Inrecent decades, it has been shown that epidemi-ological surveillance is one of the most valuable tool that public health has, since it allows us to have an overview of the population general health, thus allowing to...
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Inrecent decades, it has been shown that epidemi-ological surveillance is one of the most valuable tool that public health has, since it allows us to have an overview of the population general health, thus allowing to anticipate outbreaks of epidemics by helping in timely interventions. Currently there is an increase in cases of dengue disease in several regions of Peru. Therefore, to control this outbreak and to help population centers and human settlements that are far from the city this work puts forward a drone system with an object recognition algorithm. Drones are very efficient in terms of surveillance, allowing easy access to places that are difficult for humans. In this way, drones can carry out the field work that is required in epidemiological surveillance, carrying out photography or video work in real time, and thus identifying infectious foci of diverse diseases. In this work, an object detection algorithm that uses convolutional neural networks and a stable detection model is designed, this allows the detection of water reservoirs that are possible infectious sources of dengue. In addition the efficiency of the algorithm is evaluated through the statistical curves of precision and sensitivity that result of the training of the neural network. To validate the efficiency obtained, the model was applied to test images related to dengue, achieving an efficiency of 99.2%.
The low accuracy rates of text-shape dividers for digital ink diagrams are hindering their use in real world applications. While recognition of handwriting is well advanced and there have been many recognition approac...
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ISBN:
(纸本)9783642130212
The low accuracy rates of text-shape dividers for digital ink diagrams are hindering their use in real world applications. While recognition of handwriting is well advanced and there have been many recognition approaches proposed for hand drawn sketches, there has been less attention on the division of text and drawing. The choice of features and algorithms is critical to the success of the recognition, yet heuristics currently form the basis of selection. We propose the use of data mining techniques to automate the process of building textshape recognizers. This systematic approach identifies the algorithms best suited to the specific problem and generates the trained recognizer. We have generated dividers using data mining and training with diagrams from three domains. The evaluation of our new recognizer on realistic diagrams from two different domains, against two other recognizers shows it to be more successful at dividing shapes and text with 95.2% of strokes correctly classified compared with 86.9% and 83.3% for the two others.
Hand-drawn diagrams are frequently used as the first visualization of a model. Converting these preliminary diagrams into a specific formal format is time consuming. Computer based sketch-tools can offer support durin...
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ISBN:
(纸本)9781920682743
Hand-drawn diagrams are frequently used as the first visualization of a model. Converting these preliminary diagrams into a specific formal format is time consuming. Computer based sketch-tools can offer support during the informal sketching stage and automatic conversion to formal representations. Entity Relationship diagrams are particularly difficult to convert because of their characteristics such as cardinality notations. We extend the general diagram sketching tool InkKit with domain semantics to successfully recognize and automatically convert Entity Relationship diagrams. This approach takes advantage of sketching as the preferred initial design realization while minimizing the effort required to translate the initial design to a functional prototype.
Different problems of robot learning and planning have received considerable attention, recently. In particular, we can mention robot task learning. Robot learning from demonstration is especially important for robots...
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Different problems of robot learning and planning have received considerable attention, recently. In particular, we can mention robot task learning. Robot learning from demonstration is especially important for robots that operate in unstructured environments. The effectiveness of such learning depends strongly on the quality of vision-based analysis of human hand and body gestures. In this paper, we consider a method of recognition of human hand and body gestures that based on a modified longest common subsequence algorithm with adaptive parameters.
In the process of solving a wide range of tasks concerning the Earth surface remote sensing and its state monitoring, the main role is played by the algorithm of the surface image forming and the algo
ISBN:
(纸本)9781467389808
In the process of solving a wide range of tasks concerning the Earth surface remote sensing and its state monitoring, the main role is played by the algorithm of the surface image forming and the algo
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