Loreta Adriana Suta

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The problem we address in this paper is object segmentation applied to plant recognition. The image can contain one or more plants on a natural background. More precisely, we aim to segment flowers. This approach poses several challenges, such as texture, multiple colors that form one object, natural background, non-homogeneous regions, etc. We propose an(More)
This paper presents a local no-reference blur assessment method in natural macro-like images. The purpose is to decide the blurriness of the object of interest. In our case, it represents the first step for a plant recognition system. Blur detection works on small non-overlapping blocks using wavelet decomposition and edge classification. At the block level(More)
In this paper we present a global no macro-like images. The purpose is to study the possibility of global assessment based on the an analyzed image. In our case, it represents the first step for a plant recognition system. Blur detection works on small non overlapping blocks using wavelet decomp global images. A set of rules is obtained by a supervised(More)
This paper is dedicated to the problem of automatic skyline extraction in digital images. The study is motivated by the needs, expressed by urbanists, to describe in terms of geometrical features, the global shape created by man-made buildings in urban areas. Skyline extraction has been widely studied for navigation of Unmanned Aerial Vehicles (drones) or(More)
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