nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive Silhouette Extraction in Dynamic Environments Using Fuzzy Logic

Extracting a human silhouette from an image is the enabling step for many high-level vision processing tasks, such as human tracking and activity analysis. Although there are a number of silhouette extraction algorithms proposed in the literature, most approaches work efficiently only in constrained environments where the background is relatively simple and static. In a previous paper, we addressed some of the challenges in silhouette extraction and human tracking in a real-world unconstrained environment where the background is complex and dynamic. We extracted features from image regions, accumulated the feature information over time, fused high-level knowledge with low-level features, and built a time-varying background model. A problem with our system is that by adapting the background model, objects moved by a human are difficult to handle. In order to reinsert them into the background, we run the risk of cutting off part of the human silhouette, such as in a quick arm movement. In this paper, we develop a fuzzy logic inference system to detach the silhouette of a moving object from the human body. Our experimental results demonstrate that the fuzzy inference system is very efficient and robust.
Power-rate-distortion analysis for wireless video communication under energy constraints

Mobile devices performing video coding and streaming over wireless and pervasive communication networks are limited in energy supply. To prolong the operational lifetime of these devices, an embedded video encoding system should be able to adjust its computational complexity and energy consumption as demanded by the situation and its environment. To analyze, control, and optimize the rate-distortion (R-D) behavior of the wireless video communication system under the energy constraint, we develop a power-rate-distortion (P-R-D) analysis framework, which extends the traditional R-D analysis by including another dimension, the power consumption. Specifically, in this paper, we analyze the encoding mechanism of typical video coding systems, and develop a parametric video encoding architecture which is fully scalable in computational complexity. Using dynamic voltage scaling (DVS), an energy consumption management technology recently developed in CMOS circuits design, the complexity scalability can be translated into the energy consumption scalability of the video encoder. We investigate the R-D behavior of the complexity control parameters and establish an analytic P-R-D model. Both theoretically and experimentally, we show that, using this P-R-D model, the video coding system is able to automatically adjust its complexity control parameters to match the available energy supply of the mobile device while maximizing the picture quality. The P-R-D model provides a theoretical guideline for system design and performance optimization in mobile video communication under energy constraints.
Spatially supervised recurrent convolutional neural networks for visual object tracking

In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking. Our recurrent convolutional network exploits the history of locations as well as the distinctive visual features learned by the deep neural networks. Inspired by recent bounding box regression methods for object detection, we study the regression capability of Long Short-Term Memory (LSTM) in the temporal domain, and propose to concatenate high-level visual features produced by convolutional networks with region information. In contrast to existing deep learning based trackers that use binary classification for region candidates, we use regression for direct prediction of the tracking locations both at the convolutional layer and at the recurrent unit. Our experimental results on challenging benchmark video tracking datasets show that our tracker is competitive with state-of-the-art approaches while maintaining low computational cost.
A New TV World for Kids - When ZUI Meets Deep Learning

In this work, we propose a novel application of Zoomable User Interface (ZUI) for TV world. With the utilization of latest advances of deep learning, visual tags of video titles can be semi-automatically extracted. By creating a cascaded visual tag tree structure, the ZUI representation problem is converted into an optimization problem of choosing tags to achieve the least user interactions. To the best of our knowledge, this is the world’s first effort in adopting ZUI on TV display with an optimized solution to achieve the best user experiences. Experimental results indicate that this invention is favored by kids, due to its nature of visual richness, intuitive, and user friendly.
MPEG-4 video streaming quality evaluation in IEEE 802.11e WLANs

The IEEE 802.11 working group is currently working on a new standard called IEEE 802.11e to support quality of service (QoS) in WLANs. 802.11e introduces a so-called hybrid coordination function (HCF) containing two medium access mechanisms: enhanced distributed channel access (EDCA) and HCF controlled channel access (HCCA). In the EDCA mechanism, many QoS parameters are introduced including minimum contention window (CWmin), maximum contention window (CWmax), arbitration inter frame space (AIFS) and transmission opportunity limit (TXOPlimit). In this paper, we experimentally assess the MPEG-4 video streaming performance over 802.11e. In particular, we discuss in detail how the human satisfaction of streaming video is affected by the main QoS parameters in IEEE 802.11e WLANs. We measure the level of end user satisfaction together with the network performance and give recommendations regarding the network design and the parameter settings.
Resource allocation and performance analysis of wireless video sensors

Wireless video sensor networks (WVSNs) have been envisioned for a wide range of important applications, including battlefield intelligence, security monitoring, emergency response, and environmental tracking. Compared to traditional communication system, the WVSN operates under a set of unique resource constraints, including limitations with respect to energy supply, on-board computational capability, and transmission bandwidth. The objective of this paper is to study the resource utilization behavior of a wireless video sensor and analyze its performance under the resource constraints. More specifically, we develop an analytic power-rate-distortion (P-R-D) model to characterize the inherent relationship between the power consumption of a video encoder and its rate-distortion performance. Based on the P-R-D analysis and a simplified model for wireless transmission power, we study the optimum power allocation between video encoding and wireless transmission and introduce a measure called achievable minimum distortion to quantify the distortion under a total power constraint. We consider two scenarios in wireless video sensing, small-delay wireless video monitoring and large-delay wireless video surveillance, and analyze the performance limit of the wireless video sensor in each scenario. The analysis and results obtained in this paper provide an important guideline for practical wireless video sensor design.
From rate-distortion analysis to resource-distortion analysis

The ultimate goal in communication system design is to control and optimize the system performance under resource constraints. As the communication paradigm evolves from the conventional desktop computing, wired, and centralized communication to current mobile, wireless, distributed, and massive communication, video encoding and transmission operate under more and more resource constraints. In traditional video communication applications, such as digital TV broadcast, the major constraint is in the form of transmission bandwidth or storage space, which determines the encoding bit rate. Rate-distortion (R-D) theories have been developed to model the relationship between the coding bit rate and signal distortion. For video communication over mobile devices, the video encoding and transmission operate under additional resource constraints, such as energy supply and on-board computation capability. Therefore, there is a need to extend the traditional R-D analysis to resource-distortion analysis by incorporating the new resource constraints into the R-D analysis framework. In distributed and massive wireless video sensor networks, the resource utilization behaviors of individual video sensors should be well-coordinated through network-level rate allocation and optimum routing so as to maximize the overall performance. In this paper, we start from the classical R-D theory developed by Shannon over 50 years ago, and then review the R-D modelling techniques for modern image and video compression systems. We study the resource-distortion analysis framework for video communication over wireless devices. As one step further, we present the research problem of resource allocation and performance optimization for video compression and communication over a network of wireless communication devices.
Supervised Feature Learning Network Based on the Improved LLE for face recognition

Deep neural networks (DNNs) have been successfully applied in the fields of computer vision and pattern recognition. One drawback of DNNs is that most of existing DNNs models and their variants usually need to learn a very large set of parameters. Another drawback of DNNs is that DNNs does not fully take the class label and local structure into account during the training stage. To address these issues, this paper proposes a novel approach, called Supervised Feature Learning Network Based on the Improved LLE (SFLNet) for face recognition. The goal of SFLNet is to extract features efficiently. Thus SFLNet consists of learning kernels based on the improved Locally Linear Embedding (LLE) and multiscale feature analysis. Instead of taking image pixels as the input of LLE algorithm, the improved LLE uses linear discriminant kernel distance (LDKD). Besides, the outputs of the improved LLE are convolutional kernels, not the dimensional reduction features. Mutiscale feature analysis enhances the insensitive to complex changes caused by large pose, expression, or illumination variations. So SFLNet has better discrimination and is more suitable for face recognition task. Experimental results on Extended Yale B and AR dataset shows the impressive improvement of the proposed method and robustness to occlusion when compared with other state-of-art methods.
Video coding using joint temporal-spatial compensation

In motion compensation (MC) based video coding system, macroblock data is always predicted by its temporal neighborhood in its previous frame. In this work, we observe that the macroblock data can often be better predicted by its spatial neighborhood than its temporal neighborhood, especially at low coding bit rates. Based on this observation, we propose a joint temporal-spatial compensation (JTSC) scheme for video coding, with the conventional MC being its subset. Our experimental results demonstrate that the coded picture quality is significantly improved by this novel JTSC scheme.
Energy Minimization of Portable Video Communication Devices Based on Power-Rate-Distortion Optimization

Portable video communication devices operate on batteries with limited energy supply. However, video compression is computationally intensive and energy-demanding. Therefore, one of the central challenging issues in portable video communication system design is to minimize the energy consumption of video encoding so as to prolong the operational lifetime of portable video devices. In this work, based on power-rate-distortion (P-R-D) optimization, we develop a new approach for energy minimization by exploring the energy tradeoff between video encoding and wireless communication and exploiting the nonstationary characteristics of input video data. Both analytically and experimentally, we demonstrate that incorporating the third dimension of power consumption into conventional R-D analysis gives us one extra dimension of flexibility in resource allocation and allows us to achieve significant energy saving. Within the P-R-D analysis framework, power is tightly coupled with rate, enabling us to trade bits for joules and perform energy minimization through optimum bit allocation. We analyze the energy saving gain of P-R-D optimization. We develop an adaptive scheme to estimate P-R-D model parameters and perform online resource allocation and energy optimization for real-time video encoding. Our experimental studies show that, for typical videos with nonstationary scene statistics, using the proposed P-R-D optimization technology, the energy consumption of video encoding can be significantly reduced (by up to 50%), especially in delay-tolerant portable video communication applications.
Low-Complexity and Reliable Moving Objects Detection and Tracking for Aerial Video Surveillance with Small UAVS

Moving objects detection and tracking is the first and enabling step for many high-level UAV surveillance tasks, including cooperative UAV path planning, navigation control, and automated information analysis. In this work, we develop a low-complexity and reliable moving object detection algorithm by exploring the ideas of uncertainty analysis and spatiotemporal activity clustering. More specifically, the authors develop a fast and efficient algorithm to estimate the global vehicle-camera motion. Image regions (blocks) with local motion was detected using statistical hypothesis testing. Using spatiotemporal clustering, the authors group these moving blocks into moving objects with physical meanings, such as moving vehicles or persons. Our extensive experimental results demonstrate the efficiency of the proposed algorithm.
Fast and accurate rate prediction and picture quality control for wavelet image coding

By introducing the concepts of characteristic rate curves and rate curve decomposition, a novel framework for rate-distortion (R-D) analysis and source modeling is developed in this work. With this framework, a fast algorithm is proposed to accurately estimate the R-D curve before quantization and coding for wavelet-based image coders. The proposed algorithm is applied to the SPIHT (set partitioning in hierarchical trees) and stack-run (SR) encoders. Our extensive experimental results show that the relative R-D curve estimation error is less than 5%.
Transmission Distortion-optimized Unequal Loss Protection for video transmission over packet erasure channels

In this paper, we study the problem of Transmission Distortion-optimized Unequal Loss Protection (TD-ULP) under rate constraints for non-scalable video transmission over packet erasure channels. Based on a packet-level transmission distortion modeling scheme, we estimate the amount of contribution of each video packet to the reconstructed video quality, which defines the priority level of each packet. Unequal amounts of protections are then allocated to different video packets according to their priority levels as well as the dynamic channel conditions. The optimal ULP resource allocation is formulated as a constrained nonlinear optimization problem. An evolutionary algorithm based on Particle Swarm Optimization (PSO) is developed to obtain the optimal resource allocation. Our extensive experimental results demonstrate the effectiveness of the proposed TD-ULP scheme, which outperforms existing methods by up to 2dB gain in reconstructed video quality. 1
Deep convolutional neural network based species recognition for wild animal monitoring

We proposed a novel deep convolutional neural network based species recognition algorithm for wild animal classification on very challenging camera-trap imagery data. The imagery data were captured with motion triggered camera trap and were segmented automatically using the state of the art graph-cut algorithm. The moving foreground is selected as the region of interests and is fed to the proposed species recognition algorithm. For the comparison purpose, we use the traditional bag of visual words model as the baseline species recognition algorithm. It is clear that the proposed deep convolutional neural network based species recognition achieves superior performance. To our best knowledge, this is the first attempt to the fully automatic computer vision based species recognition on the real camera-trap images. We also collected and annotated a standard camera-trap dataset of 20 species common in North America, which contains 14, 346 training images and 9, 530 testing images, and is available to public for evaluation and benchmark purpose.
Lowpass filtering of rate-distortion functions for quality smoothing for real-time video recording and streaming

In this work, we introduce the concept of low-pass filtering of rate-distortion (R-D) functions and develop a smoothed rate control (SRC) framework for real-time video recording and streaming. Theoretically, we prove that using a geometric averaging filter the SRC algorithm is able to maintain a very smooth video presentation quality while achieving the target bit rate automatically. The proposed SRC algorithm has very low computational complexity and implementation cost. Our extensive experimental results demonstrate that the proposed SRC algorithm significantly reduces the picture quality variation in the encoded video clips while matching the encoding bit rate target very accurately.
Resource-Distortion Modeling for Video Streaming over Mesh Networks with Priority-Based Packet Scheduling

Video streaming over mesh networks operates under stringent network resource constraints, with a large number of video sessions competing for limited network resources. In this work, we aim to establish so-called resource-distortion models to characterize the inherent relationship between the allocated network resources to each video session and its end-to-end video distortion or presentation quality at the receiver end. We observed that priority-based packet scheduling has significant impact on such resource-distortion relationship. Using ANN-based learning methods, we develop an end-to-end packet delay bound violation (PDBV) probability model for video streaming over multi-hop networks with priority-based packet scheduling. We then derive a quadratic video transmission distortion model to capture the unique behavior of priority-based packet scheduling and its impact on the end-to-end video distortion. Based on these resource-distortion models, we are able to predict the end-to-end distortion for video streaming over multi-hop networks with priority-based packet scheduling. Our extensive experimental results demonstrate that the proposed method is very accurate.
Multimodal Medical Image Registration Based on Feature Spheres in Geometric Algebra

Multi-modal image registration in medical image analysis is very challenging as the appearance of body structures in images from different imaging devices can be very different. In this paper, we propose a new method [GA-speeded up robust features (SURF)], which incorporates the geometric algebra (GA) into SURF framework, to detect features from images. We model the volumetric data and register the multi-modal medical images using feature spheres formulated in conformal geometric algebra (CGA). Specifically, we first extract features from medical images using GA-SURF. Second, we construct the feature spheres using the feature points and find the correspondence of feature spheres in the two images using CGA. With that, we can register the images based on the correspondence of the feature spheres. The experimental results evaluated by RIRE have shown that our method can register the multi-modal images with high accuracy. The maximum registration error is less than  $4mm$ .
Scene text detection based on component-level fusion and region-level verification

In this paper, we present a novel scene text detection method that combines the advantages of component-based methods and region-based methods, while overcoming their inherent limitations. We first extract text regions as candidates, and then aggregate these text components in these regions into words and text lines. To separate non-text components in the background from text components, we perform both character-level filtering and word-level classification with a trained linear SVM (support vector machine) classifier. Our extensive experiments on ICDAR2003 and ICDAR2011 datasets have shown that our method outperforms the state-of-the-art methods in text detection.
Task-driven progressive part localization for fine-grained recognition

In this paper we propose a task-driven progressive part localization (TPPL) approach for fine-grained object recognition. Most existing methods follow a two-step approach which first detects salient object parts to suppress the interference from background scenes and then classifies objects based on features extracted from these regions. The part detector and object classifier are often independently designed and trained. In this paper, our major finding is that the part detector should be jointly designed and progressively refined with the object classifier so that the detected regions can provide the most distinctive features for final object recognition. Specifically, we start with a part-based SPP-net (Part-SPP) as our baseline part detector. We then develop a task-driven progressive part localization framework, which takes the predicted boxes of Part-SPP as an initial guess, then examines new regions in the neighborhood, searching for more discriminative image regions to maximize the recognition performance. This procedure is performed in an iterative manner to progressively improve the joint part detection and object classification performance. Experimental results on the Caltech-UCSD-200-2011 dataset demonstrate that our method outperforms state-of-the-art fine-grained categorization methods both in part localization and classification, even without requiring a bounding box during testing.
Reweighted Low-Rank Matrix Analysis With Structural Smoothness for Image Denoising

In this paper, we develop a new low-rank matrix recovery algorithm for image denoising. We incorporate the total variation (TV) norm and the pixel range constraint into the existing reweighted low-rank matrix analysis to achieve structural smoothness and to significantly improve quality in the recovered image. Our proposed mathematical formulation of the low-rank matrix recovery problem combines the nuclear norm, TV norm, and  $l_{1}$  norm, thereby allowing us to exploit the low-rank property of natural images, enhance the structural smoothness, and detect and remove large sparse noise. Using the iterative alternating direction and fast gradient projection methods, we develop an algorithm to solve the proposed challenging non-convex optimization problem. We conduct extensive performance evaluations on single-image denoising, hyper-spectral image denoising, and video background modeling from corrupted images. Our experimental results demonstrate that the proposed method outperforms the state-of-the-art low-rank matrix recovery methods, particularly for large random noise. For example, when the density of random sparse noise is 30%, for single-image denoising, our proposed method is able to improve the quality of the restored image by up to 4.21 dB over existing methods.
Multimedia communication systems: techniques, standards, and networks [Book Review]

Reviewed:K.R. Rao, Z.S. Bojkovic, and D.A. Milovanovic, Multimedia Communication Systems: Techniques, Standards, and Networks, Prentice Hall, 2002, $69, 576 pp., ISBN 013031398X. One primary accomplishment of this book is that it presents readers with a clear picture ofmultimedia communication and demystifies the various technologies developed in theinternational standards and research literature.