A novel Features from Accelerated Segment Test algorithm based on LBP on image matching

Guo, Lisha; Li, Junshan; Zhu, Yinghong; Tang, ZongQi

doi:10.1109/iccsn.2011.6013732

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…In equation (5), image p at a point x,y contains the sum of all the source pixels from 0,0 to x, y. Once this precomputation has been completed, the expression for the box-filtering process can be rewritten entirely in terms of sums starting at 0:…”

Section: Figure 5: Image Preprocessingmentioning

confidence: 99%

“…More importantly, in our study, we propose a new type of image classification, raw mineral copper-cobalt classification, which presents a lot of challenges since the shape of the stones are different. Many researchers have studied image feature extraction FIGURE 1: Type of Image classification strategies [2]- [5], ranging from traditional techniques like Binary Robust Independent Elementary Features (BRIEF) [6] to deep learning techniques like Learning Local Features from Images [7]. Convolutional neural networks (CNN) can replace traditional feature extraction techniques because they have a great ability to extract complex features that express the image in much more detail, learn task-specific characteristics, and are considerably more efficient.…”

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confidence: 99%

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Neural Architecture Search Net-Based Feature Extraction With Modular Neural Network for Image Classification of Copper/ Cobalt Raw Minerals

Dahouda

Joe

2022

IEEE Access

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Image processing is one of the most quickly evolving technologies today and it is an approach for applying operations on an image to improve it or extract relevant information from it. It is a critical research field in engineering and computer science. However, analyzing a high number of variables demands a lot of memory and processing resources, and it can cause a classification algorithm to overfit on training samples and underfit on test samples. As a result, various strategies, such as extraction can be used to reduce the number of features in a dataset by producing new ones from old ones. In this paper, we firstly propose a deep learning-based feature extraction approach with a modular neural network where we employ a pre-trained network, neural architecture search net (NASNet), as a feature extractor on a custom dataset of raw minerals of copper and cobalt images. It allows the input image to be feed-forwarded while performing feature learning and feature map, then stopping at a pooling layer before the fully connected (FC) layer in the NASNet to extract and save the outputs of that layer in dumped files. Secondly, the extracted features are then used as training data to build a deep neural network and machine learning algorithms for the image classification of copper and cobalt raw minerals. The experimental results show that the NASNet extracts the features efficiently, and the proposed modular neural network performs well with the boosting-decision tree as a classifier, which gives higher accuracy of 91% than 90% of the deep neural network; moreover, the precision is 1 higher than 0.98 for the deep neural network.

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Section: Figure 5: Image Preprocessingmentioning

confidence: 99%

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confidence: 99%

Neural Architecture Search Net-Based Feature Extraction With Modular Neural Network for Image Classification of Copper/ Cobalt Raw Minerals

Dahouda

Joe

2022

IEEE Access

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“…The main idea of this study was to extract features using one of the classic algorithms for obtaining keypoints, such as scale-invariant feature transform (SIFT) [21], speeded up robust features (SURF) [22], features from accelerated segment test (FAST) [23], or binary robust invariant scalable keypoints (BRISK) [24], and then create samples with found features. It should be noted that if these algorithms processed the original image, the found points would probably cover the entire image; in the case of a simple image where a ship is at sea, all points could be placed on this object or water or waves, but there may be an image with some additional background with many possible points.…”

Section: Feature Extractionmentioning

confidence: 99%

Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism

Połap¹,

Wlodarczyk–Sielicka

2020

Sensors

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The existing methods for monitoring vessels are mainly based on radar and automatic identification systems. Additional sensors that are used include video cameras. Such systems feature cameras that capture images and software that analyzes the selected video frames. Methods for the classification of non-conventional vessels are not widely known. These methods, based on image samples, can be considered difficult. This paper is intended to show an alternative way to approach image classification problems; not by classifying the entire input data, but smaller parts. The described solution is based on splitting the image of a ship into smaller parts and classifying them into vectors that can be identified as features using a convolutional neural network (CNN). This idea is a representation of a bag-of-words mechanism, where created feature vectors might be called words, and by using them a solution can assign images a specific class. As part of the experiment, the authors performed two tests. In the first, two classes were analyzed and the results obtained show great potential for application. In the second, the authors used much larger sets of images belonging to five vessel types. The proposed method indeed improved the results of classic approaches by 5%. The paper shows an alternative approach for the classification of non-conventional vessels to increase accuracy.

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“…The algorithm calculated a Bresenham circle of radius 3 for each point in an image [8] and each pixel in the circle is clockwise assigned an addressing integer number from the range 1-16 [14]. If a set of contiguous pixels in the circle have greater intensity value than the intensity of a candidate pixel plus a threshold value or all darker than the intensity of minus threshold value , then is classified as point of interest [9]. Values of and 𝑡 can be chosen experimentally [8].…”

Section: B Features From Accelerated Segment Testmentioning

confidence: 99%