Accurate wild animal recognition using PCA, LDA and LBPH

Kamencay, Patrik; Trnovszký, Tibor; Benčo, Miroslav; Hudec, Róbert; Sýkora, Peter; Satnik, Andrej

doi:10.1109/elektro.2016.7512036

Cited by 21 publications

(8 citation statements)

References 11 publications

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“…The significant reason for binary patterns (Fig. 8(a)) is to sum up the local structure in a block through comparison of each pixel with its neighborhood [40]. Each pixel coded with a sequence of bits is colligated with the connection between the pixel and one of its neighbors.…”

Section: Resultsmentioning

confidence: 99%

Image-based Individual Cow Recognition using Body Patterns

Bello¹,

Talib²,

Mohamed³

et al. 2020

IJACSA

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The existence of illumination variation, non-rigid object, occlusion, non-linear motion, and real-time implementation requirement has made tracking in computer vision a challenging task. In order to recognize individual cow and to mitigate all the challenging tasks, an image processing system is proposed using the body pattern images of the cow. This system accepts an input image, performs processing operation on the image, and output results in form of classification under certain categories. Technically, convolutional neural network is modeled for the training and testing of each pattern image of 1000 acquired images of 10 species of cow which will pass it through a series of convolution layers with filters, pooling, fully connected layers and softmax function for the pattern images classification with probabilistic values between 0 and 1. The performance evaluation of the proposed system for both training and testing data was carried out for each cow's identification and 92.59% and 89.95% accuracies were achieved respectively.

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Section: Resultsmentioning

confidence: 99%

Image-based Individual Cow Recognition using Body Patterns

Bello¹,

Talib²,

Mohamed³

et al. 2020

IJACSA

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“…The Principal Component Analysis (PCA) technique is classified among the excellent unsupervised dimensionality reduction techniques. Its major aim is to transform image data or features from a higher dimensional space to a lower dimensional space [18][19][20][21][22].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Image compression of surface defects of the hot-rolled steel strip using Principal Component Analysis

Boudiaf

Boubendira

Harrar

et al. 2019

Matériaux & Techniques

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The quality control of steel products by human vision remains tedious, fatiguing, somewhat fast, rather robust, sketchy, dangerous or impossible. For these reasons, the use of the artificial vision in the world of quality control has become more than necessary. However, these images are often large in terms of quantity and size, which becomes a problem in quality control centers, where engineers are unable to store these images. For this, efficient compression techniques are necessary for archiving and transmitting the images. The reduction in file size allows more images to be stored in a disk or memory space. The present paper proposes an effective technique for redundancy extraction using the Principal Component Analysis (PCA) approach. Furthermore, it aims to study the effects of the number of eigenvectors employed in the PCA compression technique on the quality of the compressed image. The results revealed that using only 25% of the eigenvectors provide very similar compressed images compared to the original ones, in terms of quality. These images are characterized by high compression ratios and a small storage space.

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“…In this paper, the main point is comparison of different tracking algorithms from the state-of-the-art with the proposed algorithm which uses Local Binary Pattern Histograms www.ijacsa.thesai.org (LBPH) [9] and Tensorflow for detecting, recognizing and tracking the recognized person in other videos. LBPH approach is compared with Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) for animal recognition previously [10], which showed efficient results for a small test dataset. Tensorflow can be used in heterogeneous and large scale environments; it is mainly an advantage when it comes to the performance of an algorithm [11].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Detection, Recognition and Tracking Rates of the different Algorithms

Kavuri¹,

Prakash²

2019

IJACSA

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This article discusses the approach of human detection and tracking in a homogeneous domain using surveillance cameras. This is a vast area in which significant research has been taking place from more than a decade and the paper is about detection of a human and its face in a given video and stores Local Binary Pattern Histogram (LBPH) features of the detected faces. Once a human is detected in the video, that person will be given a label and him/her is tracked in different video taken by multiple cameras by the application of machine learning and image processing with the help of OpenCV. Many algorithms were used for detecting, recognizing and tracking till date, thus in this paper, main thing is the comparison of the proposed algorithm with some among the state-of-the-art algorithms. And also shows how the proposed algorithm is better than the other chosen algorithms.

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Accurate wild animal recognition using PCA, LDA and LBPH

Cited by 21 publications

References 11 publications

Image-based Individual Cow Recognition using Body Patterns

Image-based Individual Cow Recognition using Body Patterns

Image compression of surface defects of the hot-rolled steel strip using Principal Component Analysis

Performance Comparison of Detection, Recognition and Tracking Rates of the different Algorithms

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