Gradient-based edge detection for motion estimation in H.264/AVC

Paul, Anand; Wu, Jiaji; Yang, Jar‐Ferr

doi:10.1049/iet-ipr.2009.0256

Cited by 21 publications

(6 citation statements)

References 13 publications

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“…On the other hand, the determination of thresholds for detecting ZQDCT coefficients plays an important role in the time saving of DCT, Q, IQ and IDCT processes. In a practical application, the proposed algorithm can be used to combine with any fast motion estimation or inter‐mode decision algorithm, such as those in [14–17], which allows achieving a faster and quality video coding with, for example, the IPPP structure.…”

Section: Resultsmentioning

confidence: 99%

Improved algorithm for detecting zero‐quantised discrete cosine transform coefficients in H.264/AVC (revised version)

Wang

Siu

2013

IET Image Processing

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In this study, an efficient approach for detecting zero-quantised discrete cosine transform (DCT) coefficients for video coding is developed. Compared with conventional detection methods of zero-quantised DCT coefficients used in H.264/AVC, the proposed algorithm has two major features. First, a new classification of patterns for DCT, quantisation, inverse quantisation and inverse discrete cosine transform processes is proposed. By taking a zigzag scanning order into the classification, the quantised DCT coefficients can be coded efficiently. Second, the thresholds for detecting zero-quantised DCT coefficients are determined by combining the Gaussian distribution with a theoretical analysis of the DCT and quantisation in H.264/AVC. Experimental results show that the proposed algorithm achieves an average timesaving of more than 40% compared with the algorithm in reference software JM12.

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Section: Resultsmentioning

confidence: 99%

Improved algorithm for detecting zero‐quantised discrete cosine transform coefficients in H.264/AVC (revised version)

Wang

Siu

2013

IET Image Processing

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“…This paper is primarily focused on the first two steps of dermoscopic analysis of melanoma that include preprocessing and segmentation [ 9 ]. Image processing approaches [ 10 , 11 ] and research in related domains such as retinal lesion detection and classification [ 12 – 14 ] are also somewhat relevant to the problem at hand of skin lesion segmentation, feature extraction, and classification.…”

Section: Introductionmentioning

confidence: 99%

Computer Based Melanocytic and Nevus Image Enhancement and Segmentation

Jamil

Akram

Khalid

et al. 2016

BioMed Research International

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Digital dermoscopy aids dermatologists in monitoring potentially cancerous skin lesions. Melanoma is the 5th common form of skin cancer that is rare but the most dangerous. Melanoma is curable if it is detected at an early stage. Automated segmentation of cancerous lesion from normal skin is the most critical yet tricky part in computerized lesion detection and classification. The effectiveness and accuracy of lesion classification are critically dependent on the quality of lesion segmentation. In this paper, we have proposed a novel approach that can automatically preprocess the image and then segment the lesion. The system filters unwanted artifacts including hairs, gel, bubbles, and specular reflection. A novel approach is presented using the concept of wavelets for detection and inpainting the hairs present in the cancer images. The contrast of lesion with the skin is enhanced using adaptive sigmoidal function that takes care of the localized intensity distribution within a given lesion's images. We then present a segmentation approach to precisely segment the lesion from the background. The proposed approach is tested on the European database of dermoscopic images. Results are compared with the competitors to demonstrate the superiority of the suggested approach.

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“…In the literature, various techniques have been proposed for image segmentation, such as histogram-based methods, clustering methods, and mathematical morphology [11], among which active contour models (ACMs) [12] are wellknown technique in image segmentation [13][14][15][16][17][18][19]. The core part of ACMs for image segmentation is that a curve is being employed as a contour, while it is being adapted subject to characteristics from the image, and then objects can be extracted by optimizing an energy function.…”

Section: Introductionmentioning

confidence: 99%

“…The core part of ACMs for image segmentation is that a curve is being employed as a contour, while it is being adapted subject to characteristics from the image, and then objects can be extracted by optimizing an energy function. Edge detection has been successfully applied to many fields, such as feature extraction, image registration, object detection, and motion tracking [9][10][11][12][13][14][15][16]. There is no need for ACMs to establish statistical models, so it seems to be rather convenient and feasible to apply ACMs to different types of satellite images.…”

Section: Introductionmentioning

confidence: 99%

Change Detection in Synthetic Aperture Radar Images Based on Fuzzy Active Contour Models and Genetic Algorithms

Shi

Paul

et al. 2014

Mathematical Problems in Engineering

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This paper presents an unsupervised change detection approach for synthetic aperture radar images based on a fuzzy active contour model and a genetic algorithm. The aim is to partition the difference image which is generated from multitemporal satellite images into changed and unchanged regions. Fuzzy technique is an appropriate approach to analyze the difference image where regions are not always statistically homogeneous. Since interval type-2 fuzzy sets are well-suited for modeling various uncertainties in comparison to traditional fuzzy sets, they are combined with active contour methodology for properly modeling uncertainties in the difference image. The interval type-2 fuzzy active contour model is designed to provide preliminary analysis of the difference image by generating intermediate change detection masks. Each intermediate change detection mask has a cost value. A genetic algorithm is employed to find the final change detection mask with the minimum cost value by evolving the realization of intermediate change detection masks. Experimental results on real synthetic aperture radar images demonstrate that change detection results obtained by the improved fuzzy active contour model exhibits less error than previous approaches.

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Gradient-based edge detection for motion estimation in H.264/AVC

Cited by 21 publications

References 13 publications

Improved algorithm for detecting zero‐quantised discrete cosine transform coefficients in H.264/AVC (revised version)

Improved algorithm for detecting zero‐quantised discrete cosine transform coefficients in H.264/AVC (revised version)

Computer Based Melanocytic and Nevus Image Enhancement and Segmentation

Change Detection in Synthetic Aperture Radar Images Based on Fuzzy Active Contour Models and Genetic Algorithms

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