nils-herrmann/clean_scirepeval huggingface dataset:
Reference frame selection for loss-resilient texture & depth map coding in multiview video conferencing

In a free-viewpoint video conferencing system, the viewer can choose any desired viewpoint of the 3D scene for observation. Rendering of images for arbitrarily chosen viewpoint can be achieved through depth-image-based rendering (DIBR), which typically employs “texture-plus-depth” video format for 3D data exchange. Robust and timely transmission of multiple texture and depth maps over bandwidth-constrained and loss-prone networks is a challenging problem. In this paper, we optimize transmission of multiview video in texture-plus-depth format over a lossy channel for free viewpoint synthesis at decoder. In particular, we construct a recursive model to estimate the distortion in synthesized view due to errors in both texture and depth maps, and formulate a rate-distortion optimization problem to select reference pictures for macroblock encoding in H.264 in a computation-efficient way, in order to provide unequal protection to different macroblocks. Results show that the proposed scheme can outperform random insertion of intra refresh blocks by up to 0.73 dB at 5% loss.
Iterative Side-Information Generation in a Mixed Resolution Wyner-Ziv Framework

We propose a mixed resolution framework based on full resolution key frames and spatial-reduction-based Wyner-Ziv coding of intermediate nonreference frames. Improved rate-distortion performance is achieved by enabling better side-information generation at the decoder side and better rate-allocation at the encoder side. The framework enables reduced encoding complexity by low resolution encoding of the nonreference frames, followed by Wyner-Ziv coding of the Laplacian residue. The quantized transform coefficients of the residual frame are mapped to cosets without the use of a feedback channel. A study to select optimal coding parameters in the creation of the memoryless cosets is made. Furthermore, a correlation estimation mechanism that guides the parameter choice process is proposed. The decoder first decodes the low resolution base layer and then generates a super-resolved side-information frame at full resolution using past and future key frames. Coset decoding is carried using side-information to obtain a higher quality version of the decoded frame. Implementation results are presented for the H.264/AVC codec.
On the use of motion-based frame rejection in temporal averaging denoising for segmentation of echocardiographic image sequences

We have recently introduced an algorithm for semi-automatic segmentation of the left ventricular wall in short-axis echocardiographic images (EMBC 30:218–221). In its preprocessing stage, the algorithm uses temporal averaging for image denoising. Motion estimation is used to detect and reject frames that do not correlate well with the set of images being averaged. However, the process of estimating motion vectors is computationally intense, which increases the algorithm's computation time. In this work, we evaluate the viability of replacing the motion estimation stage with less computationally intense approaches. Two alternative techniques are evaluated. The ventricular contours obtained from each of the three algorithm variants were quantitatively and qualitatively compared with contours manually-segmented by a specialist. We show that it is possible to reduce the algorithm's computational load without significantly reducing the segmentation quality. The proposed algorithms are also compared with three other techniques from the literature.
H∞ Estimation and Array Algorithms for Discrete-time Descriptor Systems

This paper deals with H<sub>∞</sub> recursive estimation problem for general time-variant descriptor systems. Firstly, a Riccati-based recursion for predicted estimates is developed (the H<sub>∞</sub> filtered estimates recursion was presented in a previous work). In the sequel, the predicted and filtered H<sub>∞</sub> filters are presented in information form with the respective array algorithms. A numerical example is presented to illustrate the effectiveness of the proposed algorithms
CQR codes: Colored quick-response codes

This paper proposes a new way to store/transmit information using a Colored QR Code structure. Instead of using only black and white modules, the proposed code is designed to employ 5 different RGB colors (red, green, blue, black and white), which enables twice as much storage capacity compared to traditional binary QR Codes. Reed-Solomon error-correcting code with a theoretical correction capability of 38.41% is also applied. In our experiments, each Colored QR Code in the test set is printed, scanned using a 3.2 megapixel digital camera and decoded. We show that the proposed scheme can consistently decode 1024 bits of information stored on a 1.3 cm × 1.3 cm printed area.
Mixed-resolution distributed video codec without motion estimation at the encoder

Inspired by recent results showing that Wyner-Ziv coding using a combination of source and channel coding may be more efficient than pure channel coding, we have applied coset codes for the source coding part in the transform domain for Wyner-Ziv coding of video. The framework is a mixed-resolution approach where reduced encoding complexity is achieved by low resolution encoding of non-reference frames and regular encoding of the reference frames. Different from our previous works, no motion estimation is carried at the encoder, neither for the low resolution frames nor the reference frames. The entropy coders of the H.264/AVC codec were tuned to improve their performance for encoding the cosets. An encoding mode with lowest encoding complexity than H.264/AVC intra mode is achieved. Experimental results show a competitive rate-distortion performance especially at low bit rates.
Progressive communication for interactive light field image data streaming

Light field (LF) imaging captures multiple intensities and directions of light per pixel during acquisition in a 3D scene, so that novel images of different viewpoints or focal points can be synthesized. However, transmitting all LF data before viewer observation incurs a large startup delay. To avoid such delay, we propose a new interactive LF streaming framework, where a client periodically requests viewpoint images, and in response a server synthesizes and transmits each requested image as a carefully chosen sparse linear combination of sub-aperture images. For each received synthesized image, the client “decodes” and recovers a new sub-aperture image using a cache of known sub-aperture images. As the cache of decoded sub-aperture images grows over time, the client becomes capable of synthesizing new view/focal point images, reducing overall transmission cost. Experimental results show that our proposed scheme can deliver synthesized images at high quality, even though only sparse sub-aperture images are used for synthesis. Moreover, compared with a scenario where the requested synthesized images are always transmitted, our proposed scheme achieves a significant reduction in accumulated rate when a sufficient number of images are transmitted.
Low-saliency prior for disocclusion hole filling in DIBR-synthesized images

Although images as viewed from intermediate virtual viewpoints can be synthesized using texture and depth maps from nearby camera views via depth-image-based rendering (DIBR), the rendered images contain disocclusion holes - spatial regions that were not visible in the reference views due to foreground object occlusion - that requires proper filling. In this paper, we introduce a new signal prior into the hole filling problem formulation: given disocclusion holes are part of the background and background tends to have low visual saliency, the extrapolated signal into the holes must also be of low saliency. Mathematically, we add a low-saliency prior to an exemplar-based inpainting algorithm, so that the best-matched block has both small matching cost and is of low visual saliency. Moreover, we compute a suitable Lagrange multiplier value for the saliency cost term via analysis of the reference images. Experimental results show that using a low-saliency prior can improve performance by 0.5 dB over a previous hole filling scheme.
Motion-Based Side-Information Generation for a Scalable Wyner-Ziv Video Coder

A motion-based side-information generation scheme with semi super-resolution for a scalable Wyner-Ziv coder framework is introduced. It is known that the performance of any Wyner-Ziv coder is heavily dependent on the efficiency of the side-information generation. We propose an iterative block based scheme to generate a semi super-resolution frame using the past and future reference frames which should be coded at full-resolution. To enable this side-information generation the framework should allow for low encoding complexity, reducing the spatial resolution only in the non-reference frames. The enhancement layer is produced using a residual frame of the reduced resolution encoded frame. The decoder first decodes the low resolution base layer and iteratively generates the side-information, along with channel decoding, to obtain a higher quality version of the decoded frame. Results of the implementation of the framework using the motion-based side-information in the H.263+ and H.264 standards are presented.
A Model and Simulation Framework for Exploring Potential Impacts of Land Use Policies: The Brazilian Cerrado Case

Land use and cover change (LUCC) research is important to support insightful management of Earth's land use to avoid irreversible damage. However, LUCC is a complex process that relates the interaction between natural and social systems at different temporal and spatial scales. Nevertheless, the representation of different interaction patterns through agent-based modeling and multi-agent systems can contribute to decision support for sustainability that are essential to a better understanding of real environmental problems, including social, economic and physical aspects. Thus, this article presents the use of a Multi-agent System for Environmental Simulation (MASE) for exploring potential impacts of land use policies. MASE is a freeware multi-agent model system to simulate LUCC dynamics that is illustrated with the Brazilian Cerrado case. Considering the experimental results, we consider that MASE represents an interesting alternative for LUCC decision support.
Loss-Resilient Coding of Texture and Depth for Free-Viewpoint Video Conferencing

Free-viewpoint video conferencing allows a participant to observe the remote 3D scene from any freely chosen viewpoint. An intermediate virtual viewpoint image is typically synthesized using two pairs of transmitted texture and depth maps from two neighboring captured viewpoints via depth-image-based rendering (DIBR). To maintain high quality of synthesized images, it is imperative to contain the adverse effects of network packet losses that may arise during texture and depth video transmission. Towards this goal, we develop an integrated approach that exploits the representation redundancy inherent in the multiple streamed videos-a voxel in the 3D scene visible to two captured views is sampled and coded twice in the two views. In particular, at the receiver we first develop an error concealment strategy that adaptively blends corresponding pixels in the two captured views during DIBR, so that pixels from the more reliable transmitted view are weighted more heavily. We then couple it with a sender-side optimization of reference picture selection (RPS) during real-time video coding, so that blocks containing pixel samples of voxels that are visible in both views are more error-resiliently coded in one view only, given adaptive blending will mitigate errors in the other view. Further, synthesized view distortion sensitivities to texture versus depth errors are analyzed, so that relative importance of texture and depth code blocks can be computed for system-wide RPS optimization. Finally, quantization parameter (QP) is adaptively selected per frame, optimally trading off source distortion due to compression with channel distortion due to potential packet losses. Experimental results show that the proposed scheme can outperform previous work by up to 2.9 dB at 5% packet loss rate.
Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks

This paper addresses the problem of continuous gesture recognition from sequences of depth maps using Convolutional Neural networks (ConvNets). The proposed method first segments individual gestures from a depth sequence based on quantity of movement (QOM). For each segmented gesture, an Improved Depth Motion Map (IDMM), which converts the depth sequence into one image, is constructed and fed to a ConvNet for recognition. The IDMM effectively encodes both spatial and temporal information and allows the fine-tuning with existing ConvNet models for classification without introducing millions of parameters to learn. The proposed method is evaluated on the Large-scale Continuous Gesture Recognition of the ChaLearn Looking at People (LAP) challenge 2016. It achieved the performance of 0.2655 (Mean Jaccard Index) and ranked 3rd place in this challenge.
Measuring face familiarity and its application to face recognition

The familiarity of faces is one of the key factors that come into play during human face analysis. However, there is very little research that studies face familiarity. In this paper, two methods are proposed to quantitatively measure the degree of familiarity of a face with respect to a known set. The methods are in accordance with the psychological study. In particular, non-negative matrix factorization (NMF) is extended to learn a localized non-overlapping subspace representation of commonly experienced facial patterns from known faces. The familiarity of a given face is then measured based on its reconstruction error after being projected into the learned extended NMF subspaces. A subjective study involving 50 subjects indicates the proposed familiarity measurement is in line with human judgments. Furthermore, the familiarity vector generated during the measuring process is employed for face recognition. Experiments based on the standard FERET evaluation protocol demonstrates the efficacy of the familiarity based representation for face recognition.
CMOS sensor cross-talk compensation for digital cameras

This paper presents two algorithms for removing the crosstalk effect in CMOS sensor based color-imaging systems. The algorithms work on the Bayer raw data and have low computational complexity. Experimental results on Macbeth color chart and real images demonstrates that both algorithms can effectively eliminate the crosstalk effect and produce better quality images with conventional color interpolation and correction algorithms designed for CCD image sensors. Complexity of the algorithms is also analyzed.
Recovering DC Coefficients in Block-Based DCT

It is a common approach for JPEG and MPEG encryption systems to provide higher protection for dc coefficients and less protection for ac coefficients. Some authors have employed a cryptographic encryption algorithm for the dc coefficients and left the ac coefficients to techniques based on random permutation lists which are known to be weak against known-plaintext and chosen-ciphertext attacks. In this paper we show that in block-based DCT, it is possible to recover dc coefficients from ac coefficients with reasonable image quality and show the insecurity of image encryption methods which rely on the encryption of dc values using a cryptoalgorithm. The method proposed in this paper combines dc recovery from ac coefficients and the fact that ac coefficients can be recovered using a chosen ciphertext attack. We demonstrate that a method proposed by Tang to encrypt and decrypt MPEG video can be completely broken
A Novel Video-Based Smoke Detection Method Using Image Separation

In the state-of-the-art video-based smoke detection methods, the representation of smoke mainly depends on the visual information in the current image frame. In the case of light smoke, the original background can be still seen and may deteriorate the characterization of smoke. The core idea of this paper is to demonstrate the superiority of using smoke component for smoke detection. In order to obtain smoke component, a blended image model is constructed, which basically is a linear combination of background and smoke components. Smoke opacity which represents a weighting of the smoke component is also defined. Based on this model, an optimization problem is posed. An algorithm is devised to solve for smoke opacity and smoke component, given an input image and the background. The resulting smoke opacity and smoke component are then used to perform the smoke detection task. The experimental results on both synthesized and real image data verify the effectiveness of the proposed method.
Learning structured dictionary based on inter-class similarity and representative margins

We consider the problem of learning a structured and discriminative dictionary based on sparse representation for classification task. The structure comprises class-shared and class-specific partitions which allows the separation of common and class-specific information in the data for classification. The resulting optimization problem was a max margin formulation that exploits the hinge loss function property. Comparative evaluation of the proposed classifier against four recent alternatives in a gender classification task indicates a 3-percenatge point improvement.
A new wavelet based ART network for texture classification

A new method for texture classification is proposed. It is composed of two processing stages, namely, a low level evolutionary feature extraction based on Gabor wavelets and a high level neural network based pattern recognition. This resembles the process involved in the human visual system. Gabor wavelets are exploited as the feature extractor. A neural network, fuzzy adaptive resonance theory (fuzzy ART), acts as the high level decision making and recognition system. Some modifications to the fuzzy ART make it capable of simulating the post-natal and evolutionary development of the human visual system. The proposed system has been evaluated using natural textures. The results obtained show that it is able to effectively perform the object recognition task and will find useful application in the study of the human visual system model for artificial vision.
A Pixel-Based Robust Imagewatermarking System

Robust image watermarking systems are required to be resistant to geometric attacks in addition to common image processing tasks, such as JPEG compression. However, robustness against geometric attacks, such as rotation, scaling and translation, still remains one of the most challenging research topics in image watermarking. We propose a new pixel-based watermarking system in which a binary logo is embedded, a bit per pixel, in the pixel domain of an image. The encoder of the proposed system is based on a sliding window embedding scheme that applies the local average quantization index modulation (QEM), to achieve geometric attack robustness. The decoder employs a maximum a posteriori (MAP) estimation supported by Markov random field (MRF) model to achieve robust decoding. Additionally, we demonstrate that the proposed scheme is also robust against possible watermark removal due to JPEG compression
A part-based template matching method for multi-view human detection

This paper proposes a part-based template matching method for multi-view human detection. The proposed method includes two stages: matching and verification. In particular, the best individual matching parts given a detection window are determined using an improved template matching algorithm. The hypothesis of the matched parts forming a human is then verified by employing a Bayesian-based model. The verification is not only based on the matching costs of individual parts but also how well the combining the matched parts satisfying the configuration constraints of the human body. Experimental results have shown that the proposed method is robust for detecting humans at multiple views and outperforms other template matching-based methods.
Mining Mid-Level Features for Action Recognition Based on Effective Skeleton Representation

Recently, mid-level features have shown promising performance in computer vision. Mid-level features learned by incorporating class-level information are potentially more discriminative than traditional low-level local features. In this paper, an effective method is proposed to extract mid-level features from Kinect skeletons for 3D human action recognition. Firstly, the orientations of limbs connected by two skeleton joints are computed and each orientation is encoded into one of the 27 states indicating the spatial relationship of the joints. Secondly, limbs are combined into parts and the limb's states are mapped into part states. Finally, frequent pattern mining is employed to mine the most frequent and relevant (discriminative, representative and non-redundant) states of parts in continuous several frames. These parts are referred to as Frequent Local Parts or FLPs. The FLPs allow us to build powerful bag-of-FLP-based action representation. This new representation yields state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D.
Shape VQ-based adaptive predictive lossless image coder

A new shape adaptive predictive lossless image coder is proposed. Three classes of block shapes are delineated with associated "optimum" predictors. Each image is partitioned into sub-blocks that are classified into one of the three classes using vector quantisation. The encoder then employs the predictor corresponding to the class of the block under consideration. Performance evaluation of the proposed coder in comparison with four other lossless coders including lossless JPEG indicates its superiority.
A 96 /spl times/ 64 intelligent digital pixel array with extended binary stochastic arithmetic

A chip architecture that integrates an optical sensor and a pixel level processing element based on binary stochastic arithmetic is proposed. The optical sensor is formed by an array of fully connected pixels, and each pixel contains a sensing element and a Pulse Frequency Modulator (PFM) converting the incident light to bit streams of identical pulses. The processing element is based on binary stochastic arithmetic to perform signal processing operations on the focal plane VLSI circuit. A 96 /spl times/ 64 CMOS image sensor is fabricated using 0.5/spl mu/m CMOS technology and achieves 29 /spl times/ 29/spl mu/m pixel size at 15% fill factor.
Comparison of "wavelet" filters and subband analysis structures for still image compression

The paper compares several subband analysis structures and filters for image compression using a generic subband quantisation and encoding method. The authors show that filter banks designed on the basis of an image correlation model outperform other filter banks. The DCT, lapped orthogonal transform (LOT), a cosine modulated filter bank, and an octave band orthogonal filter bank (discrete wavelet transform-DWT) perform in a similar manner. Wavelet filters used to implement the DWT are designed to maximise a coding gain metric. These filters significantly outperform filters designed using other criteria. Daubechies wavelet filters exhibit a coding gain close to these optimum filters and perform similarly for image compression. Some interesting properties relating filter time-width and step response ringing to the location of zeros are observed. Finally a modified DWT structure is proposed and shown to give an improvement over the DWT for some images.<<ETX>>