Order Image Statistics Research Articles

Reciprocating compressor valve condition monitoring using image-based pattern recognition

This work presents the development of a vibration-based condition monitoring method for early detection and classification of valve wear within industrial reciprocating compressors through the combined use of time-frequency analysis with image-based pattern recognition techniques. A common valve related fault condition is valve seat wear that is caused by repeated impact and accentuated by chatter. Seeded faults consistent with valve seat wear are introduced on the crankside discharge valves of a Dresser-Rand ESH-1 industrial compressor. A variety of operational data including vibration, cylinder pressure, and crank shaft position are collected and processed using a time-frequency domain approach. The resulting diagrams are processed as images with features extracted using 1st and 2nd order image statistics. A Bayesian classification strategy is employed with accuracy rates greater than 90% achieved using two and three-dimensional features spaces.

Statistical Steganalysis of High Capacity Image Steganography with Cryptography

Steganalysis of high capacity Wavelet based fusion image steganography with encryption, using Image quality metrics (as a set of features) is proposed. As the first order image statistics using the proposed algorithm are inherently preserved, which is desirable feature of the scheme, improving the security of algorithm against the targeted attacks .In addition comparing the present steganography scheme with two different encryption techniques, on the undetectibility ground, the generalized objective metric like SVD is used as a steganalysis tool. DFrFT encryption is found statistically and visually undetectable achieving the desired robustness though PSNR values are better in DNA encryption

Higher order image structure enables boundary segmentation in the absence of luminance or contrast cues.

Lower order image statistics, which can be described by an image's Fourier energy content, enable segmentation when they are different on either side of a boundary. We have previously demonstrated that the spatial distribution of the energy in an image (described by its higher order statistics or structure) could influence segmentation thresholds for contrast- and orientation-defined boundaries, even though it was the same on either side of the boundary and thus task irrelevant (Zavitz & Baker, 2013). Here we examined whether higher order statistics can also enable segmentation when boundaries are defined by differences in structure or density of texture elements. We used micropattern-based naturalistic synthetic textures to manipulate the sparseness, global phase alignment, and local phase alignment of carrier textures and measured segmentation thresholds based on forced-choice judgments of boundary orientation. We found that both global phase structure and sparseness, but not local phase alignment, enable segmentation and that local structure also has a small effect on segmentation thresholds in both cases. Simulations of a two-stage filter model with a compressive intermediate nonlinearity can reproduce the major features of the experimental data, segmenting boundaries defined by higher order statistics alone while capturing the influence of global image structure on segmentation thresholds.

CTDCIRS: Content based Image Retrieval System based on Dominant Color and Texture Features

There is a great need of developing efficient content based image retrieval systems because of the availability of large image databases. A new image retrieval system CTDCIRS (colortexture and dominant color based image retri eval system) to retrieve the images using three features called dynamic dominant color (DDC), Motif cooccurrence m atrix (MCM) and difference between pixels of scan pattern (DBPSP) is proposed. Initially the image is divided into eight coarse partitions using the fast color quantization algorithm and the eight dominant colors are obtained from eight partitions. Next the texture of the image is represented by the MCM and DBPSP. MCM is derived using a motif transformed image. MCM is similar to color cooccur rence matrix (CCM). MCM is the conventional pattern co occurrence matrix that calculates the probability of the occurrence of same pixel color between each pixel and its adjacent ones in each image, and this probability is considered as the attribute of the image.MCM captures third order image statistics in the local neighborhood which describes the direction of textures but not the complexity of the textures. That is why the DBPSP is also considered as one of the texture features. The three features Dominant color, MCM and DBPSP are integrated to facilitate the image retrieval system. Experimental results show that the proposed image retrieval is more efficient in retrieving the user interested images.

Passive steganalysis based on higher order image statistics of curvelet transform

Steganographic techniques accomplish covert communication by embedding secret messages into innocuous digital images in ways that are imperceptible to the human eye. This paper presents a novel passive steganalysis strategy in which the task is approached as a pattern classification problem. A critical part of the steganalyser design depends on the selection of informative features. This paper is aimed at proposing a novel attack with improved performance indices with the following implications: 1) employing higher order statistics from a curvelet sub-band image representation that offers better discrimination ability for detecting stego anomalies in images, as compared to other conventional wavelet transforms; 2) increasing the sensitivity and specificity of the system by the feature reduction phase; 3) realizing the system using an efficient classification engine, a neuro-C4.5 classifier, which provides better classification rate. An extensive experimental evaluation on a database containing 5600 clean and stego images shows that the proposed scheme is a state-of-the-art steganalyser that outperforms other previous steganalytic methods.

Analyses of Higher Order Metrics for SPIHT Based Image Compression

The increasing use of digitized images has led to the need to compress such imagery to allow economical storage and fast data transfer. Despite all the advantages of JPEG compression schemes based on DCT namely simplicity, satisfactory performance, and availability of special purpose hardware for implementation; these are not without their shortcomings. The input image needs to be blocked which results in noticeable and annoying blocking artifacts particularly at low bit rates. So over the past several years, the wavelet transform has gained widespread acceptance in signal processing in general and in image compression research in particular. SPIHT codes the individual bits of the image wavelet transform coefficients following a bit-plane sequence. The evaluation of an image compression system is a difficult problem. While mean square error and peak signal to noise ratios are easily and commonly calculated, they are widely recognized to not to be completely satisfactory. The statistical parameters include high order image statistics like skewness and kurtosis which describe the shape and symmetry of the image. The aim of this paper is to provide a uniform gauge of the performance of data compression processes. In addition, it specifies how the performance of different data compression methods should be ranked so that the best compressor for a specific application can be identified. In the present work, three compression algorithms viz. the JPEG coding, the wavelet transform coding and the SPIHT coding have been discussed and compared.

Universal Steganalysis Using Feature Selection Strategy for Higher Order Image Statistics

The purpose of image steganalysis is to detect the presence of hidden message in cover photographic images. Supervised learning is an effective and commonly used method to cope with difficulties of unknown image statistics and unknown steganography. Present paper proposes; a universal approach for steganalysis for detecting presence of hidden messages embedded within digital images. This paper describes wavelet like decomposition to build higher order statistical model of natural images. Feature selection technique like ANOVA is used to select relevant features. SVM are then used to discriminate between clean and stego images. Study of the effect of relevant features on classification accuracy may help to improve the complexity. The full text of the article is not available in the cache. Kindly refer the IJCA digital library at www.ijcaonline.org for the complete article. In case, you face problems while downloading the full-text, please send a mail to editor at editor@ijcaonline.org

The role of higher order image statistics in masking scene gist recognition

In the present article, we investigated whether higher order image statistics, which are known to be carried by the Fourier phase spectrum, are sufficient to affect scene gist recognition. In Experiment 1, we compared the scene gist masking strength of four masking image types that varied in their degrees of second- and higher order relationships: normal scene images, scene textures, phase-randomized scene images, and white noise. Masking effects were the largest for masking images that possessed significant higher order image statistics (scene images and scene textures) as compared with masking images that did not (phase-randomized scenes and white noise), with scene image masks yielding the largest masking effects. In a control study, we eliminated all differences in the second-order statistics of the masks, while maintaining differences in their higher order statistics by comparing masking by scene textures rather than by their phase-randomized versions, and showed that the former produced significantly stronger gist masking. Experiments 2 and 3 were designed to test whether conceptual masking could account for the differences in the strength of the scene texture and phase-randomized masks used in Experiment 1, and revealed that the recognizability of scene texture masks explained just 1% of their masking variance. Together, the results suggest that (1) masks containing the higher order statistical structure of scenes are more effective at masking scene gist processing than are masks lacking such structure, and (2) much of the disruption of scene gist recognition that one might be tempted to attribute to conceptual masking is due to spatial masking.

Display adaptive tone mapping

We propose a tone mapping operator that can minimize visible contrast distortions for a range of output devices, ranging from e-paper to HDR displays. The operator weights contrast distortions according to their visibility predicted by the model of the human visual system. The distortions are minimized given a display model that enforces constraints on the solution. We show that the problem can be solved very efficiently by employing higher order image statistics and quadratic programming. Our tone mapping technique can adjust image or video content for optimum contrast visibility taking into account ambient illumination and display characteristics. We discuss the differences between our method and previous approaches to the tone mapping problem.

Fixations in natural scenes: Interaction of image structure and image content

Explorative eye movements specifically target some parts of a scene while ignoring others. Here, we investigate how local image structure—defined by spatial frequency contrast—and informative image content—defined by higher order image statistics—are weighted for the selection of fixation points. We measured eye movements of macaque monkeys freely viewing a set of natural and manipulated images outside a particular task. To probe the effect of scene content, we locally introduced patches of pink noise into natural images, and to probe the interaction with image structure, we altered the contrast of the noise. We found that fixations specifically targeted the natural image parts and spared the uninformative noise patches. However, both increasing and decreasing the contrast of the noise attracted more fixations, and, in the extreme cases, compensated the effect of missing content. Introducing patches from another natural image led to similar results. In all paradigms tested, the interaction between scene structure and informative scene content was the same in any of the first six fixations on an image, demonstrating that the weighting of these factors is constant during viewing of an image. These results question theories, which suggest that initial fixations are driven by stimulus structure whereas later fixations are determined by informative scene content.

Content based image retrieval using motif cooccurrence matrix

We present a new technique for content based image retrieval using motif cooccurrence matrix (MCM). The MCM is derived from the motif transformed image. The whole image is divided into 2×2 pixel grids. Each grid is replaced by a scan motif that minimizes the local gradient while traversing the 2×2 grid forming a motif transformed image. The MCM is then defined as a 3D matrix whose ( i, j, k) entry denotes the probability of finding a motif i at a distance k from the motif j in the transformed image. Conceptually, the MCM is quite similar to the color cooccurrence matrix (CCM), however, the retrieval using the MCM is better than the CCM since it captures the third order image statistics in the local neighborhood. Experiments confirm that the use of MCM considerably improves the retrieval performance.