An Efficient Detection Approach of Content Aware Image Resizing

Content aware image resizing(CAIR)is an excellent technology used widely for image retarget.It can also be used to tamper with images and bring the trust crisis of image content to the public.Once an image is processed by CAIR,the correlation of local neighborhood pixels will be destructive.Although local binary patterns(LBP)can effectively describe the local texture,it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to noise.Therefore,to deal with the detection of CAIR,a novel forensic method based on improved local ternary patterns(ILTP)feature and gradient energy feature(GEF)is proposed in this paper.Firstly,the adaptive threshold of the original local ternary patterns(LTP)operator is improved,and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by CAIR.Secondly,the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery detection.Then,the ILTP features and the gradient energy features are concatenated into the combined features,and the combined features are used to train classifier.Finally support vector machine(SVM)is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or not.The candidate images are extracted from uncompressed color image database(UCID),then the training and testing sets are created.The experimental results with many test images show that the proposed method can detect CAIR tampering effectively,and that its performance is improved compared with other methods.It can achieve a better performance than the state-of-the-art approaches.

An Efficient Deep Learning-based Content-based Image Retrieval Framework

The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.

2D matrix based indexing with color spectralhistogram for efficient image retrieval

A novel content based image retrieval （CBIR） algorithmusing relevant feedback is presented. The proposed frameworkhas three major contributions： a novel feature descriptor calledcolor spectral histogram （CSH） to measure the similarity betweenimages; two-dimensional matrix based indexing approach proposedfor short-term learning （STL）; and long-term learning （LTL）.In general, image similarities are measured from feature representationwhich includes color quantization, texture, color, shapeand edges. However, CSH can describe the image feature onlywith the histogram. Typically the image retrieval process starts byfinding the similarity between the query image and the imagesin the database; the major computation involved here is that theselection of top ranking images requires a sorting algorithm to beemployed at least with the lower bound of O（n log n）. A 2D matrixbased indexing of images can enormously reduce the searchtime in STL. The same structure is used for LTL with an aim toreduce the amount of log to be maintained. The performance ofthe proposed framework is analyzed and compared with the existingapproaches, the quantified results indicates that the proposedfeature descriptor is more effectual than the existing feature descriptorsthat were originally developed for CBIR. In terms of STL,the proposed 2D matrix based indexing minimizes the computationeffort for retrieving similar images and for LTL, the proposed algorithmtakes minimum log information than the existing approaches.

HISTOLOGIC GRADE, AND CONTENT AND IMAGE ANALYSIS OF SOFT TISSUE TUMORS

Flow cytometry and image analysis technique were used to quantltate the nuclei of various soft tissue tumors. A single representing section from soft tissue sarcoma was used for histologic grading. Histologlc and cytometric comparative analyses showed that all 21 benign tumors were diploid. Among 62 cases of soft tissue sarcoma, 45(73%) were aneuploid. There was a significant difference in the nuclear area between benign and malignant tumors (P<0. 01), dlploid and aneuploid tumors (P<0. 05). The two new techniques are valuable In cellular quantitative measurement for soft tissue tumors.

Shift Invariance Level Comparison of Several Contourlet Transforms and Their Texture Image Retrieval Systems

In this paper, we proposed a metric to measure the shift invariance of the three different contourlet transforms. And then, using the same structure texture image retrieval system which use subband coefficients energy, standard deviation and kurtosis features with Canberra distance, we gave a comparison of their texture description abilities. Experimental results show that contourlet-2.3 texture image retrieval system has almost retrieval rates with non-sub sampled contourlet system;the two systems have better retrieval results than the original contourlet retrieval system. On the other hand, for the relatively lower redundancy, we recommend using contourlet- 2.3 as texture description transform.

Drug discovery from traditional Chinese herbal medicine using high content imaging technology

Traditional Chinese medicine(TCM) has been widely used in China and other Asia countries for thousands of years to treat or prevent human diseases. Chinese herbal medicine, one of the most important components of TCM, has unique diversities in chemical components, and thus results in a wide range of biological activities. However, pharmaceutical industry is facing a major challenge to develop a large population of novel natural products and drugs, and considerable efforts have not resulted in highvolume of novel drug discovery and productivity. At present, increasing attention has been paid to Chinese herb medicine modernization in combination with the cutting-age technologies of drug discovery, especially the high throughput selection. High content imaging is an image-based high throughput screening method by using automated microscopy and image analysis software to capture and analyze phenotypes at a large scale to investigate multiple biological features simultaneously in the biological complex. Here, we described the pipeline of the state-of-the-art high content imaging technology, summarized the applications of the high content imaging technology in drug discovery from traditional Chinese herbal medicine, and finally discussed the current challenges and future perspectives for development of high throughput image-based screening technology in novel drug research and discovery.

Global Water Vapor Content Decreases from 2003 to 2012： An Analysis Based on MODIS Data

Water vapor in the earth′s upper atmosphere plays a crucial role in the radiative balance, hydrological process, and climate change. Based on the latest moderate-resolution imaging spectroradiometer(MODIS) data, this study probes the spatio-temporal variations of global water vapor content in the past decade. It is found that overall the global water vapor content declined from 2003 to 2012(slope b = –0.0149, R = 0.893, P = 0.0005). The decreasing trend over the ocean surface(b = –0.0170, R = 0.908, P = 0.0003) is more explicit than that over terrestrial surface(b = –0.0100, R = 0.782, P = 0.0070), more significant over the Northern Hemisphere(b = –0.0175, R = 0.923, P = 0.0001) than that over the Southern Hemisphere(b = –0.0123, R = 0.826, P = 0.0030). In addition, the analytical results indicate that water vapor content are decreasing obviously between latitude of 36°N and 36°S(b = 0.0224, R = 0.892, P = 0.0005), especially between latitude of 0°N and 36°N(b = 0.0263, R = 0.931, P = 0.0001), while the water vapor concentrations are increasing slightly in the Arctic regions(b = 0.0028, R = 0.612, P = 0.0590). The decreasing and spatial variation of water vapor content regulates the effects of carbon dioxide which is the main reason of the trend in global surface temperatures becoming nearly flat since the late 1990 s. The spatio-temporal variations of water vapor content also affect the growth and spatial distribution of global vegetation which also regulates the global surface temperature change, and the climate change is mainly caused by the earth's orbit position in the solar and galaxy system. A big data model based on gravitational-magmatic change with the solar or the galactic system is proposed to be built for analyzing how the earth's orbit position in the solar and galaxy system affects spatio-temporal variations of global water vapor content, vegetation and temperature at large spatio-temporal scale. This comprehensive examination of water vapor changes promises a holistic understanding of the global climate change and potential underlying mechanisms.

Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle

Recently near-ground remote sensing using unmanned aerial vehicles(UAV)witnessed wide applications in obtaining field information.In this research,four Rapideye satellite images and eight RGB images acquired from UAV were used from early June to the end of July,2015 covering two experimental winter wheat fields,in order to monitor wheat canopy growth status and analyze the correlation among satellite images based normalized difference vegetation index(NDVI)with UAV’s RGB images based visible-band difference vegetation index(VDVI)and ground variables of the sampled grain protein contents.Firstly,through image interpretation of UAV’s multi-temporal RGB images with fine spatial resolution,the wheat canopy color changes could be intuitively and clearly monitored.Subsequently,by monitoring the changes of satellite images based NDVI as well as VDVI values and UAV’s RGB images based VDVI values,the conclusions were made that these three vegetation indices demonstrated the same and synchronized trend of increasing at the early stage of wheat growth season,reaching up to peak values at the same timing,and starting to decrease since then.The results of the correlation analysis between NDVI of satellite images and sampled grain protein contents show that NDVI has good predicative capability for mapping grain protein content before ripening growth stage around June7,2015,while the reliability of using satellite image based NDVI to predict grain protein contents becomes worse as ripening stage approaches.The regression analysis between UAV’s RGB image based VDVI and satellite image based VDVI as well as NDVI showed good coefficients of determination.It is concluded that it is feasible and practical to temporally complement satellite remote sensing by using UAV’s RGB images based vegetation indices to monitor wheat growth status and to map within-field spatial variations of grain protein contents for small scale farmlands.

Hepatic CT Image Query Based on Threshold-based Classification Scheme with Gabor Features

Hepatic computed tomography(CT) images with Gabor function were analyzed.Then a threshold-based classification scheme was proposed using Gabor features and proceeded with the retrieval of the hepatic CT images.In our experiments, a batch of hepatic CT images containing several types of CT findings was used and compared with the Zhao's image classification scheme, support vector machines(SVM) scheme and threshold-based scheme.

VR content creation and exploration with deep learning: A survey

Virtual reality(VR)offers an artificial,computer generated simulation of a real life environment.It originated in the 1960 s and has evolved to provide increasing immersion,interactivity,imagination,and intelligence.Because deep learning systems are able to represent and compose information at various levels in a deep hierarchical fashion,they can build very powerful models which leverage large quantities of visual media data.Intelligence of VR methods and applications has been significantly boosted by the recent developments in deep learning techniques.VR content creation and exploration relates to image and video analysis,synthesis and editing,so deep learning methods such as fully convolutional networks and general adversarial networks are widely employed,designed specifically to handle panoramic images and video and virtual 3 D scenes.This article surveys recent research that uses such deep learning methods for VR content creation and exploration.It considers the problems involved,and discusses possible future directions in this active and emerging research area.