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Underwater image formation is degraded by several factors, which causes the ocean to be a challenging environment for image processing. This paper aims to improve the visual servoing capability of an autonomous underwater vehicle by using pre-processing algorithms to improve the image quality. We used artificial fiducial markers to feed the visual controller. Therefore, three different methods for...
There is a possibility that vast amounts of undersea resources are buried beneath Japanese territorial sea. In order to find these undersea resources, a detailed topography including specific objects and events of seabed should be carefully surveyed. One of effective methods for this is to use seabed visualization technologies, which are applied to an autonomous underwater vehicle (AUV). Therefore,...
Text is the easiest means to record information but need not always be the best means for understanding a concept. In psychological theories, it is argued that when information is presented visually, it provides a better means to understand a concept. While techniques exist for generating text from a given image, the inverse problem that is to automatically fetch coherent images to represent a given...
The ability to ask questions is a powerful tool to gather information in order to learn about the world and resolve ambiguities. In this paper, we explore a novel problem of generating discriminative questions to help disambiguate visual instances. Our work can be seen as a complement and new extension to the rich research studies on image captioning and question answering. We introduce the first...
This paper is devoted to investigation of features that will be the most appropriate for description of high resolution satellite imagery. We developed an image description model which is based on the distribution of image object classes. Proposed model could be used for image similarity estimation.
Deep convolutional neural networks (CNNs) have been successfully applied to a wide variety of problems in computer vision, including salient object detection. To detect and segment salient objects accurately, it is necessary to extract and combine high-level semantic features with low-levelfine details simultaneously. This happens to be a challenge for CNNs as repeated subsampling operations such...
This paper presents a new method for the reconstruction of images from samples located at non-integer mesh positions. This is a common scenario for many image processing applications such as multi-image super-resolution, frame-rate up-conversion, or virtual view synthesis in multi-camera systems. The proposed method consists of an iterative procedure that employs adaptive denoising in order to reduce...
Adapting an educational environment to students considering its features and individuals is a necessity due to the large amount of learning objects in the repositories. Thus, organizing learning objects so that they can be efficiently recommended is a real need. In this way, this work presents a proposal for clustering learning objects in repositories considering the learning styles they support,...
Fully convolutional network (FCN) has been successfully applied in semantic segmentation of scenes represented with RGB images. Images augmented with depth channel provide more understanding of the geometric information of the scene in the image. The question is how to best exploit this additional information to improve the segmentation performance.,,In this paper, we present a neural network with...
The quality control of cherries harvested in the orchard is a process of great relevance for the Chilean export industry. Nowadays companies carry out this process manually, obtaining a high error rate in the measurements of color and caliber of the fruits. This article seeks to develop a system to automate this process and thus reduce measurement failures. For this, an information system was implemented...
Nowadays environmental science experiences tremendous growth of raster data: N-dimensional (N-d) arrays coming mainly from numeric simulation and Earth remote sensing. An array DBMS is a tool to streamline raster data processing. However, raster data are usually stored in files, not in databases. Moreover, numerous command line tools exist for processing raster files. This paper describes a distributed...
Automatic image cropping techniques have been developed recently to address the mismatch between the native display and image characteristics, such as resolution, aspect ratio, etc. These techniques usually rely on determining the importance of various regions in the image, or the aesthetic appeal of the final cropped image. In this work, we present a cropping method that combines bottom-up visual...
In this paper, we propose a post classification smoothing method aimed at improving the accuracy and visual appearance of sub-decimeter image classification results. Starting from the class confidence maps of a supervised classifier, we find a set of high confidence markers and propagate labels on an extended region adjacency graph. We apply the proposed method on a challenging 5cm resolution dataset...
Classification of SAR images is a challenging task as the radiometric properties of a class may not be constant throughout the image. The assumption made in most classification algorithms that a class can be modeled by constant parameters is then not valid. In this paper, we propose a classification algorithm based on two Markov random fields that accounts for local and global variations of the parameters...
Benchmark databases with subjective human opinions on image quality and rate-distortion trade-offs are vital to the development of Image Quality Assessment (IQA) models. Existing databases are typically built on and for PC platform. We present EyeQ, the first IQA benchmark1 that provides crowd-sourced subjective opinions on rate-distortion trade-offs of compressed images on both mobile and the PC...
State-of-the-art super-resolution (SR) algorithms require significant computational resources to achieve real-time throughput (e.g., 60Mpixels/s for HD video). This paper introduces FAST (Free Adaptive Super-resolution via Transfer), a framework to accelerate any SR algorithm applied to compressed videos. FAST exploits the temporal correlation between adjacent frames such that SR is only applied to...
Pedestrian detection, as an important task in video surveillance and forensics applications, has been widely studied. However, its performance is unsatisfactory especially in the low resolution conditions. In realistic scenarios, the size of pedestrians in the images is often small, and detection can be challenging. To solve this problem, this paper proposes a novel resolution-score discriminative...
In this paper, we propose a deep CNN to tackle the image restoration problem by learning the structured residual. Previous deep learning based methods directly learn the mapping from corrupted images to clean images, and may suffer from the gradient exploding/vanishing problems of deep neural networks. We propose to address the image restoration problem by learning the structured details and recovering...
In the era of constantly increasing Earth Observation (EO) data collections, information extraction and data analysis should be enhanced with a multi-temporal component enabled by the temporal resolution of satellite missions and create handy, yet powerful tools for those applications involving monitoring of land cover. The image time series, as results of the satellite revisiting period, gives you...
Optical imaging is unequivocally the most versatile and widely used visualization modality in the life sciences. Yet it has been significantly limited by photon scattering, which complicates imaging beyond a few hundred microns. For the past few years, there has been an emergence of powerful new optical and optoacoustic imaging methods that can offer high resolution imaging beyond the penetration...
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