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One of the time consuming tasks in the timber industry is the manually measurement of features of wood stacks. Such features include, but are not limited to, the number of the logs in a stack, their diameters distribution, and their volumes. Computer vision techniques have recently been used for solving this real-world industrial application. Such techniques are facing many challenges as the task is usually performed in outdoor, uncontrolled, environments. Furthermore, the logs can vary in texture and they can be occluded by different obstacles. These all make the segmentation of the wood logs a difficult task. Graph-cut has shown to be good enough for such a segmentation. However, it is hard to find proper graph weights. This is exactly the contribution of this paper to propose a method for setting the weights of the graph. To do so, we use Circular Hough Transform (CHT) for obtaining information about the foreand background regions of a stack image, and then use this together with a Local Circularity Measure (LCM) to modify the weights of the graph to segment the wood logs from the rest of the image. We further improve the segmentation by separating overlapping logs. These segmented wood logs are finally scaled and used to acquire the necessary wood stack measurements in real-world scale (in cm). The proposed system, which works automatically, has been tested on two different datasets, containing real outdoor images of logs which vary in shapes and sizes. The experimental results show that the proposed approach not only achieves the same results as the state-of-the-art systems, it produces more stable results.

Pain detection using facial images is of critical importance in many Health applications. Since pain is a spatiotemporal process, recent works on this topic employ facial spatiotemporal features to detect pain. These systems extract such features from the entire area of the face. In this paper, we show that by employing super-pixels we can divide the face into three regions, in a way that only one of these regions (about one third of the face) contributes to the pain estimation and the other two regions can be discarded. The experimental results on the UNBCMcMaster database show that the proposed system using this single region outperforms state-of-the-art systems in detecting no-pain scenarios, while it reaches comparable results in detecting weak and severe pain scenarios.

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