nils-herrmann/clean_scirepeval huggingface dataset:
Patch-based compression for Time-Varying Meshes

This paper proposes intra-frame and inter-frame coding algorithms for 3D mesh sequences generated by multiple cameras, which we call Time-Varying Meshes (TVMs). For this purpose, mesh segmentation into patches with patch alignment using principal component analysis (PCA) is proposed. The patches are used as minimum units to eliminate spatial and temporal correlation of the TVMs. For intraframe coding of geometry data, spectral compression using the Kirchhoff matrix was employed and that of color texture was conducted using vector quantization (VQ). The inter-frame coding of geometry data and color texture was achieved by the combination of patch-based matching and simple scalar quantization. The intra- and inter-frame coding are compared to the previous works and demonstrated promising results.
Visual Tracking of Pedestrians Jointly using Wi-Fi Location System on Distributed Camera Network

Object tracking with multiple cameras is a fundamental problem in wide-area surveillance application, but it has difficulties to achieve accurate and stable performance because of disjoint shot areas or initial object identification problems. We propose a novel object tracking method which jointly uses estimated location information of the target derived from a set of Wi-Fi signal strength values with video images from cameras. Apart from sensor fusion techniques proposed in the past which use another kind of sensors (eg., global positioning system (GPS), pressure sensors on the floors to detect foot steps, laser-range scanners, etc), our approach can cover wide-areas both indoor and outdoor in low cost because of propagation characteristics of Wi-Fi signals. Particle filtering is applied to achieve tracking the target from video images, with this Wi-Fi location estimation. This paper describes system architectures and the experimental results of the pedestrian tracking technique with Wi-Fi location estimation.
Sketch simplification by classifying strokes

In this paper, we propose a novel approach to creating clean line drawing from a scribbled sketch automatically. The main problem is determining which strokes of a scribbled sketch should be merged. We use a machine learning approach to solve this problem. Our method can automatically generate training data by comparing scribbled sketches with manually drawn line drawings without using annotations. In order to verify the generated training data, we merged strokes and created clean line drawings in accordance with the generated training data. In addition, we trained a support vector machine to estimate the pairs of strokes to be merged. Further, we verified that our method can create line drawings using this estimator.
Personalized Classifier for Food Image Recognition

Currently, food image recognition tasks are evaluated against fixed datasets. However, in real-world conditions, there are cases in which the number of samples in each class continues to increase and samples from novel classes appear. In particular, dynamic datasets in which each individual user creates samples and continues the updating process often has content that varies considerably between different users, and the number of samples per person is very limited. A single classifier common to all users cannot handle such dynamic data. Bridging the gap between the laboratory environment and the real world has not yet been accomplished on a large scale. Personalizing a classifier incrementally for each user is a promising way to do this. In this paper, we address the personalization problem, which involves adapting to the user's domain incrementally using a very limited number of samples. We propose a simple yet effective personalization framework, which is a combination of the nearest class mean classifier and the 1-nearest neighbor classifier based on deep features. To conduct realistic experiments, we made use of a new dataset of daily food images collected by a food-logging application. Experimental results show that our proposed method significantly outperforms existing methods.
Object-based visual effects by using multi-focus images and its real-time implementation

We propose a novel approach to image content manipulation which produces special visual effects on objects in a scene. It uses multiple differently focused images and generates a new image by linear filtering of them. The proposed scheme enables us to arbitrarily handle object based visual effects such as blurring, enhancement, shifting, etc. The novelty of this work is that it does not require any segmentation of objects in the scene although it generates special effects selectively applied to the objects. In this paper, the principle and the derivation of the filters are described, the results obtained for real images and its real-time implementation are shown.
Hierarchical mesh decomposition and motion tracking for Time-Varying-Meshes

This paper proposes a system for automatic segmentation and motion tracking of time-varying-meshes (TVM). Our approach is based on skeleton-based hierarchical mesh decomposition by distance calculation. The properties of the human skeleton structure are used to define the decomposition of each TVM frame. The proposed framework is a recursive system that iterates between automatic hierarchical decomposition on minimum distance satisfaction and skeleton realignment. This is done to achieve a stable segmentation and a refined skeleton. By utilizing color information, ill-defined meshes can be successfully segmented. Results show an average disparity of 1.39% of total surface area across all segmented parts, which indicate stability of the system. In addition, motion tracking is successfully performed through the use of refined skeletons.
On sensor image compression based on pixel parallel and column parallel architectures

We propose a novel concept of an integration of compression and sensing in order to enhance performance of the image sensor. By integrating compression function on the sensor plane, the image signal that has to be readout from the sensor is significantly reduced. Thus, the integration can consequently increase the pixel rate of the sensor. The compression scheme we make use of is conditional replenishment that detects and encodes moving areas. In this paper, we discuss design and implementation of two architectures for on sensor compression. One is pixel parallel approach and the other is column parallel approach. We describe and compare both approaches.
Model-based image coding advanced video coding techniques for very low bit-rate applications

The paper gives an overview of model-based approaches applied to image coding, by looking at image source models. In these model-based schemes, which are different from the various conventional waveform coding methods, the 3-D properties of the scenes are taken into consideration. They can achieve very low bit rate image transmission. The 2-D model and 3-D model based approaches are explained. Among them, a 3-D model based method using a 3-D facial model and a 2-D model based method utilizing 2-D deformable triangular patches are described. Works related to 3-D model-based coding of facial images and some of the remaining problems are also described.
Implementations of on sensor image compression and comparisons between pixel and column parallel architectures

In order to enhance performance of an image sensor, we have been investigating a novel integration of compression and sensing. By this integration, the image signal that has to be readout from the sensor is significantly reduced. Thus, the integration can consequently leads to high pixel rate sensing. The compression scheme we make use of is conditional replenishment that detects and encodes moving areas. We have developed prototypes based on two different architectures, that are pixel parallel and column parallel architectures. We present the two prototypes and their comparisons, and show the results obtained by them.
Interactive Refinement and Editing for Time-Varying Mesh

This paper presents an automatic topology change detection and mesh editing interface for time-varying meshes (TVMs). TVM is a sequence of 3D mesh models of real-world objects generated by multiple-view images. The topology change detection is achieved by extracting skeletons from TVMs and tracking the motion of the skeletons. An interactive mesh editing tool is also developed. As a result, the time consistency of the mesh topology can be maintained, enabling us to apply a lot of mesh processing algorithms to TVM. The validity of our system is confirmed by applying the edited TVM sequence to motion tracking. Experimental results demonstrate that the tracking accuracy is improved by 393 mm in the best case.
Multimodal learning for image popularity prediction on social media

Billions of photographs are uploaded to the Internet every day through image sharing services, e.g., Flickr, Instagram, etc. The growing size of these social media poses new challenges in social popularity prediction. In this paper, we try to use a multimodal learning approach which uses both tag feature and visual feature for popularity prediction. We compare several multimodal approaches with unimodal approaches in our experiments and show the results.
Distributed Representation of Dish Names in Food Related Web Services for Associative Search and Nutrition Estimation

Food related web services, such as recipe websites and food journaling apps, are rapidly gaining popularity. Data from service providers and that generated by users often coexist in these services. Compared to the former, the latter, due to its randomness and lack of organization, is often difficult to incorporate into common service features like recommendation making and associative searching. This work addresses this problem by investigating the effectiveness of multiple text embedding methods in generating distributed representations of dish titles that accurately capture the concepts of the dishes which these titles are referring to. We evaluated the practical potential of these "food vectors" with a retrieval task and a nutrition estimation task, and concluded that a method based on word embedding and LSTM is most suited for these purposes.
MangaWall: Generating manga pages for real-time applications

Recent advances in non-photorealistic rendering provide the best convenience for automatic image to manga conversion. However, we are facing a dilemma that the fine-grained manga conversion algorithms always involve computation intensive processes (e.g. image over-segmentation, brute-force feature matching, and energy optimization), which make it inapplicable to real-time applications. On the other hand, commercial manga Apps available for smart phones usually rely on simple edge detection and halftoning or hatching. Although the processing can be finished in several seconds, the conversion quality is not satisfactory yet. In this paper, we propose MangaWall to automatically convert and organize photos into manga pages. Our goal is to establish a lightweight manga rendition framework as well as generate high-quality images. To achieve this, the manga structure is enhanced by flow-based DoG operation and image vectorization. A multi-layer tone mapping and contrast-aware halftoning method is then proposed to render the manga-like screentone patterns. Besides, for multi-image input, a full binary tree-based layout representation is employed to efficiently organize manga images onto the same page canvas. Our MangaWall can be applied to real-time applications. It takes less than 0.7 second to convert a 1024 × 768 image into manga on Laptop PC.
Geometry compression for time-varying meshes using coarse and fine levels of quantization and run-length encoding

Time-varying meshes (TVM) is a new 3-D scene representation which are generated from multiple cameras. It captures highly detailed shape and texture as well as movement of real-world moving objects. Compression is a key technology for supporting TVM applications such as education, interactive broadcasting, and intangible heritage archiving. Previous works focused on compression of 3-D animation that has the same topology throughout the entire sequences. Unfortunately, the topology of TVMs change with time which makes it difficult to compress TVMs. In this paper, we propose a geometry encoder for TVMs. The encoder finds spatial and temporal redundancy by coarse and fine level quantization. Thereafter, vertex information is converted into binary sequences. And then, the binary sequences are encoded using run-length encoding (RLE). Experimental results show that vertices of TVMs which require 96 bits per vertex (bpv) are compressed to 1.9-15.4 bpv while maintaining a small geometric distortion ranging from 0.7 times 10-4 to 1.3 times 10-3 % of the maximum error.
Coarse-to-fine strategy for efficient cost-volume filtering

Cost-volume filtering is one of the most widely known techniques to solve general multi-label problems, however it is problematically inefficient when the label space size is extremely large. This paper presents a coarse-to-fine strategy of the cost-volume filtering that handles efficiently and accurately multi-label problems with a large label space size. Based upon the observation that true labels at the same image coordinate of different scales are highly correlated, we truncate unimportant labels for the cost-volume filtering by leveraging the labeling output of lower scales. Experimental results show that our algorithm achieves much higher efficiency than the original cost-volume filtering while enjoying the comparable accuracy to it.
Structural motion segmentation based on probabilistic clustering

In order to extract a meaningful scene structure from an image sequence, the global and local motion of moving objects are taken into consideration. Firstly, the image sequences are roughly separated into the regions of moving objects based on probabilistic clustering with mixture models using optical flow and the image intensity. For each moving object cluster, parametric motion estimation and segmentation can be obtained by iterative estimation of the affine motion parameters and region modification according to a criterion using the Gauss-Newton iterative optimization algorithm.
Degree of loop assessment in microvideo

This paper presents a degree-of-loop assessment method for microvideo clips. Loop video is one of the popular features in microvideo, but there are so many non-loop video tagged with “loop” on microvideo services. This is because upload-ers or spammers also know that loop video is popular and they want to draw attention from viewers. In this paper, we statistically analyze the scene dynamics of the video by using color, optical flow, saliency maps, and evaluate the degree-of-loop. We have collected more than 1,000 video clips from Vine and subjectively evaluated their degree-of-loop. Experimental results show that our proposed algorithm can classify loop/non-loop video with 85.7% accuracy and categorize them into five degree-of-loop categories with 61.5% accuracy.
Wide dynamic range imaging by sensitivity adjustable CMOS image sensor

In this paper, we propose the wide dynamic range imaging system by using the sensitivity adjustable CMOS image sensor. This method is effective to capture a scene that has both the very bright area and the dark area at the same time. Proposed sensor has the adjustable sensitivity pixels and the sensitivity is changed by switching the extra capacitor in each pixel circuit. We designed and implemented the 200 /spl times/ 200 pixels of the prototype VLSI chip. Verification result shows the sensitivity changes 6 dB of range. We also made the wide dynamic range image capture system with this prototype and observed the appropriate output of the image sensor.
A Euclidean-geodesic shape distribution for retrieval of time-varying mesh sequences

This paper proposes a Euclidean-geodesic shape distribution for the more accurate retrieval of time-varying meshes, which are 3D mesh sequences of real-world objects generated by multiple cameras. The Euclidean-geodesic shape distribution derives from a combination of the modified shape distribution algorithm, which analyzes the global shape features of 3D models, and the geodesic shape distribution algorithm, which is used to investigate topological changes. The optimal weighting for the two algorithms is investigated experimentally. Experimental results show that the performance for similar motion retrieval is better than that of conventional algorithms, being improved by 2% on average and by 5% in the best case.
On sensor image compression

In this paper, we propose a novel image sensor which compresses image signals on the sensor plane. Since an image signal is compressed on the sensor plane by making use of the parallel nature of image signals, the amount of signal read out from the sensor can be significantly reduced. Thus, the potential applications of the proposed sensor are high pixel rate cameras and processing systems which require very high speed imaging or very high resolution imaging. The very high bandwidth is the fundamental limitation to the feasibility of those high pixel rate sensors and processing systems. Conditional replenishment is employed for the compression algorithm. In each pixel, current pixel value is compared to that in the last replenished frame. The value and the address of the pixel are extracted and coded if the magnitude of the difference is greater than a threshold. Analog circuits have been designed for processing in each pixel. A first prototype of a VLSI chip has been fabricated. Some results of experiments obtained by using the first prototype are shown in this paper.
Mixed spatial and SNR scalability for TVM geometry coding

Time Varying Meshes (TVMs) are sequences of three-dimensional models created from multi-camera images. Like other kinds of mesh sequences, it has a great need for compression, in order to become largely usable. Alongside with other types of signals, mesh coding has received considerable attention towards scalable coding, which is a very important feature in many kinds of coded signals. Scalable mesh coding can be divided basically into three types of scalability: temporal, spatial and SNR (signal-to-noise ratio). In this work we focus on the use of block-based and run-length encoding aiming at achieving a combination of spatial and SNR scalability for the geometrical information of TVM. Our results have shown that it is possible to achieve scalability with only a minor increase in rate over a previous coding method.
Deformation of Time-Varying-Mesh Based on Semantic Human Model

In this paper, an approach is presented for deformation of time-varying-mesh, which is a sequence of 3D mesh models. The deformation here is a process to generate mid-frames between two frames that transform smoothly from one to the other. Motion vectors between two frames are extracted based on a semantic human model with an assumption of the articulated object with piecewise-rigid motions. For this purpose, each mesh model is transformed to a volumetric model, where a distance field is constructed. In the first frame, the user manually segments the volumetric model into the semantic human model. Then, fast motion estimation of the volumetric models is performed between the two frames with a cost function based on the distance field. Using the estimated motion vectors, a realistic deformation of a mesh model is achieved. Lastly, mid-frames are interpolated linearly. The technique can be applied in many areas such as frame rate up-conversion and motion blending.
Sketch2Manga: Sketch-based manga retrieval

We propose a sketch-based method for manga image retrieval, in which users draw sketches via a Web browser that enables the automatic retrieval of similar images from a database of manga titles. The characteristics of manga images are different from those of naturalistic images. Despite the widespread attention given to content-based image retrieval systems, the question of how to retrieve manga images effectively has been little studied. We propose a fine multi-scale edge orientation histogram (FMEOH) whereby a number of differently sized squares on a page can be indexed efficiently. Our experimental results show that FMEOH can achieve greater accuracy than a state-of-the-art sketch-based retrieval method .
Multi-media ambiance communication based on actual moving pictures

Multi-media ambiance communication refers to a means of shared-space communication, that makes use of actual moving pictures captured by video camera, combined with the laws of perspective, as used in painting and the visual characteristics of human beings, to establish a photo-realistic quality three-dimensional image space that users can naturally feel part of. We aim to enable this ambiance communication by basing the shared-space on actual moving pictures rather than on an accurate three-dimensional image space, such as is used in computer graphics.
An image-based indoor positioning for digital museum applications

In this paper, we present an image-based indoor positioning system for digital museum application using omniderectional images to which location information is embedded. By using robust image matching by PCA-SIFT, fast nearest neighbor search algorithm based Locaity Sensitive Hashing (LSH), and the confidence measure which takes into consideration of the location of the objects in the query image, our system can estimate users' locations with high accuracy and in a short time. We conducted some experiments of image matching and arguments about the results.
An intelligent facial image coding driven by speech and phoneme

The authors propose and compare two types of model-based facial motion coding schemes, i.e. synthesis by rules and synthesis by parameters. In synthesis by rules, facial motion images are synthesized on the basis of rules extracted by analysis of training image samples that include all of the phonemes and coarticulation. This system can be utilized as an automatic facial animation synthesizer from text input or as a man-machine interface using the facial motion image. In synthesis by parameters, facial motion images are synthesized on the basis of a code word index of speech parameters. Experimental results indicate good performance for both systems, which can create natural facial-motion images with very low transmission rate. Details of 3-D modeling, algorithm synthesis, and performance are discussed.<<ETX>>
128/spl times/128 pixels image sensor for on-sensor-compression

We have been investigating a novel integration of sensing and compression on an image sensor. By integration, the number of pixels in the image signal that has to be readout from the sensor can be significantly reduced, and the integration, can consequently increase the pixel rate of the sensor. We present a new compression sensor which has 128/spl times/128 pixels. We have made the prototype based on a column parallel architecture and improved the processing circuits of the new prototype to achieve lower power dissipation and higher processing speed in comparison with our previous prototypes. It is verified that the processing circuits can be operated at 5000 frames/second.
Robust photometric stereo using sparse regression

This paper presents a robust photometric stereo method that effectively compensates for various non-Lambertian corruptions such as specularities, shadows, and image noise. We construct a constrained sparse regression problem that enforces both Lambertian, rank-3 structure and sparse, additive corruptions. A solution method is derived using a hierarchical Bayesian approximation to accurately estimate the surface normals while simultaneously separating the non-Lambertian corruptions. Extensive evaluations are performed that show state-of-the-art performance using both synthetic and real-world images.