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Crop segmentation from the images captured in the outdoor field is a complex task in agriculture automation, let alone detecting some specific crops with one method. Cotton, as one of the four major economic crops, is of great significance to the development of the national economy. In this paper, a novel strategy based on the deep learning is utilized to establish the crop classifier in the RGB vector...
Crop segmentation is a frequently concerned problem for computer vision applications in agriculture. Tassel is a typical agronomic trait in the crop breeding process. Tassel trait characterization also requires fine-grained shape extraction. However, previous methods are usually dependent of category, which is hard to transfer to other cultivars with different colors. To address this, the goal of...
Fast object detection is the most important part of the unmanned surface vehicles (USV) which make it possible for the USV to avoid the obstacle automatically and navigate autonomously. So, it is necessary to find a fast and accurate object detection method. In practice, the significant difficulty is that the environment is quite complicated which make the object uncertain. The obstacle may be a person,...
The depth image has greatly broadened various applications of computer vision, however, it is seldom explored in the field of salient object detection. In this paper, we propose a learning-based approach for extracting saliency from RGB-D images. For best fitting the contrast-based stimulus that guides the saliency search in human vision system, massive visual attributes that are extracted from several...
This paper presents a new method for salient object detection based on a sophisticated appearance comparison of multisize superpixels. Those superpixels are modeled by multivariate normal distributions in CIE-Lab color space, which are estimated from the pixels they comprise. This fitting facilitates an efficient application of the Wasserstein distance on the Euclidean norm (\(\operatorname {\mathcal {W}}_{2}\) ...
We present a new segment-based method for saliency detection based on multi-size superpixels that combines local and global saliency cues. We extract superpixels at several scales and represent each superpixel with a normal distribution in CIE-Lab space estimated from its associated pixels. Global saliency is computed by grouping similar superpixels to estimate the spatial distribution of colors,...
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