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Obesity, overweight, allergy and additives related disease are common health problems worldwide nowadays. Many of these problems directly relate to food consumption and dietary habits. Hence keep tracking food intake and knowing the consumed food ingredients are essential for a healthy life. However, tracking daily consumption is tedious, and seldom be kept for long. This paper presented a mobile solution to leverage these troublesome and provide health advices on food consumption adaptively. Users can simply take photos of their meals as they usually do for dietary record. Our system then automatically recognizes the food type and provides useful statistics for analysis. We can know better the packaged food with barcode scanning and text recognition of food labels. All captured information provide basis for proper alert on allergen, suggestions to achieve health and balanced dietary. A preliminary study reveals that this is welcomed by most users care about their dietary.

Mesh representation of vasculature is fundamental to many medical applications. The benefit is a clean and tidy appearance in terms of visualization, as well as the possibility of applying computer-assisted intervention and preoperative planning for patients. A vasculature mesh is often reconstructed by iso-surfacing its segmented volume data. Clinicians are usually interested in both the vasculature and its centerline. In this paper, we introduce a mesh centerline extraction approach in the case that volume data are unavailable. The extraction method is inspired by an observation that the vasculature is generally composed of piecewise cylindrical shapes. This observation leads to a conceptually simple but effective strategy to tackle the challenging problem of vasculature centerline extraction, which gracefully combines a branch segmentation scheme and a series of advanced techniques in discrete geometry processing. Our method competes favorably with three state-of-the-art methods in the completeness and accuracy of the extracted centerlines from real human vessels, including the pathological vasculature. Our method also usually leads to maximal and mean extraction errors of less than 1% and 0.5%, respectively.

Exterior orientation parameters’ (EOP) estimation using space resection plays an important role in topographic reconstruction for push broom scanners. However, existing models of space resection are highly sensitive to errors in data. Unfortunately, for lunar imagery, the altitude data at the ground control points (GCPs) for space resection are error-prone. Thus, existing models fail to produce reliable EOPs. Motivated by a finding that for push broom scanners, angular rotations of EOPs can be estimated independent of the altitude data and only involving the geographic data at the GCPs, which are already provided, hence, we divide the modeling of space resection into two phases. Firstly, we estimate the angular rotations based on the reliable geographic data using our proposed mathematical model. Then, with the accurate angular rotations, the collinear equations for space resection are simplified into a linear problem, and the global optimal solution for the spatial position of EOPs can always be achieved. Moreover, a certainty term is integrated to penalize the unreliable altitude data for increasing the error tolerance. Experimental results evidence that our model can obtain more accurate EOPs and topographic maps not only for the simulated data, but also for the real data from Chang’E-1, compared to the existing space resection model.