BIDIRECTIONAL ASSOCIATIVE MEMORY ENSEMBLE

The multiple classifier system （MCS）, composed of multiple diverse classifiers or feed-forward neural networks, can significantly improve the classification or generalization ability of a single classifier. Enlightened by the fundamental idea of MCS, the ensemble is introduced into the quick learning for bidirectional associative memory （QLBAM） to construct a BAM ensemble, for improving the storage capacity and the error-correction capability without destroying the simple structure of the component BAM. Simulations show that, with an appropriate ＂overproduce and choose＂ strategy or ＂thinning＂ algorithm, the proposed BAM ensemble significantly outperforms the single QLBAM in both storage capacity and noise-tolerance capability.

Method for detecting 2D grapevine winter pruning location based on thinning algorithm and Lightweight Convolutional Neural Network

In viticulture,there is an increasing demand for automatic winter grapevine pruning devices,for which detection of pruning location in vineyard images is a necessary task,susceptible to being automated through the use of computer vision methods.In this study,a novel 2D grapevine winter pruning location detection method was proposed for automatic winter pruning with a Y-shaped cultivation system.The method can be divided into the following four steps.First,the vineyard image was segmented by the threshold two times Red minus Green minus Blue(2R−G−B)channel and S channel;Second,extract the grapevine skeleton by Improved Enhanced Parallel Thinning Algorithm(IEPTA);Third,find the structure of each grapevine by judging the angle and distance relationship between branches;Fourth,obtain the bounding boxes from these grapevines,then pre-trained MobileNetV3_small×0.75 was utilized to classify each bounding box and finally find the pruning location.According to the detection experiment result,the method of this study achieved a precision of 98.8%and a recall of 92.3%for bud detection,an accuracy of 83.4%for pruning location detection,and a total time of 0.423 s.Therefore,the results indicated that the proposed 2D pruning location detection method had decent robustness as well as high precision that could guide automatic devices to winter prune efficiently.

Automatic Extraction of Urban Road Centerlines from High-Resolution Satellite Imagery Using Automatic Thresholding and Morphological Operation Method

The commercial high-resolution imaging satellite with 1 m spatial resolution IKONOS is an important data source of information for urban planning and geographical information system (GIS) applications. In this paper, a morphological method is proposed. The proposed method combines the automatic thresholding and morphological operation techniques to extract the road centerline of the urban environment. This method intends to solve urban road centerline problems, vehicle, vegetation, building etc. Based on this morphological method, an object extractor is designed to extract road networks from highly remote sensing images. Some filters are applied in this experiment such as line reconstruction and region filling techniques to connect the disconnected road segments and remove the small redundant. Finally, the thinning algorithm is used to extract the road centerline. Experiments have been conducted on a high-resolution IKONOS and QuickBird images showing the efficiency of the proposed method.

Using image processing technology to create a novel fry counting algorithm

Quantitative counting of fry is required for multiple reasons,including breeding,transportation,and sales and creating a fish-counting algorithm using image processing technology is proposed as a useful method.The images for fish fry were preprocessed to extract the details of the target fish,using methods including binarization,dilation,and erosion.The thinning and the connected area algorithms were then independently adopted to count the fish fry within the image.The advantages and the disadvantages of the two algorithms under different densities were also compared,revealing that the fish fry pictures with a clear contrast between the foreground and background resulted in a higher accuracy.The main purpose of this research is to solve the problem of fry counting in the case of high density and high overlap.This research provides a reference point for this method based on image technology.