nils-herrmann/clean_scirepeval huggingface dataset:
Pinta: a system for visualizing the anatomical structures of the brain from MR imaging

Pinta is a system for segmentation and visualization of anatomical structures obtained from serial sections reconstructed from magnetic resonance imaging. The system approaches the segmentation problem by assigning each volumetric region to an anatomical structure. This is accomplished by satisfying constraints at the pixel level, slice level, and volumetric level. Each slice is represented by an attributed graph, where nodes correspond to regions and links correspond to the relations between regions. These regions are obtained by grouping pixels based on similarity and proximity. The slice level attributed graphs are then coerced to form a volumetric attributed graph, where volumetric consistency can be verified. The volumetric graph is used to ensure consistency from symbolic representations obtained from individual slices. The system allows errors to be made at the slice level, yet removes them when the volumetric consistency cannot be verified. Once the segmentation is complete, the 3D surfaces of the brain can be constructed and visualized.<<ETX>>
Classification of tumor histopathology via sparse feature learning

Our goal is to decompose whole slide images (WSI) of histology sections into distinct patches (e.g., viable tumor, necrosis) so that statistics of distinct histopathology can be linked with the outcome. Such an analysis requires a large cohort of histology sections that may originate from different laboratories, which may not use the same protocol in sample preparation. We have evaluated a method based on a variation of the restricted Boltzmann machine (RBM) that learns intrinsic features of the image signature in an unsupervised fashion. Computed code, from the learned representation, is then utilized to classify patches from acurated library of images. The system has been evaluated against a dataset of small image blocks of 1k-by-1k that have been extracted from glioblastoma multiforme (GBM) and clear cell kidney carcinoma (KIRC) from the cancer genome atlas (TCGA) archive. The learned model is then projected on each whole slide image (e.g., of size 20k-by-20k pixels or larger) for characterizing and visualizing tumor architecture. In the case of GBM, each WSI is decomposed into necrotic, transition into necrosis, and viable. In the case of the KIRC, each WSI is decomposed into tumor types, stroma, normal, and others. Evaluation of 1400 and 2500 samples of GBM and KIRC indicates a performance of 84% and 81%, respectively.
Surface recovery from planar sectional contours

We propose an approach for surface recovery from planar sectional contours. The surface is reconstructed based on the so-called "equal importance criterion" which suggests that every point in the region contributes equally to the reconstruction process. The problem is then formulated in terms of a partial differential equation, and the solution is efficiently calculated from a distance transformation. To make the algorithm valid for different application purposes, both the isosurface and the primitive representations of the object surface are derived. The isosurface is constructed by a partial differential equation, which can be solved iteratively. The traditional distance interpolating method, which was used by several researchers for surface reconstruction, is an approximate solution of the PDE. The primitive representations are approximated by Voronoi diagram transformation of the surface space. Isosurfaces have the advantage that subsequent geometric analysis of the object can be easily carried out while primitive representation is easy to visualize. The proposed technique allows for surface recovery at any desired resolution, thus avoiding the inherent problems of correspondence, tiling, and branching.
Evaluating Real-Time Mirroring of Head Gestures Using Smart Glasses

Mirroring occurs when one person tends to mimic the non-verbal communication of their counterparts. Even though mirroring is a complex phenomenon, in this study, we focus on the detection of head-nodding as a simple non-verbal communication cue due to its significance as a gesture displayed during social interactions. This paper introduces a computer vision-based method to detect mirroring through the analysis of head gestures using wearable cameras (smart glasses). In addition, we study how such a method can be used to explore perceived competence. The proposed method has been evaluated and the experiments demonstrate how static and wearable cameras seem to be equally effective to gather the information required for the analysis.
Dynamic facial expression recognition using Laplacian Eigenmaps-based manifold learning

In this paper, we propose an integrated framework for tracking, modelling and recognition of facial expressions. The main contributions are: (i) a view- and texture independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker; (ii) the complexity of the non-linear facial expression space is modelled through a manifold, whose structure is learned using Laplacian Eigenmaps. The projected facial expressions are afterwards recognized based on Nearest Neighbor classifier; (iii) with the proposed approach, we developed an application for an AIBO robot, in which it mirrors the perceived facial expression.
A grayscale hit-or-miss transform based on level sets

The hit-or-miss transform (HMT) is a powerful morphological tool for the processing of binary images. There have been several attempts to generalize it to grayscale images, based on the grayscale erosion. The goal is to obtain a translation invariant recognition tool, with some robustness regarding small deformations and variations of illumination. We propose a definition of the hit-or-miss transform based on level sets. We call it the level set hit-or-miss transform (LSHMT). We compare its performance with that of another grayscale HMT found in the literature. The task performed is that of face localization on grayscale images, based on a set of face patterns. The empirical results on a database show the greater robustness of the LSHMT. The generalization of LSHMT using connected and morphological operators by reconstruction are proposed as feasible lines of research to increase its robustness. We are also working in its generalization to color images and image sequences.
A Neural Probe With Up to 966 Electrodes and Up to 384 Configurable Channels in 0.13 $\mu$m SOI CMOS

<italic>In vivo</italic> recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13-<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m SOI Al CMOS) a 384-channel configurable neural probe for large-scale <italic>in vivo</italic> recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 <inline-formula> <tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m wide, 10 mm long, 20 <inline-formula> <tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m thick), achieving a crosstalk of <inline-formula> <tex-math notation="LaTeX">$-\text{64.4}$</tex-math></inline-formula> dB. The probe base (5 <inline-formula> <tex-math notation="LaTeX">$\times$</tex-math></inline-formula> 9 mm<inline-formula><tex-math notation="LaTeX">$^2$ </tex-math></inline-formula>) implements dual-band recording and a 171.6 Mbps digital interface. Measurement results show a total input-referred noise of 6.4 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula>V<inline-formula><tex-math notation="LaTeX">$_{\mathrm{rms}}$</tex-math></inline-formula> and a total power consumption of 49.1 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>W/channel.
Online Learning for Human-Robot Interaction

This paper presents a novel approach for incremental subspace learning based on an online version of the non-parametric discriminant analysis (NDA). For many real-world applications (like the study of visual processes, for instance) there is impossible to know beforehand the number of total classes or the exact number of instances per class. This motivated us to propose a new algorithm, in which new samples can be added asynchronously, at different time stamps, as soon as they become available. The proposed technique for NDA-eigenspace representation has been applied to the problem of online face recognition for human-robot interaction scenario.
Ultra-high-density in-vivo neural probes

The past decade has witnessed an explosive growth in our ability to observe and measure brain activity. Among different functional brain imaging techniques, the electrical measurement of neural activity using neural probes provides highest temporal resolution. Yet, the electrode density and the observability of currently available neural probe technologies fall short of the density of neurons in the brain by several orders of magnitude. This paper presents opportunities for neural probes to utilize advances in CMOS technology for increasing electrode density and observability of neural activity, while minimizing the tissue damage. The authors present opportunities for neural probes to adapt advanced CMOS technologies and discuss challenges in terms of maintaining the signal integrity and implementing data communication.
You are fired! Nonverbal role analysis in competitive meetings

This paper addresses the problem of social interaction analysis in competitive meetings, using nonverbal cues. For our study, we made use of “The Apprentice” reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status and predicting the fired candidates. The current study was carried out using nonverbal audio cues. Results obtained from the analysis of a full season of the show, representing around 90 minutes of audio data, are very promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words.
Simultaneous 3D face pose and person-specific shape estimation from a single image using a holistic approach

This paper presents a new approach for the simultaneous estimation of the 3D pose and specific shape of a previously unseen face from a single image. The face pose is not limited to a frontal view. We describe a holistic approach based on a deformable 3D model and a learned statistical facial texture model. Rather than obtaining a person-specific facial surface, the goal of this work is to compute person-specific 3D face shape in terms of a few control parameters that are used by many applications. The proposed holistic approach estimates the 3D pose parameters as well as the face shape control parameters by registering the warped texture to a statistical face texture, which is carried out by a stochastic and genetic optimizer. The proposed approach has several features that make it very attractive: (i) it uses a single grey-scale image, (ii) it is person-independent, (iii) it is featureless (no facial feature extraction is required), and (iv) its learning stage is easy. The proposed approach lends itself nicely to 3D face tracking and face gesture recognition in monocular videos. We describe extensive experiments that show the feasibility and robustness of the proposed approach.
High-precision orientation and skew detection for texts in scanned documents

This paper describes an approach towards an orientation and skew detection for texts in scanned documents. Before using OCR systems to obtain character information from images, a preprocessing stage, comprising a number of adjustments, has to be performed in order to obtain accurate results. One important operation that has to be considered is the skew correction, or deskewing, of the image, a fault that arises from an incorrect scanning process. This paper presents an iterative method for detecting the text orientation and skew angle, method based on histogram processing.
Three-Dimensional Face Pose Detection and Tracking Using Monocular Videos: Tool and Application

Recently, we have proposed a real-time tracker that simultaneously tracks the 3D head pose and facial actions in monocular video sequences that can be provided by low quality cameras. This paper has two main contributions. First, we propose an automatic 3-D face pose initialization scheme for the real-time tracker by adopting a 2-D face detector and an eigenface system. Second, we use the proposed methods-the initialization and tracking-for enhancing the human-machine interaction functionality of an AIBO robot. More precisely, we show how the orientation of the robot's camera (or any active vision system) can be controlled through the estimation of the user's head pose. Applications based on head-pose imitation such as telepresence, virtual reality, and video games can directly exploit the proposed techniques. Experiments on real videos confirm the robustness and usefulness of the proposed methods.
Characterizing conversational group dynamics using nonverbal behaviour

This paper addresses the novel problem of characterizing conversational group dynamics. It is well documented in social psychology that depending on the objectives a group, the dynamics are different. For example, a competitive meeting has a different objective from that of a collaborative meeting. We propose a method to characterize group dynamics based on the joint description of a group members' aggregated acoustical nonverbal behaviour to classify two meeting datasets (one being cooperative-type and the other being competitive-type). We use 4.5 hours of real behavioural multi-party data and show that our methodology can achieve a classification rate of upto 100%.
Multi-observation face recognition in videos based on label propagation

In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons' identity assessment), since it is highly required by popular social media search engines like Facebook and Youtube. Recently, several approaches for graph-based label propagation were proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot cope properly with the rapid and frequent changes in data appearance, a phenomenon intrinsically related with video sequences. In this paper, we propose a novel approach for efficient and adaptive graph construction, based on a two-phase scheme: (i) the first phase is used to adaptively find the neighbors of a sample and also to find the adequate weights for the minimization function of the second phase; (ii) in the second phase, the selected neighbors along with their corresponding weights are used to locally and collaboratively estimate the sparse affinity matrix weights. Experimental results performed on Honda Video Database (HVDB) and a subset of video sequences extracted from the popular TV-series `Friends' show a distinct advantage of the proposed method over the existing standard graph construction methods.
On the application of morphological heteroassociative neural networks

Morphological neural networks (MNN) have been proposed as an alternative neural computation paradigm. We explore the potential of heteroassociative MNN (HMNN) for a practical task, such as that of robust scene recognition. Scene recognition could be of use for self-localization in a vision-based navigation framework for mobile robots. HMNN have a big potential for real time application because its recall process is very fast. We present some experimental results that illustrate our ideas.
Appearance-based face recognition using a supervised manifold learning framework

Many natural image sets, depicting objects whose appearance is changing due to motion, pose or light variations, can be considered samples of a low-dimension nonlinear manifold embedded in the high-dimensional observation space (the space of all possible images). The main contribution of our work is represented by a Supervised Laplacian Eigemaps (S-LE) algorithm, which exploits the class label information for mapping the original data in the embedded space. Our proposed approach benefits from two important properties: i) it is discriminative, and ii) it adaptively selects the neighbors of a sample without using any predefined neighborhood size. Experiments were conducted on four face databases and the results demonstrate that the proposed algorithm significantly outperforms many linear and non-linear embedding techniques. Although we've focused on the face recognition problem, the proposed approach could also be extended to other category of objects characterized by large variance in their appearance.
Face localization based on the morphological multi-scale fingerprint

We propose the use of morphological multi-scale fingerprints (MMF) for face localization. The MMF is computed as the local maxima and minima preserved up to a certain scale in a multi-scale analysis based on morphological erosion and dilation. This approach belongs to a class of global image feature extraction approaches, that can be combined with others to ensure robust face localization. No structural relationships between face elements is taken into account. We compare this approach to the eigenface approach to face detection with clear superior results.
Time multiplexed active neural probe with 678 parallel recording sites

We present a high density CMOS neural probe with active electrodes (pixels), consisting of dedicated in-situ circuits for signal source amplification. The complete probe contains 1356 neuron sized (20×20 μm2) pixels densely packed on a 50 μm thick, 100 μm wide and 8 mm long shank. It allows simultaneous high-performance recording from 678 electrodes and a possibility to simultaneously observe all of the 1356 electrodes with increased noise. This considerably surpasses the state of the art active neural probes in electrode count and flexibility. The measured action potential band noise is 12.4 μVrms, with just 3 μW power dissipation per electrode amplifier and 45 μW per channel (including data transmission).
Morphological neural networks for vision based self-localization

Morphological neural networks (MNN) have been proposed as associative memories (with its two cases: autoassociative and heteroassociative). In this paper we are involved with heteroassociative MNN (HMNN). We propose their use for self-localization in a vision-based navigation framework for mobile robots. HMNN can be trained in a single computation step. Their storage capacity bound is the dimension of the patterns, and they have perfect recall of the patterns under very mild conditions. Recall is also very fast, because the MNN recall does not involve the search for an energy minimum.
Morphological neural networks for robust visual processing in mobile robotics

Morphological Neural Networks (MNN) have been proposed as associative (with its two cases: autoassociative and heteroassociative) memories. In this paper we are involved with Heteroassociative MNN (HMNN). We propose their utilization as a preprocessing step for human shape detection, in a vision-based navigation problem for mobile robots. MNN can be trained in a single computing step, they possess unlimited storing capacity, and they have perfect recall of the pattens. Recall is also very fast, because the MNN recall does not involve the search for an energy minimum.
Assessing the Influence of Mirroring on the Perception of Professional Competence Using Wearable Technology

Nonverbal communication is an intrinsic part in daily face-to-face meetings. A frequently observed behavior during social interactions is mirroring, in which one person tends to mimic the attitude of the counterpart. This paper shows that a computer vision system could be used to predict the perception of competence in dyadic interactions through the automatic detection of mirroring events. To prove our hypothesis, we developed: (1) A social assistant for mirroring detection, using a wearable device which includes a video camera and (2) an automatic classifier for the perception of competence, using the number of nodding gestures and mirroring events as predictors. For our study, we used a mixed-method approach in an experimental design where 48 participants acting as customers interacted with a confederated psychologist. We found that the number of nods or mirroring events has a significant influence on the perception of competence. Our results suggest that: (1) Customer mirroring is a better predictor than psychologist mirroring; (2) the number of psychologist's nods is a better predictor than the number of customer's nods; (3) except for the psychologist mirroring, the computer vision algorithm we used worked about equally well whether it was acquiring images from wearable smartglasses or fixed cameras.
Detecting and tracking of 3D face pose for human-robot interaction

Faces play a major role in many HCI systems, because they represent a rich source of information. Being able to estimate the 3D face pose in real-time, we can get a clue about user's intentions or to assess which object become his/her focus of attention. This paper has two main contributions. First, we propose an automatic 3D face pose initialization scheme for our real-time tracker by adopting a 2D face detector and an eigenface system. Second, we use the proposed methods - the initialization and tracking - for controlling the orientation of an AIBO camera. We show how the changes in user's face movement can be imitated by the robot's camera and how it can be applied to map an indoor scene.
Person-Specific Face Shape Estimation under Varying Head Pose from Single Snapshots

This paper presents a new method for person-specific face shape estimation under varying head pose of a previously unseen person from a single image. We describe a featureless approach based on a deformable 3D model and a learned face subspace. The proposed approach is based on maximizing a likelihood measure associated with a learned face subspace, which is carried out by a stochastic and genetic optimizer. We conducted the experiments on a subset of Honda Video Database showing the feasibility and robustness of the proposed approach. For this reason, our approach could lend itself nicely to complex frameworks involving 3D face tracking and face gesture recognition in monocular videos.
22.7 A 966-electrode neural probe with 384 configurable channels in 0.13µm SOI CMOS

In vivo recording of neural action-potential (AP) and local-field-potential (LFP) signals requires the use of high-resolution penetrating probes. Driven by the need for large-scale recording and minimal tissue damage, a technology roadmap has been defined for next-generation probes aiming to maximize the number of recording sites while minimizing the probe dimensions . In this paper we present a 384-channel configurable active neural probe for high-density recording which implements in situ buffering under each electrode to minimize the crosstalk between adjacent metal lines along the shank and other parasitic effects inherent to traditional passive probes [2]. Up to 966 selectable, neuron-sized electrodes (12×12μm2) were densely packed along a narrow (70μm) and thin (20μm) implantable shank using integrated CMOS. With twice the number of electrodes compared to state-of-the-art neural probes [2], our design achieves the highest electrode count in a single shank reported so far.