nils-herrmann/clean_scirepeval huggingface dataset:
Sign language recognition by image analysis

The Sign Language Recognition (SLR) Problem is a highly important research topic, because of its ability to increase the interaction between the people who are hearing-impaired or impediment in speech. We propose a simple but robust system. The proposed system consists of three main steps. First we apply segmentation to the face and hand region by using Fuzzy C-Means Clustering (FCM) and Thresholding. FCM is a clustering technique which employs fuzzy partitioning, in an iterative algorithm. After the face and hands are segmented, the feature vectors are extracted. The feature vectors are chosen among the low level features such as the bounding ellipse, bounding box, and center of mass coordinates, since they are known to be more robust to segmentation errors due to low resolution images. In total there are 23 features for each hand. After the feature vectors are extracted, they are used for recognition with discrete Hidden Markov Model (HMM). Recognition stage is composed of two stages, namely training and classification. The Baum Welch algorithm is used for HMM training. In classification part the likelihood of each HMM is calculated and the HMM with the highest likelihood is chosen. In order to measure the success rate of the system, the eNTERFACE dataset is used. In this dataset 8 different American Sign Language example classified and in user independent case, is shown to be working with 94.19% accuracy.
Visual image search improved with geometric consistency

In this work, BoW, VLAD, and Hamming Embedding(HE) methods are examined that are used in image retrieval systems. Also shows that results can be improved using geometric consistency with binary signature obtained from Hamming Embedding. Oxford and Paris dataset are used for tests and obtained test results are comparatively examined over average precision rate. From results, observed that by adding geometric consistency information using binary signatures makes improvement on performance.
A parametric video quality model based on source and network characteristics

The increasing demand for streaming video raises the need for flexible and easily implemented Video Quality Assessment (VQA) metrics. Although there are different VQA metrics, most of these are either Full-Reference (FR) or Reduced-Reference (RR). Both FR and RR metrics bring challenges for on-the-fly multimedia systems due to the necessity of additional network traffic for reference data. No-Reference (NR) video metrics, on the other hand, as the name suggests, are much more flexible for user-end applications. This introduces a need for robust and efficient NR VQA metrics. In this paper, an NR VQA metric considering spatiotemporal information, bit rate, and packet loss rate characteristics of a video content is proposed. The proposed metric is evaluated on EPFL-PoliMI dataset, which includes different video content characteristics. The experimental results show that the proposed metric is a robust and accurate NR VQA metric towards diverse video content characteristics.
Implementation of a wireless streaming system for universal multimedia access

This document describes a universal multimedia access system and its implementation details. In the context of this document, universal multimedia access means accessing multimedia content over ubiquitous computer networks, using different computing platforms. The computer networks involve both wired and wireless networks, and computing platforms involve wired PC, mobile PC and personal digital assistants (PDA). The system is built on a client/server architecture. Video data is H.263 coded and carried over RTP. The Java Media Framework is utilized and its capabilities are extended with special plug-ins when necessary.
YesilcamGAN: Automatic face translation to Yesilcam artists

In this paper, we propose a method that can automatically translate regular human faces to Yesilcam artists. Throughout the paper, two important contributions are presented. First, since a classifier based approach is used, the proposed method automatically selects the most similar artist and makes the face translation. Second, so as to obtain the latent information about faces, a pre-trained model is utilized at the encoder part. Experiments show that visual results which are perceptually promising, Yesilcam artist-like yet preserving facial features of a person, can be synthesized.
A novel adaptive pre screener for ground penetrating radar

This paper describes a novel pre-screener algorithm for landmine detection with a ground penetrating radar (GPR). The pre-screener algorithms are used for finding anomalies that are potential locations of interest. Thus, their processing time is as important as their true detection rate and false alarm rate. The proposed approach is based on Kernel Least Mean Square algorithm. Although Least Mean Square (LMS) based approach has already been used in the literature, KLMS based approach is a novel application for landmine detection with GPR. In this study, KLMS approach is compared with LMS approach in terms of processing time, false alarm rate, and true detection rate.
A Multi-View Video Codec Based on H.264

H.264 is the current state-of-the-art monoscopic video codec providing almost twice the coding efficiency with the same quality comparing the previous codecs. With the increasing interest in 3D TV, multi-view video sequences that are provided by multiple cameras capturing the three dimensional objects and/or scene are more widely used. Compressing multi-view sequences independently with H.264 (simulcast) is not efficient since the redundancy between the closer cameras is not exploited. In order to reduce these redundancies, we propose a multi-view video codec based on H.264 using disparity estimation/compensation as well as motion estimation/compensation. In order to effectively search for disparity/motion without increasing computational complexity, we modified the buffering structure of H.264 and implemented several referencing modes. Our results show that for closely located cameras, our codec outperforms simulcast H.264 coding. For sparsely located cameras, our method can still improve coding gain depending on the video characteristics.
Color and shape based traffic sign detection

This paper proposes two different methods in order to improve the performance of traffic sign detection process. These methods are used for efficient and robust detection of red or blue colored, circular, octagonal, rectangular and triangular traffic signs. Proposed methods use both color and shape features of the traffic signs. Both of the methods gather the color and edge information of the image in order to form a search domain for the shape based detection algorithm. The main difference between the methods appears in the fusion process of color and shape features of the gathered images. Although methods employ different algorithms for color segmentation, they both use HSV color domain. Tests on static images show improvement especially on false positive and detection rate.
A map-based approach to resolution enhancement of hyperspectral images

Hyperspectral imaging is widely used in many fields such as geology, medicine, meteorology, and so on. Despite the high spectral resolution, the spatial resolution of the hyperspectral sensors is severely limited. In this paper, we propose a novel maximum a posteriori (MAP)-based approach based on the joint superresolution of the abundance maps, to enhance the resolution of hyperspectral images. In the proposed approach, first, the endmembers and their abundance maps are estimated using Vertex Component Analysis (VCA) and Fully Constrained Least Squares (FCLS), respectively. Second, a high resolution (HR) abundance map is reconstructed for each low resolution (LR) abundance map using a MAP-based approach. In the MAP-formulation data, smoothness and edge preservation constraints are extended to include a unity constraint term specific to abundances. Finally, HR hyperspectral images are reconstructed using the HR abundance maps. The proposed algorithm is tested on both synthetic images and real image sequences. The experimental results and comparative analysis verify the effectiveness of the proposed algorithm.
Vertex partitioning based Multiple Description Coding of 3D dynamic meshes

In this paper, we propose a Multiple Description Coding (MDC) method for reliable transmission of compressed time consistent 3D dynamic meshes. It trades off reconstruction quality for error resilience to provide the best expected reconstruction of 3D mesh sequence at the decoder side. The method is based on partitioning the mesh vertices into two sets and encoding each set independently by a 3D dynamic mesh coder. The encoded independent bitstreams or socalled descriptions are transmitted independently. The 3D dynamic mesh coder is based on predictive coding with spatial and temporal layered decomposition. In addition, the proposed method allows for different redundancy allocations by duplicating a number of encoded spatial layers in both sets. The algorithm is evaluated with redundancy-rate-distortion curves and flexible trade-off between redundancy and side distortions can be achieved.
Two-Way/Hybrid Clustering Architecture for Peer to Peer Systems

In this paper, we propose a novel hybrid topology and interest based clustering to organize the overlay network in order to reduce startup latency and service interruption probability. Our method uses a clustering technique to organize the peers according to their interests and locality information. The proposed technique is compared with the current unstructured P2P architectures in terms of average hit time, hit ratio for searched content, and the maximum rate of information (in bits per second) that can be transmitted over P2P network caused by protocol and overlay- level connectivity. The simulation results show that the proposed system outperforms the current unstructured P2P architectures.
Impact of scalability in video transmission in promotion-capable differentiated services networks

Transmission of high quality video over the Internet faces many challenges including unpredictable packet loss characteristics of the current Internet and the heterogeneity of receivers in terms of their bandwidth and processing capabilities. To address these challenges, we propose an architecture in this paper that is based on the temporally scalable and error resilient video coding mode of the H.263+ codec. In this architecture, the video frames are transported over a new generation IP network that supports differentiated services (Diffserv). We also propose a novel two rate three color promotion-capable marker (trTCPCM) to be used at the edge of the Diffserv network. Our simulation study demonstrates that an average of 30 dB can be achieved in the case of highly congested links.
Demo paper: Real time 3D video streaming: A mobile approach

In this study, 3D video streaming is taken from a different perspective: how 3D streaming will adapt if both the streamer and the receiver are mobile? The full system chain is completed on mobile devices. Video is captured from a stereo camera pair, compressed by using H.264/AVC compression standard and streamed on TCP on a low power embedded system with OMAP3530 SoC ARM processor. A mobile device with autostereoscopic display is used on the receiver side. Being based on mobile devices, this system permits the possibility of 3D video communication and 3D streaming between mobile recipients on wireless network. For the system competence, the performance is tested and the relevant results are presented.
Multi-threaded architectures and benchmark tests for real-time multi-view video decoding

3D video based on multi-view representations is becoming widely popular. Real-time encoding/decoding of such video is an important concern as the number and resolution of views increase. We present systematic methods for design and optimization of real-time multi-view video encoding/decoding algorithms using multi-core processors and provide benchmark results. The proposed multi-core decoding architectures are fully compliant with the current JVT-MVC international standard, and enable multi-threaded processing with negligible loss of encoding efficiency. Benchmark results show that multi-core processors and multi-threading decoding is necessary for real-time multiview video decoding and display.
Texture preserving multi frame super resolution with spatially varying image prior

This paper proposes a new maximum a posteriori (MAP) based super-resolution (SR) image reconstruction method targeting edges and textures in images. Unlike conventional MAP based SR image reconstruction methods a spatially varying image prior is employed which is updated according to the frequency content of the reconstructed image at each iteration at different locations. Two alternative methods based on discrete cosine transforms (DCT) and Gabor filters are proposed for determining the image prior. The proposed method is validated through simulations and real experiments which clearly demonstrates significant visual improvements especially on edges and textures compared to state-of-the-art SR methods.
Comparative analysis of hyperspectral feature extraction methods in vegetation classification

To perform an accurate vegetation classification in hyperspectral data, feature extraction process prior to classification is very important. Success rates of classifiers in vegetation are rather limited compared to classification of other types of materials. Therefore, it is required to perform an effective feature extraction before classification. Principle Component Analysis(PCA) is a common and easily applicable method for this purpose. However, PCA is not an optimal method for distinguishing between different plant species. In this study, the reasons for PCA not being an adequate method for this purpose are discussed and alternative useful feature extraction methods in classification of plant species are examined. Tests were performed for Spectrally Segmented PCA(SSPCA), Discrete Wavelet Transform(DWT) and Genetic Algorithm(GA) feature extraction methods, their effects on classifier performances were compared and it was observed that all of the mentioned alternatives had noticable improvements in classification performances.
Color super resolution in HSV domain

In this paper, a new color super-resolution (SR) image reconstruction method for HSV domain is presented. Applying classical SR algorithms to each channel of the HSV video causes an artifact due to the cyclic nature of the hue channel. Proposed method solves this problem without converting the HSV video to RGB video. The proposed method reconstructs the saturation and value channels using maximum aposteriori based SR algorithm. For the hue channel the problematic pixels causing the artifacts are masked and a nearest neighbor interpolation is applied. The proposed method is compared to the classical approach where HSV video is converted to RGB domain and each channel is processed by a MAP based SR algorithm. Peak-signal-to-noise-ratio (PSNR) measures show that the proposed approach does not lower the PSNR values. In this paper, it has also been shown that increasing the resolution of the hue channel does not affect the detail perception. Hence applying SR methods to only S and V channels is sufficient to increase the real-time performance of the image processing systems.
Atmospheric Effects Removal for the Infrared Image Sequences

Accurate correction of atmospheric effects on data captured by an infrared (IR) camera is crucial for several applications such as vegetation monitoring, temperature monitoring, satellite images, hyperspectral imaging, numerical model simulations, surface properties characterization, and IR measurement interpretation. Atmospheric effects depend on the temporal changes, i.e., year, season, day, hour, etc., and on the geometry between the camera and the measured scene, i.e., line of sight. The orientation and the optical depth of the camera significantly affect the variation of the geometry across the pixels. In this paper, we propose a method to estimate the range and zenith angle of each pixel using only the Global Positioning System (GPS) coordinates of the camera and a point of interest in the scene. The estimated geometry and measured meteorological data are used to obtain the spectral atmospheric transmittance and path radiance. Furthermore, we propose an atmospheric effects removal, i.e., atmospheric correction, method that considers the spectral characteristics of the detector, lens, and filter. The proposed atmospheric correction process is analyzed in detail with the simultaneous measurements of two IR cameras. In this process, an enhanced temperature calibration method is developed and it is shown that the temperature accuracy for the dynamic range of the IR camera is very close to the noise equivalent temperature difference (NETD) value of the camera.
Markov zinciri tabanlı Güvenilirlik modellemesi

Çok-akişli eşler-arasi video, gelecek nesil isteğe bağli video sistemleri iÇin kullanilmaya aday teknolojilerdendir. Çok-akişli video sistemlerinin sorunlarindan birisi, eş dalgalanmasi adi verilen, eşlerin ağla olan bağlantilarinin kopup düzelmesi durumudur. Eş dalgalanmasinin bütşn eşler-arasi sistemlerde olumsuz etkileri olduğu bilinmektedir. Bu etkinin hafifletilmesi iÇin Çeşitli hata kontrollü alt-akiş kodlama teknikleri önerilmiğtir. Bu makalede, eş dalgalanmasi ve hata kontrolünün rolünü incelemek amaciyla sürekli zamanli Markov zinciri tabanli bir rassal model ünerilmektedir. Bu model kullanilarak, hata kontrolünün rolü, gelecek nesil Çok-akişli eşler-arasi isteğe bağli video sistemlerinin tasariminda kullanilmak üzere nicel olarak ortaya konmaktadir.
Face classification with support vector machine

A new approach to feature based frontal face recognition with Gabor wavelets and support vector machines is presented in this paper. The feature points are automatically extracted using the local characteristics of each individual face. A kernel that computes the similarity between two feature vectors, is used to map the face features to a space with higher dimension. To find the identity of a test face, the possible labels of each feature vector of that face is found with support vector machines, then the last decision is made by considering all of those labels. By using Gabor features the number of support vectors is reduced compared to directly using the actual image data, and also a better generalization performance is achieved.
Performance analysis of local indexing methods for video copy detection

Technological developments and increases of the copyright infringements have led a demand on developing automatic video copy detection systems. Generally, for this task, even if visual local descriptors are preferred, descriptors should be indexed according to make an effective search. In this work, performance of three indexing methods which are defined in literature is compared for video copy detection task. Additionally estimating geometric relation between video frames has positive effect on performance is shown. Evaluation of proposed methods are tested on TRECVID 2009 content based video copy detection dataset and from obtained results, product quantization method yields much more accurate result with respect to classical bag-of-word method is observed.
Automatic color accuracy tests for camera performance comparison

There are numerous criteria which are being used to measure camera performance and for determining such criteria, different tests are applied in different test environments. Within this framework, color accuracy testing at camera performance is one of foremost of such tests. In the scope of this paper, a method has been proposed to reduce user interaction in the color accuracy tests in the literature. At the same time, with the color constancy concept, it has been shown that color variation between the different test setups should also be considered as an important criterion on the camera performance.
Evaluation of stereo video coding schemes for mobile devices

Mobile devices such as mobile phones, personal digital assistants and personal video/game players are somehow converging and getting more powerful, thus enabling 3D mobile devices a reality. In order to store or transmit stereo video in these devices, coding techniques from both monoscopic video coding and multi-view video coding can be used. In this work, we analyze the possible stereoscopic encoding schemes for mobile devices. We have used rate-distortion curves for coding efficiency and decoding speed tests for decoder complexity. Depending on the processing power and memory of the mobile device, we concluded to use two of the settings used in our experiments.
Feasible local content representation for image-in-video search on large-video collection

In this paper, we tackle content-based image-in-video search on large video archive. Particularly in this problem, the computation cost of a proposed method is another important parameter that should be considered alongside of the success rate achieved due to the high volume of data. With our proposed method in this paper, content representation of multimedia data is achieved quite fast compared to other local approaches. Additionally, approximately %16 improvement is introduced at the success rate with respect to the method that uses global approach for content representation on Stanford I2V dataset.
On vehicle aggressive driving behavior detection using visual information

Most of the traffic accidents are caused by human related factors. One of the most important human related factor in terms of traffic accident risk is aggressive driving behavior. Although driver aggressiveness is related with psychological reasons, it can be detected by observation of driving behavior using different sensing devices. This work presents a driver aggressiveness detection method exploiting the visual data obtained by on vehicle camera. The proposed method uses this visual data in order to extract features such as lane departure rate and possible collision time by detection of road lines and vehicles on the road. These features are used to train a classifier which decides on whether an aggressive behavior is performed in related driving session. According to conducted tests the proposed method is observed to achieve 90% correct detection rate of aggressive driving behavior.
End-To-End Stereoscopic Video Streaming System

Today, stereoscopic and multi-view video are among the popular research areas in the multimedia world. In this study, we have designed a platform consisting of stereo-view capturing, real time transmission and display. At the display stage, end users view video in 3D by using polarized glasses. Stereoscopic video is compressed in an efficient way by using stereoscopic video coding techniques and streamed using real time protocols on the sender side. Receiver can view the content of the video built from multiple channels as mono or stereo depending on its display and bandwidth capabilities. The entire system is built by modifying available open source systems whenever possible
Context based super resolution image reconstruction

In this paper a context based super-resolution (SR) image reconstruction method is proposed. The proposed maximum a-posteriori (MAP) based estimator identifies local gradients and textures for selecting the optimal SR method for the region of interest. Texture segmentation and gradient map estimation are done prior to the reconstruction stage. Gradient direction is used for optimal noise reduction along the edges for non-textured regions. On the other hand, regularization term is cancelled for textured regions so that the resultant method reduces to maximum likelihood (ML) solution. It is demonstrated on Brodatz Texture Database that ML solution gives the best PSNR values on textures compared to the regularized SR methods in the literature. Experimental results show that the proposed hybrid method has superior performance in terms of Peak Signal-to-Noise-Ratio (PSNR), Structural Similarity Index Measure (SSIM) compared the SR methods in the literature.