Fast and efficient search for MPEG-4 video using adjacent pixel intensity difference quantization histogram feature

<p>Generally, satellite images contain very significant information about geographical features such as rivers, roads, building and bridges etc of the earth. Geographic Information System (GIS) requires these features for automatic detection and it has been corrupted by various types of noise. Curvelet Transform (CT) is used in the proposed system for denoising the images. Advantages of multi resolution image such as line, compatibility of human visual system and edge detection are provided. Then K-Means clustering is used in this system for segmentation purpose after the pre processing done.</p><p>First, K-Means algorithm is used for segmenting background and water then extraction of bridges is done based on pixel intensity difference. </p>

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Detection of Image Seam Carving Using a Novel Pattern

Computational Intelligence and Neuroscience ◽

10.1155/2019/9492358 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Ming Lu ◽

Shaozhang Niu

Keyword(s):

Feature Selection Method ◽

Intensity Difference ◽

Support Vector ◽

Seam Carving ◽

Image Resizing ◽

Pixel Intensity ◽

Histogram Of Oriented Gradient ◽

Local Neighborhood ◽

Test Sets ◽

Occurrence Pattern

Seam carving is an excellent content-aware image resizing technology widely used, and it is also a means of image tampering. Once an image is seam carved, the distribution of magnitude levels for the pixel intensity differences in the local neighborhood will be changed, which can be considered as a clue for detection of seam carving for forensic purposes. In order to accurately describe the distribution of magnitude levels for the pixel intensity differences in the local neighborhood, local neighborhood magnitude occurrence pattern (LNMOP) is proposed in this paper. The LNMOP pattern describes the distribution of intensity difference by counting up the number of magnitude level occurrences in the local neighborhood. Based on this, a forensic approach for image seam carving is proposed in this paper. Firstly, the histogram features of LNMOP and HOG (histogram of oriented gradient) are extracted from the images for seam carving forgery detection. Then, the final features for the classifier are selected from the extracted LNMOP features. The LNMOP feature selection method based on HOG feature hierarchical matching is proposed, which determines the LNMOP features to be selected by the HOG feature level. Finally, support vector machine (SVM) is utilized as a classifier to train and test by the above selected features to distinguish tampered images from normal images. In order to create training sets and test sets, images are extracted from the UCID image database. The experimental results of a large number of test images show that the proposed approach can achieve an overall better performance than the state-of-the-art approaches.

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Noise-Aware and Light-Weight VLSI Design of Bilateral Filter for Robust and Fast Image Denoising in Mobile Systems

Sensors ◽

10.3390/s20174722 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4722

Author(s):

Sung-Joon Jang ◽

Youngbae Hwang

Keyword(s):

Large Scale ◽

Hypothesis Test ◽

Vlsi Design ◽

Bilateral Filter ◽

Test Method ◽

Mobile Systems ◽

Noise Model ◽

Statistical Hypothesis ◽

Intensity Difference ◽

Pixel Intensity

The range kernel of bilateral filter degrades image quality unintentionally in real environments because the pixel intensity varies randomly due to the noise that is generated in image sensors. Furthermore, the range kernel increases the complexity due to the comparisons with neighboring pixels and the multiplications with the corresponding weights. In this paper, we propose a noise-aware range kernel, which estimates noise using an intensity difference-based image noise model and dynamically adjusts weights according to the estimated noise, in order to alleviate the quality degradation of bilateral filters by noise. In addition, to significantly reduce the complexity, an approximation scheme is introduced, which converts the proposed noise-aware range kernel into a binary kernel while using the statistical hypothesis test method. Finally, blue a fully parallelized and pipelined very-large-scale integration (VLSI) architecture of a noise-aware bilateral filter (NABF) that is based on the proposed binary range kernel is presented, which was successfully implemented in field-programmable gate array (FPGA). The experimental results show that the proposed NABF is more robust to noise than the conventional bilateral filter under various noise conditions. Furthermore, the proposed VLSI design of the NABF achieves 10.5 and 95.7 times higher throughput and uses 63.6–97.5% less internal memory than state-of-the-art bilateral filter designs.

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