A Fast Algorithm of Convex Hull Vertices Selection for Online Classification

Ding, Shuguang; Nie, Xiangli; Qiao, Hong; Zhang, Bo

doi:10.1109/tnnls.2017.2648038

Cited by 29 publications

(10 citation statements)

References 27 publications

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“…Image segmentation results needed to be repaired using morphological algorithms. The morphological algorithms used in this paper included dilation (see Equation (4)), blob analysis (see Equation (5)), and area filling surrounded by convex hull points [31,32] (see Equation 6 As is shown in Figure 8, the segmentation result of the threshold segmentation algorithm was partially misjudged because of the interference of the broken substrate, vermiculite, and shadow. Image segmentation results needed to be repaired using morphological algorithms.…”

Section: C1 C2 C1 C2mentioning

confidence: 99%

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Identification of Cabbage Seedling Defects in a Fast Automatic Transplanter Based on the maxIOU Algorithm

et al. 2020

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The automatic identification of seedling defects is an important technology of an intelligent automatic transplanting machine, which effectively improves the quality of the transplanting machine’s operation. The accurate segmentation of seedling substrate and seedling region is the key to the success of the seedling defect recognition algorithm. This paper proposes the maxIOU algorithm to calculate the image segmentation threshold: The image G channel and excess green color space were selected as the color space for the segmentation of the substrate region and seedling region by analyzing the color histogram. Several images were randomly selected from the dataset to generate a training set and were labeled manually as the ground truth. The training set images were segmented using a threshold of zero to 255, and the intersection over union (IOU) were calculated using the algorithm segmented result and the ground truth. The threshold corresponding to the average IOU maximum was used as the segmentation threshold. After image segmentation, three features (area of the substrate, area of the seedling, and filling ratio of the lower part of the substrate) were obtained by the algorithm, and the image was identified for whether there was an empty conveyor belt, seedling deficiency, multiple seedlings, skew, and damaged substrate. The algorithm was tested on the automatic transplanter test platform. The experiment results were as follows: Firstly, the image segmentation threshold was calculated by the maxIOU method. The color component interval corresponding to the segmented substrate region was [0, 24] in the G channel, and the color component interval corresponding to the segmented seedling region was [21, 255] in the excess green channel. The average IOU of the substrate area was 0.854, and the average IOU of the seedling area was 0.820 in the verification experiment. Secondly, a dataset including 431 normal seedling images and 69 defective seedling images (empty conveyor belt, seedling deficiency, multiple seedlings, skew, and damaged substrate) was identified for defects. The accuracy, precision, and recall were 97.6%, 97.4%, and 99.8%. The processing time was 71.4 ms. The conclusion of the experiment was as follows: the maxIOU algorithm had high accuracy in the segmentation of the substrate and seedling region. The defect identification algorithm had high accuracy for defect identification of cabbage seedlings, and the algorithm had good real-time performance, which can be applied to high speed field transplanters.

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Section: C1 C2 C1 C2mentioning

confidence: 99%

Section: C1 C2 C1 C2mentioning

confidence: 99%

Identification of Cabbage Seedling Defects in a Fast Automatic Transplanter Based on the maxIOU Algorithm

et al. 2020

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“…Thus, the concepts of convex hull and α-hull will be reviewed briefly below. Definition 4. convex hull [30]: Given a set P ¼ fp 0 ; p 1 ; . .…”

Section: Geometric Model For Event Monitoringmentioning

confidence: 99%

A Comprehensive Data Gathering Network Architecture in Large-Scale Visual Sensor Networks

et al. 2020

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The fundamental utility of the Large-Scale Visual Sensor Networks (LVSNs) is to monitor specified events and to transmit the detected information back to the sink for achieving the data aggregation purpose. However, the events of interest are usually not uniformly distributed but frequently detected in certain regions in real-world applications. It implies that when the events frequently picked up by the sensors in the same region, the transmission load of LVSNs is unbalanced and potentially cause the energy hole problem. To overcome this kind of problem for network lifetime, a Comprehensive Visual Data Gathering Network Architecture (CDNA), which is the first comparatively integrated architecture for LVSNs is designed in this paper. In CDNA, a novel α-hull based event location algorithm, which is oriented from the geometric model of α-hull, is designed for accurately and efficiently detect the location of the event. In addition, the Chi-Square distribution event-driven gradient deployment method is proposed to reduce the unbalanced energy consumption for alleviating energy hole problem. Moreover, an energy hole repairing method containing an efficient data gathering tree and a movement algorithm is proposed to ensure the efficiency of transmitting and solving the energy hole problem. Simulations are made for examining the performance of the proposed architecture. The simulation results indicate that the performance of CDNA is better than the previous algorithms in the realistic LVSN environment, such as the significant improvement of the network lifetime.

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“…Singh et al [21] proposed the convex hull approach in conjunction with Gaussian mixture model in order to improve the detection accuracy without capitalizing much of computation time. CHVS [22] is a fast convex hull vertices selection algorithm for online classification and the proposed method converts convex hull into a linear equation problem with a low computational complexity.…”

Section: Related Workmentioning

confidence: 99%

An Indexing Method to Construct Unbalanced Layers for High-Dimensional Data in Mobile Environments

Ihm

Hur

2017

Wireless Communications and Mobile Computing

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A top-query processing is widely used in many applications and mobile environments. An index is used for efficient query processing and layer-based indexing methods are representative to perform the top-query processing efficiently. However, the existing methods have a problem of high index building time for multidimensional and large data; thus, it is difficult to use them. In this paper, we proposed a new concept of constructing layer-based index, which is called unbalanced layer (UB-Layer). The existing methods construct a layer as a balanced layer with outermost data and wrap the rest of the input data. However, UBLayer constructs a layer as an unbalanced layer that does not wrap the rest of the data. To construct UB-Layer, we fist divide the dimension of the input data into divided-dimensional data and compute the convex hull in each divided-dimensional data. And then, we combine divided-convex hull to build UB-Layer. We also propose UB-SelectAttribute algorithm for dividing the dimension with major attributes. We demonstrate the superiority of the proposed methods by the performance experiments.

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A Fast Algorithm of Convex Hull Vertices Selection for Online Classification

Cited by 29 publications

References 27 publications

Identification of Cabbage Seedling Defects in a Fast Automatic Transplanter Based on the maxIOU Algorithm

Identification of Cabbage Seedling Defects in a Fast Automatic Transplanter Based on the maxIOU Algorithm

A Comprehensive Data Gathering Network Architecture in Large-Scale Visual Sensor Networks

An Indexing Method to Construct Unbalanced Layers for High-Dimensional Data in Mobile Environments

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