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Recent studies on image stitching have been extensively conducted to stitch panoramic or 360° images using a small number of input images. The stitching of aerial images, that are captured by unmanned aerial vehicles (UAVs), has various practical applications. In this paper, we propose a fast adaptive stitching algorithm for handling numerous aerial images. First, the proposed method analyzes the relative positions and overlapping regions of the UAV image footprints by exploiting their geotag information. Based on the analysis, an adaptive selection algorithm is proposed to eliminate the densely overlapped images from among all the UAV images. Then, the proposed method sequentially performs fast feature extraction and feature matching. Finally, a local warp method, with a smooth transition for overlapping regions, is introduced to alleviate the blurring artifacts and achieve highly accurate image alignment. The experiments are conducted for various scenarios to generate seamless terrestrial mosaic images of large areas. The proposed method improves the visual quality of the stitched image, by decreasing the estimated reprojection error and the number of observed visual distortions. In addition, the proposed method can substantially reduce the processing time compared with conventional stitching methods.

In the image forgery problems, previous works has been chiefly designed considering only one of two forgery types: copy-move and splicing. In this paper, we propose a scheme to handle both copy-move and splicing image forgery by concurrently classifying the image forgery types and localizing the forged regions. The structural correlations between images are employed in the forgery clustering algorithm to assemble relevant images into clusters. Then, we search for the matching of image regions inside each cluster to classify and localize tampered images. Comprehensive experiments are conducted on three datasets (MICC-600, GRIP, and CASIA 2) to demonstrate the better performance in forgery classification and localization of the proposed method in comparison with state-of-the-art methods. Further, in copy-move localization, the source and target regions are explicitly specified.

Image splicing is one of the most common methods for digital image tampering. In this paper, an efficient Markov features based algorithm is proposed for image splicing detection. The proposed algorithm first extracts two types of Markov features, coefficient-wise Markov features and block-wise Markov features in the discrete cosine transform (DCT) domain. The former are obtained by exploiting correlations between consecutive coefficients and the latter are computed by exploiting coefficient correlations between adjacent blocks. Then, a feature vector is obtained by combining these two Markov features and it is fed into support vector machine (SVM) for the classification of authentic and spliced images. The experimental results show that the proposed method not only achieves much higher detection accuracy but also reduces the total running time significantly in comparison with state-of-the-art methods.

The discrete Fourier transform (DFT) is the most widely used technique for determining the frequency spectra of digital signals. However, in the sliding transform scenario where the transform window is shifted one sample at a time and the transform process is repeated, the use of DFT becomes difficult due to its heavy computational burden. This paper proposes an optimal sliding DFT (oSDFT) algorithm that achieves both the lowest computational requirement and the highest computational accuracy among existing sliding DFT algorithms. The proposed oSDFT algorithm directly computes the DFT bins of the shifted window by simply adding (or subtracting) the bins of a previous window and an updating vector. We show that the updating vector can be efficiently computed with a low complexity in the sliding transform scenario. Our simulations demonstrate that the proposed algorithm outperforms the existing sliding DFT algorithms in terms of computational accuracy and processing time.

Copy-move forgery (CMF), which copies a part of an image and pastes it into another region, is one of the most common methods for digital image tampering. For CMF detection (CMFD), we propose a fast and robust approach that can handle several geometric transformations including rotation, scaling, sheering, and reflection. In the proposed CMFD design, keypoints and their descriptors are extracted from the image based on the Scale Invariant Feature Transform (SIFT). Then, an improved matching operation that can handle multiple copy-move forgeries is performed to detect matched pairs located in duplicated regions. Next, the geometric transformation between duplicated regions is estimated using a subset of reliable matched pairs which are obtained using the SIFT scale space representation. In our simulation, we present comparative results between the proposed algorithm and state-of-the-art ones with proven performance guarantees.

Tempcore process simulator (TPS) has been developed in this study to analyze the microstructural evolution of quenched and tempered rebar. There has been an increasing need to relate the complex microstructures to the resulting properties of quenched and tempered rebar. However, information on such relationships typically requires precise thermal histories imposed on the workpiece. Therefore, TPS, capable of simulating the Tempcore process, has been developed to produce high-fidelity data. TPS mainly consists of a vacuum induction furnace, pilot rolling mill, box furnace, and cooling unit to simulate shop floor operations. A series of experimental tests were successfully carried out with various parameters, such as reheating temperature, water flow, water pressure, and cooling time. The effects of chemical compositions and cooling time on the microstructural evolution and mechanical properties of quenched and tempered rebar have been analyzed to validate the performance of TPS. The results show that TPS can simulate the Tempcore process with a high degree of fidelity and reliability.

The discrete cosine transform (DCT) has been successfully used for a wide range of applications in digital signal processing. While there are efficient algorithms for implementing the DCT, its use becomes difficult in the sliding transform scenario where the transform window is shifted one sample at a time and the transform process is repeated. In this paper, a new two-dimensional sliding DCT (2-D SDCT) algorithm is proposed for fast implementation of the DCT on 2-D sliding windows. In the proposed algorithm, the DCT coefficients of the shifted window are computed by exploiting the recursive relationship between 2-D DCT outputs of three successive windows. The theoretical analysis shows that the computational requirement of the proposed 2-D SDCT algorithm is the lowest among existing 2-D DCT algorithms. Moreover, the proposed algorithm enables independent updating of each DCT coefficient.

Digital image forgery is becoming increasingly popular with the rapid progress of digital media editing tools. Copy-move forgery (CMF) is one of the most common methods of digital image forgery. For CMF detection (CMFD), we propose an upsampled log-polar Fourier (ULPF) descriptor that is robust to several geometric transformations including rotation, scaling, sheering, and reflection. We first describe the theoretical background of the ULPF representation. Then, we propose a feature extraction algorithm that can extract rotation and scale invariant features from the ULPF representation. In addition, we analyze the common CMFD processing pipeline and improve a part of processing pipeline to efficiently handle various types of tampering attacks. In our simulation, we present comparative results between the proposed feature descriptor and state-of-the-art ones with proven performance guarantees.

An advanced cooling unit was developed and characterized to accelerate the cooling of steel rebars during the Tempcore process. Compared to conventional flat-surfaced coolers, the newly developed cooling unit uses a corrugated surface to generate turbulence. To estimate the cooling performance of the corrugated cooler, the heat transfer coefficient during the Tempcore process was obtained using a finite-volume method. The turbulence induced by the corrugated cooler reduces the vapor volume fractions and increases the heat transfer coefficient by up to 10%. The obtained heat transfer coefficient for convective and nucleate boiling was then applied in the model to predict the volume of martensite formed in the outer layer of the bar after the Tempcore process. The corrugated cooling unit was assembled and incorporated into an experimental simulator to evaluate its ability to accelerate cooling of rebars. For rebars treated in the corrugated cooler, a 6.54% increase in the martensite volume fraction with a 13 °C decrease in the self-tempering temperature was achieved. This result confirms that the developed corrugated cooler can be a reasonable alternative to increase cooling efficiency of Tempcore facility without upgrading the capacity of the water supplying system.

Discrete orthogonal transforms such as the discrete Fourier transform (DFT), discrete Hartley transform (DHT), and Walsh?Hadamard transform (WHT) play important roles in the fields of digital signal processing, filtering, and communications. In recent years, there has been a growing interest in the sliding transform process where the transform window is shifted one sample at a time and the transform process is repeated.

The cooling process referred to as Tempcore has been applied to produce a high-strength rebar. Excellent rebar with strength and weldability can be manufactured from mild steel without the addition of alloying elements by using the Tempcore process. However, there are limitations to evaluating the effect of various chemical compositions and cooling conditions within a site facility. In this study, we developed an apparatus to simulate the Tempcore process and obtained microstructures with a hardened surface layer, an intermediate region and a soft inner core. The experimental apparatus has been equipped with a cooler set that is the same as the site facility and consists of a pump line that supplies pressure of 12-13 bar and flow rate of up to . In accordance with the simulation result of steel grade SD400 that requires more than 400 MPa of yield strength, both the hardened area ratio and the hardness with respect to each cooling depth were found to agree well with the product.

This paper proposes an advanced encryption of MP3 and MPEG-4 coder with a quality degradation-based security model. For the MP3 audio, the magnitude and phase information of modified discrete cosine transform (MDCT) coefficients is encrypted. DCT coefficients and motion vectors (MVs) are used for the scrambling of the MPEG-4 video. This encryption scheme has a level of security, secures in perception, keeps format compliance, and obtains better time efficiency by reducing the encrypted volumes of multimedia contents. These properties make it practical to incorporate encryption and decryption process into compression and decompression process, and thus suitable for secure A/V transmission or sharing. Experimental results indicate that the proposed technique provides levels of security for a flexible business model and achieves a simple and coding-efficient architecture with no adverse impact on error resilience.

The 3D video extension of High Efficiency Video Coding (3D-HEVC) is the state-of-the-art video coding standard for the compression of the multiview video plus depth format. In the 3D-HEVC design, new depth-modeling modes (DMMs) are utilized together with the existing intraprediction modes for depth intracoding. The DMMs can provide more accurate prediction signals and thereby achieve better compression efficiency. However, testing the DMMs in the intramode decision process causes a drastic increase in the computational complexity. In this paper, we propose a fast mode decision algorithm for depth intracoding. The proposed algorithm first performs a simple edge classification in the Hadamard transform domain. Then, based on the edge classification results, the proposed algorithm selectively omits unnecessary DMMs in the mode decision process. Experimental results demonstrate that the proposed algorithm speeds up the mode decision process by up to 37.65% with negligible loss of coding efficiency.

Multiple reference frame motion estimation (MRFME) is one of the main features of recent video coding standards such as H.264/AVC and high efficiency video coding. This paper presents a fast search strategy for avoiding redundant computations during MRFME. The proposed method reduces the number of pixels to be examined using the precalculated rate-distortion costs of previously searched frames. Experimental results show that the proposed method reduces the complexity of the motion estimation (ME) process by up to 81.43 %.

Alzheimer’s disease (AD) is a leading cause of dementia, which causes serious health and socioeconomic problems. A progressive neurodegenerative disorder, Alzheimer’s causes the structural change in the brain, thereby affecting behavior, cognition, emotions, and memory. Numerous multivariate analysis algorithms have been used for classifying AD, distinguishing it from healthy controls (HC). Efficient early classification of AD and mild cognitive impairment (MCI) from HC is imperative as early preventive care could help to mitigate risk factors. Magnetic resonance imaging (MRI), a noninvasive biomarker, displays morphometric differences and cerebral structural changes. A novel approach for distinguishing AD from HC using dual-tree complex wavelet transforms (DTCWT), principal coefficients from the transaxial slices of MRI images, linear discriminant analysis, and twin support vector machine is proposed here. The prediction accuracy of the proposed method yielded up to 92.65 ± 1.18 over the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, with a specificity of 92.19 ± 1.56 and sensitivity of 93.11 ± 1.29, and 96.68 ± 1.44 over the Open Access Series of Imaging Studies (OASIS) dataset, with a sensitivity of 97.72 ± 2.34 and specificity of 95.61 ± 1.67. The accuracy, sensitivity, and specificity achieved using the proposed method are comparable or superior to those obtained by various conventional AD prediction methods.

In this paper, a recursive sliding transform (RST) algorithm is proposed for the fast implementation of the Walsh Hadamard Transform (WHT) on sliding windows. The proposed RST algorithm accelerates the sliding WHT process by recursively reducing the order of WHT. Theoretical analysis shows that the proposed RST algorithm requires only 1.33 additions per sample for each WHT basis vector regardless of the size or dimension of the basis vector. The computational requirement of the proposed RST algorithm is the lowest among existing fast sliding WHT algorithms.

The discrete Fourier transform (DFT) produces a Fourier representation for finite-duration data sequences. In addition to its theoretical importance, the DFT plays a key role in the implementation of a variety of digital signal-?processing algorithms. Several algorithms including the fast Fourier transform (FFT) and the Goertzel algorithm have been introduced for the fast implementation of the DFT [1], [2].

The three-dimensional (3D) extension of the high-efficiency video coding (3D-HEVC) standard adopts new depth-modelling modes (DMMs) to provide an alternative prediction scheme for depth-map intra-coding. In 3D-HEVC, although edges in depth maps can be accurately estimated by utilising the DMMs, testing the DMMs in the mode decision introduces a huge computational load to the encoder. A fast mode decision algorithm is proposed that can significantly reduce the computational overhead incurred by the DMMs. Experimental results indicate that the proposed algorithm reduces the encoding complexity by 33.89% on average without any penalty in coding efficiency.

Motion estimation (ME) has a variety of applications in image processing, pattern recognition, target tracking, and video compression. In modern video compression standards such as H.264/AVC and HEVC, multiple reference frame ME (MRFME) is adopted to reduce the temporal redundancy between successive frames in a video sequence. In MRFME, the motion search process is conducted using additional reference frames, thereby obtaining better prediction signal as compared to single reference frame ME (SRFME). However, its high computational complexity makes it difficult to be utilized in real-world applications. In order to reduce the computational complexity of MRFME, this paper proposes a level-set-based ME algorithm (LSME) without any penalty in the rate-distortion (RD) performance. First, the proposed algorithm partitions the motion search space into multiple level sets based on a rate constraint. The proposed algorithm then controls the ME process on the basis of the predetermined level sets. Experimental results show that the proposed algorithm reduces the ME time by up to 83.46% as compared to the conventional full search (FS) algorithm.

The level set approach is a powerful tool for segmenting images. This paper proposes a method for segmenting brain tumor images from MR images. A new signed pressure function (SPF) that can efficiently stop the contours at weak or blurred edges is introduced. The local statistics of the different objects present in the MR images were calculated. Using local statistics, the tumor objects were identified among different objects. In this level set method, the calculation of the parameters is a challenging task. The calculations of different parameters for different types of images were automatic. The basic thresholding value was updated and adjusted automatically for different MR images. This thresholding value was used to calculate the different parameters in the proposed algorithm. The proposed algorithm was tested on the magnetic resonance images of the brain for tumor segmentation and its performance was evaluated visually and quantitatively. Numerical experiments on some brain tumor images highlighted the efficiency and robustness of this method.

The two-dimensional (2D) discrete Fourier transform (DFT) in the sliding window scenario has been successfully used for numerous applications requiring consecutive spectrum analysis of input signals. However, the results of conventional sliding DFT algorithms are potentially unstable because of the accumulated numerical errors caused by recursive strategy. In this letter, a stable 2D sliding fast Fourier transform (FFT) algorithm based on the vector radix (VR) 2 × 2 FFT is presented. In the VR-2 × 2 FFT algorithm, each 2D DFT bin is hierarchically decomposed into four sub-DFT bins until the size of the sub-DFT bins is reduced to 2 × 2; the output DFT bins are calculated using the linear combination of the sub-DFT bins. Because the sub-DFT bins for the overlapped input signals between the previous and current window are the same, the proposed algorithm reduces the computational complexity of the VR-2 × 2 FFT algorithm by reusing previously calculated sub-DFT bins in the sliding window scenario. Moreover, because the resultant DFT bins are identical to those of the VR-2 × 2 FFT algorithm, numerical errors do not arise; therefore, unconditional stability is guaranteed. Theoretical analysis shows that the proposed algorithm has the lowest computational requirements among the existing stable sliding DFT algorithms.

As the preceding research for the design of gangway in the next generation high speed train, the aero-acoustic noise at the gangway is calculated. For this purpose, the shape of gangway with mud flaps is assumed as the two-dimensional cavity. Then, 5 gap sizes between mud flaps of gangway are selected and parametric study is performed according to the gap sizes. From this study, the aerodynamic features such as vortex shedding, pressure, etc. are computed. Also, the aero-acoustic properties of tonal noise and overall noise are analyzed at the 3 locations of microphone and the relation between the gap size of mud flap and the noise level is assessed. Through this study, it is shown that the noise characteristics of base and specific models are better than those of other models.

In scalable video coding (SVC) as an extension of H.264/advanced video coding (AVC), a computationally expensive exhaustive mode decision is employed to select the best coding mode for each macroblock (MB). In order to reduce computational complexity, we propose a fast mode decision algorithm for SVC which uses an all-zero block (AZB) detection. Based on the empirical analysis of the inter-layer correlation of the AZB, the MB at the enhancement layer (EL) is predicted whether to be the AZB. Then, only predicted MBs are examined by the AZB detection algorithm. Since the mode decision can be terminated by detecting the AZB, we determine the processing order of the candidate modes at the EL in order to terminate the mode decision in the early stage. The proposed algorithm can be combined with other conventional fast mode decision methods in order to further reduce the computational complexity of those methods. Experimental results show that the proposed algorithm can significantly speed up the encoding process, especially for low bit-rate video sequences, without deteriorating the coding efficiency of SVC.

In scalable video coding (SVC), an exhaustive mode decision is performed to search for the best mode at each macroblock. Although this method achieves an optimal trade-off between rate and distortion, it introduces an extreme computational burden on the encoder. In this letter, we propose a fast mode decision algorithm that can reduce the computational load of the mode decision for SVC. We statistically derive the expectation of the rate-distortion cost (RDcost) increase caused by skipping each mode in the mode decision. In the proposed algorithm, the encoder performs the mode decision using a small number of modes that are determined based on the expected increase of the RDcost. Experimental results show that the proposed algorithm can reduce the computational complexity significantly with negligible video quality degradation and bitrate increment.

The scalable video coding (SVC) standard adopts a simple interlayer intra prediction (ILIP) method for encoding scalable video sequences. In the conventional ILIP, a prediction signal for the macroblock (MB) of the enhancement layer (EL) is obtained by simply upsampling the colocated block of the base layer (BL). We propose an improved ILIP method by generalizing the original one adopted in the SVC standard. In the proposed ILIP method, the MB of the EL is predicted using all MBs of the BL. Experimental results show that the proposed algorithm can reduce the bit rate by 1.91% to 6.44%, as compared with the conventional ILIP, while the average PSNR is not decreased.

The scalable video coding (SVC) standard adopts four post-processing error concealment (EC) methods as a non-normative tool in order to conceal a lost picture in transmission. In this paper, we first introduce an error tracking model with which the concealment error and propagation error can be estimated. Then, we analyze the four EC methods using the error tracking model. Until now, there have been few studies on how to conceal SVC bitstream corrupted by burst errors. Based on the results of the error analysis, we propose a selection-based EC strategy to conceal burst errors for SVC bitstream. In the proposed strategy, one of the four EC methods is adaptively selected such that the visual degradation is minimized for each lost picture. The experimental results show that the proposed EC strategy can improve the EC performance without increasing the computational complexity. Moreover, the proposed strategy does not require any modification of the conventional SVC codec since it utilizes only the EC methods adopted in the standard.

Scalable video coding (SVC) is currently developed as an extension of H.264/AVC. In the SVC encoder, an exhaustive search technique is employed to select the best coding mode for each macro block (MB). This technique achieves an optimal trade-off between rate and distortion, but it requires an extremely large encoding time. In this paper, we propose a fast mode decision algorithm for inter-layer coding at the enhancement layer. The proposed algorithm predicts the mode of each MB at the enhancement layer using the modes of a co-located MB and its neighboring MBs at the base layer. The proposed method can achieve a time saving of up to 74% in spatial scalability and 63% in coarse grain quality scalability with negligible loss of quality and bit rate increment.

The scalable video coding (SVC) standard adopts the inter-layer residual prediction (ILRP) algorithm to encode the residual signal of the enhancement layer (EL). The ILRP reduces the number of bits required for encoding the residual signal but incurs excessive encoding time. In this paper, we propose a fast encoding algorithm for SVC-based video streaming. In this algorithm, the ILRP is selectively applied to the coding modes depending on their Lagrangian rate-distortion costs. Experimental results show that the proposed algorithm can reduce computational complexity with negligible video quality degradation and bitrate increments.

Ultra-High Definition Television (U-HDTV) promises to offer a strong sense of reality to viewers by utilizing extremely high spatial and temporal resolution. In order to compress U-HDTV video sequences in a practical way, the video sequence is divided into sub-sequences and each sub-sequence is processed. However, the merged sequence of independently decoded sub-sequences inherently exhibits a distortion along the boundary of the sub-sequences, namely sub-sequence boundary distortion (SBD). In this paper, we first introduce a method to quantitatively measure the SBD. Then, we propose a post-processing algorithm that reduces the SBD. Experimental results indicate that the SBD is efficiently alleviated by the proposed post-processing algorithm.

Context-based Adaptive Binary Arithmetic Coding (CA-BAC) is adopted as an entropy coding tool for main profile of the video coding standard H.264/AVC. CABAC achieves higher degree of redundancy reduction by estimating the conditional probability of each binary symbol which is the input to the arithmetic coder. This paper presents an entropy coding method based on CABAC. In the proposed method, the binary symbol is coded using more precisely estimated conditional probability, thereby leading to performance improvement. We apply our method to the standard and evaluate its performance for different video sources and various quantization parameters (QP). Experiment results show that our method outperforms the original CABAC in term of coding efficiency, and the average bit-rate savings are up to 1.2%.

Currently deployed mobile networks including High Speed Downlink Packet Access (HSDPA) offer only best-effort Quality of Service (QoS). In wireless best effort networks, the bandwidth variation is a critical problem, especially, for mobile devices with small buffers. This is because the bandwidth variation leads to packet losses caused by buffer overflow as well as picture freezing due to high transmission delay or buffer underflow. In this paper, in order to provide seamless video streaming over HSDPA, we propose an efficient real-time video streaming method that consists of the available bandwidth (AB) estimation for the HSDPA network and the transmission rate control to prevent buffer over-flows/underflows. In the proposed method, the client estimates the AB and the estimated AB is fed back to the server through real-time transport control protocol (RTCP) packets. Then, the server adaptively adjusts the transmission rate according to the estimated AB and the buffer state obtained from the RTCP feedback information. Experimental results show that the proposed method achieves seamless video streaming over the HSDPA network providing higher video quality and lower transmission delay.

Video transmission over mobile worldwide interoperability for microwave access (WiMAX) can be serverly degraded due to the effect of fading and handoff. In this paper, we propose a channel adaptive error resilience scheme for video transmission over mobile WiMAX. When the channel condition begins to trigger handoff, the current frame is stored in the long-term memory for the forward error correction, and the following frames are encoded by using double motion vectors (MVs) in the sense of multi-hypothesis motion compensation. Even if a whole frame is lost, we can reconstruct the following frames using the stored frame in the long-term memory. However, the error propagation still remains in this forward error resilience method. To refresh the erroneous frames to the decoder, the encoder utilizes the channel adaptive refreshing (CAR). In the CAR, the channel rate is first predicted using channel parameter, a carrier to interference and noise ratio (CINR), and the encoder adaptively determines the number of blocks to be encoded in the intra mode based on the feedback information. Performance evaluations are presented to demonstrate the effectiveness of the proposed method.