Reviewerly/exHarmony huggingface dataset:
On Feature-Based SAR Image Registration: Appropriate Feature and Retrieval Algorithm

An investigation on the appropriate feature and parameter retrieval algorithm is conducted for feature-based registration of synthetic aperture radar (SAR) images. The commonly used features such as tie points, Harris corner, SIFT, and SURF are comprehensively evaluated. SURF is shown to outperform others on criteria such as the geometrical invariance of feature and descriptor, the extraction and matching speed, the localization accuracy, as well as the robustness to decorrelation and speckling. The processing result reveals that SURF has nice flexibility to SAR speckles for the potential relationship between Fast-Hessian detector and refined Lee filter. Moreover, the use of Fast-Hessian to oversampled images with unaltered sampling step helps to improve the registration accuracy to subpixel (i.e., &lt;1 pixel). As for parameter retrieval, the widely used random sample consensus (RANSAC) is inappropriate because it may trap into local occlusion and result in uncertain estimation. An extended fast least trimmed squares (EF-LTS) is proposed, which behaves stable and averagely better than RANSAC. Fitting SURF features with EF-LTS is hence suggested for SAR image registration. The nice performance of this scheme is validated on both InSAR and MiniSAR image pairs.
Digital Image Restoration Using NL Means with Robust Edge Preservation Technique

We present a novel approach for image denoising with spatial domain edge preservation method to yield denoised image, from an image which is distorted by additive white Gaussian noise without loss of detail information of an image. Denoising of noise corrupted images while preserving its attributes like edges and fine details is an extreme challenge. During image acquisition and transmission often noise gets added in the image which degrades the image quality. Algorithms like NL means and BM3D have been very successful in this aspect. The weight assigned to the fellow pixels for calculating replacement for a noisy pixels have a pivotal role in these algorithms. In NL means, the weight assigned to a pixel depends only on its relative magnitude with respect to its neighborhood. In this paper we are proposing a robust mechanism for weight calculation which also considers the orientation of a pixel. Using this mechanism we got improvement in PSNR and visual quality of the de-noised image as compared to the NL means method and also got improvement in structural similarity as compared to the BM3D. From the experimental results it can be seen that our proposed algorithm is superior in comparison to the NL means and BM3D technique of image denoising, considering the factors like PSNR, SSIM and accordingly conclusions are drawn.
Generalization of Parameter Recovery in Binocular Vision for a Planar Scene

In this paper, we consider a mobile platform with two cameras directed towards the floor. In earlier work, this specific problem geometry has been considered under the assumption that the cameras have been mounted at the same height. This paper extends the previous work by removing the height constraint, as it is hard to realize in real-life applications. We develop a method based on an equivalent problem geometry, and show that much of previous work can be reused with small modification to account for the height difference. A fast solver for the resulting nonconvex optimization problem is devised. Furthermore, we propose a second method for estimating the height difference by constraining the mobile platform to pure translations. This is intended to simulate a calibration sequence, which is not uncommon to impose. Experiments are conducted using synthetic data, and the results demonstrate a robust method for determining the relative parameters comparable to previous work.
Large scale image retrieval with DCNN and local geometrical constraint model

Image retrieval, which refers to browse, search and retrieve the images of the same scene or object from a large database of digital images, has attracted increasing interests in recent years. This paper proposes a coarse-to-fine method for fast indexing with Deep Convolutional Neural Network(DCNN) and Local Geometrical Constraint Model. We first use a vector quantized DCNN feature descriptors and exploit enhanced Locality-sensitive hashing(LSH) techniques for fast coarse-grained retrieval. Then, we focus on obtaining high-precision preserved matches for fine-grained retrieval. This is formulated as a maximum likelihood estimation of a Bayesian model with latent variables indicating whether matches in the putative set are inliers or outliers. We impose the non-parametric global geometrical constraints on the correspondence using Tikhonov regularizers in a reproducing kernel Hilbert space. To ensure the well-posedness of the problem, we develop a local geometrical constraint that can preserve local structures among neighboring feature points, and it is also robust to a large number of outliers. The problem is solved by using the Expectation Maximization algorithm. Extensive experiments on real near-duplicate images for both feature matching and image retrieval demonstrate that the results of the proposed method outperform current state-of-the-art methods.
Dynamic Object Indexing Technique for Distortionless Video Synopsis

With a development of observation cameras, the measure of caught recordings extends. Physically dissecting and recovering reconnaissance video is work concentrated and costly. It is substantially more important to create a video description and the video can be observed in a good manner. So, here we describe a novel video outline way to deal with produce consolidated video, which utilizes a protest following technique for extracting imperative items. This strategy will create video objects and a crease cutting technique to gather the first video. Finally, output results that our proposed strategy can accomplish a high buildup rate while safeguarding all the imperative objects of intrigue. Hence, in this method, we can empower clients to see the synopsis video with high impact.
An ASIC Crypto Processor for 254-Bit Prime-Field Pairing Featuring Programmable Arithmetic Core Optimized for Quadratic Extension Field

An ASIC crypto processor optimized for the 254-bit prime-field optimal-ate pairing over Barreto-Naehrig (BN) curve is proposed. The data path of the proposed crypto processor is designed to compute five Fp2 operations, a multiplication, three addition/subtractions, and an inversion, simultaneously. We further propose a design methodology to automate the instruction scheduling by using a combinatorial optimization solver, with which the total cycle count is reduced to 1/2 compared with ever reported. The proposed crypto processor is designed and fabricated by using a 65nm silicon-on-thin-box (SOTB) CMOS process. The chip measurement result shows that the fabricated chip successfully computes a pairing in 0.185ms when a typical operating voltage of 1.20V is applied, which corresponds to 2.8× speed up compared to the current state-of-the-art pairing implementation on ASIC platform.
Guaranteed Bang for the Buck: Modeling VDI Applications to Identify Storage Requirements

In the cloud environment, most services are provided by virtual machines (VMs). Identifying storage requirements of VMs is challenging, but it is essential for good user experiences while optimizing use of storage resources. Determining the storage configuration necessary to support and satisfy VMs first requires an accurate description of the VM configurations, and the problem is further exacerbated by the diversity and special characteristics of the VMs. In this paper, we study Virtual Desktop Infrastructure (VDI), a prevalent and complicated VM application, to identify and characterize storage requirements of VMs and determine how to meet such requirements with minimal storage resources and cost. We first create a model to describe the behavior of VDI, and we collect real VDI traces to populate this model. The model allows us to identify the storage requirements of VDI and determine the potential bottlenecks of a given storage configuration. Based on this information, we can tell what capacity and minimum capability a storage configuration needs in order to support and satisfy given VDI configurations. We show that our model can describe more fine-grained VM behavior varying with time and virtual disk types compared with the rules of thumb currently used in industry.
Conditional states and entropy in qudit-qubit systems

We examine, in correlated mixed states of qudit-qubit systems, the set of all conditional qubit states that can be reached after local measurements at the qudit based on rank-1 projectors. While for a similar measurement at the qubit, the conditional post-measurement qudit states lie on the surface of an ellipsoid, for a measurement at the qudit we show that the set of post-measurement qubit states can form more complex solid regions. In particular, we show the emergence, for some classes of mixed states, of sets which are the convex hull of solid ellipsoids and which may lead to cone-like and triangle-like shapes in limit cases. We also analyze the associated measurement dependent conditional entropy, providing a full analytic determination of its minimum and of the minimizing local measurement at the qudit for the previous states. Separable rank-2 mixtures are also discussed.
Automatic Detection and Tracking of Plumes from 67P/Churyumov–Gerasimenko in <i>Rosetta</i>/OSIRIS Image Sequences

Abstract Solar system bodies such as comets and asteroids are known to eject material from their surface in the form of jets and plumes. Observations of these transient outbursts can offer insight into the inner workings and makeup of their originating body. However, the detection of and response to these events has thus far been manually controlled by ground operations, limiting the response time, due to the light time delay of ground communications. For distant bodies, the delay can exceed the duration of temporary events, making it impossible to respond with follow-up observations. To address this need, we developed a computer vision methodology for detecting plumes of the comet 67P/Churyumov–Gerasimenko from imagery acquired by the OSIRIS scientific camera system. While methods exist for the automatic detection of plumes on spherical and near-convex solar system bodies, this is the first work that addresses the case of highly irregularly shaped bodies such as 67P/Churyumov–Gerasimenko. Our work is divided into two distinct components: an image processing pipeline that refines a model-based estimate of the nucleus body, and an iterative plume detection algorithm that finds regions of local intensity maxima and joins plume segments across successively higher altitudes. Finally, we validate this method by comparing automatically labeled images to those labeled by hand, and find no significant differences in variability. This technique has utility in both ground-based analysis of plume sequences as well as onboard applications, such as isolating short sequences of high activity for priority downloading or triggering follow-up observations with additional instruments.
Differential Coding-Based Medical Image Compression

Modern trends of technology face the challenges of cost-effective massive data storage and transmission. Image compression is a master key for this issue. Basically, the process of image compression reduces redundant and irrelevant information from the original data resulting in reduced data file size. Vector quantization is a lossy image compression technique which helps to achieve higher compression with less computation complexity. The aim of the proposed work is to develop a novel medical image compression method that blends differential encoding and vector quantization (VQ). The basic idea is to transform the input image blocks into a set of difference vectors (difference between the each pixel intensity value and its respective mean). The difference vectors are normalized to preserve the sign and further quantized to generate the codebook. The algorithm is also investigated with other statistical moments like median and mode for finding the difference vectors. The experimental results with test medical images have demonstrated better performance of the proposed method when compared to similar methods.
Anatomical Segmentation of Human Brain MRI Using Morphological Masks

Segmenting the anatomical parts of the human brain from MRI is a challenging task in medical image analysis. There is no evident scale for the distribution of intensity over a region in medical images. Region growing is performed to generate a mask to segment the anatomical parts from MRI. A new tailored version of dilation is used in renovating the segmentation mask. This custom-made dilation differs from typical dilation in computation. The structuring element size is fixed, and the anchor point norm is changed from usual dilation. The neighborhood evaluation is made only for certain pixels that are satisfied by the proposed constraints; thus, estimation is not made throughout the image. The computation of the classical dilation is reduced with the proposed custom-made dilation.
Bio-smart Materials for Product Design Innovation: Going Through Qualities and Applications

Based on the principles of emerging bio-smart materials, in the framework of design-driven material innovation approach and cross-disciplinary research practices, the aim of this paper is to make clear how these materials are creating new opportunities to realize answers to the complex needs of contemporary society, while defining bio-smart materials main qualities, and questioning the implications on research design practices. We present a review of case studies of bio-smart materials applications in order to demonstrate that their diffusion is underway, especially in some application sector characterized by more complexity and a higher propensity for the use of biotechnological innovations. The examples described, are framed in three specific areas: biomedical design, sports design, and design for the environment that are particularly interested of the development and application of novel materials for both their performances and sustainability.
Noise bias compensation for tone mapped noisy image using prior knowledge

A large number of studies have been made on denoising of a digital noisy image. In regression filters, a convolution kernel was determined based on the spatial distance or the photometric distance. In non-local mean (NLM) filters, pixel-wise calculation of the distance was replaced with patch-wise one. Later on, NLM filters have been developed to be adaptive to the local statistics of an image with introduction of the prior knowledge in a Bayesian framework. Unlike those existing approaches, we introduce the prior knowledge, not on the local patch in NLM filters but, on the noise bias (NB) which has not been utilized so far. Although the mean of noise is assumed to be zero before tone mapping (TM), it becomes non-zero value after TM due to the non-linearity of TM. Utilizing this fact, we propose a new denoising method for a tone mapped noisy image. In this method, pixels in the noisy image are classified into several subsets according to the observed pixel value, and the pixel values in each subset are compensated based on the prior knowledge so that NB of the subset becomes close to zero. As a result of experiments, effectiveness of the proposed method is confirmed.
Control Design and Visual Autonomous Navigation of Quadrotor

In this paper, an autonomous navigation and obstacle avoidance system based on monocular camera has been designed and implemented which enables the quadrotor to navigate in a previously unknown GPS-denied environment. Moreover, four controllers have been designed, simulated and their performance were compared. The various control strategies are LQR, PID, Feedback Linearization with pole placement and Sliding Mode control.Sensor data and the camera video stream have been used by a Key-frame visual simultaneous localization and mapping (SLAM) system to compute the location of the drone and generate the 3D map of the environment in the form of point cloud. This point cloud data is clustered and used for obstacle detection. Moreover a probabilistic roadmap (PRM) algorithm has been used to generate a collision-free path that will be followed by the drone based on the chosen controller.We implemented our approach on a Parrot ARDrone2.0, and the theoretical results have been validated with experiments. All computations are performed on a ground station, which is connected to the drone via wireless LAN.
Detection of Gaze Direction for Human–Computer Interaction

Eye guide is an assistive specialized apparatus intended for the incapacitated or physically disabled individuals who were not able to move parts of their body, especially people whose communications are limited only to eye movements. The prototype consists of a camera and a computer. The system recognizes gazes in four directions and performs required user actions in related directions. The detected eye direction can then be used to control the applications. The facial regions which form the images are extracted using the skin color model and connected-component analysis. When the eye regions are detected, the tracking is performed. The system models consist of image processing, face detector, face tracker, and eyeblink detection. The eye guide system potentially helps as a computer input control device for the disabled people with severe paralysis.
Adaptation of Assistant Based Speech Recognition to New Domains and Its Acceptance by Air Traffic Controllers

In air traffic control rooms, paper flight strips are more and more replaced by digital solutions. The digital systems, however, increase the workload for air traffic controllers: For instance, each voice-command must be manually inserted into the system by the controller. Recently the AcListant® project has validated that Assistant Based Speech Recognition (ABSR) can replace the manual inputs by automatically recognized voice commands. Adaptation of ABSR to different environments, however, has shown to be expensive. The Horizon 2020 funded project MALORCA (MAchine Learning Of Speech Recognition Models for Controller Assistance), proposed a more effective adaptation solution integrating a machine learning framework. As a first showcase, ABSR was automatically adapted with radar data and voice recordings for Prague and Vienna. The system reaches command recognition error rates of 0.6% (Prague) resp. 3.2% (Vienna). This paper describes the feedback trials with controllers from Vienna and Prague.
Characterization of Yubileyny Field Ore Potential Based on New Structure-Substance Information

Spatial-genetic relation of a polycomponent ore mineralization (Au, Ag, PGE, Cu, Pb, Zn, Ni) with dynamometamorphic complex, formed by the picrite-komatiite-tholeiite series rocks of Kelyana-Irokinda greenstone belt, is identified.Productivity of various ore mineralization morphostructural types (quartz-vein, zones of veining and sulfide dissemination) differing in mineral composition and content of useful components is considered.It is shown that the main industrial value is represented by veined and disseminated ores with relatively poor useful components content, but at the same time forming large-capacity ore zones.The occurrence extent, localization conditions and ore zones hyperthetical resources are favorable for the production of economically cost-effective open pit (quarry) mining operations.An effective sampling method is recommended to carry out the prospect and exploration of large-capacity deposits on the Yubileyny field and other similar to its Transbaikalia ore objects.
Visualizing Graph Differences from Social Media Streams

We propose KGdiff, a new interactive visualization tool for social media content focusing on entities and relationships. The core component is a layout algorithm that highlights the differences between two graphs. We apply this algorithm on knowledge graphs consisting of named entities and their relations extracted from text streams over different time periods. The visualization system provides additional information such as the volume and frequency ranking of entities and allows users to select which parts of the graph to visualize interactively. On Twitter and news article collections, KGdiff allows users to compare different data subsets. Results of such comparisons often reveal topical or geographical changes in a discussion. More broadly, graph differences are useful for a wide range of relational data comparison tasks, such as comparing social interaction graphs, identifying changes in user behavior, or discovering differences in graphs from distinct sources, geography, or political stance.
[Papers] Unsupervised Multi-class Object Discovery by Spherical Clustering of Deep Features

In this paper, we propose a novel method for multi-class co-localization; it offers unsupervised localization of the main object in each image from an image set containing multiple kinds of main objects. Our method utilizes deep features to tackle the co-localization problem. Deep features, which can be extracted by pre-trained neural networks, are effective against unsupervised co-localization from multi-class image set. Based on spherical clustering, we classify deep features into several clusters, and choose one dominant cluster for each image or each image set. Experiments show that this very simple approach is significantly better than conventional state-of-the-art techniques in terms of localization accuracy. Moreover, multi-class co-localization experiments show that our method has the potential to classify the object in each image at the same time as achieving localization.
A Survey of Approaches for Promoting Honest Recommendations in Reputation Systems

The efficiency of the reputation mechanism fully depends on the number of received recommendations and the quality of each of them, but a peer may not be willing to provide honest recommendations actively in order to pursue its own interest. To address this problem, a number of schemes have been proposed. It is therefore necessary to give an overview of the representative schemes. In this paper, we present a comprehensive discussion on approaches for promoting honest recommendations in reputation systems. We first classify the existing schemes into two categories: protecting the privacy of recommenders and providing incentive to recommenders. The latter can then be sub-divided into two categories: market-based incentive schemes and policy-based incentive schemes. We then survey some representative schemes in the literature belonging to each category, and summarize their unique characteristics and working principles. Moreover, some open problems in each category are also discussed.
Algorithm for Base Action Set Generation Focusing on Undiscovered Sensor Values

Previous machine learning algorithms use a given base action set designed by hand or enable locomotion for a complicated task through trial and error processes with a sophisticated reward function. These generated actions are designed for a specific task, which makes it difficult to apply them to other tasks. This paper proposes an algorithm to obtain a base action set that does not depend on specific tasks and that is usable universally. The proposed algorithm enables as much interoperability among multiple tasks and machine learning methods as possible. A base action set that effectively changes the external environment was chosen as a candidate. The algorithm obtains this base action set on the basis of the hypothesis that an action to effectively change the external environment can be found by observing events to find undiscovered sensor values. The process of obtaining a base action set was validated through a simulation experiment with a differential wheeled robot.