Performance analysis of video segmentation

Aditya, Hafidz Dayu; Gayatri, Thota; Santosh, Taji Taramati; Ankalaki, Shilpa; Majumdar, Jharna

doi:10.1109/icaccs.2017.8014567

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…These masking-video segmentation methodologies face the same issues as the proposed shot boundary detector methodology -the efficiency of a segmentation technique may vary with the category or genre of the video being analysed [4]. Although the area of segmentation differs from what is addressed in this article, both are affected by the genre of the video being analysed.…”

Section: Literature Overviewmentioning

confidence: 99%

Parameter Analysis of the Jensen-Shannon Divergence for Shot Boundary Detection in Streaming Media Applications

Klerk¹,

Venter²,

Hoffman³

2018

SAIEE Afr. Res. J.

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Shot boundary detection is an integral part of multimedia, be it video management or video processing. Multiple boundary detection techniques have been developed throughout the years, but are only applicable to very specific instances. The Jensen-Shannon divergence (JSD) is one such a technique that can be implemented to detect the shot boundaries in digital videos. This paper investigates the use of the JSD algorithm to detect shot boundaries in streaming media applications. Furthermore, the effects of the various parameters used by the JSD technique, on the accuracy of the detected boundaries, are quantified by the recall and precision metrics all the while keeping track of how they affect the execution time.

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Section: Literature Overviewmentioning

confidence: 99%

Parameter Analysis of the Jensen-Shannon Divergence for Shot Boundary Detection in Streaming Media Applications

Klerk¹,

Venter²,

Hoffman³

2018

SAIEE Afr. Res. J.

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“…Histogram Normalisasi didasarkan pada peran pentingnya kualitas visual dalam aplikasi berbagai bidang, seperti pengawasan keamanan [1], [2], industri media dan entertainment [3], serta bidang ilmu pengetahuan dan teknologi. Dalam beberapa aplikasi, seperti sistem pengawasan CCTV [4], video yang diperoleh sering kali memiliki tingkat kontras yang rendah dan detail yang tidak jelas [5], [6], [7] yang menyulitkan identifikasi dan analisis gambar.…”

Section: Pendahuluan Peningkatan Kontras Pencahayaan Dengan Menggunakanunclassified

Penerapan Normalisasi Histogram untuk Peningkatan Kontras Pencahayaan pada Pengamatan Visual CCTV

Saluky,

Marine,

Bahiyah

2023

JPIT

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Low Contrast can cause low image quality and make it difficult for proper image analysis. One technique to improve image quality is to increase the lighting contrast. One method that is often used is histogram normalization, which can increase image contrast by balancing the distribution of pixels across a range of pixel values. The purpose of this research is to apply the histogram normalization method to images and compare the results before and after the normalization process. The images used in this study are self-made images and images from public databases. The results of the study show that normalized histograms can increase image contrast and improve low image quality due to inadequate lighting. Thus, histogram normalization can be used as a technique to improve image quality in various applications, including medical image processing, satellite image processing, and security surveillance.

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“…Video segmentation has been a focus of computer vision and image processing, and it plays a key role in some fields such as the target classification, tracking and recognition [9], [10]. In recent years, some related works have been proposed, such as the optical flow [11], temporal difference [12] and background subtraction [13]- [15].…”

Section: Related Workmentioning

confidence: 99%

“…Step 5 : The light-shadow discriminant function is applied to eliminate the pixels of light and shadow by (10), and then the foreground objects are obtained.…”

Section: E Description Of Igasmmentioning

confidence: 99%

A GMM-Based Segmentation Method for the Detection of Water Surface Floats

2019

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Gaussian Mixture Model (GMM) is a widely used approach for the background subtraction and the moving objects detection. However, the classical GMM probably detects incorrectly and cannot deal with the shadows with a pixel-level and time-domain classification, and thus it cannot monitor the water surface floats effectively. To solve this problem, an improved GMM-based automatic segmentation method (IGASM) is proposed to detect the water surface floats in this paper, where the background updating strategy is improved to segment the water surface floats more effectively. Besides, the GMM results are mapped into an HSV color space, and a light-shadow discriminant function is applied to solve the problems of light and shadow. Then, a morphological method is used to smooth the extracted foregrounds. Finally, Graph Cuts algorithm is applied to optimized the segmentation results according to the spatial information of video images. Experimental results demonstrate that IGASM can detect the water surface floats quickly and accurately, and the influences of light, shadows and ripples of water surface can be eliminated as much as possible. INDEX TERMS Background subtraction, Gaussian mixture model, graph cuts, light-shadow discriminant function, video segmentation, water surface floats.

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Performance analysis of video segmentation

Cited by 5 publications

References 5 publications

Parameter Analysis of the Jensen-Shannon Divergence for Shot Boundary Detection in Streaming Media Applications

Parameter Analysis of the Jensen-Shannon Divergence for Shot Boundary Detection in Streaming Media Applications

Penerapan Normalisasi Histogram untuk Peningkatan Kontras Pencahayaan pada Pengamatan Visual CCTV

A GMM-Based Segmentation Method for the Detection of Water Surface Floats

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