基于SIFT 匹配的多视点立体图像零视差调整

针对目标跟踪过程中目标可能出现的快速变化和严重遮挡等问题,提出了一种基于新的子空间表示的目标跟踪算法。采用距离不变量对尺度不变特征变换(SIFT)特征点匹配对进行提纯。用提纯后的特征点匹配对,通过线性拟合得到仿射变化参数。在粒子滤波的理论框架下,采用快速的迭代算法,建立目标的主分量(PCA)子空间表示,结合计算得到的仿射变化参数,构造有效的目标观测模型完成跟踪。同时,采用在线学习的方法对SIFT特征点和PCA子空间进行定时更新。大量实验表明,提出的算法能快速有效地完成对姿态和形状剧烈变化的目标的精确跟踪。     

A wavelet transform based contrast enhancement method for underwater acoustic images

Multidimensional Systems and Signal Processing - Dredging the surface of the ocean to identify both living and non living things nowadays has become an unproblematic task with the help of the...     

A multi-exposure fusion framework for contrast enhancement of hazy images employing dynamic stochastic resonance

Current imaging devices coupled with advanced hardware and software are smart enough to enhance low light images taken in clear weather. But in hazy or foggy environments, the captured images are of degraded quality. To address this issue, image processing algorithms are employed to enhance the degraded images to make useful for extracting meaningful features. In this study, we propose a haze removal algorithm to improve the color and contrast of images captured in hazy environments. The first step involves generation of images with various exposures using the theory of dynamic stochastic resonance. The images are then fused in a multi-scale fusion framework crafting weight maps viz. haze density, chromaticity, and luminance gradient. The fusion process focuses on uniformly enhancing the dark and bright regions of the image. However, it may overemphasize haze affected regions. Therefore, in the second step, the atmospheric scattering equation is referred and its modified version is applied that accomplishes the haze removal task. Quantitative and qualitative analyses demonstrate the effectiveness of the proposed method.     

Computer generated images vs. digital photographs: A synergetic feature and classifier combination approach

The development of powerful and low-cost hardware devices allied with great advances on content editing and authoring tools have promoted the creation of computer generated images (CG) to a degree of unrivaled realism. Differentiating a photo-realistic computer generated image from a real photograph (PG) can be a difficult task to naked eyes. Digital forensics techniques can play a significant role in this task. As a matter of fact, important research has been made by the scientific community in this regard. Most of the approaches focus on single image features aiming at detecting differences between real and computer generated images. However, with the current technology advances, there is no universal image characterization technique that completely solves this problem. In our work, we (1) present a complete study of several CG versus PG approaches; (2) create a large and heterogeneous dataset to be used as a training and validation database; (3) implement representative methods of the literature; and (4) devise automatic ways for combining the best approaches. We compared the implemented methods using the same validation environment showing their pros and cons with a common benchmark protocol. We collected approximately 4850 photographs and 4850 CGs with large diversity of image content and quality. We implemented a total of 13 methods. Results show that this set of methods can achieve up to 93% of accuracy when used without any form of machine learning fusion. The same methods, when combined through the implemented fusion schemes, can achieve an accuracy rate of 97%, representing a reduction of 57% of the classification error over the best individual result.     

A robust method for fingerprinting digital images

In this paper, a method to fingerprint digital images is proposed, and different watermarked copies with different identification string are made. After determining the number of the customers and the length of the watermark string, this method chooses some values inside the digital image using a characteristic function, and adds watermarks to these values in a way that can protect the product against the attacks happened by comparing two fingerprinted copies. The watermarks are a string of binary numbers -1s and 1s. Every customer will be distinguished by a series of 1s and -1s generated by a pseudo-random generator. The owner of the image can determine the number of customers and the length of the string as well as this method will add another watermarking values to watermark string to protect the product.     

一种有效的低对比度水下图像增强算法

针对水下图像光照不均且对比度低的问题,提出一种新的水下图像增强算法。该算法首先利用去背景法获得照明均匀的前景图像;然后对传统的Butterworth同态滤波器进行改进,提高其高频增益并与相角结合,使得前景图像经改进的Butterworth同态滤波器滤波后,低频成分得到很好的抑制而高频成分被有效放大,从而获得高对比度的水下图像。实验结果表明,与其他算法相比,本文算法能有效地提高图像对比度。     

多层级及对比度提升的红外和可见光图像融合

提出了一种针对红外和可见光图像的有效融合算法。首先,在Petrovi?多层级图像融合体系的基础上,在特征级信息中加入高频边缘分量,提高了融合质量;其次,分析了图像特点及目标的物理特性,提出了针对融合结果的对比度提升方法,进一步了提高融合质量。通过主观观察以及客观指标对比证明,该方法获得了优于其他比较方法的融合结果,并且对比度提升方法明显提高了各类方法的红外和可见光图像融合质量,效果好,具有广泛的适用性。     

Multi-view convolutional vision transformer for 3D object recognition

With the rapid development of three-dimensional (3D) vision technology and the increasing application of 3D objects, there is an urgent need for 3D object recognition in the fields of computer vision, virtual reality, and artificial intelligence robots. The view-based method projects 3D objects into two-dimensional (2D) images from different viewpoints and applies convolutional neural networks (CNN) to model the projected views. Although these methods have achieved excellent recognition performance, there is not sufficient information interaction between the features of different views in these methods. Inspired by the recent success achieved by vision transformer (ViT) in image recognition, we propose a hybrid network by taking advantage of CNN to extract multi-scale local information of each view, and of transformer to capture the relevance of multi-scale information between different views. To verify the effectiveness of our multi-view convolutional vision transformer (MVCVT), we conduct experiments on two public benchmarks, ModelNet40 and ModelNet10, and compare with those of some state-of-the-art methods. The final results show that MVCVT has competitive performance in 3D object recognition.     

基于小波的信息保持型雷场图像融合算法

介绍了一种基于多分辨率小波变换的雷场图像融合算法,并针对红外图像和可见光全色波段图像的各小波域高频分量分别采取局部加法融合处理,在最大程度上避免了融合过程中的信息丢失,提高了雷场融合影像的几何分辨率。实验表明：通过文中给出的算法模型可以在保持图像光谱信息的前提下有效提高图像的分辨率,能够充分利用雷场的多源遥感图像信息,是一种实用的信息融合模型。     

A novel unsupervised multiple feature hashing for image retrieval and indexing (MFHIRI)

Recently, techniques that can automatically figure out the incisive information from gigantic visual databases are urging popularity. The existing multi-feature hashing method has achieved good results by fusing multiple features, but in processing these multi-features, fusing multi-features into one feature will cause the feature dimension to be very high, increasing the amount of calculation. On the one hand, it is not easy to discover the internal ties between different features. This paper proposes a novel unsupervised multiple feature hashing for image retrieval and indexing (MFHIRI) method to learn multiple views in a composite manner. The proposed scheme learns the binary codes of various information sources in a composite manner, and our scheme relies on weighted multiple information sources and improved KNN concept. In particular, here we adopt an adaptive weighing scheme to preserve the similarity and consistency among binary codes. Precisely, we follow the graph modeling theory to construct improved KNN concept, which further helps preserve different statistical properties of individual sources. The important aspect of improved KNN scheme is that we can find the neighbors of a data point by searching its neighbors’ neighbors. During optimization, the sub-problems are solved in parallel which efficiently lowers down the computation cost. The proposed approach shows consistent performance over state-of-the-art (three single-view and eight multi-view approaches) on three broadly followed datasets viz. CIFAR-10, NUS-WIDE and Caltech-256.     

基于多尺度边缘表示的偏振图像二次融合算法

针对传统成像技术探测信息量少以及传统图像融合方法计算量大,不能凸显边缘轮廓的问题,提出一种基于多尺度边缘表示的偏振图像二次融合方法。首先,对同时偏振成像系统获取的4个偏振角度图像进行多尺度边缘去噪、配准预处理。然后一次融合,即将预处理后的4个偏振角度图像进行二进小波分解,在不同尺度上对低频、高频系数进行特定组合,计算得到Stokes向量［I Q U V］T,线偏振度(DOLP)和偏振相位角(AOP)等偏振信息;二次融合,即是根据边缘相关性原则融合偏振强度图像和线偏振度图像经小波分解后的高频系数,低频用区域能量的方法。最后,采用交替投影迭代法重构融合图像的低频、高频系数,得到重构图像。实验结果表明,本文算法的融合性能优于传统方法。以小波变换法为参考,平均值提高65.48%,标准差提高95.98%,平均梯度是传统小波的6.45倍,边缘强度是小波变换的5.54倍。本算法提高了运算速度,结合了强度图像亮度信息和线偏振度图像反映不同物体性质的特点,突出了目标轮廓细节,能很好地识别隐藏、伪装的目标。     

User assisted disparity remapping for stereo images

This study concentrates on user assisted disparity remapping for stereo image footage, i.e. the disparity of an object of interest is altered while leaving the remaining scene unattended. This application is useful in the sense that it provides a method for emphasizing/de-emphasizing an object on the scene by adjusting its depth with respect to the camera. The proposed technique can also be used as a post-processing step for retargeting stereoscopic footage on different display sizes and resolutions. The proposed technique involves an MRF-based energy minimization step for interactive stereo image segmentation, for which user assistance on only one of the stereo pairs is required for determining the location of stereo object pair. A key contribution of the proposed study is elimination of dense disparity estimation step from the pipeline. This step is realized through a sparse feature matching technique between the stereo pairs. Moreover, by the help of the proposed technique, novel disparity adjusted views are synthesized using the produced stereo object segments and background information for the images. Qualitative and quantitative evaluation of the generated segments and the disparity adjusted images prove the functionality and superiority of the proposed technique.     

基于ReliefF特征加权和KNN的自然图像分类方法

为对自然图像有效准确地分类,提出了一种对图像低层特征和KNN分类算法中的近邻样本分别进行加权的分类方法。针对不同类别图像的视觉特征的差异,通过ReliefF算法计算训练集中每个类别的特征权值,利用此权值来改进待测图像与训练集中图像的距离度量;按照不同近邻到待测样本的距离远近,为不同近邻赋予权值来改进KNN算法在类别决策上的不足。实验结果表明该方法较传统KNN和特征加权KNN方法,准确性提高且对不同K值具有良好的鲁棒性。     

基于邻域统计信息的红外与可见光图像融合

针对红外图像与可见光图像的融合问题,提出了一种基于邻域统计信息的图像融合新算法。首先对图像进行多尺度分解,得到一系列子带系数,然后针对各子带系数的物理特性,提出了高低频规则不同的图像融合算法。对于图像低频部分,首先定义基于邻域统计信息的目标和场景特征参数,然后设计了加权系数自适应变化的加权平均融合策略;对于图像高频部分,首先定义邻域系数分布特征参数,然后设计了受邻域统计信息调制的系数比较取大融合策略。实验结果表明该算法能够很好地将红外图像与可见光图像进行融合,且融合效果优于其他一些算法。     

A zero-watermark algorithm for multiple images based on visual cryptography and image fusion

At present, it is difficult for the multiple images zero-watermark algorithm to protect all the images in the image set, and repeated operations will reduce the efficiency of the algorithm. To solve these issues, the proposed algorithm can design a reasonable copyright protection scheme according to the number of images in the image set to realize the protection of all images, and reduce the cost of time and storage. The gray-weighted average image fusion method is used to fuse multiple normalized standard images into one image. The LWT(Lifting the Wavelet Transform)-QR decomposition is applied to the effective area of the fusion image to obtain the robust feature image. Non-extended visual cryptography is used to enhance the security of the algorithm. A zero-watermark image is obtained by using the XOR manipulation for the feature image and the public shared image. Experimental results demonstrate that the proposed algorithm has good performance.     

基于改进直方图的红外图像增强方法

为了能在动态范围压缩的同时增强红外图像的对比度,提出了一种基于Sobel梯度直方图均衡算法（gradient histogram equalization,GHE）。与以往的直方图均衡化（histogram equalization,HE）方法不同,该方法自适应地为图像强梯度的灰阶分配高对比度,保留并增强16 bit图像中更多的细节。随后使用双Gamma映射对映射曲线进行调整,有效地抑制图像亮部的过曝现象,同时提高暗部的细节。该方法相比于传统的直方图均衡化算法在暗区细节处理、过曝抑制、对比度增强等方面都有较好的效果。     

一种多传感器多目标信息融合方法

文中提出一种多传感器探测多个目标时的目标信息融合方法，采用聚类分析方法完成目标信息分类，采用Dempster-Shafer方法完成目标性质可信度计算，采用总概率最大值法和极大似然法对同一目标的多个测量值进行综合计算。     

灰度融合图像目标与背景感知对比度客观评价模型

目标与背景感知对比度是影响可见光与红外灰度融合图像质量的主要因素之一。现有的对比度评价模型未能充分考虑人眼视觉特性。因此,基于韦伯对比度模型的形式,结合人眼亮度掩盖特性,提出了一种简单有效的融合图像目标与背景感知对比度评价模型。利用模拟图像和现实场景灰度融合图像的主观评价分数来检验客观评价模型。结果表明,与现有的5种图像对比度评价模型相比,所提出的目标与背景感知对比度客观评价模型能够给出更接近人眼主观感受的评价结果,有效地实现灰度融合图像目标与背景感知对比度的客观评价。