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Sketch portrait generation benefits a wide range of applications such as digital entertainment and law enforcement. Although plenty of efforts have been dedicated to this task, several issues still remain unsolved for generating vivid and detail-preserving personal sketch portraits. For example, quite a few artifacts may exist in synthesizing hairpins and glasses, and textural details may be lost in the regions of hair or mustache. Moreover, the generalization ability of current systems is somewhat limited since they usually require elaborately collecting a dictionary of examples or carefully tuning features/components. In this paper, we present a novel representation learning framework that generates an end-to-end photo-sketch mapping through structure and texture decomposition. In the training stage, we first decompose the input face photo into different components according to their representational contents (i.e., structural and textural parts) by using a pre-trained Convolutional Neural Network (CNN). Then, we utilize a Branched Fully Convolutional Neural Network (BFCN) for learning structural and textural representations, respectively. In addition, we design a Sorted Matching Mean Square Error (SM-MSE) metric to measure texture patterns in the loss function. In the stage of sketch rendering, our approach automatically generates structural and textural representations for the input photo and produces the final result via a probabilistic fusion scheme. Extensive experiments on several challenging benchmarks suggest that our approach outperforms example-based synthesis algorithms in terms of both perceptual and objective metrics. In addition, the proposed method also has better generalization ability across dataset without additional training., Comment: Published in TIP 2017

The purpose of this exploratory was to examine the possible impact of the content adaptive selling, listening and customer orientation, based sales performance. Eighty eight sales people were surveyed by questionnaire. Result indicated that adaptive selling positively influence sales performance. Listening positively influence sales performance. Customer orientation positively influence sales performance. Listening is a stronger influence on sales performance. All result was using a hipotesis test. Keywords : adaptive selling, listening, customer orientation.

Near-eye multi-focal (light field) displays are known to be more advantageous than conventional stereoscopic 3D displays, because they can alleviate the vergence-accommodation conflict. While stereoscopic 3D displays are restricted to projecting images on a single focal plane, near-eye light field displays can form multiple focal planes and volumetrically render 3D data. Most existing near-eye light field displays use simplistic, uniformly spaced focal plane configuration (in dioptric space). In this paper, we present a novel technique that optimizes the focal plane configuration based on characteristics of the content to be rendered. We show that this technique can significantly improve the perceived visual quality of content visualized on such displays.

In this paper, a content-adaptive deblocking method is proposed to improve the visual quality of block-based DCT compressed videos. We find that the edge information obtained through the global orientation energy edge detection (OEED) on an initially deblocked image provides a robust partition of local directional features (LDFs). Based on this partition, for the directional featured region, we design corresponding filter orientation and thresholds to best preserve image details while reducing blocking artifacts; for the consecutive non-featured region, we impose extra smoothing to suppress the visually severe blocking artifacts. Experimental results demonstrate that our method effectively improves the visual quality while well maintaining the objective fidelity of block-based DCT compressed videos, compared with the H.264/AVC deblocking method and other existing directional deblocking methods.

We optimize automultiscopic displays built by stacking a pair of modified LCD panels. To date, such dual-stacked LCDs have used heuristic parallax barriers for view-dependent imagery: the front LCD shows a fixed array of slits or pinholes, independent of the multi-view content. While prior works adapt the spacing between slits or pinholes, depending on viewer position, we show both layers can also be adapted to the multi-view content, increasing brightness and refresh rate. Unlike conventional barriers, both masks are allowed to exhibit non-binary opacities. It is shown that any 4D light field emitted by a dual-stacked LCD is the tensor product of two 2D masks. Thus, any pair of 1D masks only achieves a rank-1 approximation of a 2D light field. Temporal multiplexing of masks is shown to achieve higher-rank approximations. Non-negative matrix factorization (NMF) minimizes the weighted Euclidean distance between a target light field and that emitted by the display. Simulations and experiments characterize the resulting content-adaptive parallax barriers for low-rank light field approximation.

Portable and mobile multimedia production faces special challenges posed by transmission bandwidth limitations. This paper describes experimental studies of the multimodal quality of compressed television materials for mobile and portable devices, discusses required optimizations and proposes a mathematical model for content adaptive bit rate allocation. Study results are used to discuss the characteristics of multimodal quality and support the proposition that, if multimodal audiovisual perceptual quality is taken into account, it will improve the quality of new portable and mobile multimedia applications.

In this paper, a method for Lagrange multiplier selection is proposed in the context of rate-distortion optimisation for wavelet-based scalable video coding targeting quality scalability. Despite the prevalence of the conventional method for Lagrange multiplier selection in hybrid video coding, the underlying formulation is not applicable to wavelet-based scalable video coding. To address the inherent challenges, a thorough analysis of the rate-distortion models for transform video coding is provided with regard to low and middle-to-high bit-rates, respectively. Based on the analysis, the models are consolidated according to experimental observations and the consolidated rate-distortion models serve as the basis for the derivation of the Lagrange multiplier. Considering the influence of the open-loop prediction structure on the rate-distortion performance, the Lagrange multiplier is initially derived for a single-targeted bit-rate. Moreover, the method for Lagrange multiplier selection in scalable video coding aiming at multiple-targeted bit-rates is proposed in a general sense of bit-rate range, varying from low to high bit-rates, building on the initially derived Lagrange multiplier for a single-targeted bit-rate. The proposed Lagrange multiplier is content adaptive and well suited for wavelet-based scalable video coding where quantisation steps are unavailable. Detailed performance evaluation of the proposed method for wavelet-based scalable video coding is provided with regard to a given targeted bit-rate and multiple-targeted bit-rates, respectively. The experimental results have demonstrated the effectiveness of the proposed Lagrange multiplier for rate-distortion optimisation considering quality scalability in wavelet-based scalable video coding.

Skin detection plays an important role in applications such as face detection and tracking, person detection and pornography detection. While previous studies focus on pixel-based skin color detection techniques that individually classify each pixel as skin color or non-skin color, this study presents a region-based algorithm for detecting skin color. The proposed algorithm uses a special region, called key skin region, as the basis to classify skin color. A performance comparison with conventional skin classifiers, including the Bayesian classifier, the unimodal Gaussian classifier and the Gaussian mixture classifier, is made in this study. Experimental results show that the proposed algorithm outperforms other tested skin classifiers. Furthermore, the skin regions detected by the proposed algorithm, especially facial regions, are nearly complete with no hollow holes in these regions. This property can simplify the complexity of implementing applications that use skin color as their basis, such as face detection and face tracking.

We propose a new pre-roll delay-distortion optimization (DDO) framework that allows determination of the minimum pre-roll delay and distortion while ensuring continuous playback for on-demand content-adaptive video streaming over limited bitrate networks. The input video is first divided into temporal segments, which are assigned a relevance weight and a maximum distortion level, called relevance-distortion policy, which may be specified by the user. The system then encodes the input video according to the specified relevance-distortion policy, whereby the optimal spatial and temporal resolutions and quantization parameters, also called encoding parameters, are selected for each temporal segment. The optimal encoding parameters are computed using a novel, multi-objective optimization formulation, where a relevance weighted distortion measure and pre-roll delay are jointly minimized under maximum allowable buffer size, continuous playback, and maximum allowable distortion constraints. The performance of the system has been demonstrated for on-demand streaming of soccer videos with substantial improvement in the weighted distortion without any increase in pre-roll delay over a very low-bitrate network using AVC/H.264 encoding.

A content adaptive, network aware approach for joint optimization of wireless video transmission is presented in this article. The joint system optimization approach has been developed in the framework of the IST PHOENIX project (www.ist-phoenix.org). After a description of the considered cross-layer approach and of the information to be exchanged among the system component blocks, the concept of "JSCC/D controllers" is introduced. Results obtained through the demonstrator realized as proof-of-concept for the described paradigm are then shown, confirming the validity of the described joint optimization approach

Cinematic virtual reality provides an immersive visual experience by presenting omnidirectional videos of real-world scenes. A key challenge is to develop efficient representations of omnidirectional videos in order to maximize coding efficiency under resource constraints, specifically, number of samples and bitrate. We formulate the choice of representation as a multi-dimensional, multiple-choice knapsack problem and show that the resulting representations adapt well to varying content. We also show that separation of the sampling and bit allocation constraints leads to a computationally efficient solution using Lagrangian optimization with only minor performance loss. Results across images and videos show significant coding gains over standard representations.

Source camera identification (SCI) is an important topic in image forensics. One of the most effective fingerprints for linking an image to its source camera is the sensor pattern noise, which is estimated as the difference between the content and its denoised version. It is widely believed that the performance of the sensor-based SCI heavily relies on the denoising filter used. This study proposes a novel sensor-based SCI method using content adaptive guided image filter (CAGIF). Thanks to the low complexity nature of the CAGIF, the proposed method is much faster than the state-of-the-art methods, which is a big advantage considering the potential real-time application of SCI. Despite the advantage of speed, experimental results also show that the proposed method can achieve comparable or better performance than the state-of-the-art methods in terms of accuracy.

This paper employs the concept of the content-adaptive residual and presents a low-dimensional feature set for detecting the grayscale steganography in spatial domain. The testing image is first segmented into three kinds of areas, that is, the smooth, edge, and textural areas. Then, different pixel predictors are used to calculate the residuals responded to different areas. The yielded different co-occurrence matrices are finally collected as the steganalytic features. Experiments reported show that the proposed method is effective and yields good performances when detecting popular steganographic algorithms such as LSB matching, EA, and HUGO.

Remote engineering systems are valuable tools to give visual assistance and remote support e.g. in Structural Health Monitoring (SHM) or Non-destructive Testing (NDT). They allow discussing a second opinion with a remote expert and thus reducing the human factor during testing and monitoring. For an optimal impression of the situation, the second person requires both a camera view of the location and the screen view of the SHM/NDT system used. The Online Maintenance Assistance (OMA) system implements this two-view collaboration. Remote partners can see and actively control measuring equipment, while observing details of the location in the camera window. Due to varying screen signals and communication properties, an adaption for optimal visual quality of the camera and screen signal compression is required. The adaption is performed in a Machine Learning System (MLS), which uses an adapted k-nearest neighbors algorithm to adapt signal compression parameters to the measurement task and the communication channel. doi: 10.12783/SHM2015/148

This paper provides a motion-based content-adaptive depth map enhancement algorithm to enhance the quality of the depth map and reduce the artifacts in the synthesized views. The proposed algorithm extracts depth cues from the motion distribution at the specific scenario of camera movement to align the distribution of depth and motion. In real world scenarios, when the camera is panning in horizontal direction, the nearer distance between the object and the camera, the larger motion will be, and vice versa; therefore, we could interpret the depth from motion in this. Moreover, in the scenario of fixed camera, the depth cue from motion could be derived in the same approach, and the depth variation within one moving object shall be small. Hence, the depth values of moving object should not be rapidly changing. In addition, this paper also employs the bi-directional motion-compensated infinite impulse response low-pass filter to stabilize the consistency of depth maps over time. As a consequence, the algorithm so introduced not only aligns the depth map to depth cues from motion but also enhance stability and consistency of depth maps in the spatial-temporal domain. Experiment results via enhanced depth maps show that the synthesized results would be better in both objective and subjective measurement in comparison with the results using original depth maps and the state-of-the-art depth enhancement algorithms.

In this abstract, we present preliminary evaluation of a full 4D reconstruction method aimed at cardiac gated emission tomography. The proposed method uses content adaptive mesh model (CAMM) to represent the 3D volumetric data. The nodes of the CAMM are then moved from a gate to gate, according to the motion obtained from a motion estimation algorithm, to form 4D deformable content adaptive mesh model (DCAMM).

This paper proposes an efficient content adaptive screen image scaling scheme for the real-time screen applications like remote desktop and screen sharing. In the proposed screen scaling scheme, a screen content classification step is first introduced to classify the screen image into text and pictorial regions. Afterward, we propose an adaptive shift linear interpolation algorithm to predict the new pixel values with the shift offset adapted to the content type of each pixel. The shift offset for each screen content type is offline optimized by minimizing the theoretical interpolation error based on the training samples respectively. The proposed content adaptive screen image scaling scheme can achieve good visual quality and also keep the low complexity for real-time applications., ICIP 2013

Integrating efficient visual perceptual cues into standardized video coding framework can improve performance significantly. In this paper we propose to enhance AVS encoder by using the latest just noticeable distortion (JND) model to adjust DCT coefficients of prediction residues in a content adaptive way. To better modeling JND profile in AVS integer DCT domain, we further derive the JND mapping from the classical DCT domain to AVS Integer DCT domain. The experiment shows that the proposed algorithm can reduce the bitrate by about 13% on average, compared to the AVS standard encoder at similar visual quality.

In this paper, a content adaptive in-loop depth map filter is proposed for HEVC based 3DV video coding to improve the quality of the synthesized views. The proposed depth map filtering scheme is block based by reusing the TU (transform unit) split structure in HEVC. Firstly, TU are classified into 3 categories: flat, directional, and textureless, according to the characteristics of the depth map. Then four kinds of filters are designed by considering the characteristics of the current TU and neighbor TUs jointly. The proposed scheme is incorporated into HTM3.0- the reference software of HEVC based 3D video coding. The experimental results show both subjective and objective quality improvement. The average BD-rate reduction is 0.65% for synthesis views in HD test sequences, and the maximum BD-rate reduction is up to 1.6%.

Limited by long acquisition time of 2D ghost imaging, current ghost imaging systems are so far inapplicable for dynamic scenes. However, it's been demonstrated that nature images are spatiotemporally redundant and the redundancy is scene dependent. Inspired by that, we propose a content-adaptive computational ghost imaging approach to achieve high reconstruction quality under a small number of measurements, and thus achieve ghost imaging of dynamic scenes. To utilize content-adaptive inter-frame redundancy, we put the reconstruction under an iterative reweighted optimization, with non-uniform weight computed from temporal-correlated frame sequences. The proposed approach can achieve dynamic imaging at 16fps with 64×64-pixel resolution.

This paper proposes a content-adaptive blending method, smart compositing, for displaying two overlapped video frames on the same screen while preserving the readability of both. A pixel-wise adaptive blending factor map is generated according to the edge and saturation information of the content of only the overlay frame. Using this blending factor map, regions of the overlay frame with edges or saturated color are assigned to be more opaque and the remaining regions are assigned to be more transparent. A halo is also created around the edges of the overlay content which enhances the edges and disambiguates them from the underlying frame. The proposed method is suitable for overlaying many different types of content (e.g. drawings, slides, texts, and pictures) and does not require any information (e.g., an opacity mask) from the application which generates the content. This method has low computational complexity and has been implemented in real-time.

Disparity estimation is a highly complex and time consuming process in multi-view video encoders. Since multiple views taken from a 2D camerea array need to be coded at every time instance, the complexity of the encoder plays an important role besides its rate-distortion performance. In previous papers we have introduced a new frame type called D frame that exploits the stron geometrical correspondence between views, thereby reducing the complexity of the encoder. By employing D frames instead of some of the P frames in the prediction structure, significant compexity gain can be achieved if the trhreshold value which is a keystone element to adjust the complexity at the cost of quality and/or bit-rate is selected wisely. In this work, a new adaptive method to calculate the threshold value automatically from existing information during the encoding process is presented. In this method, the threshold values are generated for each block of each D frame to increase the accuracy. The algorithm is applied to several image sets and 20.6% complexity gain is achieved using the automatically generated threshold values without compromising qaulity or bit-rate.

This paper presents a new self-adapting and content-sensitive optimization technique for the H.264/AVC in-loop deblocking filter [1], focussing on the visual enhancement of the perceived reconstruction quality. Performed frame-wisely at the encoder side, the proposed algorithm first identifies visually important image regions in the currently decoded and blocky frame, including natural and artificial edge areas, and then optimizes the filter inherent threshold decisions, finding the best trade-off between preserving natural and suppressing artificial edges. As fast optimization criterion, the low complex Edge-PSNR [2] is employed, which has proved a very good congruence to the human visual quality sensation, much better than standard PSNR. In this work, the optimization behavior of all thresholds is analyzed, showing their mostly good-natured curve shape for convex optimization, but high content dependency of the optimal values. The in-loop coding results for AVC evidence the approach's high capability for visual improvement, whose general design is easily portable to other AVC deblocking based architectures like HEVC.

This paper presents a content-adaptive coding scheme for immersive networked experience of sports events, in particular, soccer games. We assume that future sports events are captured by an array of fixed high-definition cameras which provide multiview image sequences for a free-viewpoint immersive networked experience in a home environment. We discuss a content-adaptive coding scheme for image sequences that exploits properties of such sequences and that permits efficient user interactions. In this work, we construct a rate distortion model for an image sequence to obtain the optimal bitrate allocation among static and dynamic content items. The optimal bitrate allocation results in a rate distortion performance of the coding scheme that outperforms that of conventional H.264/AVC coding significantly.

In order to restore the information loss in video communication, a content adaptive intra error concealment method is proposed in this paper. Different images and different packet loss rates of the video sequences are used in the experiment. The experimental results show that the improved method has the better subjective quality and the higher PSNR than the bilinear interpolation and the semi-adaptive error concealment method.

A content adaptive method for single image dehazing is proposed in this work. Since the degradation level affected by haze is related to the depth of the scene and pixels in each specific part of the image (such as trees, buildings or other objects) tend to have similar depth to the camera, we assume that the degradation level affected by haze of each region is the same That is, the transmission in each region should be similar as well. Based on this assumption, each input image is segmented into different regions and transmission is estimated for each region followed by refinement by soft matting. As a result, the hazy images can be successfully recovered. The experimental results demonstrate that the proposed method performs satisfactorily.

In prior work, we proposed an unequal error protection algorithm based on Wyner-Ziv coding for error resilient video transmission. In subsequentwork it was demonstrated that using either of content adaptive unequal error protection or feedback aided unequal error protection individually improved error resilience performance. In the current paper we propose to combine the use of a content adaptive function, implemented at the encoder, with the channel loss feedback provided by the decoder. The experimental results demonstrate improved rate distortion performance.

Realistic finite element (FE) head models for neuro-electromagnetic imaging are getting more attention due to their analytic advantages over conventional models. To improve the numerical efficiency, we have previously developed a novel mesh generation scheme that produces FE head models automatically that are content-adaptive to given MR images. MRI content-adaptive FE meshes (cMeshes) represent the electrically conducting domain more effectively with less number of nodes and elements, thus lessen the computational loads. In general, the cMesh generation is affected by the selection of feature maps derived from MRI. In this study, we have tested the effects of various feature maps on the generation of cMesh FE head models. Also we have evaluated the quality of cMesh FE head models to check their suitability for neuro-electromagnetic imaging using EEG and MEG. The results suggest that the cMesh FE head models with properly selected feature maps do show acceptable quality to be used in neuro-electromagnetic imaging.

Most artifact reduction techniques blur the details of the images while removing various coding artifacts. In this paper, we propose a novel and explicit approach for classifying blocks into detailed regions, intermediate regions and smooth regions. The classification is based on the information content of the underlying region, which is quantized by local entropy calculated on the PDF of the pixel intensity distribution. The entropy is used as an indicator of how much smoothing is needed for a certain region. Experimental results show that our proposed algorithm can preserve the details and reduce coding artifacts better than more expensive state of the art techniques.

This paper describes a new method of watermarking based on visual contents and using four spaces insertion of insertion to take advantage of properties of each of them. This is realized initially by segmenting image in unrefined regions and then adapting every space of insertion to these regions in order to maximize the invisibility of the marked image. Experimental results show that the watermark is robust to several distortions including geometric alterations, JPEG coding at different qualities, STIRMA4RK attack, filtering and cropping.

Multiple reference frame selection adopted by the state-of-art H.264 video compression standard offers substantial performance gain. The temporal search range control, as a consequence, is crucial for maintaining the coding performance with minimum complexity. In this paper, we investigate the relationships between the reference frame buffer utilization and the optimal search range. A content-adaptive algorithm is proposed to control the search range dynamically during the encoding process. Experimental results show that our algorithm can rapidly adapt to the video characteristics and effectively reduce the complexity with negligible coding performance penalty.

In this paper, we propose a low-complexity content-adaptive background skipping scheme for region-of-interest (ROI) video coding in real-time wireless video telephony applications. Based on the real-time content information of the current and previous frames, such as foreground shape deformation, foreground and background motion, and background texture complexity, the proposed algorithm dynamically decides whether to skip non-ROI macroblocks of current frame and reallocate saved bits to ROI and coded non-ROI. In this work, we proposed and compared three bit reallocation strategies, and the one predicting the future events using a Bayesian model is found the most efficient. Experimental results indicate that the proposed scheme significantly outperforms other methods by up to 2.5 dB

This paper presents a content-adaptive system for streaming reconstructed soccer goal events over networks with bandwidth limited to 1.5Mbps or below. The reconstruction module analyzes a soccer video to produce corresponding panoramic field model with localized motion trajectories to provide tactic analysis for user's better comprehension of the goal events. The transmission module designs 3 schemes for different bandwidth conditions (150Kbps-1.5Mbps), (56-150Kbps), and (

We propose a content-adaptive interpolation algorithm for digital images. The proposed algorithm makes use of a non-linear spatial varying filter (SVF), which is simple but efficient in enlarging images. First, edge orientation statistics in an image are exploited, and then the information is used to determine suitable weights of neighboring pixels when interpolating the current pixel. Using the proposed approach, we can generate pixel values to replace missing ones such that sharp edges can be accurately reconstructed. Simulation results show that the proposed algorithm obtains better results in both subjective and objective quality measurements over existing methods.

Conventional rate control schemes focus on the adjustment of quantization values to retain a certain buffer level, and buffer adaptive frame skippings are used to avoid buffer overflow at low bit-rates. In this paper, a content adaptive frame-skipping scheme is proposed to achieve the balance between spatial and temporal quality. The occurrence of frame skipping is jointly dependent on the temporal and spatial contents of the video, and on the fullness of the buffer. This helps to achieve a balanced spatial and temporal quality and to enhance the overall perceptual quality. Experimental results show that the new scheme is simple but very effective, with large average PSNR gains and consistently improved visual quality, and the improvement in perceptual quality is much significant than that in average PSNR's.

This paper presents a memory-saving long-term prediction algorithm. It takes advantage of the better image quality possible with this technique, but is characterized by an extreme reduction in the amount of memory space required. In reference picture selection mode (H.263 standard, Annex N), coder and decoder use not only the previous picture for prediction, but also information from less recent pictures. Contrary to Annex N, in the method that we propose only parts, e.g. individual blocks or areas of past pictures, are stored in a reference buffer. We present two new concepts, both for selecting picture areas for storing and for deleting superfluous blocks from the long-term memory. By suitable selection of the picture areas, our algorithm achieves a similar picture quality to H.263+, Annex N, but uses only a fraction of the picture buffer. In this way it is possible to address around 50 earlier pictures with the same amount of memory required for only 5 pictures. Moreover, our approach requires no signaling and produces no delay.

Unconstrained motion compensated temporal filtering (UMCTF) is a very general and flexible framework for temporal filtering. It allows the selection of many different filters as well as decomposition structures to allow easy adaptation to video content, bandwidth variations, complexity requirements, and in conjunction with embedded coding can provide spatio-temporal-SNR scalability. In this paper we demonstrate the content-adaptive filter selection provided within the UMCTF framework. We show improvements in coding efficiency as well as in decoded visual quality using content-adaptive filters, at different granularities.

We propose the use of a content-adaptive volumetric mesh model for fully three-dimensional (3D) tomographic image reconstruction. In the proposed framework, the image to be reconstructed is first modeled by an efficient mesh representation. The image is then obtained through estimation of the nodal values from the measured data. The use of a mesh representation can alleviate the ill-posed nature of the reconstruction problem, thereby leading to improved quality in the reconstructed images. In addition, it reduces the data storage requirement, resulting in efficient algorithms. The proposed methods are tested using gated cardiac-perfusion images. Initial results demonstrate that the proposed approach achieves good performance when compared to several commonly used methods for image reconstruction, and produces results very rapidly.

The introduction of HDTV asks for spatial up-conversion techniques enabling the display of standard resolution material. Recently, X. Li and M. Orchard proposed the 'New Edge-Directed Interpolation' (NEDI) algorithm for high quality up-scaling of natural images. We shall show that the method, although it generally behaves well, introduces annoying artifacts in fine-textured areas. Based on an analysis of these artifacts and taking advantage of the temporal correlation between video images, we propose an improved NEDI algorithm. In our evaluation, we compare the performance of the original and the improved NEDI method on a significant set of test images. We conclude from both subjective and objective measures that the proposed modifications improve the overall performance of NEDI.

Fourier ptychography (FP) is a recently reported technique, for large field-of-view and high-resolution imaging. Specifically, FP captures a set of low-resolution images, under angularly varying illuminations, and stitches them together in the Fourier domain. One of FP's main disadvantages is its long capturing process, due to the requisite large number of incident illumination angles. In this Letter, utilizing the sparsity of natural images in the Fourier domain, we propose a highly efficient method, termed adaptive Fourier ptychography (AFP), which applies content adaptive illumination for FP, to capture the most informative parts of the scene's spatial spectrum. We validate the effectiveness and efficiency of the reported framework, with both simulated and real experiments. Results show that the proposed AFP could shorten the acquisition time of conventional FP, by around 30%-60%.

A novel content-adaptive music watermarking technique is proposed in this article. To optimally balance inaudibility and robustness when embedding and extracting watermarks, the embedding scheme is highly related to the music structure and human auditory system (HAS). A note-based segmentation method is proposed and used for music vocal/instrumental boundary detection. A multiple bit hopping and hiding scheme with different embedding parameters is applied to vocal and instrumental frames of the music. The experimental results in inaudibility and robustness are provided to support all novel features in the proposed watermarking scheme.

The design of computer-assisted decision (CAD) systems for different biomedical imaging scenarios is a challenging task in computer vision. Sometimes, this challenge can be attributed to the image acquisition mechanisms since the lack of control on the cameras can create different visualizations of the same imaging site under different rotation, scaling, and illumination parameters, with a requirement to get a consistent diagnosis by the CAD systems. Moreover, the images acquired from different sites have specific colors, making the use of standard color spaces highly redundant. In this paper, we propose to tackle these issues by introducing novel region-based texture, and color descriptors. The proposed texture features are based on the usage of analytic Gabor filters (for compensation of illumination variations) followed by the calculation of first- and second-order statistics of the filter responses and making them invariant using some trivial mathematical operators. The proposed color features are obtained by compensating for the illumination variations in the images using homomorphic filtering followed by a bag-of-words approach to obtain the most typical colors in the images. The proposed features are used for the identification of cancer in images from two distinct imaging modalities, i.e., gastroenterology and dermoscopy . Experiments demonstrate that the proposed descriptors compares favorably to several other state-of-the-art methods, elucidating on the effectiveness of adapted features for image characterization.