nils-herrmann/clean_scirepeval huggingface dataset:
Robotic-assisted serious game for motor and cognitive post-stroke rehabilitation

Stroke is a major cause of long-term disability that can cause motor and cognitive impairments. New technologies such as robotic devices and serious games are increasingly being developed to improve post-stroke rehabilitation. The aim of the present project was to develop a ROBiGAME serious game to simultaneously improve motor and cognitive deficits (in particular hemiparesis and hemineglect). In this context, the difficulty level of the game was adapted to each patient's performance, and this individualized adaptation was addressed as the main challenge of the game development. The game was implemented on the REAplan end-effector rehabilitation robot, which was used in continuous interaction with the game. A preliminary feasibility study of a target pointing game was run in order to validate the game features and parameters. Results showed that the game was perceived as enjoyable, and that patients reported a desire to play the game again. Most of the targets included in the game design were realistic, and they were well perceived by the patients. Results also suggested that the cognitive help strategy could include one visual prompting cue, possibly combined with an auditory cue. It was observed that the motor assistance provided by the robot was well adapted for each patient's impairments, but the study results led to a suggestion that the triggering conditions should be reviewed. Patients and therapists reported the desire to receive more feedback on the patient's performances. Nevertheless, more patients and therapists are needed to play the game in order to give further and more comprehensive feedback that will allow for improvements of the serious game. Future steps also include the validation of the motivation assessment module that is currently under development.
Masterpiece: physical interaction and 3D content-based search in VR applications

Virtual reality interfaces can immerse users into virtual environments from an impressive array of application fields, including entertainment, education, design, and navigation. However, history teaches us that no matter how rich the content is from these applications, it remains out of reach for users without a physical way to interact with it. Multimodal interfaces give users a way to interact with the virtual environment (VE) using more than one complementary modality. Masterpiece (which is short for multimodal authoring tool with similar technologies from European research utilizing a physical interface in an enhanced collaborative environment) is a platform for a multimodal natural interface. We integrated Masterpiece into a new authoring tool for designers and engineers that uses 3D search capabilities to access original database content, supporting natural human-computer interaction
An evaluation criterion of saliency models for video seam carving

Modeling human attention has been arousing a lot of interest due to its numerous applications. The process that allows us to focus on some more important stimuli is defined as the “attention”. Seam carving is an approach to resize images or video sequences while preserving the semantic content. To define what is important, gradient was first used but due to limitations of this approach, saliency models, which are able to predict whether regions in the images attract human attention, are now used. Most of them are optimized to conform a ground truth like eye tracking but there is no way to know the efficiency of the saliency models applied in a specific application like in seam carving. In this paper, we propose a criterion, based on the quantity of geometric deformation and the image's reduction, which evaluate the quality of a resizing by seam carving. This criterion is applied on evaluation of image (SCES) or video (SCED) resizing. We validate our criterion with subjective evaluation and used it to rank state of the art saliency models for seam carving. Evaluation of the image by SCES gives a Spearman correlation of -0.92196 and a Pearson correlation of -0.8812. For the video, the final SCED gives a Spearman correlation of -0.81351 and a Pearson correlation of -0.80581.
Biologically plausible context recognition algorithms

In this paper, four new approaches of global context recognition algorithms (gist) are introduced. They are able to automatically distinguish context differences like buildings, coast, home (indoor), mountain or streets. All proposed models are biologically plausible and are able to deal with both color and gray-level images. They use Gabor or Log-Gabor filters to extract features that better mimic human visual perception. Those features are then classified using a Mahalanobis space (when a subset of features is extracted) or in a high-dimensional Gaussian space (when all features are taken into account) with Support Vector Machines (SVM). The proposed models are compared to a standard state of the art gist model to proof their efficiency.
Rare: A new bottom-up saliency model

In this paper, a new bottom-up visual saliency model is proposed. Based on the idea that locally contrasted and globally rare features are salient, this model will be called “RARE” in the following sections. It uses a sequential bottom-up features extraction where first low-level features as luminance and chrominance are computed and from those results medium-level features as image orientations are extracted. A qualitative and a quantitative comparison are achieved on a 120 images dataset. The RARE algorithm powerfully predicts human fixations compared with most of the freely available saliency models.
Video saliency based on rarity prediction: Hyperaptor

Saliency models are able to provide heatmaps highlighting areas in images which attract human gaze. Most of them are designed for still images but an increasing trend goes towards an extension to videos by adding dynamic features to the models. Nevertheless, only few are specifically designed to manage the temporal aspect. We propose a new model which quantifies the rarity natively in a spatiotemporal way. Based on a sliding temporal window, static and dynamic features are summarized by a time evolving "surface" of different features statistics, that we call the "hyperhistogram". The rarity-maps obtained for each feature are combined with the result of a superpixel algorithm to have a more object-based orientation. The proposed model, Hyperaptor stands for hyperhistogram-based rarity prediction. The model is evaluated on a dataset of 12 videos with 2 different references along 3 different metrics. It is shown to achieve better performance compared to state-of-the-art models.
Saliency and Human Fixations: State-of-the-Art and Study of Comparison Metrics

Visual saliency has been an increasingly active research area in the last ten years with dozens of saliency models recently published. Nowadays, one of the big challenges in the field is to find a way to fairly evaluate all of these models. In this paper, on human eye fixations, we compare the ranking of 12 state-of-the art saliency models using 12 similarity metrics. The comparison is done on Jian Li's database containing several hundreds of natural images. Based on Kendall concordance coefficient, it is shown that some of the metrics are strongly correlated leading to a redundancy in the performance metrics reported in the available benchmarks. On the other hand, other metrics provide a more diverse picture of models' overall performance. As a recommendation, three similarity metrics should be used to obtain a complete point of view of saliency model performance.
Audio-visual attention: Eye-tracking dataset and analysis toolbox

Although many visual attention models have been proposed, very few saliency models investigated the impact of audio information. To develop audio-visual attention models, researchers need to have a ground truth of eye movements recorded while exploring complex natural scenes in different audio conditions. They also need tools to compare eye movements and gaze patterns between these different audio conditions. This paper describes a toolbox that answer these needs by proposing a new eye-tracking dataset and its associated analysis ToolBox that contains common metrics to analysis eye movements. Our eye-tracking dataset contains the eye positions gathered during four eye-tracking experiments. A total of 176 observers were recorded while exploring 148 videos (mean duration = 22 s) split between different audio conditions (with or without sound) and visual categories (moving objects, landscapes and faces). Our ToolBox allows to visualize the temporal evolution of different metrics computed from the recorded eye positions. Both dataset and ToolBox are freely available to help design and assess visual saliency models for audiovisual dynamic stimuli.
Tumor Detection using Airways Asymmetry

A novel tumor detection technique on CT scan images of the neck area is detailed in this paper. This technique is based on an airways' symmetry evolution within slices. The algorithm proposes to the physician a set of three slices where a tumor (if it exists) should mostly be located. Then, he will just have to browse the three slices instead of almost 100 in a CT scan. Our method is very effective and shows no false alarms within the patients in our database. In each of our tests the tumors were found to be close to one of the three proposed slices
A CBIR-based evaluation framework for visual attention models

The computational models of visual attention, originally proposed as cognitive models of human attention, nowadays are being used as front-ends to numerous vision systems like automatic object recognition. These systems are generally evaluated against eye tracking data or manually segmented salient objects in images. We previously showed that this comparison can lead to different rankings depending on which of the two ground truths is used. These findings suggest that the saliency models ranking might be different for each application and the use of eye-tracking rankings to choose a model for a given application is not optimal. Therefore, in this paper, we propose a new saliency evaluation framework optimized for object recognition. This paper aims to answer the question: 1) Is the application-driven saliency models rankings consistent with classical ground truth like eye-tracking? 2) If not, which saliency models one should use for the precise CBIR applications?.
Abnormal motion selection in crowds using bottom-up saliency

This paper deals with the selection of relevant motion from multi-object movement. The proposed method is based on a multi-scale approach using features extracted from optical flow and global rarity quantification to compute bottom-up saliency maps. It shows good results from four objects to dense crowds with increasing performance. The results are convincing on synthetic videos, simple real video movements, a pedestrian database and they seem promising on very complex videos with dense crowds. This algorithm only uses motion features (direction and speed) but can be easily generalized to other dynamic or static features. Video surveillance, social signal processing and, in general, higher level scene understanding can benefit from this method.
Camera-based degraded character segmentation into individual components

In this article, we present a novel fully automatic character segmentation for camera-based images. This is a top-down approach inspired by the human visual system: the high level step is based on color clustering while the low level one on the phase of log-Gabor filters. First, this latter step deals with remaining touching characters after the color clustering approach and then takes into account the neighborhood information to address the problem of broken characters. Results are very encouraging and the method should be fast enough to be used on embedded platforms like personal digital assistants.
Fast and automatic tumoral area localisation using symmetry

Our research deals with a fully automatic and fast localization of possible tumoral areas on computed tomography scanner (CT scan) images. The aim of this method is not to segment tumors but only to highlight the areas where tumor has the greater probability to be located. To achieve this task, we use the bilateral symmetry of the human body and the asymmetry introduced by the presence of tumors. Our work was initially dedicated to the head and neck area but it should work well for any other body part and even better for more symmetric areas like the brain.
Emotion identification by facial landmarks dynamics analysis

In this paper we concentrate our efforts on the analysis of the facial landmarks dynamics as being a relevant method to access the subject's emotion. Given the person's facial landmarks we describe their trajectory with respect to the neutral pose and out of this trajectory we extract relevant features that are subsequently entered into a classification system for the actual recognition of emotion. This procedure provides a robust estimation, that has little sensibility with respect to landmark accuracy. Moreover it brings promising results for complex emotion classes. The approach is extensively tested on the facto emotion database, namely Cohn-Kanade+. The reached performance is comparable with state of the art, but on the complete palette of possible emotions.
Maximum-A-Posteriori estimation for global spatial coherence recovery based on Matting Laplacian

Global spatial coherence is an important criterion in the performance evaluation of many image applications, such as image segmentation, image enhancement, depth estimation, motion estimation, and many others. In this paper, we treat the recovery of spatial coherence as a Maximum-A-Posteriori (MAP) estimation problem, with a generalized spatial-coherence prior model based on Matting Laplacian (ML) matrix. Besides, to enhance computational efficiency, a cell-based Matting-Laplacian (CML) framework is further proposed. In our experiments, we demonstrate that the proposed approach can greatly improve the spatial coherence of the output results in variant applications, like the shape-from-focus process and the SIFT-flow refinement process.
Two systolic architectures for modular multiplication

The authors present two systolic architectures to speed up the computation of modular multiplication in RSA cryptosystems. In the double-layer architecture, the main operation of Montgomery's algorithm is partitioned into two parallel operations after using the precomputation of the quotient bit. In the non-interlaced architecture, we eliminate the one-clock-cycle gap between iterations by pairing off the double-layer architecture. We compare our architectures with some previously proposed Montgomery-based systolic architectures, on the basis of both modular multiplication and modular exponentiation. The comparisons indicate that our architectures offer the highest speed, lower hardware complexity, and lower power consumption.
Contrast-based color image segmentation

In this letter, we propose a color image segmentation algorithm based on contrast information. Given a color image, we use contrast information, instead of the commonly used derivative information, to detect edges. To fit for human's visual perception, the CIE L/sup */a/sup */b/sup */ color space is used and the /spl Delta/E/sub ab/ color difference is adopted as the measure of color contrast. A subjective experiment is made to demonstrate the weak correlation between the perceived color contrast and the levels of (L/sup */,a/sup */,b/sup */). This experiment implies the feasibility of using a single-threshold scheme to suppress perceptually faint boundaries. A complete segmentation scheme is proposed and the simulation results demonstrate the superiority of this approach in providing reasonable and reliable color image segmentation.
Image contrast enhancement based on intensity-pair distribution

Current contrast enhancement algorithms sometimes come with undesired drawbacks, like the loss of tiny details, enhancement of image noise, occasional over-enhancement, and unnatural look of the processed images. In this paper, we propose a new approach for contrast enhancement based on the use of a so-called intensity-pair distribution. This distribution possesses both local information and global information of the image content. By analyzing the content of intensity-pair distribution, a set of expansion forces are generated for contrast enhancement while another set of anti-expansion forces are generated to suppress image noise. To avoid over-enhancement and preserve the natural look of the processed images, a magnitude mapping function is also proposed. Experimental results show that the proposed algorithm does provide a flexible and reliable way for contrast enhancement.
Information Preserving Color Transformation for Protanopia and Deuteranopia

In this letter, we proposed a new recoloring method for people with protanopic and deuteranopic color deficiencies. We present a color transformation that aims to preserve the color information in the original images while maintaining the recolored images as natural as possible. Two error functions are introduced and combined together to form an objective function using the Lagrange multiplier with a user-specified parameter lambda. This objective function is then minimized to obtain the optimal settings. Experimental results show that the proposed method can yield more comprehensible images for color-deficient viewers while maintaining the naturalness of the recolored images for standard viewers.
Unsupervised hierarchical clustering based on sequential partitioning and merging

In this talk, we present an unsupervised hierarchical clustering method based on the split-and-merge scheme. In the splitting phase, we sequentially partition the feature space of the given data into smaller cells so that the probability distribution of the feature points within each cell follows a Gaussian function. In the merging phase, we sequentially merge cells of similar feature property into larger cells to construct a hierarchical description of the data. This hierarchical representation is very efficient and effective in describing the inherent structure of the data, especially data of high dimension. An application of this unsupervised hierarchical clustering algorithm in image segmentation is also presented to demonstrate the feasibility of this new approach.
A surface-based vacant space detection for an intelligent parking lot

We proposed a surface-based vacant parking space detection system. Unlike many car-oriented or space-oriented methods, the proposed system is parking-lot-oriented. In the system, we treat the whole parking lot as a structure consisting of plentiful surfaces. A surface-based hierarchical framework is then proposed to integrate the 3-D scene information with the patch-based image observation for the inference of vacant space. To be robust, the feature vector of each image patch is extracted based on the Histogram of Oriented Gradients (HOG) approach. By incorporating these texture features into the proposed probabilistic models, we could systematically infer the optimal hypothesis of parking statuses while dealing with occlusion effect, shadow effect, perspective distortion, and fluctuation of lighting condition in both day time and night time.
An Efficient Approach for Dynamic Calibration of Multiple Cameras

In this paper, we propose a new algorithm for dynamic calibration of multiple cameras. Based on the mapping between a horizontal plane in the 3-D space and the 2-D image plane on a panned and tilted camera, we utilize the displacement of feature points and the epipolar-plane constraint among multiple cameras to infer the changes of pan and tilt angles for each camera. This algorithm does not require a complicated correspondence of feature points. It can be applied to surveillance systems with wide-range coverage. It also allows the presence of moving objects in the captured scenes while performing dynamic calibration. The sensitivity analysis of our algorithm with respect to measurement errors and fluctuations in previous estimations is also discussed. The efficiency and feasibility of this approach has been demonstrated in some experiments over real scenery.
A Bayesian Hierarchical Framework for Multitarget Labeling and Correspondence With Ghost Suppression Over Multicamera Surveillance System

In this paper, the main purpose is to locate, label, and correspond multiple targets with the capability of ghost suppression over a multicamera surveillance system. In practice, the challenges come from the unknown target number, the interocclusion among targets, and the ghost effect caused by geometric ambiguity. Instead of directly corresponding objects among different camera views, the proposed framework adopts a fusion-inference strategy. In the fusion stage, we formulate a posterior distribution to indicate the likelihood of having some moving targets at certain ground locations. Based on this distribution, a systematic approach is proposed to construct a rough scene model of the moving targets. In the inference stage, the scene model is inputted into a proposed Bayesian hierarchical detection framework, where the target labeling, target correspondence, and ghost removal are regarded as a unified optimization problem subject to 3-D scene priors, target priors, and foreground detection results. Moreover, some target priors, such as target height, target width, and the labeling results are iteratively refined based on an expectation-maximization (EM) mechanism to further boost system performance. Experiments over real videos verify that the proposed system can systematically determine the target number, efficiently label moving targets, precisely locate their 3-D locations, and effectively tackle the ghost problem.
Image recolorization for the colorblind

In this paper, we propose a new re-coloring algorithm to enhance the accessibility for the color vision deficient (or colorblind). Compared to people with normal color vision, people with color vision deficiency (CVD) have difficulty in distinguishing between certain combinations of colors. This may hinder visual communication owing to the increasing use of colors in recent years. To address this problem, we re-color the image to preserve visual detail when perceived by people with CVD. We first extract the representing colors in an image. Then we find the optimal mapping to maintain the contrast between each pair of these representing colors. The proposed algorithm is image content dependent and completely automatic. Experimental results on natural images are illustrated to demonstrate the effectiveness of the proposed re-coloring algorithm.
A novel structure for digital image stabilizer

In this paper a new architecture for digital image stabilizer (DIS) is presented. The system utilizes a matching algorithm on the Gray-coded bit-plane of the video sequence, which greatly reduces the complexity and enables the real-time processing capability in its motion estimating mechanism. According to the algorithm, a flexible system architecture containing software and hardware blocks is proposed. The proposed design is computationally efficient and is thus well suited as a low-cost solution for DIS in camcorders. In practice, the system has been validated on a mixed FPGA/DSP-based prototype.