Solea aegyptiaca (Chabanaud, 1927) is one of the most common Soleid species in southern Tunisian waters. This study provides the first detailed information on the reproduction biology of S. aegyptiaca in these areas. Samples of S. aegyptiaca were collected by trawl from the Gulf of Gabes (Tunisia) between April 2013 and March 2015. A total of 1638 specimens were examined, ranging from 9.7 to 30.7 cm total length. The sex ratio was in favour of males in smaller size classes and females in larger size classes. The macroscopic analysis of the gonads and the progression of the monthly values of the gonadosomatic index (GSI) indicated that the reproductive season extended from October to February, with GSI peaking in November and December for males and females respectively, and that spawning occurs once a year from November to February. The utilization of lipid reserves, stored predominantly in the liver as well as in muscles, was depicted. The estimated average length at first maturity was 22.31 ± 0.41 cm for males and 23.19 ± 0.184 cm for females. Total fecundity of mature females ranged from 14,160–62,700 eggs per fish, showing a significant increase with size, with an average of 33,020 ± 5239 eggs per fish.
Local feature detection and description are widely used for object recognition such as augmented reality applications. There have been a number of evaluations and comparisons between feature detectors and descriptors and between their different implementations. Those evaluations are carried out on random sets of image structures. However, feature detectors and descriptors respond differently depending on the image structure. In this paper, we evaluate the overall performance of the most efficient detectors and descriptors in terms of speed and efficiency. The evaluation is carried out on a set of images of different object classes and structures with different geometric and photometric deformations. This evaluation would be useful for detecting the most suitable detector and descriptor for a particular object recognition application. Moreover, multi-object applications such as digilog books could change the detector and descriptor used based on the current object. From the results, it has been observed that some detectors perform better with certain object classes. Differences in performance of the descriptors vary with different image structures.
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