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One of the main problems in Computer Vision and Close Range Digital Photogrammetry is 3D reconstruction. 3D reconstruction with structured light is one of the existing techniques and which still has several problems, one of them the identification or classification of the projected targets. Approaching this problem is the goal of this paper. An area based method called template matching was used for target classification. This method performs detection of area similarity by correlation, which measures the similarity between the reference and search windows, using a suitable correlation function. In this paper the modified cross covariance function was used, which presented the best results. A strategy was developed for adaptative resampling of the patterns, which solved the problem of deformation of the targets due to object surface inclination. Experiments with simulated and real data were performed in order to assess the efficiency of the proposed methodology for target detection. The results showed that the proposed classification strategy works properly, identifying 98% of targets in plane surfaces and 93% in oblique surfaces.

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Sixty-six trairão (Hoplias lacerdae) fingerlings (average weight of 2.0±0.5 g and total length of 5.8±0.2 cm), trained to accept dry rations, were allotted to six 15-L aquariums, with aeration and controlled temperature (24.0±0.5°C), in a density of 0.7 juveniles/L, aiming to evaluate the effects of darkness on fish productive performance. The treatments consisted of two photoperiods: 12 hours light: 12 hours dark (12L:12D) and 0 hour light: 24 hours dark (OL:24D), with three replicates. Fingerlings were fed ad libitum a commercial extruded diet (42% CP), twice a day. The aquariums were cleaned daily for excrement withdrawal through siphoning, exchanging 1/4 total volume. At the end of the experiment (30 days), weight gain, feed:gain ratio and survival and cannibalism rates were evaluated. The results showed that darkness did not affect the productive performance of trairão juveniles.

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