Detection of objects on the image using a sliding window mode

Глумов, Н. И.; Kolomiyetz, E.I.; Sergeyev, V. V.

doi:10.1016/0030-3992(95)93752-d

Cited by 32 publications

(13 citation statements)

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“…This classifier generates the best hyperplane that classifies samples as positive (hypocotyls) and negative (no hypocotyls) examples. During the hypocotyl detection stage, the sliding window approach (Glumov et al ., ) is used to perform an exhaustive search for hypocotyls. Finally, by keeping the highest scored windows as true positives, polynomial regression is used to define a curve that passes through all the detected hypocotyls.…”

Section: Resultsmentioning

confidence: 99%

MyROOT: a method and software for the semiautomatic measurement of primary root length in Arabidopsis seedlings

et al. 2019

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Summary Root analysis is essential for both academic and agricultural research. Despite the great advances in root phenotyping and imaging, calculating root length is still performed manually and involves considerable amounts of labor and time. To overcome these limitations, we developed MyROOT, a software for the semiautomatic quantification of root growth of seedlings growing directly on agar plates. Our method automatically determines the scale from the image of the plate, and subsequently measures the root length of the individual plants. To this aim, MyROOT combines a bottom‐up root tracking approach with a hypocotyl detection algorithm. At the same time as providing accurate root measurements, MyROOT also significantly minimizes the user intervention required during the process. Using Arabidopsis, we tested MyROOT with seedlings from different growth stages and experimental conditions. When comparing the data obtained from this software with that of manual root measurements, we found a high correlation between both methods (R2 = 0.997). When compared with previous developed software with similar features (BRAT and EZ‐Rhizo), MyROOT offered an improved accuracy for root length measurements. Therefore, MyROOT will be of great use to the plant science community by permitting high‐throughput root length measurements while saving both labor and time.

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Section: Resultsmentioning

confidence: 99%

MyROOT: a method and software for the semiautomatic measurement of primary root length in Arabidopsis seedlings

et al. 2019

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“…The deterministic sliding-window algorithm detects specular, shiny or reflective objects by looking at changes in regional variation between hue and intensity; the original area where this algorithm was employed was in video processing [72]. For IEDs, areas where hue does not change significantly, but the intensity does, are good candidates for a non-reflective (Non-IED) region; conversely, areas, where the opposite is true, are good candidates for a shiny, reflective (IED) surface, potentially indicating the presence of an IED.…”

Section: Deterministic Image Analysismentioning

confidence: 99%

Shallow buried improvised explosive device detection via convolutional neural networks

Colreavy‐Donnelly

Caraffini

Kühn

et al. 2020

ICA

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The issue of detecting improvised explosive devices, henceforth IEDs, in rural or built-up urban environments is a persistent and serious concern for governments in the developing world. In many cases, such devices are plastic, or varied metallic objects containing rudimentary explosives, which are not visible to the naked eye and are difficult to detect autonomously. The most effective strategy for detecting land mines also happens to be the most dangerous. This paper intends to leverage the use of a Convolutional Neural Network (CNN) to aid in the discovery of such IEDs. As part of a related project, an autonomous sensor array was used to detect the devices in terrains too hazardous for a human to survey. This paper presents a CNN and its training methodology, suitable to make use of the sensor system. This convolutional neural network can accurately distinguish between a potential IED and surrounding undergrowth and natural features of the environment in real-time. The training methodology enabled the CNN to successfully recognise the IEDs with an accuracy of 98.7%, in well-lit conditions. The results are evaluated against other convolutional neural systems as well as against a deterministic algorithm, showing that the proposed CNN outperforms its competitors including the deterministic method.

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“…We pad all the training examples by 0s and they become squares of 30×30 then, every extended image is cropped by sliding a square 28 × 28 window over 9 possible positions. Hence, every image produces 9 versions among which one is the original image itself [4]. For the sake of simplicity, we use a simple black and white diagonal input image to show the sliding window process in Figure 6.…”

Section: Sliding Window L2 Metricmentioning

confidence: 99%

“…As an example, authors in [1], demonstrate that for k = 1 case the k-NN classification error is lower bounded by the twice the Bayes errorrate. Such studies regarding mathematical properties of k-NN led to further research and investigation including new rejection approaches in [2], refinements with respect to Bayes error rate in [3], and distance weighted approaches in [4]. Moreover, soft computing [5] methods and fuzzy methods [6] have also been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

MNIST Dataset Classification Utilizing k-NN Classifier with Modified Sliding-Window Metric

Grover

Toghi

2019

Advances in Intelligent Systems and Computing

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The MNIST dataset of the handwritten digits is known as one of the commonly used datasets for machine learning and computer vision research. We aim to study a widely applicable classification problem and apply a simple yet efficient K-nearest neighbor classifier with an enhanced heuristic. We evaluate the performance of the K-nearest neighbor classification algorithm on the MNIST dataset where the L2 Euclidean distance metric is compared to a modified distance metric which utilizes the sliding window technique in order to avoid performance degradation due to slight spatial misalignments. The accuracy metric and confusion matrices are used as the performance indicators to compare the performance of the baseline algorithm versus the enhanced sliding window method and results show significant improvement using this proposed method.

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Detection of objects on the image using a sliding window mode

Cited by 32 publications

References 1 publication

MyROOT: a method and software for the semiautomatic measurement of primary root length in Arabidopsis seedlings

MyROOT: a method and software for the semiautomatic measurement of primary root length in Arabidopsis seedlings

Shallow buried improvised explosive device detection via convolutional neural networks

MNIST Dataset Classification Utilizing k-NN Classifier with Modified Sliding-Window Metric

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