In recent years, there has been a growing demand for urban digitization using cameras and light detection and ranging (LiDAR). Shadows are a condition that affects measurement the most. Therefore, shadow detection technology is essential. In this study, we propose shadow detection utilizing the LiDAR intensity that depends on the surface properties of objects but not on irradiation from other light sources. Unlike conventional LiDAR-intensity-aided shadow detection methods, our method embeds the un-correlation between luminance and LiDAR intensity in each position into the optimization. The energy, which is defined by the un-correlation between luminance and LiDAR intensity in each position, is minimized by graph-cut segmentation to detect shadows. In evaluations on KITTI and Waymo datasets, our shadow-detection method outperformed the previous methods in terms of multiple evaluation indices.

We propose a non-photorealistic rendering method to automatically generate reaction-diffusion-pattern-like images from photographic images. The proposed method uses smoothing filter with a circular window, and changes the size of the circular window depending on the position in photographic images. By partially changing the size of the circular window, the size of reaction-diffusion patterns can be changed partially. To verify the effectiveness of the proposed method, experiments were conducted to apply the proposed method to various photographic images.

We propose a generalization of the rolling guidance filter (RGF) to a similarity-based clustering (SBC) algorithm which can handle general vector data. The proposed RGF-based SBC algorithm makes the similarities between data clearer than the original similarity values computed from the original data. On the basis of the similarity values, we assign cluster labels to data by an SBC algorithm. Experimental results show that the proposed algorithm achieves better clustering result than the result by the naive application of the SBC algorithm to the original similarity values. Additionally, we study the convergence of a unimodal vector dataset to its mean vector.

Based on TLS-ESPRIT algorithm, this paper proposes a weighted spatial smoothing DOA estimation algorithm to address the problem that the conventional TLS-ESPRIT algorithm will be disabled to estimate the direction of arrival (DOA) in the scenario of coherent sources. The proposed method divides the received signal array into several subarrays with special structural feature. Then, utilizing these subarrays, this paper constructs the new weighted covariance matrix to estimate the DOA based on TLS-ESPRIT. The auto-correlation and cross-correlation information of subarrays in the proposed algorithm is extracted sufficiently, improving the orthogonality between the signal subspace and the noise subspace so that the DOA of coherent sources could be estimated accurately. The simulations show that the proposed algorithm is superior to the conventional spatial smoothing algorithms under different signal to noise ratio (SNR) and snapshot numbers with coherent sources.

This paper presents a method for many-to-one voice conversion using phonetic posteriorgrams (PPGs) based on an adversarial training of deep neural networks (DNNs). A conventional method for many-to-one VC can learn a mapping function from input acoustic features to target acoustic features through separately trained DNN-based speech recognition and synthesis models. However, 1) the differences among speakers observed in PPGs and 2) an over-smoothing effect of generated acoustic features degrade the converted speech quality. Our method performs a domain-adversarial training of the recognition model for reducing the PPG differences. In addition, it incorporates a generative adversarial network into the training of the synthesis model for alleviating the over-smoothing effect. Unlike the conventional method, ours jointly trains the recognition and synthesis models so that they are optimized for many-to-one VC. Experimental evaluation demonstrates that the proposed method significantly improves the converted speech quality compared with conventional VC methods.

The eyes are continuously fluctuating during fixation. These fluctuations are called fixational eye movements. Fixational eye movements consist of tremors, microsaccades, and ocular drifts. Fixational eye movements aid our vision by shaping spatial-temporal characteristics. Here, it is known that photoreceptors, the first input layer of the retinal network, have a spatially non-uniform cell alignment called the cone mosaic. The roles of fixational eye movements are being gradually uncovered; however, the effects of the cone mosaic are not considered. Here we constructed a large-scale visual system model to explore the effect of the cone mosaic on the visual signal processing associated with fixational eye movements. The visual system model consisted of a brainstem, eye optics, and photoreceptors. In the simulation, we focused on the roles of fixational eye movements on signal processing with sparse sampling by photoreceptors given their spatially non-uniform mosaic. To analyze quantitatively the effect of fixational eye movements, the capacity of information processing in the simulated photoreceptor responses was evaluated by information rate. We confirmed that the information rate by sparse sampling due to the cone mosaic was increased with fixational eye movements. We also confirmed that the increase of the information rate was derived from the increase of the responses for the edges of objects. These results suggest that visual information is already enhanced at the level of the photoreceptors by fixational eye movements.

A non-photorealistic rendering method has been proposed for generating oil-film-like images from photographic images by bilateral infra-envelope filter. The conventional method has a disadvantage that it takes much time to process. We propose a method for generating oil-film-like images that can be processed faster than the conventional method. The proposed method uses an iterative process with upper and lower smoothing filters. To verify the effectiveness of the proposed method, we conduct experiments using Lenna image. As a result of the experiments, we show that the proposed method can process faster than the conventional method.

Sparse arrays can usually achieve larger array apertures than uniform linear arrays (ULA) with the same number of physical antennas. However, the conventional direction-of-arrival (DOA) estimation algorithms for sparse arrays usually require the spatial smoothing operation to recover the matrix rank which inevitably involves heavy computational complexity and leads to a reduction in the degrees-of-freedom (DOFs). In this paper, a low-complex DOA estimation algorithm by exploiting the discrete Fourier transform (DFT) is proposed. Firstly, the spatial spectrum of the virtual array constructed from the sparse array is established by exploiting the DFT operation. The initial DOA estimation can obtain directly by searching the peaks in the DFT spectrum. However, since the number of array antennas is finite, there exists spectrum power leakage which will cause the performance degradation. To further improve the angle resolution, an iterative process is developed to suppress the spectrum power leakage. Thus, the proposed algorithm does not require the spatial smoothing operation and the computational complexity is reduced effectively. In addition, due to the extention of DOF with the application of the sparse arrays, the proposed algorithm can resolve the underdetermined DOA estimation problems. The superiority of the proposed algorithm is demonstrated by simulation results.

Edge-preserving smoothing filter smoothes the textures while preserving the information of sharp edges. In image processing, this kind of filter is used as a fundamental process of many applications. In this paper, we propose a new approach for edge-preserving smoothing filter. Our method uses 2D local filter to smooth images and we apply indicator function to restrict the range of filtered pixels for edge-preserving. To define the indicator function, we recalculate the distance between each pixel by using edge information. The nearby pixels in the new domain are used for smoothing. Since our method constrains the pixels used for filtering, its running time is quite fast. We demonstrate the usefulness of our new edge-preserving smoothing method for some applications.

A novel image enhancement method for vein recognition is introduced. Inspired by observation that the intensity of the vein vessel changes rapidly during the smoothing process compared to that of background (i.e., skin tissue) due to its thin and long shape, we propose to exploit the smoothing speed as a restoration weight for the vein image enhancement. Experimental results based on the CASIA multispectral palm vein database demonstrate that the proposed method is effective to improve the performance of vein recognition.

Organic electronics devices can be applicable to implant sensors. The noises in the acquired data can be removed by smoothing using sliding windows. We developed a new criterion for window-size decision based on smoothness and similarity (SSC). The smoothed curve fits the raw data well and is sufficiently smooth.

Depth image based rendering (DIBR), which is utilized to render virtual views with a color image and the corresponding depth map, is one of the key procedures in the 2D to 3D conversion process. However, some troubling problems, such as depth edge misalignment, disocclusion occurrences and cracks at resampling, still exist in current DIBR systems. To solve these problems, in this paper, we present a robust depth image based rendering scheme for stereoscopic view synthesis. The cores of the proposed scheme are two depth map filters which share a common domain transform based filtering framework. As a first step, a filter of this framework is carried out to realize texture-depth boundary alignments and directional disocclusion reduction smoothing simultaneously. Then after depth map 3D warping, another adaptive filter is used on the warped depth maps with delivered scene gradient structures to further diminish the remaining cracks and noises. Finally, with the optimized depth map of the virtual view, backward texture warping is adopted to retrieve the final texture virtual view. The proposed scheme enables to yield visually satisfactory results for high quality 2D to 3D conversion. Experimental results demonstrate the excellent performances of the proposed approach.

This paper proposes Deep Neural Network (DNN)-based Voice Conversion (VC) using input-to-output highway networks. VC is a speech synthesis technique that converts input features into output speech parameters, and DNN-based acoustic models for VC are used to estimate the output speech parameters from the input speech parameters. Given that the input and output are often in the same domain (e.g., cepstrum) in VC, this paper proposes a VC using highway networks connected from the input to output. The acoustic models predict the weighted spectral differentials between the input and output spectral parameters. The architecture not only alleviates over-smoothing effects that degrade speech quality, but also effectively represents the characteristics of spectral parameters. The experimental results demonstrate that the proposed architecture outperforms Feed-Forward neural networks in terms of the speech quality and speaker individuality of the converted speech.

We propose an edge-preserving multiscale image decomposition method using filters for non-equispaced signals. It is inspired by the domain transform, which is a high-speed edge-preserving smoothing method, and it can be used in many image processing applications. One of the disadvantages of the domain transform is sensitivity to noise. Even though the proposed method is based on non-equispaced filters similar to the domain transform, it is robust to noise since it employs a multiscale decomposition. It uses the Laplacian pyramid scheme to decompose an input signal into the piecewise-smooth components and detail components. We design the filters by using an optimization based on edge-preserving smoothing with a conversion of signal distances and filters taking into account the distances between signal intervals. In addition, we also propose construction methods of filters for non-equispaced signals by using arbitrary continuous filters or graph spectral filters in order that various filters can be accommodated by the proposed method. As expected, we find that, similar to state-of-the-art edge-preserving smoothing techniques, including the domain transform, our approach can be used in many applications. We evaluated its effectiveness in edge-preserving smoothing of noise-free and noisy images, detail enhancement, pencil drawing, and stylization.

Recently, Gan and Luo have proposed a direction-of-arrival estimation method for uncorrelated and coherent signals in the presence of multipath propagation [3]. In their method, uncorrelated and coherent signals are distinguished by rotational invariance techniques and the property of the moduli of eigenvalues. However, due to the limitation of finite number of sensors, the pseudo-inverse matrix derived in this method is an approximate one. When the number of sensors is small, the approximation error is large, which adversely affects the property of the moduli of eigenvalues. Consequently, the method in [3] performs poorly in identifying uncorrelated signals under such circumstance. Moreover, in cases of small number of snapshots and low signal to noise ratio, the performance of their method is poor as well. Therefore, in this letter we first study the approximation in [3] and then propose an improved method that performs better in distinguishing between uncorrelated signals and coherent signals and in the aforementioned two cases. The simulation results demonstrate the effectiveness and efficiency of the proposed method.

In developing an automatic system of a single tooth length measurement on x-ray image, since a tooth shape is assumed to be straight and curve, an algorithm which can accurately deal with straight and curve is required. This paper proposes an automatic algorithm for measuring the length of single straight and curve teeth. In the algorithm consisting of control point determination, curve fitting, and length measurement, PCA is employed to find the first and second principle axes as vertical and horizontal ones of the tooth, and two terminal points of vertical axis and the junction of those axes are determined as three first-order control points. Signature is then used to find a peak representing tooth root apex as the forth control point. Bezier curve, Euclidean distance, and perspective transform are finally applied with determined four control points in curve fitting and tooth length measurement. In the experiment, comparing with the conventional PCA-based method, the average mean square error (MSE) of the line points plotted by the expert is reduced from 7.548 pixels to 4.714 pixels for tooth image type-I, whereas the average MSE value is reduced from 7.713 pixels and 7.877 pixels to 4.809 pixels and 5.253 pixels for left side and right side of tooth image type-H, respectively.

In this paper, the spatial smoothing (SS) method is extended to the wideband multipath case. By reordering the array input signal and the weight vector, the corresponding covariance matrix of each subarray can be constructed conveniently. Then, a novel wideband beamforming algorithm, based on the SS method (SS-WB), can be achieved by linearly constrained minimum variance (LCMV). Further improvement of the output signal-to-interference-plus-noise ratio (SINR) for SS-WB can be obtained by removing the desired signal in the observed array data with the reconstruction of covariance matrix, which is denoted as wideband beamformer based on modified SS method (MSS-WB). Both proposed algorithms can reduce the desired signal cancellation due to the super decorrelation ability of SS method and MSS-WB can lead to a significantly improved output SINR. The simulations verify their effectiveness in the multipath environment.

Network selection is one of the hot issues in the fusion of heterogeneous wireless networks (HWNs). However, most of previous works only consider selecting single-access network, which wastes other available network resources, rarely take account of multi-access. To make full utilization of available coexisted networks, this paper proposes a novel multi-access selection algorithm based on joint utility optimization for users with multi-mode terminals. At first, the algorithm adopts exponential smoothing method (ESM) to get smoothed values of received signal strength (RSS). Then we obtain network joint utility function under the constraints of bandwidth and number of networks, with the consideration of trade-off between network benefit and cost. At last, Lagrange multiplier and dual optimization methods are used to maximize joint utility. Users select multiple networks according to the optimal association matrix of user and network. The simulation results show that the proposed algorithm can optimize network joint utility, improve throughput, effectively reduce vertical handoff number, and ensure Quality of Service (QoS).

Text Categorization (TC) is a task of classifying a set of documents into one or more predefined categories. Centroid-based method, a very popular TC method, aims to make classifiers simple and efficient by constructing one prototype vector for each class. It classifies a document into the class that owns the prototype vector nearest to the document. Many studies have been done on constructing prototype vectors. However, the basic philosophies of these methods are quite different from each other. It makes the comparison and selection of centroid-based TC methods very difficult. It also makes the further development of centroid-based TC methods more challenging. In this paper, based on the observation of its general procedure, the centroid-based text classification is treated as a kind of ranking task, and a unified framework for centroid-based TC methods is proposed. The goal of this unified framework is to classify a text via ranking all possible classes by document-class similarities. Prototype vectors are constructed based on various loss functions for ranking classes. Under this framework, three popular centroid-based methods: Rocchio, Hypothesis Margin Centroid and DragPushing are unified and their details are discussed. A novel centroid-based TC method called SLRCM that uses a smoothing ranking loss function is further proposed. Experiments conducted on several standard databases show that the proposed SLRCM method outperforms the compared centroid-based methods and reaches the same performance as the state-of-the-art TC methods.

We propose a new face relighting method using an illuminance template generated from a single reference portrait. First, the reference is wrapped according to the shape of the target. Second, we employ a new spatially variant edge-preserving smoothing filter to remove the facial identity and texture details of the wrapped reference, and obtain the illumination template. Finally, we relight the target with the template in CIELAB color space. Experiments show the effectiveness of our method for both grayscale and color faces taken from different databases, and the comparisons with previous works demonstrate a better relighting effect produced by our method.