Rich Texture Research Articles

An improved multi-focus image fusion algorithm based on multi-scale weighted focus measure

This paper focuses on developing an improved multi-focus image fusion (MFIF) algorithm. Existing spatial domain algorithms dependent on the obtained fusion decision map still lead to unexpected ghosting, blurred, edges as well as blocking effects such that the visual effect of image fusion is seriously degraded. To overcome these shortages, an improved MFIF algorithm is developed with the help of a novel multi-scale weighted focus measure and a decision map optimization technique. First, a novel multi-scale measurement template is designed in order to effectively extract the gradient information of rich texture regions, smooth regions as well as transitional regions between the aforementioned regions simultaneously. Then, an improved calculation scheme of the focus score matrix is designed based on the weighted sum of the focus measure maps in each region window centered on a concerned pixel, under which the advantage of pixel-by-pixel weighting is employed. In what follows, an initial decision map is obtained in light of the focus score matrix combined with threshold filtering, which is employed to eliminate the small isolated regions caused by some misclassified pixels. Furthermore, an accurate decision map is received with the help of the optimization capability of guided filtering to avoid edge unexpected artificial textures. In comparison with block-based fusion algorithms, our algorithm developed in this paper extracts the focus regions pixel-by-pixel, thereby helping to reduce the blocking effects that appear in the fusion image. Finally, some intensive comparison analysis based on common datasets is performed to verify the superiority over state-of-the-art methods in both visual qualitative and quantitative evaluations.

GAN-FM: Infrared and Visible Image Fusion Using GAN With Full-Scale Skip Connection and Dual Markovian Discriminators

A good result of infrared and visible image fusion should not only maintain significant contrast for distinguishing targets from the backgrounds, but also contain rich scene textures to cater for human visual perception. However, previous fusion methods usually do not fully utilize the information, and hence their fused results sacrifice either the salience of thermal targets or the sharpness of textures. To address this challenge, we propose a novel Generative Adversarial Network with Full-scale skip connection and dual Markovian discriminators (GAN-FM) to fully preserve effective information in infrared and visible images. First, a full-scale skip connected generator is designed to extract and fuse deep features of different scales, which can promote the direct transmission of shallow high-contrast features to the deep level, preserving the thermal radiation targets from the semantic level. As a result, the fused image can maintain significant contrast. Second, we propose two Markovian discriminators to establish adversarial games with the generator, so as to estimate probability distributions of infrared and visible modalities at the same time. Unlike conventional global discriminator, the Markovian discriminators try to distinguish each patch of input images, thus the attention of network is restricted to local regions and the fused results are forced to contain more details. In addition, we propose an effective joint gradient loss to ensure the harmonious coexistence of contrast and texture, which prevents the background texture pollution caused by the edge diffusion of the high-contrast target regions. Extensive qualitative and quantitative experiments demonstrate that our GAN-FM has advantages over the state-of-the-art methods in preserving significant contrast and rich textures. Moreover, we apply the fused image generated by our method to object detection and image segmentation, which can effectively improve the performance.

Multimodal Medical Image Fusion Based on Gabor Representation Combination of Multi-CNN and Fuzzy Neural Network

Aiming at the current multimodal medical image fusion methods that cannot fully characterize the complex textures and edge information of the lesion in the fused image, a method based on Gabor representation of multi-CNN combination and fuzzy neural network is proposed. This method first filters the CT and MR image sets through a set of Gabor filter banks with different proportions and directions to obtain different Gabor representations pairs of CT and MR, each pair of different Gabor representations is used to train the corresponding CNN to generate a G- CNN and multiple G- CNN form a G- CNN group, namely G- CNNs; then when fusing CT and MR images, CT and MR are represented by Gabors to get Gabor representation pairs firstly, each Gabor representation pair is put into the corresponding trained G- CNN for preliminary fusion, then use the fuzzy neural network to fuse multiple outputs of the G- CNNs to obtain the final fused image. Compared with the nine recent state-of-the-art multimodal fusion methods, the average mutual information of the three groups of experiments has increased by 13%, 10.3%, and 10% respectively; the average spatial frequency has increased by 10.3%, 20%, and 10.7%; the average standard deviation has increased respectively 12.4%, 10.8%, 14.4%; the average edge retention information increased by 33.5%, 22%, and 43%. The experimental results show that the proposed fusion method is significantly better than the other comparative fusion methods in objective evaluation and visual quality. It has the best performance on the four indicators and can better integrate the rich texture features and the clear edge information of the source images into the final fused image, which improves the quality of multimodal medical image fusion, and effectively assists doctors in disease diagnosis.

Genres and Multilingual Contexts: The Translational Culture of Nineteenth-Century Calcutta

Nineteenth-century Calcutta has been widely researched to understand its role in the making of a ‘modern’ India. However, the ‘translational’ culture of this period has not received enough attention. The present article traces what it terms Calcutta’s ‘translational culture’ by examining a palimpsest of languages and genres through the mediating role of translation. Nineteenth-century was a time when several languages were competing for space in the making of modern Bengali prose. Most of the writers of the time were negotiating a plural and multilingual domain and experimenting withnew styles of prose and poetry writing. Two such examples can be seen in the works of Michael Madhusudan Dutt (1824 –1873), and Kaliprassana Singha (1841 -1870). These writers were instrumental in the making of new genres and were negotiating multiple languages and linguistic registers that included –Sanskrit, Bengali with its different elite and colloquial registers, English,and several European languages and literatures. In juxtaposing Dutt and Singha, the present article attempts to point towards a parallel history of the nineteenth-century Calcutta traced through moments of transactions, translations,and negotiations among languages, ideas,and world views. Languages and literary genres in this case become a testimony to the rich texture of social and cultural negotiations that went into the making of the modernist Bengali prose and indicative of its palimpsestic and translational nature.

Interventions: An Interview with C. Dale Young by Frederick Luis Aldama

InterventionsAn Interview with C. Dale Young by Frederick Luis Aldama Frederick Luis Aldama Click for larger view View full resolution An M. D. and award-winning poet, fiction author, editor and teacher, C. Dale Young stands among our greats: Avicenna, John Keats, Anton Chekhov, Nawal El Saadawi, Atul Gawande, Rafael Campo, and, of course, William Carlos Williams. Like Williams, C. Dale is mixed Latinx. Like Williams, C. Dale seeks a unity of poetic effect built in and through objects and actions, more than ideas. Through masterful calibrations of sounds and rhythms as well as lyric with narrative form, his poems guide and glide us into rich textures of place and the alpha to omega of a Whitmanian expansive existence. Already at an early age C. Dale cultivated a passion for the arts and sciences. He would later parlay this into undergraduate studies of molecular biology, poetry, and studio art at Boston College. Our twenty-first-century renaissance man, C. Dale deepened his knowledge in these areas, receiving an MFA and MD from the University of Florida. Today, C. Dale writes, publishes, practices medicine, and teaches future generations of MFAs. With Prometeo (2021), C. Dale adds to his extraordinary body of work, including, The Day Underneath the Day (2001); The Second Person (2007), Torn (2011), The Halo (2016), and Affliction (2018). Gentle, graceful, and drop-of-pen attentive, C. Dale Young recently shared with me deep insights into his creative process, worldview, and life. Frederick Luis Aldama: It’s been too long, C. Dale, since our last interview. And now with the dropping of Prometeo, yet again you’ve taken the poetic form to new extraordinary heights. There’s so much that resonates with your earlier work, and yet you take me, my heart, and my head to a whole new place. All the poems work so beautifully together. Did you conceive of Prometeo as an organic whole? C. Dale Young: I wish I could say that that’s how it worked. But that’s never how it’s worked for any of my books. I honestly never have any sense of a book until I’m almost two-thirds through writing the poems. I have this kind of joke that if I knew what the book was going to be, I would then ruin all the poems. That I would start trying to tailor the poems and it would be disastrous. I think part of the reason that a lot of my books feel coherent is that I’m an obsessive-compulsive person. I tend to come back at things over and over, trying to understand them in different ways. That’s what holds a lot of the books together. I didn’t appreciate how much Prometeo was in conversation with other books of mine until I got the very first copy and sat down and read it. I was surprised. I discovered a lot of linkages between these poems and earlier poems. I wrote the first of these poems in 2013. And I think I wrote the last of the poems in late 2018, almost 2019. And I think it was around 2017 that I had a sense of the book. FLA: You’ve chosen to publish Prometeo and other collections with the Four Way Press. Why? CDY: It’s partly technical and legal. My very first book, The Day Underneath the Day, was published by Northwestern University Press. They did not have a right of first refusal. When I approached them with the second book, The Second Person, they never rejected it, but it just kind of sat there; the editor that I had had left the press, and there wasn’t really a poetry editor at that time. The book was picked up by Zoo Press. It was supposed to appear at the AWP 2015 in Vancouver. I arrived and there was no table from Zoo Press. It was April 1st, so I thought it was an amazing April Fool’s joke. In fact, there was a big scandal. The director of Zoo Press had taken all the money. They never published the book. I was teaching at Warren Wilson, and Martha Rhodes, the director of...

Queering the Global Filipina Body: Contested Nationalisms in the Filipina/o Diaspora by Gina K. Velasco

Reviewed by: Queering the Global Filipina Body: Contested Nationalisms in the Filipina/o Diaspora by Gina K. Velasco Anna M. Moncada Storti (bio) Queering the Global Filipina Body: Contested Nationalisms in the Filipina/o Diaspora by Gina K. Velasco. Urbana: University of Illinois Press, 2020, 176 pp., $98.15 hardcover, $26.00 paper, $19.95 ebook. Filipinas—too often stereotyped in contemporary popular culture as care workers, sex workers, and mail-order brides—evoke the gendered and sexual politics of the Philippine nation, but how have those in the Filipinx diaspora reconciled with such tropes? Gina K. Velasco's interdisciplinary study, Queering the Global Filipina Body: Contested Nationalisms in the Filipina/o Diaspora, reimagines representational politics, boldly challenging the discursive terrain which indexes Filipinas as "devalued, flexible" (2) laborers within capitalism's global labor market. Extending theoretical work across Filipinx Studies, transnational feminism, and queer of color critique, Velasco exhibits the indispensable use of queer diasporic analysis, illuminating a defining tension within Filipina/o American cultural production from the 1990s to the mid-2010s: queer and feminist critiques of the heteropatriarchal nation, on the one hand, and the liberatory potential of popular nationalism as a form of anti-imperial resistance, on the other. Gender, sexuality, and race emerge as consistent vectors of analysis, as do migration, labor, and homonationalism. A mixed methodological study, Queering the Global Filipina Body combines ethnography with visual cultural analysis as it introduces a queer engagement with national belonging. Required reading for Filipinx scholars and activists alike, this book brings rich texture to the queer, feminist, and leftist pursuits that embolden the diasporic nationalisms of the US's former colony. It is an ideal text to assign in graduate seminars and advanced undergraduate courses in feminist theory, queer diaspora, and critical ethnic studies. Unique to Velasco's debut monograph is its extension of a "loving" critique, one foregrounded from a position of solidarity. Originally discussed in a previously published essay, the author's notion of "loving" critique articulates shared political commitment with the subject under inspection. Indeed, while the book forwards a sustained critique of the nation's ruthless investment in heteropatriarchy, it also wholeheartedly believes in the political potential of diasporic nationalism. These "contested nationalisms" shape the book's organizational structure; the first two chapters intervene into the gendered and sexual politics that undergird representations of the global Filipina body as a national symbol; the last two chapters spotlight Filipinx performance art, shifting the book's attention towards queer renditions of the global Filipina body which disrupt [End Page 343] both the heteronormative and masculinist ethos of Filipina/o American cultural nationalism and the homonationalism of US-based LGBT politics. Each chapter centers a different iteration of the global Filipina body. Chapter 1 looks closely at the balikbayan, the migrant who returns. Drawing on fieldwork conducted at the Philippine Studies Program—a heritage language program based at the University of the Philippines, Diliman—Velasco brings romanticized narratives of home to bear on the tensions that arise when Filipina/o Americas travel back to the "motherland." Regarded with "envy and scorn" (26), as traitors with a coveted privilege, Philippine expatriates awaken a deep-seated sense of national betrayal. As sites of politicization, heritage language programs incite a nationalist consciousness in Filipina/o Americans whose affinity to the island country complicates notions of betrayal. Heritage programs function as affective structures of diasporic nationalism that expose its participants to the Philippine revolutionary charge not without reproducing the nation's upholding of the nuclear family. Insofar as Philippine revolutionary nationalism is characterized through figures like Andrés Bonifacio and José Rizal, the implicitly male (diasporic or otherwise) national subjectivity is contingent upon its opposite: the feminized global Filipina body. Through interviews and participant observation, Velasco triangulates the heterosexual male balikbayan, the global Filipina body, and the queer balikbayan, addressing the contradictory and divergent commitments that define queer subjects' investments in anti-imperial nationalism. With critical and historical context, Velasco outlines the fraught ways in which efforts for liberation sustain the reproductive logic of the nation. Chapter 2 asks how representations of the Filipina trafficked woman / sex worker collapse narratives of sexual, affective, and domestic labor to satisfy alternative political...

A Novel Coarse-to-Fine Sea-Land Segmentation Technique Based on Superpixel Fuzzy C-Means Clustering and Modified Chan-Vese Model

The sea-land segmentation for optical remote sensing images (RSIs) has a valuable role in water resources and coastal zones management. However, it is challenging because optical RSIs mainly suffer from low contrast, intensity inhomogeneity with mixed pixels, and large image size with redundant information. This paper introduces a novel coarse-to-fine sea-land segmentation method that incorporates Superpixel Fuzzy C-Means (SPFCM) and a Modified Chan-Vese active contour model (MCV). First, the image is over-segmented into superpixels to reduce the information redundancy and utilize spectral and spatial information. The SPFCM employs local relationships among neighboring superpixels to cluster the superpixels based on their color and texture features, deal with mixed pixels, and produce coarse segmentation results. The CV depends on the initial contour. If the contour is incorrectly initialized, the CV may trap in local minima and needs many iterations. The purpose of the SPFCM result is to provide an automatic initial contour for an MCV model instead of manual initialization to improve the CV model's performance. Finally, color and texture features are combined in vector-valued images to solve the traditional CV's problems to deal with complicated nature images with intensity inhomogeneity or rich texture features to produce fine segmentation results. The proposed method is evaluated and achieved an average accuracy of 98.9%, an average Jaccard Similarity Coefficient (JSC) equals 97.1%, an average Disc Similarity Coefficient (DSC) equals 98.5%, and an average recall equals 99.3%. The proposed method results are promising, and it outperformed the results of other state-of-the-art sea-land segmentation methods.

Dan Shen's “Dual Dynamics” and Dual Audience of Literary Fairy Tales

Dan Shen's “Dual Dynamics” and Dual Audience of Literary Fairy Tales

An Infrared and Visible Image Fusion Method Guided by Saliency and Gradient Information

Infrared and visible image fusion is a hot topic due to the perfect complementarity of their information. There are two key problems in infrared and visible image fusion. One is how to extract significant target areas and rich texture details from the source images, and the other is how to integrate them to produce satisfactory fused images. To tackle these problems, we propose a novel fusion framework in this paper. A multi-level image decomposition method is used to obtain the base layer and detail layer of the source image. For the fusion of base layer, an ingenious fusion strategy guided by the saliency map of source image is designed to improve the intensity of salient targets and the visual quality of the fused image. For the fusion of detail layer, an efficient approach by introducing the enhanced gradient information is presented to boost the detail features and sharpen the edges of the fused image. Experimental results demonstrate that, compared with fifteen classical and advanced fusion methods, the proposed image fusion framework has better performance in both subjective and objective evaluation.

Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks

One major limitation of remote-sensing images is bad weather conditions, such as haze. Haze significantly reduces the accuracy of satellite image interpretation. To solve this problem, this paper proposes a novel unsupervised method to remove haze from high-resolution optical remote-sensing images. The proposed method, based on cycle generative adversarial networks, is called the edge-sharpening cycle-consistent adversarial network (ES-CCGAN). Most importantly, unlike existing methods, this approach does not require prior information; the training data are unsupervised, which mitigates the pressure of preparing the training data set. To enhance the ability to extract ground-object information, the generative network replaces a residual neural network (ResNet) with a dense convolutional network (DenseNet). The edge-sharpening loss function of the deep-learning model is designed to recover clear ground-object edges and obtain more detailed information from hazy images. In the high-frequency information extraction model, this study re-trained the Visual Geometry Group (VGG) network using remote-sensing images. Experimental results reveal that the proposed method can recover different kinds of scenes from hazy images successfully and obtain excellent color consistency. Moreover, the ability of the proposed method to obtain clear edges and rich texture feature information makes it superior to the existing methods.

GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion

Visible images contain rich texture information, whereas infrared images have significant contrast. It is advantageous to combine these two kinds of information into a single image so that it not only has good contrast but also contains rich texture details. In general, previous fusion methods cannot achieve this goal well, where the fused results are inclined to either a visible or an infrared image. To address this challenge, a new fusion framework called generative adversarial network with multiclassification constraints (GANMcC) is proposed, which transforms image fusion into a multidistribution simultaneous estimation problem to fuse infrared and visible images in a more reasonable way. We adopt a generative adversarial network with multiclassification to estimate the distributions of visible light and infrared domains at the same time, in which the game of multiclassification discrimination will make the fused result to have these two distributions in a more balanced manner, so as to have significant contrast and rich texture details. In addition, we design a specific content loss to constrain the generator, which introduces the idea of main and auxiliary into the extraction of gradient and intensity information, which will enable the generator to extract more sufficient information from source images in a complementary manner. Extensive experiments demonstrate the advantages of our GANMcC over the state-of-the-art methods in terms of both qualitative effect and quantitative metric. Moreover, our method can achieve good fused results even the visible image is overexposed. Our code is publicly available at https://github.com/jiayi-ma/GANMcC .

Contextual Ethics: Taking the Lead from Wittgenstein and Løgstrup on Ethical Meaning and Normativity

AbstractA prominent trend in moral philosophy today is the interest in the rich textures of actual human practices and lives. This has prompted engagements with other disciplines, such as anthropology, history, literature, law and empirical science, which have produced various forms ofcontextual ethics. These engagements motivate reflections on why and how context is important ethically, and such metaethical reflection is what this article undertakes. Inspired by the work of the later Wittgenstein and the Danish theologian K.E. Løgstrup, I first describe one of the ways in which context plays a central role with regard to ethical meaning and normativity. I then examine how ‘context’ is to be defined, and finally I discuss some of the questions which arise when giving context prominence in ethics – namely, how to delimit the scope of relevant context, the relevant traits of a particular context and what ‘the ethical’ is.

Research and Development of Palmprint Authentication System Based on Android Smartphones

The security problems of online transactions by smartphones reveal extreme demand for reliable identity authentication systems. With a lower risk of forgery, richer texture, and more comfortable acquisition mode, compared with face, fingerprint, and iris, palmprint is rarely adopted for identity authentication. In this paper, we develop an effective and full-function palmprint authentication system regarding the application on an Android smartphone, which bridges the algorithmic study and application of palmprint authentication. In more detail, an overall system framework is designed with complete functions, including palmprint acquisition, key points location, ROI segmentation, feature extraction, and feature coding. Basically, we develop a palmprint authentication system having user-friendly interfaces and good compatibility with the Android smartphone. Particularly, on the one hand, to guarantee the effectiveness and efficiency of the system, we exploit the practical Log-Gabor filter for feature extraction and discuss the impact of filtering direction, downsampling ratio, and discriminative feature coding to propose an improved algorithm. On the other hand, after exploring the hardware components of the smartphone and the technical development of the Android system, we provide an open technology to extend the biometric methods to real-world applications. On the public PolyU databases, simulation results suggest that the improved algorithm outperforms the original one with a promising accuracy of 100% and a good speed of 0.041 seconds. In real-world authentication, the developed system achieves an accuracy of 98.40% and a speed of 0.051 seconds. All the results verify the accuracy and timeliness of the developed system.

Research on Laser-Visual Fusion-based Simultaneous Localization and Mapping

In consideration of the restrictions of the single-sensor SLAM system in different application scenarios, this paper proposes a laser-visual sensor fusion strategy on the basis of analyzing the strengths and weaknesses of laser SLAM and visual SLAM. In the local mapping stage, the laser stability is used to facilitate the visual SLAM system while in the global map generation stage, the point cloud map with rich texture information as constructed by the visual SLAM system is used to improve the missing map information of the laser SLAM system and finally obtain a globally consistent 2D grid map. Through indoor real scene experiments, the fused SLAM system is verified so as to improve the accuracy of localization and mapping and yield a favorable robustness by combining the strengths of both effectively.

A Denoising Method Based on Pulse Interval Compensation for High-Speed Spike-Based Image Sensor

In order to eliminate the noise on the pulse interval of high-speed spike-based image sensor, a pulse interval compensation (PIC) algorithm is proposed. Based on the analysis of the principle of sensor noise generation, a pulse interval error model is constructed and the compensation algorithm is developed. First, raw pulse data is obtained from the sensor system, and then the coefficient matrix is used to compensate the non-uniform pulse interval caused by the dark current. Then, the guided filter separates high-frequency noise from scenes details and the pulse interval compensation matrix is obtained in the corresponding pulse interval. Finally, the pulse interval compensation is performed on the raw pulse data for correction. In static scene experiments, the proposed method is compared with the existing state-of-the-art methods and shows a clear advantage in denoising in both low texture and rich texture scenes. In the experiment to film a turntable at a rotation speed of 2000 r/min, 3000 r/min, 6000 r/min, the average roughness decreases from 0.2830 to 0.2034, 0.2937 to 0.2139 and 0.3252 to 0.2501 respectively. These experiments proved that the proposed method can effectively eliminate the noise of the spike-based image sensor.

Scale-Residual Learning Network for Scene Text Detection

Detecting incidentally captured text in the wild remains an open problem due to challenging factors including unconstrained scenarios and large scale variation. In this paper, we establish a large-scale scene text detection dataset (LS-Text), containing 36, 000 images and 270, 783 text instances with various scales and complex scenarios, to promote the research of text detection. We propose a Scale-residual Learning Network (SLN) to deal with the scale variation problem in a progressive optimization manner. Specifically, we integrate both learnable feature concatenation and feature up-sampling operator. It can effectively eliminate the residuals between the outputs of SLN and ground-truth text instances by processing both the Feature Fusion Residuals (FFR) and the Scale Transformation Residuals (STR), simultaneously. By stacking multi-scale feature maps in a deep-to-shallow manner, SLN continuously optimizes feature representation by accumulating strong semantic information and rich texture details in a scale-residual learning way. Extensive experimental results on five challenging datasets demonstrate the state-of-the-art performance of the proposed SLN model, and the challenging aspects related to real-world scenarios of the proposed LS-Text dataset. Both the source code of SLN and the LS-Text dataset are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SLN-Text-Detection</uri> .

Delineation of built-up land change from SAR stack by analysing the coefficient of variation

One main challenge in detecting built-up land cover changes using synthetic aperture radar (SAR) instruments is that complicated backscattering behaviours and the superimposition of speckles on rich textures cause a large number of false alarms. Using trajectory-based analyses from time-series SAR imagery can mitigate false alarms since the temporal variability in backscattering during construction improves discrimination capability. This paper presents an approach towards the detection of built-up land change based on a single-channel SAR stack. The proposed methodology includes the generation of a change indicator, the Markov modelling procedure and the delineation of changes over built-up areas. The generation of the change indicator aims to provide a feature with abundant contrast between changed and stable areas, a high signal-to-noise ratio and detail preservation. To this end, all temporal information is converted into a map of the coefficient of variation. After error removal, this change detector is combined with a Markov random field (MRF) criterion function. Rather than MRF modelling by iteration with very complex stochastic models, we propose using SAR temporal trajectory under a hypothesis test framework and interferometric coherence series to establish conditional density for each class. Then, the Graph-cuts theory is applied to delineate the boundary between changed and stable areas, followed by a binary classification procedure based on speckle divergence to exclude natural areas. The technique is tested on both synthetic data and two TerraSAR-X datasets covering representative areas with rich texture. We found that in a complex built environment that is challenging for classical change indicators and state-of-the-art techniques, the presented method can provide smaller overall error with better detail preservation.

Bayesian fusion for infrared and visible images

Infrared and visible image fusion has been a hot issue in image fusion. In this task, a fused image containing both the gradient and detailed texture information of visible images as well as the thermal radiation and highlighting targets of infrared images is expected to be obtained. In this paper, a novel Bayesian fusion model is established for infrared and visible images. In our model, the image fusion task is cast into a regression problem. To measure the uncertainty in a better manner, we formulate the model in a hierarchical Bayesian manner. Aiming at making the fused image satisfy the human visual system, the model incorporates the total-variation (TV) penalty. Subsequently, the model is efficiently inferred by the expectation-maximization (EM) algorithm, where the fusion weight can be automatically inferred and adaptive to the source images. The performance of our algorithm is investigated and compared with several state-of-the-art approaches on TNO and NIR image fusion datasets. In comparison with other methods, the novel model can generate better fused images with highlighting targets and rich texture details, which may potentially improve the reliability of the target automatic detection and recognition system.

Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation

This paper presents a method for estimating the six Degrees of Freedom (6DoF) pose of texture-less primitive-shaped objects from depth images. As the conventional methods for object pose estimation require rich texture or geometric features to the target objects, these methods are not suitable for texture-less and geometrically simple shaped objects. In order to estimate the pose of the primitive-shaped object, the parameters that represent primitive shapes are estimated. However, these methods explicitly limit the number of types of primitive shapes that can be estimated. We employ superquadrics as a primitive shape representation that can represent various types of primitive shapes with only a few parameters. In order to estimate the superquadric parameters of primitive-shaped objects, the point cloud of the object must be segmented from a depth image. It is known that the parameter estimation is sensitive to outliers, which are caused by the miss-segmentation of the depth image. Therefore, we propose a novel estimation method for superquadric parameters that are robust to outliers. In the experiment, we constructed a dataset in which the person grasps and moves the primitive-shaped objects. The experimental results show that our estimation method outperformed three conventional methods and the baseline method.

A COARSE-TO-FINE BAND REGISTRATION FRAMEWORK FOR MULTI/HYPERSPECTRAL REMOTE SENSING IMAGES CONSIDERING CLOUD INFLUENCE

Abstract. Band registration is one of the most critical steps in the production of multi/hyperspectral images and determines the accuracy of applications directly. Because of the characteristics of imaging devices in some multi/hyperspectral satellites, there may be a time difference between bands during push-broom imaging, which leads to displacements of moving clouds with respect to the ground. And a large number of feature points may gather around cloud contours due to the high contrast and rich texture, resulting in building a transformation more suitable for moving clouds and making ground objects ghosted and blurred. This brings a big challenge for registration methods based on feature extraction and matching. In this paper, we propose a novel coarse-to-fine band registration framework for multi/hyperspectral images containing moving clouds. In the coarse registration stage, a cloud mask is generated by grayscale stretching, morphology and other operations. Based on this mask, a coarse matching of cloud-free regions is performed to eliminate large misalignment between bands. In the refinement stage, low-rank analysis and RASL (Robust Alignment by Sparse and Low-rank decomposition) are used to optimize the rank of coarse results to achieve fine registration between bands. After experiments on a total of 102 images (83 cloudy images and 19 cloud-free images with all 32 bands) from Zhuhai-1 hyperspectral satellite, our method can achieve a registration accuracy of 0.6 pixels in cloudy images, 0.41 pixels in cloud-free images, which is enough for subsequent applications.