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In this paper, we study how to construct a planar cubic G1 Hermite interpolation curve with minimal length and curvature variation energy. This problem is solved by a bi-objective optimization model. We prove that there is a unique solution to this problem and give the expression of the solution. Some numerical examples show that the proposed method makes the curvature variation energy of the curve as small as possible when the length of the curve as short as possible. [ABSTRACT FROM AUTHOR]

The large scale of surveillance video and the high requirement of compression in time requires a low complexity and high efficiency compression algorithm to compress surveillance video. Motion search is a very time-consuming procedure in video coding. In the recent video coding standards such as HEVC/H.265, this procedure becomes more flexible by utilizing the division structure of Coding Units (CUs) and Predicting Units (PUs). However, for surveillance videos that are often captured by fixed-view cameras, the used motion search strategy still does not make full use of their intrinsic characteristics. To address this problem, we propose a PU-Adaptive Search (PA-Search) method for surveillance videos. In PA-Search, a background model is firstly constructed for a super group of pictures and then a background-foreground representation (BFR) is derived for each frame in this group. Utilizing the BFR, PUs are classified into four categories, namely, Full Background PUs (FBPUs), Background PUs (BPUs), Foreground PUs (FPUs), and hybrid foreground-background PUs (XPUs). In PA-Search, zero motion vector (zero-MV) and non-sub-pixel search are assigned to FBPUs and an error-tolerant search algorithm is also performed to reduce the influence of PU mis-classifications; while for non-FBPUs, adaptive search range is calculated according to the PU category and its size, and a BFR-based early-termination algorithm is also used to reduce the search complexity. Moreover, an early terminate partition algorithm is adopted by Full Background CUs to further reduce the encoding time. Experimental results demonstrate the advantage of the proposed PA-Search on HEVC reference software HM-16.0. PA-Search can reduce the number of search points and the total encoding time averagely by 66.90% and 46.69% over TZ Search, while maintaining the coding efficiency. [ABSTRACT FROM AUTHOR]

Generating ground truth to design object detectors for large geographic areas covered by hundreds of satellite images poses two major challenges: one algorithmic and the other rooted in human–computer interaction considerations. The algorithmic challenge relates to minimizing the human annotation burden by collecting only those ground truth samples that are likely to improve the classifier. And the human–computer interaction challenge relates to the temporal latencies associated with scanning all the images to find those ground truth samples and eliciting annotations from a user. We address the algorithmic challenge by using the now well-known concepts from Active Learning, albeit with a significant departure from how Active Learning has traditionally been presented in the literature: we present a human-operated active learning framework, rather than relying on previously collected fully labeled datasets for simulated experiments. And, we address the human-computer interaction challenge by using a distributed approach that relies on multiple virtual machines working in parallel to carry out randomized scans in different portions of the geographic area in order to generate the active-learning based samples for human annotation. We demonstrate our wide-area framework for two infrequently occurring objects over large geographic areas in Australia. One is for detecting pedestrian crosswalks in a region that spans 180,000 sq. km, and the other is for detecting power transmission-line towers in a region that spans 150,000 sq. km. Using randomly selected unseen regions for measuring detector performance, the crosswalk detector works with 92% precision and 72% recall, and the transmission-line tower detector with 80% precision and 50% recall. [ABSTRACT FROM AUTHOR]

In this paper, we present a new algebraic method to solve the perspective-three-point (P3P) problem, which directly computes the rotation and position of a camera in the world frame without the intermediate derivation of the points in the camera frame. Unlike other online methods, the proposed method uses an “object” coordinate frame, in which the known 3D coordinates are sparse, facilitating formulations of the P3P problem. Additionally, two auxiliary variables are introduced to parameterize the rotation and position matrix, and then a closed-form solution for the camera pose is obtained from subsequent substitutions. This algebraic approach makes the processes more easily followed and significantly improves the performance. Experimental results demonstrated that our method offers accuracy and precision comparable to the existing state-of-the art methods but it has better computational efficiency. [ABSTRACT FROM AUTHOR]

More than a decade ago, the point pair features (PPFs) were introduced, showing a great potential for 3D object detection and pose estimation under very different conditions. Many modifications have been made to the original PPF, in each case showing varying degrees of improvement for specific datasets. However, to the best of our knowledge, no comprehensive evaluation of these features has been made. In this work, we evaluate PPFs on a large set of 3D scenes. We not only compare PPFs to local point cloud descriptors, but also investigate the internal variations of PPFs (different types of relations between two points). Our comparison is made on 7 publicly available datasets, showing variations on a number of parameters, e.g. acquisition technique, the number of objects/scenes and the amount of occlusion and clutter. We evaluate feature performance both at a point-wise object-scene correspondence level and for overall object detection and pose estimation in a RANSAC pipeline. Additionally, we also present object detection and pose estimation results for the original, voting based, PPF algorithm. Our results show that in general PPF is the top performer, however, there are datasets, which have low resolution data, where local histogram features show a higher performance than PPFs. We also found that PPFs compared to most local histogram features degrade faster under disturbances such as occlusion and clutter, however, PPFs still remain more descriptive on an absolute scale. The main contribution of this paper is a detailed analysis of PPFs, which highlights under which conditions PPFs perform particularly well as well as its main weaknesses. [ABSTRACT FROM AUTHOR]

In this paper two non-stationary forms of Chaikin’s perturbation subdivision scheme, mentioned in Dyn et al. (2004), have been proposed with tension parameter ω . Comparison among the proposed subdivision schemes and the existing non-stationary subdivision scheme depicts that the trigonometric form is more efficient in the reproduction of circles and ellipses and the hyperbolic form is more suitable for the construction of many analytical curves. [ABSTRACT FROM AUTHOR]

Although appearance-based trackers have been greatly improved in the last decade, they still struggle with challenges that are not fully resolved. Of these challenges, occlusions, which can be long lasting and of a wide variety, are often ignored or only partly addressed due to the difficulty in their treatments. To address this problem, in this study, we propose an occlusion-aware particle filter framework that employs a probabilistic model with a latent variable representing an occlusion flag. The proposed framework prevents losing the target by prediction of emerging occlusions, updates the target template by shifting relevant information, expands the search area for an occluded target, and grants quick recovery of the target after occlusion. Furthermore, the algorithm employs multiple features from the color and depth domains to achieve robustness against illumination changes and clutter, so that the probabilistic framework accommodates the fusion of those features. This method was applied to the Princeton RGBD Tracking Dataset, and the performance of our method with different sets of features was compared with those of the state-of-the-art trackers. The results revealed that our method outperformed the existing RGB and RGBD trackers by successfully dealing with different types of occlusions. [ABSTRACT FROM AUTHOR]

NASA MSFCs airborne Hurricane Imaging Radiometer (HIRAD) uses interferometric aperture synthesis to produce high resolution wide swath images of scene brightness temperature (Tb) distribution at four discrete C-band microwave frequencies (4.0, 5.0, 6.0 and 6.6 GHz). Images of ocean surface wind speed under heavy precipitation such as in tropical cyclones, is inferred from these measurements. The baseline HIRAD Tb reconstruction algorithm had produced prominent along-track streaks in the Tb images. Particularly the 4.0 GHz channel had been so dominated by the streaks as to be unusable.The loss of a frequency channel had compromised the final wind speed retrievals. During 2016, the HIRAD team made substantial progress in developing a quality controlled signal processing technique for the HIRAD data collected in 2015’s Tropical Cyclone Intensity (TCI) experiment and reduced the effect of streaks in all channels including 4.0 GHz. [ABSTRACT FROM AUTHOR]

• Coarse-to-fine strategies are combined with attention mechanisms to ensure the effectiveness of attention maps. • SAMs are introduced to model the long-range dependencies across fundus vessels. • CSAUNet is constructed to precisely segment fundus vessels. • The CSAUNet can achieve state-of-the-art performance. Vessel segmentation contributes to precise diagnosis of fundus diseases. However, due to the existence of micro vessels and class imbalance, it remains a challenging task to accurately segment vessels from fundus images. To address this issue, a coarse-to-fine framework, namely cascade self-attention U-shaped network (CSAUNet), is proposed in this paper. Firstly, a self-attention U-shaped network (SAUNet) is pre-trained to roughly locate the fundus vessels. Afterwards, the pretrained SAUNet is cascaded with a residual self-attention UNet (Res-SAUNet), and the rough segmentation results of the pretrained SAUNet are regarded as the spatial attention maps to force the Res-SAUNet to focus on significant regions. In addition, self-attention modules (SAMs) are introduced in both the SAUNet and Res-SAUNet to effectively model the long-range dependencies across fundus vessels. The experimental results show that the accuracy, sensitivity, specificity and dice similarity coefficient (DSC) of CSAUNet on three public databases, including DRIVE, STARE and synthetic images (SI), reach 96.76%/83.4%/98.1%/82.07%, 97.28%/83.04%/98.62%/84% and 0.9915/0.9539/0.9962/0.9612, respectively. In addition, the coarse-to-fine framework can improve the DSC and sensitivity on DRIVE, STARE and SI by 1.24%/2.37%, 1.69%/1.04% and 0.8%/0.72%, respectively, and the incorporation of SAMs can further improve the DSC on DRIVE, STARE and SI by 0.37%, 0.7% and 0.49%, respectively, which indicates that CSAUNet can improve the segmentation performance for fundus vessels. [ABSTRACT FROM AUTHOR]

In this work we present first results of a laboratory manufactured filter-fluorometer to study differences in intensity and position of the main peaks of three porphyrins that appear during the Heme-Synthesis. Porphyrins play a major role in Photodynamic Therapy (PDT) for cancer treatment. Within the Heme-Synthesis, Porphyrins such as Protoporphyrin IX (PPIX) and its two precursors Coproporphyrin III (CPIII) and Uroporphyrin III (UPIII) represent photochemical agents that can interact with light to show fluorescence or generate Reactive Oxygen Species (ROS) to destroy cells. A major problem that arises is determining the ideal time slot to begin treatment after drug application. Our work is meant to show a way to solve this problem by looking at concentration changes of precursors appearing in Heme-Synthesis and using these changes to predict the occurence of PPIX inside the mitochondria. [ABSTRACT FROM AUTHOR]

The work is reached to study temperature transients in thin-film Hall sensors. We experimentally study InSb thin-film Hall sensor. We find transients of voltage with amplitude about 10 μ V on the sensor ports after current switching. We demonstrate by direct measurements that the transients is caused by thermo-e.m.f., and both non-stationarity and heterogeneity of temperature in the film. We find significant asymmetry of temperature field for different direction of the current, which is probably related to Peltier effect. The result can be useful for wide range of scientist who works with switching of high density currents in any thin semiconductor films. [ABSTRACT FROM AUTHOR]

This paper deals with experimental verification of reciprocal relations of nonlinear quadripole for unsteady mode in external magnetic field. We find out transients of measured voltages in the quadripole after current switch. These transients are caused by changing of current–voltage characteristics (CVC) of quadripole. We propose the reciprocal relations for linear part of full resistance matrix and its experimental verification method based on algorithm of separation of resistance matrix linear part. It is shown that the proposed reciprocal relations are valid with 10 −3 relatively accuracy even for non-stationary case in external magnetic field. [ABSTRACT FROM AUTHOR]