Original Image Tracing with Image Relational Graph for Near-Duplicate Image Elimination

2016 ◽  
pp. 324-336 ◽  
Author(s):  
Fang Huang ◽  
Zhili Zhou ◽  
Tianliang Liu ◽  
Xiya Liu
Keyword(s):  
Original Image

An Evaluation of Colour-to-Greyscale Image Conversion by Linear Anisotropic Diffusion and Manual Colour Grading

2019 ◽  
Vol 2019 (1) ◽  
pp. 69-74
Author(s):  
Aldo Barba ◽  
Ivar Farup ◽  
Marius Pedersen
Keyword(s):  
Anisotropic Diffusion ◽  
Motion Pictures ◽  
Current Paper ◽  
Original Image ◽  
Colour Image ◽  
Image Conversion ◽  
Significant Difference ◽  
Greyscale Image

In the paper "Colour-to-Greyscale Image Conversion by Linear Anisotropic Diffusion of Perceptual Colour Metrics", Farup et al. presented an algorithm to convert colour images to greyscale. The algorithm produces greyscale reproductions that preserve detail derived from local colour differences in the original colour image. Such detail is extracted by using linear anisotropic diffusion to build a greyscale reproduction from a gradient of the original image that is in turn calculated using Riemannised colour metrics. The purpose of the current paper is to re-evaluate one of the psychometric experiments for these two methods (CIELAB L* and anisotropic Δ99) by using a flipping method to compare their resulting images instead of the side by side method used in the original evaluation. In addition to testing the two selected algorithms, a third greyscale reproduction was manually created (colour graded) using a colour correction software commonly used to process motion pictures. Results of the psychometric experiment found that when comparing images using the flipping method, there was a statistically significant difference between the anisotropic Δ99 and CIELAB L* conversions that favored the anisotropic method. The comparison between Δ99 conversion and the manually colour graded image also showed a statistically significant difference between them, in this case favoring the colour graded version.

APLIKASI PENAJAMAN CITRAA STREET VIEW MENGGUNAKAN METODE GRAMD SCHMIDT SPECTRAL SHARPENING DAN HIGH PASS FILTERING

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Erna Verawati ◽  
Surya Darma Nasution ◽  
Imam Saputra
Keyword(s):  
Image Processing ◽  
Visual Information ◽  
Intensity Difference ◽  
Original Image ◽  
Filtering Method ◽  
The Road ◽  
Street View ◽  
Result Show ◽  
Filter Process ◽  
High Pass

Sharpening the image of the road display requies a degree of brightness in the process of sharpening the image from the original image result of the improved image. One of the sharpening of the street view image is image processing. Image processing is one of the multimedia components that plays an important role as a form of visual information. There are many image processing methods that are used in sharpening the image of street views, one of them is the gram schmidt spectral sharpening method and high pass filtering. Gram schmidt spectral sharpening method is method that has another name for intensity modulation based on a refinement fillter. While the high pass filtering method is a filter process that btakes image with high intensity gradients and low intensity difference that will be reduced or discarded. Researce result show that the gram schmidt spectral sharpening method and high pass filtering can be implemented properly so that the sharpening of the street view image can be guaranteed sharpening by making changes frome the original image to the image using the gram schmidt spectral sharpening method and high pass filtering.Keywords: Image processing, gram schmidt spectral sharpening and high pass filtering.

Secure Watermarking using Diophantine Equations for Authentication and Recovery

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Jayashree Nair ◽  
T. Padma
Keyword(s):  
Diophantine Equation ◽  
Diophantine Equations ◽  
Jpeg Compression ◽  
Authentication Scheme ◽  
Original Image ◽  
Invariant Features ◽  
Jpeg2000 Compression ◽  
Frequency Properties ◽  
Visual Authentication

This paper describes an authentication scheme that uses Diophantine equations based generation of the secret locations to embed the authentication and recovery watermark in the DWT sub-bands. The security lies in the difficulty of finding a solution to the Diophantine equation. The scheme uses the content invariant features of the image as a self-authenticating watermark and a quantized down sampled approximation of the original image as a recovery watermark for visual authentication, both embedded securely using secret locations generated from solution of the Diophantine equations formed from the PQ sequences. The scheme is mildly robust to Jpeg compression and highly robust to Jpeg2000 compression. The scheme also ensures highly imperceptible watermarked images as the spatio –frequency properties of DWT are utilized to embed the dual watermarks.

scholarly journals High-Capacity Reversible Data Hiding in Encrypted Images by Information Preprocessing

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xi-Yan Li ◽  
Xia-Bing Zhou ◽  
Qing-Lei Zhou ◽  
Shi-Jing Han ◽  
Zheng Liu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Capacity ◽  
Main Idea ◽  
Original Image ◽  
Chaos Encryption ◽  
Secret Information ◽  
Encrypted Image ◽  
Encrypted Images ◽  
Scanned Documents

With the development of cloud computing, high-capacity reversible data hiding in an encrypted image (RDHEI) has attracted increasing attention. The main idea of RDHEI is that an image owner encrypts a cover image, and then a data hider embeds secret information in the encrypted image. With the information hiding key, a receiver can extract the embedded data from the hidden image; with the encryption key, the receiver reconstructs the original image. In this paper, we can embed data in the form of random bits or scanned documents. The proposed method takes full advantage of the spatial correlation in the original images to vacate the room for embedding information before image encryption. By jointly using Sudoku and Arnold chaos encryption, the encrypted images retain the vacated room. Before the data hiding phase, the secret information is preprocessed by a halftone, quadtree, and S-BOX transformation. The experimental results prove that the proposed method not only realizes high-capacity reversible data hiding in encrypted images but also reconstructs the original image completely.

scholarly journals The Design and Realization of Cherry Tomato Harvesting Robot Based on IOT

2016 ◽  
Vol 12 (12) ◽  
pp. 23 ◽  
Author(s):  
Biqing Li ◽  
Yongfa Ling ◽  
Hongyan Zhang ◽  
Shiyong Zheng
Keyword(s):  
Image Recognition ◽  
Recognition Rate ◽  
Control Technology ◽  
Original Image ◽  
Cherry Tomato ◽  
Modular Control ◽  
Breakage Rate ◽  
Harvesting Efficiency ◽  
Technology Processes ◽  
Harvesting Robot

To improve the efficiency of harvesting cherry tomato and reduce its breakage rate, we design a harvesting robot base on image recognition and modular control. After image acquisition with IOT technology, the binary processing and expansion and corrosion processing of original image can effectively increase the fruit recognition rate. In addition, the use of fuzzy control technology processes the response error of manipulator. We test the performance of cherry tomato harvesting robot through harvesting experiment. The experimental results show that the harvesting efficiency significantly improves and the degree of crushing cherry tomato greatly reduces after using the cherry tomato harvesting robot.

A CRYPTO SIGNATURE SCHEME FOR IMAGE AUTHENTICATION OVER WIRELESS CHANNEL

2005 ◽  
Vol 05 (01) ◽  
pp. 135-148 ◽  
Author(s):  
QIBIN SUN ◽  
SHUIMING YE ◽  
CHING-YUNG LIN ◽  
SHIH-FU CHANG
Keyword(s):  
Packet Loss ◽  
Digital Signature ◽  
Digital Images ◽  
Image Authentication ◽  
Wireless Channel ◽  
Experimental Results ◽  
Signature Scheme ◽  
Original Image ◽  
Degraded Images ◽  
Block Shuffling

With the ambient use of digital images and the increasing concern on their integrity and originality, consumers are facing an emergent need of authenticating degraded images despite lossy compression and packet loss. In this paper, we propose a scheme to meet this need by incorporating watermarking solution into traditional cryptographic signature scheme to make the digital signatures robust to these image degradations. Due to the unpredictable degradations, the pre-processing and block shuffling techniques are applied onto the image at the signing end to stabilize the feature extracted at the verification end. The proposed approach is compatible with traditional cryptographic signature scheme except that the original image needs to be watermarked in order to guarantee the robustness of its derived digital signature. We demonstrate the effectiveness of this proposed scheme through practical experimental results.

Blind Digital Image Watermarking Algorithm Based on the Chain Code

2012 ◽  
Vol 546-547 ◽  
pp. 410-415
Author(s):  
Chun Ge Tang ◽  
Tie Sheng Fan ◽  
Lei Liu ◽  
Zhi Hui Li
Keyword(s):  
Image Quality ◽  
Digital Watermarking ◽  
Digital Image ◽  
Image Watermarking ◽  
Boundary Representation ◽  
Experimental Results ◽  
Digital Image Watermarking ◽  
Original Image ◽  
Chain Code ◽  
Decision Method

A new blind digital watermarking algorithm based on the chain code is proposed. The chain code is obtained by the characteristics of the original image -the edge contour. The feather can reflect the overall correlation of the vector image, and chain code expression can significantly reduce the boundary representation of the amount of data required. For the watermarking embedding, the original vector image is divided into sub-block images, and two bits of the watermarking information are embedded into sub-block images repeatedly by quantization. For watermarking extracting, the majority decision method is employed to determine the size of the extracted watermark. Experimental results show that the image quality is not significantly lowered after watermarking. The algorithm can resist the basic conventional attacks and has good robustness on the shear attacks.

DCT Coefficients Weighting (DCTCW)-Based Gray Wolf Optimization (GWO) for Brightness Preserving Image Contrast Enhancement

2021 ◽  
Author(s):  
Saorabh Kumar Mondal ◽  
Arpitam Chatterjee ◽  
Bipan Tudu
Keyword(s):  
Contrast Enhancement ◽  
Objective Evaluation ◽  
Brightness Distribution ◽  
Image Contrast ◽  
Optimization Techniques ◽  
Biological Behavior ◽  
Gray Wolf ◽  
Original Image ◽  
Image Characteristics ◽  
Image Contrast Enhancement

Image contrast enhancement (CE) is a frequent image enhancement requirement in diverse applications. Histogram equalization (HE), in its conventional and different further improved ways, is a popular technique to enhance the image contrast. The conventional as well as many of the later versions of HE algorithms often cause loss of original image characteristics particularly brightness distribution of original image that results artificial appearance and feature loss in the enhanced image. Discrete Cosine Transform (DCT) coefficient mapping is one of the recent methods to minimize such problems while enhancing the image contrast. Tuning of DCT parameters plays a crucial role towards avoiding the saturations of pixel values. Optimization can be a possible solution to address this problem and generate contrast enhanced image preserving the desired original image characteristics. Biological behavior-inspired optimization techniques have shown remarkable betterment over conventional optimization techniques in different complex engineering problems. Gray wolf optimization (GWO) is a comparatively new algorithm in this domain that has shown promising potential. The objective function has been formulated using different parameters to retain original image characteristics. The objective evaluation against CEF, PCQI, FSIM, BRISQUE and NIQE with test images from three standard databases, namely, SIPI, TID and CSIQ shows that the presented method can result in values up to 1.4, 1.4, 0.94, 19 and 4.18, respectively, for the stated metrics which are competitive to the reported conventional and improved techniques. This paper can be considered a first-time application of GWO towards DCT-based image CE.

An Image Segmentation Method Based on Adaptability Threshold

2013 ◽  
Vol 734-737 ◽  
pp. 2912-2916
Author(s):  
Hui Li ◽  
Ping He
Keyword(s):  
Image Segmentation ◽  
Strain Measurement ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Important Application ◽  
Original Image ◽  
Segmentation Method ◽  
One Dimensional ◽  
The One ◽  
Application Fields

Automation strain measurement of the sheet metal deforming becomes one of the important application fields of computer vision. The algorithm of image segmentation based on adaptability threshold was presented for image segmentation of metal steel. In order to validate the proposed method, it is tested and compared with Ostu method and the one-dimensional maximum entropy method. Experiment results indicate that the method is simple and effective, and has an advantage of reservation of the main features of the original image.

scholarly journals Enhancement of Satellite Imagesusing Image Sharpening Technique

2019 ◽  
Vol 8 (6S2) ◽  
pp. 351-354
Keyword(s):  
Image Processing ◽  
Color Image ◽  
Histogram Equalization ◽  
Grayscale Image ◽  
Original Image ◽  
High Quality ◽  
Wide Range ◽  
Equalization Technique ◽  
The Difference

In many image processing applications, a wide range of image enhancement techniques are being proposed. Many of these techniques demanda lot of critical and advance steps, but the resultingimage perception is not satisfactory. This paper proposes a novel sharpening method which is being experimented with additional steps. In the first step, the color image is transformed into grayscale image, then edge detection process is applied using Laplacian technique. Then deduct this image from the original image. The resulting image is as expected; After performing the enhancement process,the high quality of the image can be indicated using the Tenengrad criterion. The resulting image manifested the difference in certain areas, the dimension and the depth as well. Histogram equalization technique can also be applied to change the images color.

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