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This paper propose an autonomous landing method for unmanned aerial vehicles (UAVs), aiming to address those situations in which the landing pad is the deck of a ship. Fiducial marker are used to obtain the six-degrees of freedom (DOF) relative-pose of the UAV to the landing pad. In order to compensate interruptions of the video stream, an extended Kalman filter (EKF) is used to estimate the ship's...
The elastic properties of human tissue can be evaluated through the study of mechanical wave propagation using highframe rate imaging and motion estimation. Methods such as block-matching or phase-based motion estimation are usually used to estimate the motion induced by the mechanical wave. These methods can be time consuming because of the processing involved and the need for specific types of averaging...
Reliable localization is one of the most important parts of an MAV system. Localization in an indoor GPS-denied environment is a relatively difficult problem. Current vision based algorithms track optical features to calculate odometry. We present a novel localization method which can be applied in an environment having orthogonal sets of equally spaced lines to form a grid. With the help of a monocular...
In this paper, the problem of age estimation is addressed based on two modalities: speech utterances and speakers' face images. The proposed age estimation framework employs the Shifted Covariates REgression Analysis for Multi-way data (SCREAM) model, which combines Parallel Factor Analysis 2 and Principal Covariates Regression. SCREAM is able to extract a few latent variables from multi-way data...
In this paper, we present an impression estimation method for television commercials with a visualization method. Our method estimates the impressions viewers might have of a new proposal for a TV commercial written in text as weighted favorable factors and visualizes the estimated favorable factors. During the production of TV commercials, it is important to create commercials that clearly communicate...
Image quality assessment (IQA) plays a crucial role in monitoring quality control in image communication systems, and in benchmarking and optimizing parameters in enhancement algorithms. The full-reference IQA metrics require a good-quality reference image, obtaining which may not be practical in real-life applications. This paper, therefore, proposes a no-reference IQA metric based on the hypothesis...
Recently, visual inertial has became popular due to its excellent result. However, the excellent result severely depends on the accuracy of estimation of initial parameters. The existing method is not effective on estimating the initial parameters and lacks the function to perform the closed loop detection, which will cause the error accumulation and low accurate estimation to system's state. In the...
Attribute-based recognition models, due to their impressive performance and their ability to generalize well on novel categories, have been widely adopted for many computer vision applications. However, usually both the attribute vocabulary and the class-attribute associations have to be provided manually by domain experts or large number of annotators. This is very costly and not necessarily optimal...
When a car moves on the road, the driver must monitor the driving environment whenever there is a takeover request (TOR), even if automated driving is activated. We examined ways to determine the driver's visual attention area in order to judge whether or not the driver has confirmed safety. We observed the gaze of the subject during driving, tracking not only the head, but also the eye. Considering...
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...
This paper focuses on the spectral unmixing technique for analyzing hyperspectral image (HSI). In this paper, we first prove that the reconstruction errors and the abundance anomalies (AAs, abundances that are negative or greater than one) are effective in measuring the purity of pixels. Then, due to the continuity of the objects in the space, the endmembers are assumed to be located at some noticeable...
This work is focused on the task of multimodal saliency detection. Very few works have been developed in the field, and there are no well-established baselines or benchmarks comparable to those existing in the field of visual saliency detection. In this paper, we set out to improve an existing model by enhancing the performance of its key module: the audio-visual correlation estimation based on the...
In this paper, we present a novel approach to estimate the relative depth of regions in monocular images. There are several contributions. First, the task of monocular depth estimation is considered as a learning-to-rank problem which offers several advantages compared to regression approaches. Second, monocular depth clues of human perception are modeled in a systematic manner. Third, we show that...
Accurate human body orientation estimation (HBOE) can significantly promote the analysis of human behavior. However, conventional methods cannot holistically exploit the complementary nature of spatial and temporal information for H-BOE. Different from existing methods, we propose an end-to-end temporal-spatial deep learning framework to accurately estimate the human body orientation. In this framework,...
Reliable occluded skeletal posture estimation is a fundamentally challenging problem for vision-based monitoring techniques. This is due to several imaging related challenges introduced by existing depth-based pose estimation techniques that fail to provide accurate joint position estimates when the line of sight between the imaging device and the patient is obscured by an occluding material. In this...
Visual attention is a dynamic search process of acquiring information. However, most previous studies have focused on the prediction of static attended locations. Without considering the temporal relationship of fixations, these models usually cannot explain the dynamic saccadic behavior well. In this paper, an iterative representation learning framework is proposed to predict the saccadic scanpath...
In this paper we utilize the first large-scale "in-the-wild" (Aff-Wild) database, which is annotated in terms of the valence-arousal dimensions, to train and test an end-to-end deep neural architecture for the estimation of continuous emotion dimensions based on visual cues. The proposed architecture is based on jointly training convolutional (CNN) and recurrent neural network (RNN) layers,...
This paper presents a framework for saliency estimation and fixation prediction in videos. The proposed framework is based on a hierarchical feature representation obtained by stacking convolutional layers of independent subspace analysis (ISA) filters. The feature learning is thus unsupervised and independent of the task. To compute the saliency, we then employ a multiresolution saliency architecture...
An important concern for current IaaS cloud providers is to be falsely accused by cloud users for the response delays of their applications running on the cloud. Since cloud computing brings an additional virtualization layer between guest-OS and hardware resources, the relationship between the performance of an application and its resource consumption becomes obscure. Only monitoring resource consumption...
Animation and game are very popular in Japan. Many works in animation and game have been released, and many characters have been produced. It is an important factor to select appropriate voice actors so that we can appropriately impress the characters. Based on this idea, we are developing a prototype of voice actor recommendation tool. We recorded voice performance from portable games and calculated...
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