nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive discrete cosine transform coding with vector quantization for color images

A new vector quantization scheme in discrete cosine transform domain, named DCT-VQ and its application to color image coding are described. In this scheme, DCT-domain is partitioned into vectors which are normalized and vector-quantized using universal vector quantizers designed with multidimensional Laplacian distribution. Adaptive coding scheme is also introduced to obtain better reconstruction of images. The color image coder employs the above scheme and encodes separately three components converted from R,G,B signals. The simulations have shown that adaptive DCT-VQ exhibits better performance than a conventional adaptive cosine transform coding with scalar quantization. The decomposition of DCT-block into vectors results in much less complex coder than a vector quantizer in original space domain.
A new approach to depth range detection by producing depth-dependent blurring effect

This paper presents a new method for detecting the depth range of a scene from two differently focused images. This method first generates three images with differently emphasized blur using linear filtering of the two acquired images for more depth-sensitive estimation. In this point, this method is different from conventional approaches. The discrete Fourier transform of the ratio between the generated images is then introduced as a criterion of the depth range. Finally, through the thresholding process of the criterion according to its correspondence to the depth on the basis of the imaging model, the depth range is estimated in five depth steps. Experiments for synthesized and real images are performed to test the proposed method.
Capturing life-log and retrieval based on contexts

In "wearable computing" environments, digitization of personal experiences will be made possible by continuous recordings using a wearable video camera. This could lead to the "automatic life-log application". However it is evident that the resulting amount of video content will be enormous. Accordingly, to retrieve and browse desired scenes, a vast quantity of video data must be organized using structural information. We are developing a "context-based video retrieval system for life-log applications". This system can capture not only video and audio but also various sensor data and provides functions that make efficient video browsing and retrieval possible by using data from these sensors, some databases and various document data.
Social Attribute Annotation for Personal Photo Collection

Social attributes for photos, which simply refer to a set of labels {Who, When, Where, What}, are intrinsic attributes of an image. For instance, given a scenery photo without human bodies or faces, we cannot say the photo has no relation with social individuals. In fact, it could have been taken when we went travelling with other friends. To effectively annotate social attributes, we obtain training images from friends' SNS albums. Moreover, to cope with limited training data and organize photos in a feature-effective way, we introduce a batch-based framework, which pre-clusters photos by events. After graph learning based annotation, a post processing step is proposed to refine the annotation result. Experimental results show the effectiveness of the proposed batch-based social attribute annotation framework.
Adaptive-integration-time image sensor with real-time reconstruction function

A computational image sensor is proposed in which the pixel controls its integration time to light intensity. The integration time of each pixel is selected from among several lengths of integration time and the integration time is shortened if the pixel intensity becomes saturated. Although the integration time of each pixel varies, the pixel intensity is adjusted on the sensor in real time. The dynamic range of the pixel value output from the proposed sensor is greatly widened. A prototype of 64/spl times/48 pixels has been fabricated by using 2-poly 2-metal 0.8-/spl mu/m CMOS process. The proposed sensor has simple functions for the comparison of intermediate integration value and threshold to control the integration time and nonlinear image reconstruction. Because the maximum number of the comparison-reset operations during a frame is three, one of the four integration times can be selected pixel by pixel. The circuit and layout design of the prototype which has computational elements based on column parallel architecture are described and the fundamental functions have been verified. By the experiments, it has been verified that the sensor can achieve a wide dynamic range by adapting to light.
Sketch-Based Spatial Queries for Retrieving Human Locomotion Patterns From Continuously Archived GPS Data

We propose a system for retrieving human locomotion patterns from tracking data captured within a large geographical area, over a long period of time. A GPS receiver continuously captures data regarding the location of the person carrying it. A constrained agglomerative hierarchical clustering algorithm segments these data according to the person's navigational behavior. Sketches made on a map displayed on a computer screen are used for specifying queries regarding locomotion patterns. Two basic sketch primitives, selected based on a user study, are combined to form five different types of queries. We implement algorithms to analyze a sketch made by a user, identify the query, and retrieve results from the collection of data. A graphical user interface combines the user interaction strategy and algorithms, and allows hierarchical querying and visualization of intermediate results. We evaluate the system using a collection of data captured during nine months. The constrained hierarchical clustering algorithm is able to segment GPS data at an overall accuracy of 94% despite the presence of location-dependent noise. A user study was conducted to evaluate the proposed user interaction strategy and the usability of the overall system. The results of this study demonstrate that the proposed user interaction strategy facilitates fast querying, and efficient and accurate retrieval, in an intuitive manner.
Adaptive discrete cosine transform image coding using gain/Shape vector quantizers

An efficient discrete cosine transform image coding system using the gain/shape vector quantizers (DCT-G/S VQ) is presented. In the coding system, AC transform coefficients in a subblock are partitoned into several bands according to the Schaming's method, and the normalized AC transform coefficients of each band are quantized with the gain/shape vector quantizer designed on a spherically symmetric probability model. In addition, an adaptive DCT-G/S VQ (A-DCT-G/S VQ) is presented by incorporating a modification of the recursive quantization technique in the DCT-G/S VQ. The coding systems are simulated on color images, and their performance is compared to that of previously reported discrete cosine transform coding systems using the Max-type scalor quantizers.
Restoration and demosaicing for pixel mixture images in DSC video clips

This paper proposes restoration methods for "pixel mixture". Pixel mixture is a function for saving readout time for digital still cameras (DSCs), which mixes together two pixels on CCD. Pixel mixture reduces the number of pixels to be read out from CCD, and it enables video rate read out from DSCs of more than a million pixels. In this paper, an iterative method is presented in order to recover the image. Both restoration and demosaicing are performed at the same time. Our approach achieves the result of smaller mean square error than conventional interpolation methods.
Motion Editing in 3D Video Database

As the next generation of media, 3D video is attracting increased attention. 3D video is a sequence of three dimensional mesh models, captured and generated for a real dynamic object. In this paper, we present a simple framework of motion editing in 3D video database to re-use 3D video data. Our system is composed of two modules. In the first module, a motion database is automatically set up from original 3D video sequences off-line by analyzing the feature vectors of each frame. It is observed that our original 3D video sequences have a two-level temporal structure. A fine-to-coarse method is proposed to extract such a structure. 3D video is segmented into the fine-level structure by a three reference frame strategy and then is clustered into the coarse-level structure. In the second module, users can synthesize the motions to edit a new 3D video sequence online. A cost function is optimized to transit between two motions with the users' requirements. All the algorithms in the system are based on the analysis in feature vector space and the edited 3D video sequence is played using OpenGL.
Intra texture prediction based on repetitive pixel replenishment

A new intra prediction scheme based on intra repetitive pixel replenishment is proposed. This scheme improves intra coding efficiency by generating adaptive texture according to an intra displacement vector that reduces prediction error for cyclic patterns and differential motion vector coding. Simulation results showed that the proposed technique improved coding efficiency by up to 2.5% BD-Rate (ΔBitrate in [%]) compared with version 3.0 of common software for High Efficiency Video Coding; encoding time was increased by 20%. The total improvement in intra coding efficiency with respect to existing H.264/AVC intra prediction was 5.0% on average and with a maximum of 10.3%. Because the proposed scheme did not need to estimate the decoder-side motion, there was no significant increase in decoding time.
Service Annotation and Profiling by Review Analysis

With the increase in the number of user reviews on user review sites, useful tools for extracting good and bad points of services so that users can easily and intuitively understand the quality of the services are required. If the annotations are selected from the pre-defined list, there can always be missing keywords. Supervised annotation approaches would suffer from the same problem. In this paper, we present an unsupervised method for extracting unique aspects of services and user opinions on these aspects from plain user reviews and apply it to service annotation using Yelp's Academic Dataset. Our method is simple and easy to extend to other languages and domains. By using only the term frequency (TF), general aspects such as whether the food or service is good can be extracted. Further, what is praised particularly to the specific service can be extracted by using the term frequency and inverse document frequency (TF-IDF).
A novel image sensor with flexible sampling control

We propose a new sampling control system for an image sensor. Contrary to the random access pixels, the proposed sensor is able to read out spatially variant pixels at high speed, without inputting pixel address for each access. The sampling positions can be changed dynamically by rewriting the sampling position memory. Since the proposed sensor has sampling position memory that stores the sampling control order, it is able to easily control sampling position. We can achieve any spatially varying sampling patterns.
Fractionally spaced equalizers with adaptive sampling

A new structure of fractionally spaced equalizer (FSE) is described. A conventional FSE has taps spaced equally T/M, where T is the symbol spacing and M is an integer. Although there are many advantages of FSE in digital data transmission, the computational requirements increase with factor M. We describe a non-equally spaced FSE (NFSE) with less computational requirements. A novel tap control algorithm for NFSE under some restrictions is also presented.<<ETX>>
Pixel independent random access image sensor for real time image-based rendering system

We have been investigating a high-speed image-based rendering system. In this area, most of the conventional systems sacrifice spatial or temporal resolution for a heavy amount of input images. When the system uses a camera-array on its input, this problem is more obvious. However, the required image data for the rendering are only a portion of them, determined by the position of the imaginary-view. We propose an image based rendering system, which uses pixel independent random access image sensors to eliminate the bottleneck of the conventional systems. We have developed a prototype of a CMOS image sensor, which has 128 /spl times/ 128 pixels. We verified that the prototype chip readouts externally selected pixels at 60 frames/second.
Towards Online Impression Prediction of Oral Presentations Using Soft Coding

We have been developing impression prediction techniques for oral presentations. The contribution of this paper is two folds. First, we introduce soft code assignment for the bag-of-features (BoF) representation to improve the prediction accuracy. Second, we discuss towards online impression prediction aiming at real-time feedback to the speaker. Experimental results using over 1,600 TED presentation videos show that about 3% accuracy improvement can be achieved by the soft-coding and half amount of presentation is need to achieve comparable prediction accuracy to using the whole presentation.
A Sensor Network for Event Retrieval in a Home Like Ubiquitous Environment

We present the current status of a system based on a network of sensors for event retrieval from a home like environment. A large number of cameras, microphones and pressure based floor sensors are used for continuous data capture. The data are analyzed independently and the results recorded in a central database, where they are combined for efficient retrieval and summarization of the video and audio data. We describe the detection of basic actions, events, and faces using image analysis. The users can query the system interactively to retrieve video, audio, and key frames corresponding to events. We report results of performance evaluations of the algorithms and discuss issues related to sensing, analysis and retrieval of data.
Error analysis of 3Dc-based normal map compression and its application to optimized quantization

Normal mapping is one of the most essential technologies for realistic three-dimensional computer graphics. In conventional normal map compression such as 3Dc, only the x and y components are encoded and the z components are restored based on the normalizing condition. In this paper, we present an intuitively comprehensive error analysis for this approach. As a result, we reveal in what condition compression error becomes larger. We also present a non-linear quantization algorithm based on the formula for better compression performance than the conventional approaches. Experimental results using 300 normal map demonstrate that the PSNR is improved by 0.29 dB on average. Our algorithm is compatible with random access and highly-parallel processing on GPU.
SNAPPER: Fashion Coordinate Image Retrieval System

Fashion is one of the most important factors in our lives. A lot of people enjoy their fashion. On the other hand, they are not confident about their fashion coordination. Especially, it is a very worrisome problem to purchase a new fashion item that suits to their clothing. In this paper, we propose an image-retrieval-based system for supporting a person's fashion coordination and purchase of new fashion items. This system provides users an efficient way to know and purchase the fashion items which suit to those in their closets. The user uploads the image of his or her clothing and specifies the part which he or she puts it on. Then, our system retrieves similar items and recommends fashion coordinate from our fashion coordinate database that has been collected from fashion websites.
Temporal Segmentation of 3-D Video by Histogram-Based Feature Vectors

Three-dimensional (3-D) video, which is a sequence of time-varying mesh models generated in a multi-camera studio, is attracting increased attention, because it can record and reproduce the 3-D information of real-world objects with high accuracy. As one of the most important preprocessings for indexing, annotation, retrieval, and many other functions in management of a 3-D video database, it is necessary to temporally segment 3-D video into meaningful and manageable segments. We have developed robust and effective segmentation algorithms using histogram-based feature vector representation, striving to understand and manage 3-D video contents. We have developed two approaches to generate feature vectors by vertex positions in the mesh models: one uses the Cartesian coordinate system and the other employs the spherical coordinate system. Then, 3-D video is segmented by the motion intensity of an object, which is analyzed by the feature vectors. The segmentation algorithms we have developed are applied to three different 3-D video sequences. A statistical method is presented to evaluate the segmentation results. High recall and precision rates of 0.95 and 0.77, respectively, are achieved in the best case.
Bit allocation of vertices and colors for patch-based coding in time-varying meshes

This paper discusses bit-rate assignments for vertices, color, reference frames, and target frames in the patch-based compression method for time-varying meshes (TVMs). TVMs are nonisomorphic 3D mesh sequences of the real-world objects generated from multiview images. Experimental results demonstrate that the bit rate for vertices greatly affects the visual quality of the rendered 3D model, whereas the bit rate for color does not contribute to quality improvement. Therefore, as many bits as possible should be assigned to vertices, with 8–10 bits per vertex (bpv) per frame being sufficient for color. For interframe coding, the visual quality is improved in proportion to the bit rate of both vertices and color. However, it is demonstrated that the use of fewer bits (5∼6 bpv) is sufficient to achieve a visual quality that matches the intraframe visual quality.
Fooling Neural Networks in Face Attractiveness Evaluation: Adversarial Examples with High Attractiveness Score But Low Subjective Score

People are fond of taking and sharing photos in their social life, and a large part of it is face images, especially selfies. A lot of researchers are interested in analyzing attractiveness of face images. Benefited from deep neural networks (DNNs) and training data, researchers have been developing deep learning models that can evaluate facial attractiveness of photos. However, recent development on DNNs showed that they could be easily fooled even when they are trained on a large dataset. In this paper, we used two approaches to generate adversarial examples that have high attractiveness scores but low subjective scores for face attractiveness evaluation on DNNs. In the first approach, experimental results using the SCUT-FBP dataset showed that we could increase attractiveness score of 20 test images from 2.67 to 4.99 on average (score range: [1, 5]) without noticeably changing the images. In the second approach, we could generate similar images from noise image with any target attractiveness score. Results show by using this approach, a part of attractiveness information could be manipulated artificially.
Digitizing Personal Experiences: Capture and Retrieval of Life Log

In wearable computing environments, digitization of personal experiences will be made possible by continuous recordings using a wearable video camera. This could lead to the ``automatic life-log application''. However, it is evident that the resulting amount of video content will be enormous. Accordingly, to retrieve and browse desired scenes, a vast quantity of video data must be organized using structural information. We are developing a ``context-based video retrieval system for life-log applications''. Our life log system captures video, audio, acceleration sensor, gyro, GPS, annotations, documents, web pages, and emails, and provides functions that make efficient video browsing and retrieval possible by using data from these sensors, some databases and various document data.
Foveation scalable video coding with automatic fixation selection

Image and video coding is an optimization problem. A successful image and video coding algorithm delivers a good tradeoff between visual quality and other coding performance measures, such as compression, complexity, scalability, robustness, and security. In this paper, we follow two recent trends in image and video coding research. One is to incorporate human visual system (HVS) models to improve the current state-of-the-art of image and video coding algorithms by better exploiting the properties of the intended receiver. The other is to design rate scalable image and video codecs, which allow the extraction of coded visual information at continuously varying bit rates from a single compressed bitstream. Specifically, we propose a foveation scalable video coding (FSVC) algorithm which supplies good quality-compression performance as well as effective rate scalability. The key idea is to organize the encoded bitstream to provide the best decoded video at an arbitrary bit rate in terms of foveated visual quality measurement. A foveation-based HVS model plays an important role in the algorithm. The algorithm is adaptable to different applications, such as knowledge-based video coding and video communications over time-varying, multiuser and interactive networks.
A universal image quality index

We propose a new universal objective image quality index, which is easy to calculate and applicable to various image processing applications. Instead of using traditional error summation methods, the proposed index is designed by modeling any image distortion as a combination of three factors: loss of correlation, luminance distortion, and contrast distortion. Although the new index is mathematically defined and no human visual system model is explicitly employed, our experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error. Demonstrative images and an efficient MATLAB implementation of the algorithm are available online at http://anchovy.ece.utexas.edu//spl sim/zwang/research/quality_index/demo.html.
High dynamic range image tone mapping by maximizing a structural fidelity measure

Tone mapping operators (TMOs) that convert high dynamic range (HDR) images to standard low dynamic range (LDR) images are highly desirable for the visualization of these images on standard displays. Although many existing TMOs produce visually appealing images, it is until recently validated objective measures that can assess their quality have been proposed. Without such objective measures, the design of traditional TMOs can only be based on intuitive ideas, lacking clear goals for further improvement. In this paper, we propose a substantially different tone mapping approach, where instead of explicitly designing a new computational structure for TMO, we search in the space of images to find better quality images in terms of a recent objective measure that can assess the structural fidelity between two images of different dynamic ranges. Specifically, starting from any initial image, the proposed algorithm moves the image along the gradient ascent direction and stops until it converges to a maximal point. Our experiments show that the proposed algorithm reliably produces better quality images upon a number of state-of-the-art TMOs.