nils-herrmann/clean_scirepeval huggingface dataset:
An automatic synthesizer for CMOS operational amplifiers

A design methodology with a good trade-off between design time and quality in the automatic synthesis of CMOS operational amplifiers is presented. It is based on an iterative device sizing approach in which a multivariate interpolation technique provides a way to improve the design and the SPICE simulator gives quantitative evaluation of the resultant circuit performance in each design iteration. The methodology has been implemented and proved to be very practical and promising.<<ETX>>
Joint Prediction Algorithm and Architecture for Stereo Video Hybrid Coding Systems

3-D video will be the most prominent video technology in the next generation. Among the 3-D video technologies, stereo video systems are considered to be realized first in the near future. Stereo video systems require double bandwidth and more than twice the computational complexity relative to mono-video systems. Thus, an efficient coding scheme is necessary for transmitting stereo video. In this paper, a new structure of prediction core in stereo video coding systems is proposed from the algorithm level to the hardware architecture level. The joint prediction algorithm (JPA), which combines three prediction schemes, is proposed for high coding efficiency and low computational complexity. It makes the system outperform MPEG-4 temporal scalability and simple profile by 2-3 dB in rate-distortion performance. Besides, JPA also utilizes the characteristics of stereo video and successfully reduces about 80% computational complexity. Then, a new hardware architecture of the prediction core based on JPA and a modified hierarchical search block-matching algorithm is proposed. With a special data flow, no bubble cycles exist during the block-matching process. The proposed architecture also adopts the near-overlapped candidates reuse scheme to save the heavy burden of data access. Besides, both on-chip memory requirement and off-chip memory bandwidth can be reduced by the proposed new scheduling. Compared with the hardware requirement for the implementation of full search block-matching algorithm, only 11.5% on-chip SRAM and 3.3% processing elements are needed with a tiny PSNR drop, making it area-efficient while maintaining high stereo video quality and processing capability
Accuracy and power tradeoff in spike sorting microsystems with cubic spline interpolation

Accurate spike sorting is an important issue for neuroscientific and neuroprosthetic applications. The sorting of spikes depends on the features extracted from the neural waveforms, and a better sorting performance usually comes with a higher sampling rate (SR). However the long duration experiment on free-moving subjects is the current trend. For low power considerations, the compromise on sorting accuracy is made for the miniaturized and wireless neural recording ICs with a lower SR. In this paper, we introduce the cubic spline interpolation to strike the power and accuracy tradeoff on the recording microsystems for the off-site and on-chip spike sorter. According to the simulation results, the recorder operated with the SR of 12.5k sample per second (sps) outperforms the system with 25ksps SR on both accuracy and power if the interpolation is appropriately performed before the spike sorting.
Precompression Quality-Control Algorithm for JPEG 2000

In this paper, a precompression quality-control algorithm is proposed. It can greatly reduce computational power of the embedded block coding (EBC) and memory requirement to buffer bit streams. By using the propagation property and the randomness property of the EBC algorithm, rate and distortion of coding passes is approximately predicted. Thus, the truncation points are chosen before actual coding by the entropy coder. Therefore, the computational power, which is measured with the number of contexts to be processed, is greatly reduced since most of the computations are skipped. The memory requirement, which is measured with the amount required to buffer bit streams, is also reduced since the skipped contexts do not generate bit streams. Experimental results show that the proposed algorithm reduces the computational power of the EBC by 80% on average at 0.8 bpp compared with the conventional postcompression rate-distortion optimization algorithm . Moreover, the memory requirement is also reduced by 90%. The average PSNR degrades only about 0.1~0.3 dB, on average
Depth Map Generation for 2D-to-3D Conversion by Short-Term Motion Assisted Color Segmentation

This paper presented a novel depth map generation method - the short-term motion assisted color segmentation, which combines the pictorial, monocular and binocular depth cues of human vision. The proposed method utilizes a motion/edge registration technique to avoid the motion jitter error in common motion segmentation. And the motion/image segment adaptation algorithm matches the connected components with the motion segments. Even for static scene, the connected component algorithm is still working for the depth map generation. The experimental results show that the adaptation of motion and image segmentation improves quality and smoothness of the depth map both in the spatial and temporal domain.
Hybrid motion/depth-oriented inpainting for virtual view synthesis in multiview applications

Multiview video can provide users a 3D and virtual reality perception by its multiple viewing angles. To improve the quality of virtual view synthesized frame and remove the disocclusion region, hole filling technique is required. By classifying the different image artifact and applying proper hybrid motion/depth-oriented inpainting algorithm, the image quality will be closer to the real image. In addition, the simulation result and comparison with previous works on inpainting show that both objective evaluation estimates and subjective perceptual vision come to a better result by the system proposed in this paper.
Low Power Cache Algorithm and Architecture Design for Fast Motion Estimation in H.264/AVC Encoder System

Low power motion estimation (ME) of H.264/AVC is an important research issue because of the growing mobile applications of H.264/AVC encoder. In this paper, low power cache algorithm and architecture for fast ME of H.264/AVC is proposed in order to replace the conventional search range (SR) memory. With the block translation (BT) cache architecture, search trajectory prediction (STP) prefetching algorithm, and ultra low power cache miss hiding (CMH) strategy, 35% SR memory writing power and 67% SR memory static power are reduced for D1 videos. Combining fast ME with the proposed cache provides the total solution for low power ME hardware.
A data-interlacing architecture with two-dimensional data-reuse for full-search block-matching algorithm

This paper describes a data-interlacing architecture with two-dimensional (2-D) data-reuse for full-search blockmatching algorithm. Based on a one-dimensional processing element (PE) array and two data-interlacing shift-register arrays, the proposed architecture can efficiently reuse data to decrease external memory accesses and save the pin counts. It also achieves 100% hardware utilization and a high throughput rate. In addition, the same chips can be cascaded for different block sizes, search ranges, and pixel rates.
Fast adaptive loop filter algorithm for high efficiency video coding

Adaptive loop filter (ALF) is one of the new tools adopted in the next generation video coding standard HEVC. ALF improves both BD Rate and subjective performance by using Wiener filtering. However, the computational complexity of ALF is quite high. ALF accounts for 14% of the total encoder runtime. In this paper, two fast ALF algorithms, LCU based edge sub-sample scheme and Patch based edge sub-sample scheme, are proposed. The implementation result shows that only 0.04% and 0.16% BD Rate drop with 40% and 71% runtime is saved for LCU based edge sub-sample scheme and Patch based edge sub-sample scheme respectively.
Real-Time Human Movement Retrieval and Assessment With Kinect Sensor

The difficulty of vision-based posture estimation is greatly decreased with the aid of commercial depth camera, such as Microsoft Kinect. However, there is still much to do to bridge the results of human posture estimation and the understanding of human movements. Human movement assessment is an important technique for exercise learning in the field of healthcare. In this paper, we propose an action tutor system which enables the user to interactively retrieve a learning exemplar of the target action movement and to immediately acquire motion instructions while learning it in front of the Kinect. The proposed system is composed of two stages. In the retrieval stage, nonlinear time warping algorithms are designed to retrieve video segments similar to the query movement roughly performed by the user. In the learning stage, the user learns according to the selected video exemplar, and the motion assessment including both static and dynamic differences is presented to the user in a more effective and organized way, helping him/her to perform the action movement correctly. The experiments are conducted on the videos of ten action types, and the results show that the proposed human action descriptor is representative for action video retrieval and the tutor system can effectively help the user while learning action movements.
Robust RGB-D Hand Tracking Using Deep Learning Priors

With the irruption of inexpensive depth sensor devices, hand gesture tracking has become a topic of great interest. Two main problems to face respect other tracking algorithms are the high complexity of the hand structure, which translate in a very large amount of possible gestures, and the rapidness of the movements we are able to make when moving the hand or just the fingers. Recent approaches try to fit a 3D hand model to the observed RGB-D data by an optimization function that minimizes the error between the model and the data. However, these algorithms are very dependent on the initialization point, which are impractical to run in a natural environment. To solve these kinds of problems, it is common to use an offline data set with prelearned gestures that will serve as a first rough estimate. In concrete, we present an algorithm that uses an articulated ICP minimization function that is initialized by the parameters obtained from a data set of hand gestures trained through a deep learning framework. This setup has two strong points. First, deep learning provides a very fast and accurate estimate of performed hand gestures. Second, the articulated ICP algorithm allows capturing the possible variability of a gesture performed by different persons or slightly different gestures. Our proposed algorithm is evaluated and validated in several ways. Independent evaluations for the deep learning framework and articulated ICP are performed. Moreover, different real sequences are recorded to validate our approach and, finally, quantitative and qualitative comparisons are conducted with state-of-the-art algorithms.
A Spatial-Pyramid Scene Categorization Algorithm based on Locality-aware Sparse Coding

Scene recognition has a wide range of applications, such as object recognition and detection, content-based image indexing and retrieval, and intelligent vehicle and robot navigation. In particular, natural scene images tend to be very complex and are difficult to analyze due to changes of illumination and transformation. In this study, we investigate a novel model to learn and recognize scenes in nature by combining locality constrained sparse coding (LCSP), Spatial Pyramid Pooling, and linear SVM in end-to-end model. First, interesting points for each image in the training set are characterized by a collection of local features, known as codewords, obtained using dense SIFT descriptor. Each codeword is represented as part of a topic. Then, we employ LCSP algorithm to learn the codeword distribution of those local features from the training images. Next, a modified Spatial Pyramid Pooling model is employed to encode the spatial distribution of the local features. For the final stage, a linear SVM is employed to classify local features encoded by Spatial Pyramid Pooling. Experimental evaluations on several benchmarks well demonstrate the effectiveness and robustness of the proposed method compared to several state-of-the-art visual descriptors.
Interactive human action search using body language

Searching for human actions in a large video collection is a frequent demand in our daily lives. However, it is often not well supported by current multimedia technologies. For example, by using the traditional text-based search methods, it is not quite straightforward to give proper keywords as query input if users are uncertain about the textual or verbal descriptions of interesting actions in their mind. According to the sociological findings, the use of body language could be arguably a more natural and direct way for people to express their conscious or subconscious thoughts in a nonverbal manner. Therefore, in this paper, we propose an interactive system for human action search in videos, which is characterized by enabling the user to give a search query of interesting human actions through directly performing it. In contrast to a machines learning based recognition system, we address the problem of human action search with the approximate string matching (ASM) technique. As long as a user's actions can be matched with any sequence of the video database, they are said to be meaningful actions. The experiments demonstrate the effectiveness of our system in support of the user's search task.
Joint Enhancement and Denoising Method via Sequential Decomposition

Many low-light enhancement methods ignore intensive noise in original images. As a result, they often simultaneously enhance the noise as well. Furthermore, extra denoising procedures adopted by most methods ruin the details. In this paper, we introduce a joint low-light enhancement and denoising strategy, aimed at obtaining well-enhanced low-light images while getting rid of the inherent noise issue simultaneously. The proposed method performs Retinex model based decomposition in a successive sequence, which sequentially estimates a piece-wise smoothed illumination and a noise-suppressed reflectance. After getting the illumination and reflectance map, we adjust the illumination layer and generate our enhancement result. In this noise-suppressed sequential decomposition process we enforce the spatial smoothness on each component and skillfully make use of weight matrices to suppress the noise and improve the contrast. Results of extensive experiments demonstrate the effectiveness and practicability of our method. It performs well for a wide variety of images, and achieves better or comparable quality compared with the state-of-the-art methods.
A Framework of Enlarging Face Datasets Used for Makeup Face Analysis

There have been lots of research about face detection and face recognition. However, faces with makeup usually seriously affect the face recognition result. If we want to recognize the face with higher accuracy, it would be better to first know whether the input face is with makeup or not, and we can use corresponding makeup face or non-makeup face model to recognize it. Unfortunately, the current available datasets for face analysis do not include enough makeup and non-makeup image pairs of users. In this work, we propose a framework to efficiently increase pairs of makeup and non-makeup face images for the existing makeup face datasets. Patch-based features are extracted and support vector machine (SVM) is applied to classify whether a face image is with makeup. The technique of partial least squares (PLS) is then employed to authenticate whether a makeup photo and a non-makeup photo belong to the same person. By combining the makeup detection and the face authentication methods, we can successfully construct a larger face dataset that can be specifically used for applications of makeup face analysis.
Rectangling Stereographic Projection for Wide-Angle Image Visualization

This paper proposes a new projection model for mapping a hemisphere to a plane. Such a model can be useful for viewing wide-angle images. Our model consists of two steps. In the first step, the hemisphere is projected onto a swung surface constructed by a circular profile and a rounded rectangular trajectory. The second step maps the projected image on the swung surface onto the image plane through the perspective projection. We also propose a method for automatically determining proper parameters for the projection model based on image content. The proposed model has several advantages. It is simple, efficient and easy to control. Most importantly, it makes a better compromise between distortion minimization and line preserving than popular projection models, such as stereographic and Pannini projections. Experiments and analysis demonstrate the effectiveness of our model.
NHAD: Neuro-Fuzzy Based Horizontal Anomaly Detection in Online Social Networks

Use of social network is the basic functionality of today's life. With the advent of more and more online social media, the information available and its utilization have come under the threat of several anomalies. Anomalies are the major cause of online frauds which allow information access by unauthorized users as well as information forging. One of the anomalies that act as a silent attacker is the horizontal anomaly. These are the anomalies caused by a user because of his/her variable behavior towards different sources. Horizontal anomalies are difficult to detect and hazardous for any network. In this paper, a self-healing neuro-fuzzy approach (NHAD) is used for the detection, recovery, and removal of horizontal anomalies efficiently and accurately. The proposed approach operates over the five paradigms, namely, missing links, reputation gain, significant difference, trust properties, and trust score. The proposed approach is evaluated with three datasets: DARPA'98 benchmark dataset, synthetic dataset, and real-time traffic. Results show that the accuracy of the proposed NHAD model for 10 to 30 percent anomalies in synthetic dataset ranges between 98.08 and 99.88 percent. The evaluation over DARPA'98 dataset demonstrates that the proposed approach is better than the existing solutions as it provides 99.97 percent detection rate for anomalous class. For real-time traffic, the proposed NHAD model operates with an average accuracy of 99.42 at 99.90 percent detection rate.
An efficient algorithm for periodic halftone identification

When periodic halftoned documents (e.g., books, newspapers, and magazines) are scanned for image reproduction, moiré patterns occur. In order to avoid these moiré artifacts, it is necessary to detect the periodic halftone. This paper provides a fast Fourier transform based method to classify periodic halftoned documents. Experimental results show that the overall accuracy of this method is 97% on a large data set which contains many difficult-to-classify images. Misclassified documents tend to be extremely difficult to classify, in that they contain very small periodic halftone regions. An existing method, by comparison, has accuracy of only 70%.
Learning and Recognition of Clothing Genres From Full-Body Images

According to the theory of clothing design, the genres of clothes can be recognized based on a set of visually differentiable style elements, which exhibit salient features of visual appearance and reflect high-level fashion styles for better describing clothing genres. Instead of using less-discriminative low-level features or ambiguous keywords to identify clothing genres, we proposed a novel approach for automatically classifying clothing genres based on the visually differentiable style elements. A set of style elements, that are crucial for recognizing specific visual styles of clothing genres, were identified based on the clothing design theory. In addition, the corresponding salient visual features of each style element were identified and formulated with variables that can be computationally derived with various computer vision algorithms. To evaluate the performance of our algorithm, a dataset containing 3250 full-body shots crawled from popular online stores was built. Recognition results show that our proposed algorithms achieved promising overall precision, recall, and  ${F}$ -score of 88.76%, 88.53%, and 88.64% for recognizing upperwear genres, and 88.21%, 88.17%, and 88.19% for recognizing lowerwear genres, respectively. The effectiveness of each style element and its visual features on recognizing clothing genres was demonstrated through a set of experiments involving different sets of style elements or features. In summary, our experimental results demonstrate the effectiveness of the proposed method in clothing genre recognition.
A robust tracking algorithm for 3D hand gesture with rapid hand motion through deep learning

There are two main problems that make hand gesture tracking especially difficult. One is the great number of degrees of freedom of the hand and the other one is the rapid movements that we make in natural gestures. Algorithms based on minimizing an objective function, with a good initialization, typically obtain good accuracy at low frame rates. However, these methods are very dependent on the initialization point, and fast movements on the hand position or gesture, provokes a lost of track which are unable to recover. We present a method that uses deep learning to train a set of gestures (81 gestures), that will be used as a rough estimate of the hand pose and orientation. This will serve to a registration of non rigid model algorithm that will find the parameters of hand, even when temporal assumption of smooth movements of hands is violated. To evaluate our proposed algorithm, different experiments are performed with some real sequences recorded with Intel depth sensor to demonstrate the performance in a real scenario.
Background Extraction Using Random Walk Image Fusion

It is important to extract a clear background for computer vision and augmented reality. Generally, background extraction assumes the existence of a clean background shot through the input sequence, but realistically, situations may violate this assumption such as highway traffic videos. Therefore, our probabilistic model-based method formulates fusion of candidate background patches of the input sequence as a random walk problem and seeks a globally optimal solution based on their temporal and spatial relationship. Furthermore, we also design two quality measures to consider spatial and temporal coherence and contrast distinctness among pixels as background selection basis. A static background should have high temporal coherence among frames, and thus, we improve our fusion precision with a temporal contrast filter and an optical-flow-based motionless patch extractor. Experiments demonstrate that our algorithm can successfully extract artifact-free background images with low computational cost while comparing to state-of-the-art algorithms.
A user-attention based focus detection framework and its applications

In this paper, a generic user-attention based focus detection framework is developed to capture user focus points for video frames. The proposed framework considers both bottom-up and top-down attentions, and integrates both image-based and video-based visual features for saliency map computation. For efficiency purpose, the number of adopted features is kept as few as possible. The realized framework is extensible and flexible in integrating more features with a variety of fusion schemes. One application of the proposed framework, the user-assisted spatial resolution reduction, has also been addressed.