“…Figure 1 shows the visual definition of each criterion. In addition, if the best algorithm fails on specific frames, the possibility of re-tracking this frame using another tracking algorithm was checked out 38 . Figure 1 Criteria visual representation.…”
This study aims to compare the tracking algorithms provided by the OpenCV library to use on ultrasound video. Despite the widespread application of this computer vision library, few works describe the attempts to use it to track the movement of liver tumors on ultrasound video. Movements of the neoplasms caused by the patient`s breath interfere with the positioning of the instruments during the process of biopsy and radio-frequency ablation. The main hypothesis of the experiment was that tracking neoplasms and correcting the position of the manipulator in case of using robotic-assisted surgery will allow positioning the instruments more precisely. Another goal of the experiment was to check if it is possible to ensure real-time tracking with at least 25 processed frames per second for standard definition video. OpenCV version 4.5.0 was used with 7 tracking algorithms from the extra modules package. They are: Boosting, CSRT, KCF, MedianFlow, MIL, MOSSE, TLD. More than 5600 frames of standard definition were processed during the experiment. Analysis of the results shows that two algorithms—CSRT and KCF—could solve the problem of tumor tracking. They lead the test with 70% and more of Intersection over Union and more than 85% successful searches. They could also be used in real-time processing with an average processing speed of up to frames per second in CSRT and 100 + frames per second for KCF. Tracking results reach the average deviation between centers of neoplasms to 2 mm and maximum deviation less than 5 mm. This experiment also shows that no frames made CSRT and KCF algorithms fail simultaneously. So, the hypothesis for future work is combining these algorithms to work together, with one of them—CSRT—as support for the KCF tracker on the rarely failed frames.
“…Figure 1 shows the visual definition of each criterion. In addition, if the best algorithm fails on specific frames, the possibility of re-tracking this frame using another tracking algorithm was checked out 38 . Figure 1 Criteria visual representation.…”
This study aims to compare the tracking algorithms provided by the OpenCV library to use on ultrasound video. Despite the widespread application of this computer vision library, few works describe the attempts to use it to track the movement of liver tumors on ultrasound video. Movements of the neoplasms caused by the patient`s breath interfere with the positioning of the instruments during the process of biopsy and radio-frequency ablation. The main hypothesis of the experiment was that tracking neoplasms and correcting the position of the manipulator in case of using robotic-assisted surgery will allow positioning the instruments more precisely. Another goal of the experiment was to check if it is possible to ensure real-time tracking with at least 25 processed frames per second for standard definition video. OpenCV version 4.5.0 was used with 7 tracking algorithms from the extra modules package. They are: Boosting, CSRT, KCF, MedianFlow, MIL, MOSSE, TLD. More than 5600 frames of standard definition were processed during the experiment. Analysis of the results shows that two algorithms—CSRT and KCF—could solve the problem of tumor tracking. They lead the test with 70% and more of Intersection over Union and more than 85% successful searches. They could also be used in real-time processing with an average processing speed of up to frames per second in CSRT and 100 + frames per second for KCF. Tracking results reach the average deviation between centers of neoplasms to 2 mm and maximum deviation less than 5 mm. This experiment also shows that no frames made CSRT and KCF algorithms fail simultaneously. So, the hypothesis for future work is combining these algorithms to work together, with one of them—CSRT—as support for the KCF tracker on the rarely failed frames.
“…That provide the difference between original and reconstructed images. It measures the pixel difference between given two images [7].…”
Section: ) Peak Signal-to-noise Ratio (Psnr) and Structural Similarit...mentioning
confidence: 99%
“…In this technique, the secret image is embedded in some other cover media like image, audio, or video etc. [6][7][8][9]. However, there are some steganalysis techniques [10], that can extract the secret hidden image from the cover media.…”
Due to advancements in technology, digital images are widely used in various applications like medical field, military communication, remote sensing, etc. These images may contain sensitive and confidential information. Therefore, images are required to be protected from unauthorized access. Many image protection techniques have been proposed in past years. The most common technique to protect the images is encryption. In this technique, a secret key and an encryption algorithm are used to change the plain image into an encrypted image. The encrypted image looks like a noisy image and can easily attracts the attacker's attention. If image gets captured and stacked, sensitive information can be revealed. In this regard, Visually Meaningful Encrypted Image (VMEI) technique is developed, which initially encrypts the original image and then hides it into a reference image. The final encrypted image looks like a normal image. Hence, VMEI technique provides more security as compared to simple image encryption techniques. Therefore, a systematic survey on existing VMEI techniques is presented in this paper. The VMEI techniques are divided into different categories based on their characteristics. Moreover, this paper elaborates and investigates the improvements and analyses performed on VMEI techniques based on various evaluation parameters. These evaluation parameters are divided into different categories such as security attacks, encryption key attacks, quality analysis, and noise attacks. Finally, this paper discusses the potential applications and the future challenges of VMEI techniques.
“…In contrast, Mertens [16] proposed a pixel-based multi-exposure fusion method, which can make up for the shortcomings of discontinuous images but has a high computational complexity and low efficiency. An extremely critical method in pixel-based image fusion is pyramid decomposition [17], which constructs an image pyramid from the input image, adds a weight matrix, and finally combines the two to obtain the final fusion result. Ashish et al adopted multiresolution fusion based on a Laplacian pyramid [18], measured by entropy and contrast, which is characterized by seamless fusion and excellent improved details.…”
A heterogeneous material image enhancement method based on multi-exposure image fusion is proposed to address the problem of obtaining high-quality images from the single imaging of chips containing two extremely different reflectivity materials. First, a multi-exposure image fusion algorithm based on window segmentation and Laplacian pyramid fusion is proposed. Then, orthogonal experiments are used to optimize the parameters of the imaging system. Next, a method based on information entropy and average gray intensity is utilized to calculate the imaging exposure times of two heterogeneous materials, and two exposure time ranges are obtained that are appropriate for regions with high and low reflectivity. Finally, the subjective and objective experimental evaluations are conducted after the multi-exposure image set has been established. The results show that the fused image has a good visual effect, the information entropy is 6.29, and the average gray intensity is 131.56. In addition, time consumption is reduced by an average of 20.3% compared to the Laplace pyramid strategy. The heterogeneous material enhancement method based on multi-exposure image fusion proposed in this paper is effective and deserving of further research and application.
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