Struggles over family land? Tree crops, land and labour in Ghana's Brong-Ahafo region

Agricultural land use in much of Brong-Ahafo region, Ghana has been shifting from the production of food crops towards increased cashew nut cultivation in recent years. This article explores everyday, less visible, gendered and generational struggles over family farms in West Africa, based on qualitative, participatory research in a rural community that is becoming increasingly integrated into the global capitalist system. As a tree crop, cashew was regarded as an individual man's property to be passed on to his wife and children rather than to extended family members, which differed from the communal land tenure arrangements governing food crop cultivation. The tendency for land, cash crops and income to be controlled by men, despite women's and young people's significant labour contributions to family farms, and for women to rely on food crop production for their main source of income and for household food security, means that women and girls are more likely to lose out when cashew plantations are expanded to the detriment of land for food crops. Intergenerational tensions emerged when young people felt that their parents and elders were neglecting their views and concerns. The research provides important insights into gendered and generational power relations regarding land access, property rights and intra-household decision-making processes. Greater dialogue between genders and generations may help to tackle unequal power relations and lead to shared decision-making processes that build the resilience of rural communities.

Leaf trapping and retention of particles by holm oak and other common tree species in Mediterranean urban environments

Holm oak (Quercus ilex), a widespread urban street tree in the Mediterranean region, is widely used as biomonitor of persistent atmospheric pollutants, especially particulate-bound metals. By using lab- and field-based experimental approaches, we compared the leaf-level capacity for particles’ capture and retention between Q. ilex and other common Mediterranean urban trees: Quercus cerris, Platanus × hispanica, Tilia cordata and Olea europaea. All applied methods were effective in quantifying particulate capture and retention, although not univocal in ranking species performances. Distinctive morphological features of leaves led to differences in species’ ability to trap and retain particles of different size classes and to accumulate metals after exposure to traffic in an urban street. Overall, P. × hispanica and T. cordata showed the largest capture potential per unit leaf area for most model particles (Na+ and powder particles), and street-level Cu and Pb, while Q. ilex acted intermediately. After wash-off experiments, P. × hispanica leaves had the greatest retention capacity among the tested species and O. europaea the lowest. We concluded that the Platanus planting could be considered in Mediterranean urban environments due to its efficiency in accumulating and retaining airborne particulates; however, with atmospheric pollution being typically higher in winter, the evergreen Q. ilex represents a better year-round choice to mitigate the impact of airborne particulate pollutants.

A robust sclera segmentation algorithm

Sclera segmentation is shown to be of significant importance for eye and iris biometrics. However, sclera segmentation has not been extensively researched as a separate topic, but mainly summarized as a component of a broader task. This paper proposes a novel sclera segmentation algorithm for colour images which operates at pixel-level. Exploring various colour spaces, the proposed approach is robust to image noise and different gaze directions. The algorithm’s robustness is enhanced by a two-stage classifier. At the first stage, a set of simple classifiers is employed, while at the second stage, a neural network classifier operates on the probabilities’ space generated by the classifiers at stage 1. The proposed method was ranked the 1st in Sclera Segmentation Benchmarking Competition 2015, part of BTAS 2015, with a precision of 95.05% corresponding to a recall of 94.56%.

On fusion for robust motion segmentation

While a multitude of motion segmentation algorithms have been presented in the literature, there has not been an objective assessment of different approaches to fusing their outputs. This paper investigates the application of 4 different fusion schemes to the outputs of 3 probabilistic pixel-level segmentation algorithms. We performed an extensive experimentation using 6 challenge categories from the changedetection.net dataset demonstrating that in general simple majority vote proves to be more effective than more complex fusion schemes.

Segmentation-level fusion for iris recognition

This paper investigates the potential of fusion at normalisation/segmentation level prior to feature extraction. While there are several biometric fusion methods at data/feature level, score level and rank/decision level combining raw biometric signals, scores, or ranks/decisions, this type of fusion is still in its infancy. However, the increasing demand to allow for more relaxed and less invasive recording conditions, especially for on-the-move iris recognition, suggests to further investigate fusion at this very low level. This paper focuses on the approach of multi-segmentation fusion for iris biometric systems investigating the benefit of combining the segmentation result of multiple normalisation algorithms, using four methods from two different public iris toolkits (USIT, OSIRIS) on the public CASIA and IITD iris datasets. Evaluations based on recognition accuracy and ground truth segmentation data indicate high sensitivity with regards to the type of errors made by segmentation algorithms.