Optimized Content-Aware Image Resizing with Merging and Improved Importance Diffusion

A simple and effective content-aware image resizing method is proposed based on the row / column merging and improved importance diffusion,which preserves the important regions in an image as well as the global visual effect. By repeatedly merging two rows / columns into one row / column or inserting a new row /column between two rows / columns, this method realizes image-resolution reduction and expansion. The importance of the merged row / column is promoted and diffused to four rows / columns around the merged one,which is to avoid the unwanted image distortions resulted from excessively merging of un-important regions. In addition,the proposed method introduces the direction of gradient vector in the low-pass filter to reduce the interference caused by complex texture background and protect important content better. Furthermore,according to human mechanics principles,different weights are given to the row and column direction components of gradient vectors which can obtain better global visual effect. Experimented results show that the proposed method satisfied in not only visual effect but also objective evaluation.

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.

On-line Cache Resizing for Low-Power Microprocessors

We propose a novel scheme, called on-line cache resizing （OCR）, to dynamically resize the cache and meet the size requirement of each application. At each periodic interval, the scheme gathers the cache hit-miss statistics at runtime using an extra tag array. These executing statistics serve as inputs to an analytical model of cache energy. The scheme uses energy as a primary metric to dynamically increase/decrease the number of active cache ways for the next interval. The scheme minimizes the active cache size to save energy with minimal performance loss. The simulation with SPEC 2000 benchmarks shows that OCR results in an average of 38.4% energy saving compared with fixed-size caches, with only 2.0% performance loss.

Fast Face Detection with Multi-Scale Window Search Free from Image Resizing Using SGI Features

Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. This paper describes a novel method of fast face detection with multi-scale window search free from image resizing. We adopt statistics of gradient images (SGI) as image features and append an overlapping cell array to improve detection accuracy. The SGI feature is scale invariant and insensitive to small difference of pixel value. These characteristics enable the multi-scale window search without image resizing. Experimental results show that processing speed of our method is 3.66 times faster than a conventional method, adopting HOG features combined to an SVM classifier, without accuracy degradation.

Real-time content-aware image resizing

Content-aware image resizing is a kind of new and effective approach for image resizing, which preserves image content well and does not cause obvious distortion when changing the aspect ratio of images. Recently, a seam based approach for content-aware image resizing was proposed by Avidan and Shamir. Their results are impressive, but because the method uses dynamic programming many times, it is slow. In this paper, we present a more efficient algorithm for seam based content-aware iraage resizing, which searches seams through establishing the matching relation between adjacent rows or columns. We give a linear algorithm to find the optimal matches within a weighted bipartite graph composed of the pixels in adjacent rows or columns. Therefore, our method is fast （e.g. our method needs only about 100 ms to reduce a 768x1024 Image＇s width to 1/3 while Avidan and Shamir＇s method needs 12 s）. This supports immediate image resizing whereas Avidan and Shamir＇s method requires a more costly pre-processing step to enable subsequent real-time processing. A fast method such as the one proposed will be also needed for future real-time video resizing applications.

Fast Multi-Operator Image Resizing and Evaluation

Current multi-operator image resizing methods succeed in generating impressive results by using image similarity measure to guide the resizing process. An optimal operation path is found in the resizing space. However, their slow resizing speed caused by inefficient computation strategy of the bidirectional patch matching becomes a drawback in practical use. In this paper, we present a novel method to address this problem. By combining seam carving with scaling and cropping, our method can realize content-aware image resizing very fast. We define cost functions combing image energy and dominant color descriptor for all the operators to evaluate the damage to both local image content and global visual effect. Therefore our algorithm can automatically find an optimal sequence of operations to resize the image by using dynamic programming or greedy algorithm. We also extend our algorithm to indirect image resizing which can protect the aspect ratio of the dominant object in an image.

Review:A survey for image resizing

Image resizing is a key technique for displaying images on different devices, and has attracted much attention in the past few years. This paper reviews the image resizing methods proposed in recent years, gives a detailed comparison on their performance, and reveals the main challenges raised in several important issues such as preserving an important region, minimizing distortions, and improving efficiency. Furthermore, this paper discusses the research trends and points out the possible hotspots in this field. We believe this survey can give some guidance for researchers from relevant research areas, offering them an overall and novel view.

Image resizing by reconstruction from deep features

Traditional image resizing methods usually work in pixel space and use various saliency measures.The challenge is to adjust the image shape while trying to preserve important content.In this paper we perform image resizing in feature space using the deep layers of a neural network containing rich important semantic information.We directly adjust the image feature maps,extracted from a pre-trained classification network,and reconstruct the resized image using neuralnetwork based optimization.This novel approach leverages the hierarchical encoding of the network,and in particular,the high-level discriminative power of its deeper layers,that can recognize semantic regions and objects,thereby allowing maintenance of their aspect ratios.Our use of reconstruction from deep features results in less noticeable artifacts than use of imagespace resizing operators.We evaluate our method on benchmarks,compare it to alternative approaches,and demonstrate its strengths on challenging images.

Cluster-Based Saliency-Guided Content-Aware Image Retargeting

Straightforward image resizing operators without considering image contents （e.g., uniform scaling） cannot usually produce satisfactory results, while content-aware image retargeting aims to arbitrarily change image size while preserving visually prominent features. In this paper, a cluster-based saliency-guided seam carving algorithm for content- aware image retargeting is proposed. To cope with the main drawback of the original seam carving algorithm relying on only gradient-based image importance map, we integrate a gradient-based map and a cluster-based saliency map to generate a more reliable importance map, resulting in better single image retargeting results. Experimental results have demonstrated the efficacy of the proposed algorithm.

林权证管理系统中Resize对象的应用

重点阐述了VisualFoxPro 6 .0基础类库中Resize对象 (改变尺寸对象 )实现方法过程、应用及怎样修改来满足需求 ,该对象具有一定的实用性和典型性 。

Resizable, Rescalable and Free-Style Visualization of Hierarchical Clustering and Bioinformatics Analysis

Graphical representation of hierarchical clustering results is of final importance in hierarchical cluster analysis of data. Unfortunately, almost all mathematical or statistical software may have a weak capability of showcasing such clustering results. Particularly, most of clustering results or trees drawn cannot be represented in a dendrogram with a resizable, rescalable and free-style fashion. With the “dynamic” drawing instead of “static” one, this research works around these weak functionalities that restrict visualization of clustering results in an arbitrary manner. It introduces an algorithmic solution to these functionalities, which adopts seamless pixel rearrangements to be able to resize and rescale dendrograms or tree diagrams. The results showed that the algorithm developed makes clustering outcome representation a really free visualization of hierarchical clustering and bioinformatics analysis. Especially, it possesses features of selectively visualizing and/or saving results in a specific size, scale and style (different views).

Tribon中Resize/Respec.功能的深入探讨

介绍在TribonM2中,如何通过对核心文件d065的设置,在不激活管路的前提下,实现修改管路部件及规格,以及修正出错管路的方法。

Optimal design and earthquake-resistant design evaluation of low-rise framed RC structure

This study evaluates the seismic response of an existing two stories RC building using non-linear analysis. The original model was resized and there were obtained two buildings designed under two different methodologies to fulfill the Venezuelan codes requirements for a high seismic hazard. An elastic analysis was applied to the original building in order to verify interstory drifts;the resizing building it was designed under requirements of strong column-wake beam condition. A third building was modeled according to the seismic displacement design procedure. It were performed non-linear static analysis and 2D and 3D dynamic analyses, obtaining capacity curves, structural ductility, structural performance point, global and interstory drifts for each building. Torsional effects for the resizing building were also computed from 3D analysis. In the original building it was obtained a weak seismic behavior, while resized buildings presented good seismic performance under the Limits States evaluated in this study.

Snell Kahuna切换台常用功能的使用

英国Snell Kahuan系列切换台由于其特有的FormatFusion技术以及出色的性能,目前被广泛应用。本文将结合我台新建80平方米高清新闻演播室的使用,介绍一下该系列切换台常用功能的使用。

图片大小批量调查 JPEG Resizer

JPEG Resizer是一款图片尺寸批量修改软件，可打开多个文件或者文件夹，列出所有JPG格式的图片文件及其尺寸大小，从而一次将所有图片修改成同样的尺寸大小。具体操作很简单：首先在主界面上点击左侧的“文件”菜单，然后点击“添加文件”或者“添加文件夹”将图片添加进来，接下来点击左侧的“尺寸”选项，该软件提供了四种调整方式，

Convenient noncooperative speckle-correlation imaging method

For speckle-correlation-based scattering imaging,an iris is generally used next to the diffuser to magnify the speckle size and enhance the speckle contrast,which limits the light flux and makes the setup cooperative.Here,we experimentally demonstrate a non-iris speckle-correlation imaging method associated with an image resizing process.The experimental results demonstrate that,by estimating an appropriate resizing factor,our method can achieve high-fidelity noncooperative speckle-correlation imaging by digital resizing of the raw captions or on-chip pixel binning without iris.The method opens a new door for noncooperative high-frame-rate speckle-correlation imaging and benefits scattering imaging for dynamic objects hidden behind opaque barriers.

调整晶体管尺寸优化CMOS电路的一种全局方法

随着集成电路特征线宽的不断缩小以及相应的电路密度和工作频率的增加，功耗已经成为超大规模集成电路设计中除面积和延时外另一个需要重点考虑的问题。在电路设计阶段可以通过调整晶体管尺寸（ｒｅｓｉｚｉｎｇ）改善电路的延时及功耗参数。原先的方法仅是对各个晶体管单独进行调整，只能达到局部极小。因此采用了一种基于关键路径的全局ｒｅｓｉｚｉｎｇ方法，通过同时调整关键路径上的多个单元尺寸，进一步改善了电路的延时功耗积。另外，通过在节点权重中引入延时、面积和功耗参数，在ｒｅｓｉｚｉｎｇ过程中实现了电路各项性能之间的折衷。将此算法集成到ＳＩＳ电路设计环境中，对一些Ｂｅｎｃｈｍａｒｋ电路的实验结果表明，此方法具有一定的优化效果。

Enlarging Image by Constrained Least Square Approach with Shape Preserving

From a visual point of view, the shape of an image is mainly determined by the edges. Conventional polynomial interpolation of image enlarging methods would produce blurred edges, while edge-directed interpolation based methods would cause distortion in the non-edge areas. A new method for image enlarging is presented. The image is enlarged in two steps. In the first step, a fitting surface is constructed to interpolate the image data. To remove the zigzagging artifact for each pixel, a fitting patch is constructed using edge information as constraints. The combination of all the patches forms the fitting surface which has the shape suggested by image data. Each point on the fitting surface can be regarded as a sampling point taken from a unit square domain, which means that when the fitting surface is used to enlarge the image, each sampling domain of the enlarged pixels is also a unit square, causing the enlarged image to lose some details. To make the enlarged image keep the details as many as possible, the sampling domain of the enlarged pixels should be less than a unit square. Then, in the second step, using the points taken from the fitting surface, new pixels are computed using constrained optimization technique to form the enlarged image, and the size of the sampling domain of the enlarged pixels is inversely proportional to the size of the enlarged image. The image enlarged by the new method has a quadratic polynomial precision. Comparison results show that the new method produces resized image with better quality.

Load Shedding for Window Joins over Streams

We address several load shedding techniques over sliding window joins. We first construct a dual window architectural model including aux-windows and join-windows, and build statistics on aux-windows. With the statistics, we develop an effective load shedding strategy producing maximum subset join outputs. In order to accelerate the load shedding process, binary indexed trees have been utilized to reduce the cost on shedding evaluation. When streams have high arrival rates, we propose an approach incorporating front-shedding and rear-shedding, and find an optimal trade-off between them. As for the scenarios of variable speed ratio, we develop a plan reallocating CPU resources and dynamically resizing the windows. In addition, we prove that load shedding is not affected during the process of reallocation. Both synthetic and real data are used in our experiments, and the results show the promise of our strategies.

重启验证型软件的破解

Batch Image Resizer是一款图片编辑工具，可以对多数的图片格式文件（包括JPEG、BMP、GIF、PCX、PNG．等）进行大小调整、旋转、压印以及转换格式等操作。下面我们就来看看其注册算法。