@book {152, title = {Computer vision: models, learning, and inference}, year = {2012}, publisher = {Cambridge University Press}, organization = {Cambridge University Press}, url = {http://www.computervisionmodels.com/}, author = {Prince, Simon JD} } @proceedings {178, title = {Characterization of LQI behavior in WSN for glacier area in Patagonia Argentina}, journal = {Embedded Systems (SASE/CASE), 2013 Fourth Argentine Symposium and Conference on}, year = {2013}, month = {08/2013}, pages = {1--6}, publisher = {IEEE}, address = {Buenos Aires, Argentina}, abstract = {

One of the most important aspects before installing a Wireless Sensor Network (WSN) is a previous study of connectivity constraints that exist in the area to be covered. This study is critical to the final distribution of the sensors, with an important impact in the life of the network by reducing consumption, and on the robustness by contemplating redundancy of paths and sensors. In this paper, we present a summary of the most important aspects of a preliminary empirical study of the Link Quality Indicator (LQI), on different landscapes in the glaciers area of Patagonia Argentina. The landscapes covered varied in geographical structures with different levels of attenuation and extreme environmental conditions. Through the analysis of the Cumulative Distribution Function (CDF) of the measured LQI values, we can characterize the behavior of four different scenarios and correlate the combined effects of the environmental structure with the distance from the transmitter. The measurements performed were designed for characterizing the links at the physical layer with the purpose of defining models to estimate the Packet Error Rate (PER) for the WSN deployment stage.

}, issn = {978-1-4799-1098-4}, doi = {10.1109/SASE-CASE.2013.6636777}, url = {http://dx.doi.org/10.1109/SASE-CASE.2013.6636777}, author = {Diedrichs, Ana Laura and Robles, Mar{\'\i}a In{\'e}s and Bromberg, Facundo and Mercado, Gustavo and Dujovne, Diego} } @article {78, title = {Computer Vision: Summary and Discussion}, year = {2011}, abstract = {
Review of important concepts, Some important open problems, Feedback and course evaluation
}, keywords = {computer vision, discussion, lecture, summary}, url = {http://courses.engr.illinois.edu/cs543/sp2011/lectures/Lecture\%2029\%20-\%20Summary\%20and\%20Discussion\%20-\%20Vision_Spring2011.pdf}, author = {Derek Hoiem} } @article {209, title = {Computer Vision Approach for Low Cost, High Precision Measurement of Grapevine Trunk Diameter in Outdoor Conditions}, year = {2016}, institution = {arXiv.org}, abstract = {

Trunk diameter is a variable of agricultural interest, used mainly in the prediction of fruit trees production. It is correlated with leaf area and biomass of trees, and consequently gives a good estimate of the potential production of the plants. This work presents a low cost, high precision method for the measurement of trunk diameter of grapevines based on Computer Vision techniques. Several methods based on Computer Vision and other techniques are introduced in the literature. These methods present different advantages for crop management: they are amenable to be operated by unknowledgeable personnel, with lower operational costs; they result in lower stress levels to knowledgeable personnel, avoiding the deterioration of the measurement quality over time; and they make the measurement process amenable to be embedded in larger autonomous systems, allowing more measurements to be taken with equivalent costs. To date, all existing autonomous methods are either of low precision, or have a prohibitive cost for massive agricultural adoption, leaving the manual Vernier caliper or tape measure as the only choice in most situations. In this work we present a semi-autonomous measurement method that is susceptible to be fully automated, cost effective for mass adoption, and its precision is competitive (with slight improvements) over the caliper manual method.

}, keywords = {computer vision, Gaussian mixture model, Grapevine, Measurements, Trunk Diameter}, issn = {arXiv:1406.4845v2}, url = {http://arxiv.org/abs/1406.4845}, author = {P{\'e}rez, Diego Sebasti{\'a}n and Bromberg, Facundo and Gonzalez-Antivilo, Francisco} } @article {37, title = {Computer Vision Online}, year = {2014}, url = {http://www.computervisiononline.com/} } @article {121, title = {CV Resources}, year = {2014}, abstract = {

Computer Vision and Machine Learning resources with a focus on 3D scene understanding, parsing and reconstruction.

}, url = {http://www.cvlibs.net/links.php}, author = {Geiger, Andreas} } @article {38, title = {CVonline}, year = {2014}, url = {http://homepages.inf.ed.ac.uk/rbf/CVonline/} } @article {39, title = {CVPapers}, year = {2014}, keywords = {computer vision, link, online, papers, resources}, url = {http://www.cvpapers.com/} }