nils-herrmann/clean_scirepeval huggingface dataset:
From 2D markers in MIP to 3D vessel segmentation: A fuzzy paradigm for connected filtering

Connected filters, and in particular those relying on component trees, can be involved in vessel filtering and segmentation tasks. We propose a new component tree-based segmentation method that enables to easily obtain vascular volumes from 3D data, by simply providing 2D markers from MIP visualisations. This approach gathers the advantages of component trees (low algorithmic cost) and the user-friendly (fuzzy) handling of 3D images via 2D representations, leading to an ergonomic and fast tool. It has been successfully used for segmenting a large dataset of MRAs and CTAs visualizing Willis polygons, in the clinical context of stenosis detection.
Morphology-based cerebrovascular atlas

Cerebrovascular atlases can be used to improve medical tasks requiring the analysis of 3D angiographic data. The generation of such atlases remains however a complex and infrequently considered issue. The existing approaches rely on information exclusively related to the vessels. We alternatively investigate a new way, consisting of using both vascular and morphological information (i.e., cerebral structures) to improve the accuracy and relevance of the obtained vascular atlases. Experiments emphasize improvements in the main steps of the atlas generation process impacted by the use of morphological information. An example of cerebrovascular atlas obtained from a dataset of 56 MRAs acquired from different acquisition devices is finally provided.
A Bibliography on Digital and Computational Convexity (1961-1988)

A bibliography of 370 references of books, papers in serial journals, and conference papers, on convexity in relation to computer science is presented. The subject is divided into five topics: (1) convexity and straightness in digital images; (2) convex hull algorithms and their complexity; (3) other computational problems related to convexity; (4) miscellaneous applications; and (5) general mathematical sources. These references range in time from 1961 to September 1988.
On Idempotence and Related Requirements in Edge Detection

The energy feature detectors described by R. Owens et al. (1989) are good candidates for idempotent edge detectors. However, some of them (in particular, the Gabor energy feature detector) suffer from a serious defect that is absent in gradient-type operators: their sensitivity to gray-level shift in the original image. This leads to errors in the localization of step edges. The Fourier phase and amplitude conditions outlined by M.C. Morrone and D.C. Burr (1988) for the class of energy feature detectors guarantee a zero DC level when the convolution masks are taken in L/sup 1/; therefore, the resulting energy feature detector is invariant under grey-level shift in the original image. Also, the properties of the underlying edge model are invariant under a smoothing of the image by a Gaussian or any function in L/sup 1/ having zero Fourier phase. In particular, such a smoothing does not deteriorate the idempotence of the edge detector. Some concrete examples of energy feature detectors satisfying the Morrone conditions are described.
Annular filters for binary images

A binary annular filter removes isolated points in the foreground and the background of an image. Here, the adjective "isolated" refers to an underlying adjacency relation between pixels, which may be different for foreground and background pixels. In this paper, annular filters are represented in terms of switch pairs. A switch pair consists of two operators which govern the removal of points from foreground and background, respectively. In the case of annular filters, switch pairs are completely determined by foreground and background adjacency. It is shown that a specific triangular condition in terms of both adjacencies is required to establish idempotence of the resulting annular filter. In the case of translation-invariant operators, an annular filter takes the form X-->(X [plus sign in circle] A) [intersection] X [union or logical sum] (X [minus sign in circle] B), where A and B are structuring elements satisfying some further conditions: when A [intersection] B [intersection] (A [plus sign in circle] B) not equal Ø, it is an (idempotent) morphological filter; when A [union or logical sum] B [subset] A [plus sign in circle] B, it is a strong filter and in this case it can be obtained by composing in either order the annular opening X-->(X [plus sign in circle] A) [intersection] X and the annular closing X--> [union or logical sum] (X [minus sign in circle] B).
A computational image sensor with adaptive pixel-based integration time

An image sensor is proposed in which the pixel adapts its integration time to motion and light. The integration time of each pixel is shortened if motion is detected in the pixel or pixel intensity becomes saturated. The adaptivity of motion and light significantly enhances temporal resolution and dynamic range of the sensor. Because the integration time differs pixel-by-pixel, a scene containing both a bright and a dark region will be captured by pixels of shorter and longer integration times. Because the integration time adapts to motion, higher temporal resolution is obtained in a moving area and a higher signal-to-noise ratio in a static area. The control of the integration time is done on the sensor focal plane, with column parallel processing circuits integrated in CMOS image sensor. A prototype of 32/spl times/32 pixels has been fabricated by using 1-poly 2-metal CMOS 1-/spl mu/m process. The fundamental functions have been verified. By the experiments, it has been verified that the sensor can reduce motion blur by adapting the integration time to motion and achieve wide dynamic range by adapting to light when the minimum integration interval is 680 /spl mu/s.
A new transport scheme for hybrid delivery of content over broadcast and broadband

This paper presents a new media transport scheme that flexibly combines media components on broadcast channels and broadband networks so that end users can conveniently consume favorite content without being aware of its delivery channels. By taking advantage of the different delivery characteristics of broadcast channels and broadband networks, hybrid delivery offers an effective means of stably providing customized content to each user. In hybrid delivery systems, generic components for a wide range of users are delivered on broadcast channels, and additional components customized to users' interests or locations are delivered on broadband networks. Our proposed scheme enables each user to flexibly combine those components. We have implemented the proposed scheme in a prototype hybrid delivery system. The experimental results demonstrate that the scheme achieves hybrid delivery of content on broadcast channels and broadband networks.
Depth video camera based temporal alpha matting for natural 3D scene generation

We present a new method for fully automated video matting. This method uses depth information acquired by a depth camera to automatically compute trimaps. Trimaps segment an image into three nonoverlapping regions (foreground, background, and unknown) and generation of a highly accurate trimap is one of the most important tasks in natural alpha matting. We propose an adaptive approach to generate unknown regions according to the fuzziness of the foreground object. Fuzzy regions have wide unknown regions, whereas areas that contain sharp edges have narrow unknown areas. We further extend the standard closed-form matting method to optimize both spatial and temporal domains. Our results show the proposed method significantly reduces flickering artifacts and generates natural 3D scenes.
Integrated use of multiple image sensors to determine motion field

A scheme of motion estimation using multiple image sensors is proposed, in which the multiple image sensors work at different integration intervals or different sampling instances. Because combined use of multiple image sensors increases the temporal resolution while keeping signal to noise ratio high, the accuracy of the estimation can be improved. Both a gradient-based and a block-matching-based method are formulated for the images obtained by the multiple image sensors. The experimental results show that the proposed scheme significantly improves the accuracy and reduces computational complexity. Theoretical analysis of estimation error is also given that shows that the appropriate use of multiple imagers makes estimation more precise.<<ETX>>
Audience Ratings Prediction of TV Dramas Based on the Cast and Their Popularity

Television viewership ratings represent the popularity of TV programs and are an important indicator of the revenue generated by broadcasters from advertisements as well as other sources. Although higher ratings for a given program are beneficial for both broadcasters and advertisers, little is known about the factors that make programs more attractive to viewers. In this paper, we propose a method to predict the ratings of TV dramas before they are broadcasted by considering the cast and the staff involved with them. In order to consider the popularity of actors, we consider the number of hits received by their Wikipedia pages, and tweets related to them on Twitter. We tested our proposed method using a collection of 678 TV dramas. The experimental results showed that audience ratings can be predicted with a correlation coefficient of r = 0.845. The effect of each factor was also investigated to reveal the components significant to audience ratings.
Signal-processing based method for acquiring very high resolution images with multiple cameras and its theoretical analysis

This paper describes VHD (very high definition) image media competing in spatial resolution with the familiar film media as a new digital image media concept, and discusses a problem of image acquisition as a key technology for achieving VHD image systems. Towards the development of a VHD image acquisition system, the work presents a new signal-processing based approach using multiple different cameras. The image acquisition approach, processing and integrating multiple images taken simultaneously with multiple different cameras, produces an improved spatial resolution image with sufficiently high signal-to-noise ratio. Theoretical analysis and experimental simulations show clearly the improvement in the high frequencies and details.
Geometrically Invariant Object-Based Watermarking using SIFT Feature

In this paper, we have developed a robust object-based watermarking algorithm using the scale-invariant feature transform (SIFT) features in conjunction with a new data embedding method based on discrete cosine transform (DCT). The message is embedded in DCT spaces of randomly generated blocks in the selected object region. To recognize the object region after being distorted, its SIFT features are registered in advance. In the detection scheme, we firstly detect the object region by using feature matching. The transformation parameters are then calculated, and the message can be detected. Experimental results demonstrated that our proposed algorithm is very robust to geometrical distortions such as JPEG compression, scaling, rotation, shearing, aspect ratio change, image filtering, and so on.
Searching for nearest neighbors with a dense space partitioning

Product quantization based approximate nearest neighbor search with the use of inverted index structures have recently received increasing attention. In this paper, we propose a new inverted index structure for searching nearest neighbors in very large datasets of high dimensional data. For data indexing, our proposed method creates a dense space partitioning using multiple centroids based assigning, which generates shorter candidate lists and improves the search speed. Our experiments with a dataset of one billion SIFT features show that while achieving higher accuracy, our method demonstrates better performances on search speed compared to IV-FADC, the conventional product quantization based inverted index structure.
Tracking of humans and estimation of body/head orientation from top-view single camera for visual focus of attention analysis

This paper addresses the problem of determining a person's body and head orientations while tracking the person in an indoor environment monitored by a single top-view camera. The challenging part of this problem lies in the wide range of human postures depending on the position of the camera and articulations of the pose. In this work, a two-level cascaded particle filter approach is introduced to track humans. Color clues are used as the first level for each iteration and edge-orientation histograms are reutilized to support the tracking at the second level. To determine body and head orientations, a combination of Shape Context and SIFT features is proposed. Body orientation is calculated by matching the upper region of the body with predefined shape templates, then finding the orientation within the ranges of π/8 degrees. Then, the optical flow vectors of SIFT features around the head region are calculated to evaluate the direction and type of the motion of the body and head. We demonstrate preliminary results of our approach showing that body and head orientations are successfully estimated. A discussion on various motion patterns and future improvements for more complicated situations is also given.
Depth Estimation Based on an Infrared Projector and an Infrared Color Stereo Camera by Using Cross-Based Dynamic Programming with Cost Volume Filter

This paper presents a method to estimate a depth map using an infrared projector and a pair of infrared color cameras that can capture infrared and color images simultaneously. The infrared projector projects a dot pattern so that the cameras capture infrared images of a scene textured by the dots with which depths to surfaces in the scene can be estimated regardless of whether they have visible textures. Cost volumes are calculated for each of the infrared and color stereo images and are processed with a cost volume filter that smoothes each cost map by a cross-based local multipoint filter. The filtered infrared and color cost volumes are then integrated into a single cost volume by selecting either the infrared or color cost for each pixel according to the size of the adaptive kernel used for the cross-based local multipoint filter. This improves the accuracy where the adaptive kernel is small. We propose a method that can find optimal local curved surfaces of adaptive kernels from the cost volume by using three dynamic programmings. We experimented this depth estimation method on real-world datasets that the infrared color stereo cameras captured. We also used it for color stereo matching and showed that it works with normal color stereo cameras as well.
Highly Efficient VQ-Based Normal Map Compression using Quality Estimation Model

Normal maps play an important role in computer 3D graphics to express pseudo roughness of the surface with a small amount of polygon data. In this paper, a highly efficient normal map compression algorithm is proposed based on an estimation model to predict the quality of the images rendered with the compressed normal maps. The optimal encoding is achieved by minimizing the predicted mean square error (MSE) employing vector quantization (VQ). In addition, encoding and decoding time is fast enough for practical usage. Experimental results demonstrate that the algorithm proposed in this paper yields better compression performance than the other algorithms in the literatures.
Depth estimation for synthesizing arbitrary view images by random access IBR sensor array

We have been investigating image-based rendering (IBR) imaging system by using array of smart image sensors. The imaging system consists of random accessible image sensors and FPGA, arbitrary view images can be obtained in real-time. Although IBR system can generate more realistic images compared to model-based rendering (MBR) system, the generated images are degraded if the depth information for synthesis has some errors. In this paper, we describe a method of depth estimation for the application of the imaging system. It is based on comparing various synthesized images to an actual image obtained by a real sensor repeatedly. By using the depth information, more realistic arbitrary view images can be generated.
Multi-Sensor Fusion Tracking Using Visual Information and WI-Fl Location Estimation

In a widely distributed camera network environment, object tracking is a important function for a surveillance application. This paper describes an object tracking scheme using sensor fusion approach which is composed of visual information from cameras and location information based on Wi-Fi location estimation system. Estimated location information is calculated by a set of received signal strength values of beacon packets from Wi-Fi access points (APs) around the targets. Different from the conventional approaches which use another kind of sensors (e.g., global positioning system (GPS), pressure sensors on the floors, laser-range scanners, etc.), our approach can cover wider areas both indoor and outdoor with lower cost. Particle filter is applied to combine those two different kinds of sensory input to achieve tracking the target in a stable performance. Wi-Fi observation model is involved in a conventional visual particle filtering scheme to evaluate importance weights of each particle. In this paper, we present experimental results applied to outdoor surveillance camera environment.
Image sensor for compression and enhancement

In this paper, we propose a novel image sensor on which a video signal can be compressed. By making use of very fast analog processing on the imager plane, the compression sensor can significantly reduce the amount of pixel data output from the sensor. A prototype sensor with 32/spl times/32 pixels has been fabricated using standard 2 /spl mu/m CMOS technology. Some results of examinations are shown in this paper. We also describe a motion adaptive enhancement sensor which controls storage time independently pixel by pixel. The proposed sensor is intended to have high temporal resolution in the moving area and high SNR in the static area. According to preliminary experiments, the value of SNR increases about 5 dB compared to the video compression sensor.
Estimation of camera parameters from image sequence for model-based video coding

The authors describe a method for estimating camera parameters from image sequences for application to emerging model-based video coding systems. The parameters to be estimated include focal length, zoom, and 3-D rotation parameters. The method consists of first establishing a correspondence and then, estimating the parameters by fitting the correspondence data to a transformation model based on a perspective mapping model and a 3-D rotation and zoom operation model. They show by simulations and experiments that the proposed method successfully estimates the focal length from the image sequences, it explains very well the induced motion field of images undergoing camera operation (3-D rotation and zoom), and that it significantly outperforms conventional estimation methods, especially for wide-angled images. It is anticipated that the proposed method will be successfully applied to compensating for the motion field induced by camera operation in extracting a 3-D object model and a 3-D object motion, and to synchronizing the viewing direction and scale of an image, in model-based video coding technology.
Simultaneous acquisition of multiple images with higher dynamic range

Computational photography has redefined the possibilities of conventional photography. New effects can be obtained by mixing modified hardware with software. In this paper we present the basis of a theoretical framework for a computational camera. By using coded apertures, the framework is able to multiplex images of a scene and simultaneously acquire them. As an application, we show a system to capture images with different exposure values, a feature that is often useful in high dynamic range imaging. This framework is not limited to the acquisition of images with varying exposure values. It is also adaptable to different types of filters, e.g.: color filters. The advantages of this system are the low cost of implementation and ease of adaption to different conditions. However, the need and difficulty of the decoding process can add extra image artifacts.
Retrieval of Time-Varying Mesh and motion capture data using 2D video queries based on silhouette shape descriptors

This paper presents a retrieval system for Time-Varying Mesh (TVM) and motion capture data using 2D video queries. Previous approaches have used other TVM and motion capture data as queries and the cost for query generation was a significant issue. Instead, the proposed system uses 2D video queries, which can be easily captured by a single camera, enabling end users to retrieve 3D motion sequences such as TVM and motion capture data easily and interactively. We introduce the P-type Fourier descriptor, which is a feature of 2D contour images. TVM and computer graphics sequences rendered from motion capture data are silhouetted rendering from multiple viewpoints. Feature vectors for TVM and motion capture data are generated by applying the P-type Fourier descriptor to these silhouetted images. Experimental results using four TVM sequences and motion capture data demonstrated an average retrieval accuracy of 88% in terms of nearest neighbors.
Food Category Representatives: Extracting Categories from Meal Names in Food Recordings and Recipe Data

Food Log is a multimedia recording tool for producing food records for many individuals. In one year of operation, Food Log has produced more than one million food records for meals eaten by users. We found nearly 70,000 unique food records among these data. In analyzing them, one of the challenges is to extract meal categories from such a large number of records. In this paper, we propose a method for compressing a meal name into a shorter representation. First, we collect similar meal names using a k-nearest neighbor search. Next, we construct a word graph to model the relationship between the meal names and items in the database. We select representative words by identifying minimal paths in the word graph. Finally, we obtain a few words that represent categorical information about the original meal name. We applied the method to data in food records for both Food Log and the Rakuten Recipe database. Our results show that the method worked effectively for both datasets.
Structural edge detection based on fractal analysis for image compression

Addresses the problem of structural edge detection and discusses the possibilities of application of this edge information to image compression. For the purpose of detecting an edge efficiently, the authors first divide the image into appropriate nonoverlapping blocks and estimate the fractal dimension of each block by using the blanket-covering method. Secondly, the edge points are efficiently detected by using a wavelet transform where the dilation parameter is controlled based on a fractal dimension. The proposed method was tested on a real image using two wavelet functions, and the results are shown.<<ETX>>