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Surveillance video parsing, which segments the video frames into several labels, e.g., face, pants, left-leg, has wide applications [41, 8]. However, pixel-wisely annotating all frames is tedious and inefficient. In this paper, we develop a Single frame Video Parsing (SVP) method which requires only one labeled frame per video in training stage. To parse one particular frame, the video segment preceding...
Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but extensive literature on the subject indicates the difficulty in identifying a prior which is suitably informative, and general. Rather than imposing a prior based on theory, we propose instead to learn one from the data...
One of the key technologies to take full advantage of wind power is to establish a wind turbine (WT) generator output estimation system with high accuracy. The static feed forward artificial neural network is widely used in previous WT generator output estimation technology. However, this method has many problems such as local minimization, a lack of dynamics, edge effect, and multi-correlation. To...
Learning based methods have shown very promising results for the task of depth estimation in single images. However, most existing approaches treat depth prediction as a supervised regression problem and as a result, require vast quantities of corresponding ground truth depth data for training. Just recording quality depth data in a range of environments is a challenging problem. In this paper, we...
Human age is considered an important biometric trait for human identification or search. Recent research shows that the aging features deeply learned from large-scale data lead to significant performance improvement on facial image-based age estimation. However, age-related ordinal information is totally ignored in these approaches. In this paper, we propose a novel Convolutional Neural Network (CNN)-based...
We present a descriptor, called fully convolutional self-similarity (FCSS), for dense semantic correspondence. To robustly match points among different instances within the same object class, we formulate FCSS using local self-similarity (LSS) within a fully convolutional network. In contrast to existing CNN-based descriptors, FCSS is inherently insensitive to intra-class appearance variations because...
Spaceborne SAR interferometry (InSAR) has the potential of mapping the forest height on a global scale and a monthly/weekly basis, which can improve our understanding of the global carbon dynamics. In previous work, repeat-pass SAR interferometry from spaceborne sensors is utilized to create large-scale forest height maps based on a newly developed approach. This paper thus serves as a summary paper...
Area Sampling Frames are used for surveys including crop acreage and yield, forests, and natural resource inventories and are the foundation of the statistical program of the USDA National Agricultural Statistics Service (NASS) and many statistical survey programs around the world. An automated area frame stratification method was recently implemented into NASS operations, which is based on the objective...
There are 700,000 Rheumatoid Arthritis (RA) patients in Japan, and the number of patients is increased by 30,000 annually. The early detection and appropriate treatment according to the progression of RA are effective to improve the patient's prognosis. The modified Total Sharp (mTS) score is widely used for the progression evaluation of Rheumatoid Arthritis. The mTS score assessments on hand or foot...
The automatic generation of semantic maps from remotely sensed imagery by supervised classifiers has seen much effort in the last decades. The major focus has been on the improvement of the interplay between feature operators and classifiers, while experimental design and test data generation has been mostly neglected. This paper shows that sampling strategies applied to partition the available reference...
We aim to jointly estimate height and semantically label monocular aerial images. These two tasks are traditionally addressed separately in remote sensing, despite their strong correlation. Therefore, a model learning both height and classes jointly seems advantageous and so, we propose a multitask Convolutional Neural Network (CNN) architecture with two losses: one performing semantic labeling, and...
We propose a fully convolutional neural network (FCNN) model for ice concentration estimation from dual-polarized SAR images. Our network contains 5 convolutional layers. Tested in the Gulf of Saint Lawrence during freeze-up, the proposed model is demonstrated to generate improved ice concentration estimates compared to a CNNs with similar structure.
In this work, we consider the problem of detecting target objects in remote sensing imagery; such as detecting rooftops, trees, or cars in color/hyperspectral imagery. Many detection algorithms for this problem work by assigning a decision statistic (or “confidence”) to all, or a subset, of spatial locations in the data. A threshold is then applied to the statistics to identify detections. The detection...
In this paper, we propose a single image based high dynamic range (HDR) imaging method. In the method, the inverse camera response function (CRF) is modeled according to the empirical model of CRF firstly; then the optimal inverse CRF is resolved and multiplied by a weighting function to make it smooth in the areas near the maximum and minimum pixel values; finally, the HDR image is generated by conducting...
Sheet music has long been regarded as one of the most effective medias for musicians, music players, and amateurs to communicate with each other. It is also an intuitive way for non-professionals to learn how to play a musical instrument or sing a song. However, not all composers have willingness to share their own sheet music, especially those protected by strict copyright regulations. For amateurs...
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,...
Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computer vision, only takes the full face image as input. Our method encodes the face image using a convolutional...
Bellwether effect refers to the existence of exemplary projects (called the Bellwether) within a historical dataset to be used for improved prediction performance. Recent studies have shown an implicit assumption of using recently completed projects (referred to as moving window) for improved prediction accuracy. In this paper, we investigate the Bellwether effect on software effort estimation accuracy...
This paper presents a novel nonlinear adaptive filter method, namely, Hammerstein adaptive filter with single feedback under minimum mean square error (HAF-SF-MMSE). A single delayed output is incorporated into the estimation of the current output based on minimum mean square error criterion, and therefore the history information of output is considered. Moreover, hybrid learning rates and adaptive...
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