Roman domestic art and early Christian traditions

David Balch is well-known for his research into early Christian house churches and families. This innovative book is a collection of revised essays, which have been published separately elsewhere. It focuses on the interconnections between Roman domestic art and early Christian traditions. The main evidence comprises the frescoes and mosaics found in Italian domus (houses) and insulae (apartment buildings), including the rich findings at the cities of Pompeii and Herculaneum, which were destroyed by the grand eruption of the Vesuvius in 79 AD.Book review : Roman Domestic Art and Early House Churches / David L. Balch. ISBN : 978-3-16-149383-6. Publisher : Mohr Siebeck, Tübingen, 2008, pp. 296.http://www.hts.org.zahb201

God in the second book of Maccabees: The connection between words and deeds

This article focuses on God’s image and role in 2 Maccabees. My analysis will build on narratology, especially characterisation, and on the differentiation proposed by Barbara Schmitz in connection with the book of Judith: the distinction between God’s role as a character depicted in the narrative (God’s acts and statements) and references to God in statements about God by other characters in the narrative. How does this differentiation work out for 2 Maccabees? Does the book describe any miracles performed by God, and if so, do these take place within or outside the normal processes of nature, as God, for example, did according to Joshua 10, 11–14, which reports that God made the sun stand still at Gibeon? Contribution: Firstly, this article presents the results of a narratological analysis of 2 Maccabees. Secondly, since the statements about God and God’s actual role are prominent in the book, this analysis is important for establishing the meaning of the book for the narratees, the intended readers. Thirdly, this reading is relevant for theological studies dealing with the image and role of God in the contexts of early Judaism and early Christianity

Improved vegetation segmentation with ground shadow removal using an HDR camera

A vision-based weed control robot for agricultural field application requires robust vegetation segmentation. The output of vegetation segmentation is the fundamental element in the subsequent process of weed and crop discrimination as well as weed control. There are two challenging issues for robust vegetation segmentation under agricultural field conditions: (1) to overcome strongly varying natural illumination; (2) to avoid the influence of shadows under direct sunlight conditions. A way to resolve the issue of varying natural illumination is to use high dynamic range (HDR) camera technology. HDR cameras, however, do not resolve the shadow issue. In many cases, shadows tend to be classified during the segmentation as part of the foreground, i.e., vegetation regions. This study proposes an algorithm for ground shadow detection and removal, which is based on color space conversion and a multilevel threshold, and assesses the advantage of using this algorithm in vegetation segmentation under natural illumination conditions in an agricultural field. Applying shadow removal improved the performance of vegetation segmentation with an average improvement of 20, 4.4, and 13.5% in precision, specificity and modified accuracy, respectively. The average processing time for vegetation segmentation with shadow removal was 0.46 s, which is acceptable for real-time application

Transfer learning for the classification of sugar beet and volunteer potato under field conditions

Classification of weeds amongst cash crops is a core procedure in automated weed control. Addressing volunteer potato control in sugar beets, in the EU Smartbot project the aim was to control more than 95% of volunteer potatoes and ensure less than 5% of undesired control of sugar beet plants. A promising way to meet these requirements is deep learning. Training an entire network from scratch, however, requires a large dataset and a substantial amount of time. In this situation, transfer learning can be a promising solution. This study first evaluates a transfer learning procedure with three different implementations of AlexNet and then assesses the performance difference amongst the six network architectures: AlexNet, VGG-19, GoogLeNet, ResNet-50, ResNet-101 and Inception-v3. All nets had been pre-trained on the ImageNet Dataset. These nets were used to classify sugar beet and volunteer potato images taken under ambient varying light conditions in agricultural environments. The highest classification accuracy for different implementations of AlexNet was 98.0%, obtained with an AlexNet architecture modified to generate binary output. Comparing different networks, the highest classification accuracy 98.7%, obtained with VGG-19 modified to generate binary output. Transfer learning proved to be effective and showed robust performance with plant images acquired in different periods of the various years on two types of soils. All scenarios and pre-trained networks were feasible for real-time applications (classification time < 0.1 s). Classification is only one step in weed detection, and a complete pipeline for weed detection may potentially reduce the overall performance.</p

Transfer learning for the classification of sugar beet and volunteer potato under field conditions

Classification of weeds amongst cash crops is a core procedure in automated weed control. Addressing volunteer potato control in sugar beets, in the EU Smartbot project the aim was to control more than 95% of volunteer potatoes and ensure less than 5% of undesired control of sugar beet plants. A promising way to meet these requirements is deep learning. Training an entire network from scratch, however, requires a large dataset and a substantial amount of time. In this situation, transfer learning can be a promising solution. This study first evaluates a transfer learning procedure with three different implementations of AlexNet and then assesses the performance difference amongst the six network architectures: AlexNet, VGG-19, GoogLeNet, ResNet-50, ResNet-101 and Inception-v3. All nets had been pre-trained on the ImageNet Dataset. These nets were used to classify sugar beet and volunteer potato images taken under ambient varying light conditions in agricultural environments. The highest classification accuracy for different implementations of AlexNet was 98.0%, obtained with an AlexNet architecture modified to generate binary output. Comparing different networks, the highest classification accuracy 98.7%, obtained with VGG-19 modified to generate binary output. Transfer learning proved to be effective and showed robust performance with plant images acquired in different periods of the various years on two types of soils. All scenarios and pre-trained networks were feasible for real-time applications (classification time < 0.1 s). Classification is only one step in weed detection, and a complete pipeline for weed detection may potentially reduce the overall performance.</p