Extended performance comparison of hybrid wavelet transform for image compression with varying proportions of constituent transforms

Thepade, Sudeep D.; Dewan, Jaya H.; Lohar, Anil

doi:10.1109/icact.2013.6710497

Cited by 17 publications

(4 citation statements)

References 3 publications

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“…Paper [15] shows that the growth in software for digital images has increased the need for effective methods of image compression. The HWT performance for the compression of digital images was checked.…”

Section: Underlying the Methods Is The Task Of Automating Image Processing In Unmanned Aircraft Systems The Operability Of The Proposed Mmentioning

confidence: 99%

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Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform

Slyusar

Protsenko

Chernukha

et al. 2021

EEJET

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The tasks that unmanned aircraft systems solve include the detection of objects and determining their state. This paper reports an analysis of image recognition methods in order to automate the specified process. Based on the analysis, an improved method for recognizing images of monitored objects by a convolutional neural network using a discrete wavelet transform has been devised. Underlying the method is the task of automating image processing in unmanned aircraft systems. The operability of the proposed method was tested using an example of processing an image (aircraft, tanks, helicopters) acquired by the optical system of an unmanned aerial vehicle. A discrete wavelet transform has been used to build a database of objects' wavelet images and train a convolutional neural network based on them. That has made it possible to improve the efficiency of recognition of monitored objects and automate a given process. The effectiveness of the improved method is achieved by preliminary decomposition and approximation of the digital image of the monitored object by a discrete wavelet transform. The stages of a given method include the construction of a database of the wavelet images of images and training a convolutional neural network. The effectiveness of recognizing the monitored objects' images by the improved method was tested on a convolutional neural network, which was trained with images of 300 monitored objects. In this case, the time to make a decision, based on the proposed method, decreased on average from 0.7 to 0.84 s compared with the artificial neural networks ResNet and ConvNets. The method could be used in the information processing systems in unmanned aerial vehicles that monitor objects; in robotic complexes for various purposes; in the video surveillance systems of important objects

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Section: Underlying the Methods Is The Task Of Automating Image Processing In Unmanned Aircraft Systems The Operability Of The Proposed Mmentioning

confidence: 99%

“…The values of the kernel weights are set randomly in the region from −0.5 to 0.5. The kernel slides over the preceding map and performs a convolution operation according to formula (15). The second convolutional layer can be described by formula (16).…”

Section: Fig 5 Elements Of a Convolutional Neural Networkmentioning

confidence: 99%

Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform

Slyusar

Protsenko

Chernukha

et al. 2021

EEJET

View full text Add to dashboard Cite

show abstract

“…Furthermore, this combination demonstrated effectiveness for compression ratios between 70% and 90% at a 1:1 ratio, as well as for lower compression ratios at a 1:4 ratio. [11] H.B. Kekre, Tanuja Sarode, and Prachi Natu employed a hybrid wavelet transform with various combinations, such as 8-32, 16-16, and 32-8 of Discrete Cosine Transform (DCT) and Discrete Kekre Transform (DKT), to compress images across different color spaces.…”

Section: Reviewmentioning

confidence: 99%

Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-2 with HMM Classifier

Manoj Chavan

2024

jes

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Online signature verification stands out as a distinctive biometric feature, offering both static and dynamic attributes within 2D signature images. A Hybrid Wavelet Transform-2 (HWT-2) with a size of 256 is constructed by employing the Kronecker product of two orthogonal transforms: DCT, DHT, Haar, Hadamard, and Kekre, each with sizes of 4 and 64. The HWT enables the analysis of signals at both global and local levels, akin to wavelet transforms. HWT-2 is applied to 256 samples of online handwritten signatures, and the first 128 samples of the output are utilized as feature vectors for the verification and forgery detection of online handwritten signatures. These feature vectors are inputted into Left-Right and Ergodic Hidden Markov Model (HMM) classifiers for analysis. The HMMs are trained using 10 randomly selected genuine signature samples, and subsequently tested on the remaining 10 genuine signatures and 20 forged signatures from 40 users of the SVC 2004 signature database. This process is repeated 20 times, and the average values are computed. Among all possible combinations of HWT-2 using DCT, DHT, Haar, Hadamard, and Kekre transforms for the Left-Right HMM model, the combination of DCT 4 and DHT 64 demonstrates the best performance, with False Rejection Rate (FRR) and False Acceptance Rate (FAR) values of 3.96% and 1.48%, respectively, for state 5. Similarly, for the Ergodic HMM model, the combination of DCT 4 and DHT 64 exhibits the best performance, with FRR and FAR values of 1.10% and 2.88%, respectively, for state 5. These results indicate that combinations of HWT-2 outperform individual orthogonal transforms, and further, that HWT-2 combinations within the Ergodic HMM model offer superior performance compared to the Left-Right HMM model.

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“…The study found that for a compression ratio of 95%, the 4:1 ratio of Discrete Cosine Transform (DCT) to Haar transform yielded the most favorable results. Additionally, this combination performed well for compression ratios between 70% and 90% at a 1:1 ratio and for lower compression ratios at a 1:4 ratio [11] H.B. Kekre, Tanuja Sarode, and Prachi Natu explored a hybrid wavelet transform utilizing various combinations, such as 8-32, 16-16, and 32-8 of Discrete Cosine Transform (DCT) and Discrete Kekre Transform (DKT), to compress images across different color spaces.…”

mentioning

confidence: 99%

Comparative Analysis of Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-1 and 2 with HMM Classifier

Manoj Chavan

2024

jes

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Online signature verification is a unique biometric feature. Provides static and dynamic features for 2D signature images. Hybrid wavelet transform -1 and 2 (HWT-1 and HWT-2) of size 256 is created using the Kronecker product of two orthogonal transforms such as DCT, DHT, Haar, Hadamard and Kekre with size 4 and 64. HWT has the ability to analyze signals such as wavelet transform at global and local levels. HWT-1 and HWT-2 are used for the 256 samples of the online Handwritten signature and the first 128 samples of the output are used as feature vectors for handwritten online signature verification and forgery detection. This feature vector is given to the Left-Right and Ergodic modelled HMM classifier for analysis. HMM is trained using 10 randomly chosen genuine signature samples and is used to test remaining 10 genuine signatures and 20 forged signatures of 40 users of SVC 2004 signature database. This process is iterated 20 times and then average values are calculated. Considering all the possible combination of HWT-1 and HWT-2 for DCT, DHT, Haar, Hadamard and Kekre transform for Left Right HMM model, DCT 4 Haar 64 HWT-1 offers best performance of FRR, FAR of 1.05%, 0.99% respectively for state 3. Considering all the possible combination of HWT-1 and HWT-2 for DCT, DHT, Haar, Hadamard and Kekre transform for Ergodic HMM model, DCT 4 DHT 64 offers best performance of FRR, FAR of 1.10%, 2.88% respectively for state 5. We conclude that comparing HWT-1 and HWT-2 combinations for Left Right and Ergodic HMM model, HWT-1 for Left Right model offer better performance.

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Extended performance comparison of hybrid wavelet transform for image compression with varying proportions of constituent transforms

Cited by 17 publications

References 3 publications

Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform

Construction of an advanced method for recognizing monitored objects by a convolutional neural network using a discrete wavelet transform

Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-2 with HMM Classifier

Comparative Analysis of Handwritten Online Signature Verification and Forgery Detection Using Hybrid Wavelet Transform-1 and 2 with HMM Classifier

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