The Laplacian Pyramid as a Compact Image Code

Burt, Peter; Adelson, Edward H.

doi:10.1515/9781400827268.28

Cited by 265 publications

(359 citation statements)

References 0 publications

Supporting

Mentioning

347

Contrasting

Unclassified

Order By: Relevance

“…Computing this matrix has a complexity of O(N N d ) where N d is the number of disparity values. We study the encoding of the disparity map using two transforms, namely the Sequential (S) transform [24] and a transform we call the Laplace (L) transform due to its resemblance to the Laplacian pyramid [31]. Both provide a compact representation of discrete and piecewise-constant disparity maps.…”

Section: One-dimensional Disparity Mapmentioning

confidence: 99%

Wavelet-Based Joint Estimation and Encoding of Depth-Image-Based Representations for Free-Viewpoint Rendering

Maitre

Shinagawa²,

Minh

2008

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-We propose a wavelet-based codec for the static Depth-Image-Based Representation (DIBR), which allows viewers to freely choose the viewpoint. The proposed codec jointly estimates and encodes the unknown depth map from multiple views using a novel Rate-Distortion (RD) optimization scheme. The rate constraint reduces the ambiguity of depth estimation by favoring piecewise-smooth depth maps. The optimization is efficiently solved by a novel dynamic programming along trees of integer wavelet coefficients. The codec encodes the image and the depth map jointly to decrease their redundancy and to provide a RD-optimized bitrate allocation between the two. The codec also offers scalability both in resolution and in quality. Experiments on real data show the effectiveness of the proposed codec.

show abstract

Section: One-dimensional Disparity Mapmentioning

confidence: 99%

Wavelet-Based Joint Estimation and Encoding of Depth-Image-Based Representations for Free-Viewpoint Rendering

Maitre

Shinagawa²,

Minh

2008

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…In order to obtain better results, the training data is pre-processed in the way depicted in Figure 4 3) a Laplacian pyramid filtering technique [43] is applied in order to extract gradient information of the cropped image. Therefore, less important information in the image has been eliminated.…”

Section: Spot Classifiermentioning

confidence: 99%

A novel neural network approach to cDNA microarray image segmentation

Wang

Zineddin

Liang

et al. 2013

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.

show abstract

“…PDFB consist of a double filter bank structure for obtaining sparse expansions for typical images having smooth contours. In this double filter bank, the laplacian pyramid (LP) [16] is first used to capture the point discontinuities, and then followed by a directional filter bank (DFB) [15] to link point discontinuities into linear structure. contourlet decomposition is shown in Fig.…”

Section: Contourlet Transformmentioning

confidence: 99%

A Novel Image Steganography in Contourletdomain Using Genetic Algorithm

Ramezani¹,

Keynia²,

Ramezani³

2013

IJFCC

View full text Add to dashboard Cite

Abstract-Steganography is the science that involves communicating secret data in an appropriate multimedia carrier. This paper presents the application of contourlet Transform and Genetic Algorithm (GA) in a novel steganography scheme. We employ a genetic algorithm based mapping function to embed data in Discrete contourlet Transform coefficients in 4x4blocks on the cover image. The optimal pixel adjustment process (OPAP) is applied after embedding the message. GA employed to obtain an optimal mapping function to lessen the error difference between the cover and the stego image and use the block mapping method to preserve the local image properties. Also we applied the OPAP to increase the hiding capacity of the algorithm in comparison with other systems.According to the experimental results, the proposed method is capable of providing a larger embedding capacity without causing noticeable distortions of stego-images in comparison with similar existing methods. Moreoverthe results of our experiments show that employing two of powerful steganalyzers on stego-images produced by our method, they could not discriminate between stego and clean-images reliably.Index Terms-Steganography, discrete contourlet transform, genetic algorithm, optimal pixel adjustment process.

show abstract

The Laplacian Pyramid as a Compact Image Code

Cited by 265 publications

References 0 publications

Wavelet-Based Joint Estimation and Encoding of Depth-Image-Based Representations for Free-Viewpoint Rendering

Wavelet-Based Joint Estimation and Encoding of Depth-Image-Based Representations for Free-Viewpoint Rendering

A novel neural network approach to cDNA microarray image segmentation

A Novel Image Steganography in Contourletdomain Using Genetic Algorithm

Contact Info

Product

Resources

About