were designed for and tested on frontal face images only, which does not comply with the professional and home video scenarios. In video, faces appear at different poses and scales, and the image quality may vary as well. In this paper we analyzed to what extent well-known face matching methods are suitable for matching faces in video. We performed a comparison between the local method Elastic Bunch Graph Matching, the global approaches principle component analysis (PCA) and PCA with linear discriminant analysis (PCA+LDA). The outcome of this study is that while in cases of small face pose variations Elastic Bunch Graph Matching works slightly better, for large face pose variations the global methods provide better performance.

Accurate eye localization in low and standard definition content

In this paper we address the problem of eye localization in low and standard definition content, such as webcam-generated and TV images. We present a probabilistic eye localization method based on well-known multiscale local binary patterns (LBPs), which provide a simple and powerful spatial description of texture, and are robust to the noise typical to low and standard definition content. Our primary contribution is in the proposed method of combining the LBPs that is targeted towards achieving spatial accuracy under mentioned conditions. Evaluation performed on a standard dataset of webcam-quality images shows that our approach has superior performance with respect to the state of the art, while having a reasonable complexity and a low memory footprint.

Dialog detection in narrative video by shot and face analysis

The proliferation of captured personal and broadcast content in personal consumer archives necessitates comfortable access to stored audiovisual content. Intuitive retrieval and navigation solutions require however a semantic level that cannot be reached by generic multimedia content analysis alone. A fusion with film grammar rules can help to boost the reliability significantly. The current paper describes the fusion of low-level content analysis cues including face parameters and inter-shot similarities to segment commercial content into film grammar rule-based entities and subsequently classify those sequences into so-called shot reverse shots, i.e. dialog sequences. Moreover shot reverse shot specific mid-level cues are analyzed augmenting the shot reverse shot information with dialog specific descriptions.