nils-herrmann/clean_scirepeval huggingface dataset:
A hierarchical learning approach to calibrate allele frequencies for SNP based genotyping of DNA pools

The combination of low density SNP arrays and DNA pooling is a fast and cost effective approach to genotyping that opens up basic genomics to a range of new applications and studies. However we have identified significant limitations in the existing approach to calculating allele frequencies with DNA pooling. These limitations include a reduced ability to deal with SNP to SNP variation via the standard interpolation method. Our contribution is a new hierarchical learning framework which resolves these drawbacks. The framework involves a hierarchy of two greedily trained layers of learners. The first layer learns the bias of each SNP then applies a calibration to reduce SNP bias by mapping into a common coordinate system across all SNPs. The second layer learns an allele frequency function exploiting the global SNP data. A range of algorithms have been applied including linear regression, neural network and support vector regression. The framework has been tested on pooled samples of Black Tiger prawns that have been genotyped with low density Sequenom iPLEX panels. Analysis of pooled samples and the corresponding individually genotyped SNP samples indicate the pooling approach introduces an allele frequency RMS error of 0.12. The existing calibration approach corrects ~14% of the error. Our hierarchical approach is 4.5 times as effective by correcting for ~64% of the introduced error. This is a significant reduction and has the potential to enable genetic studies previously not possible due to allele frequency error. Although testing so far is limited to low density SNP arrays the approach was developed to generalize to other SNP genotyping technologies.
Psychophysical assessment of perceived interest in natural images: The ROI-D database

We introduce a novel region-of-interest (ROI) database for natural image content, the ROI-D database. The database consists of ROI maps created from manual selections obtained in a psychophysical experiment with 20 participants. The presented stimuli were 42 photographic images taken from 3 publicly available image quality databases. In addition to the ROI selections, dominance ratings were recorded that provide further insight into the interest of the selected ROI in relation to the background. In this paper, the experiment is described, the resulting ROI database is analysed, and possible applications of the database are discussed. The ROI-D database is made freely available to the image processing research community.
Virtual Reality-Based Human-Data Interaction

Visual Analytics (VA) is a discipline that integrates computational and human efforts, allowing for effective data exploration with interactive and insightful user interfaces. Human- Data Interaction (HDI) is a relatively new term that we interpret here as the interactive interface between a human and the visual representation of the data, analogous to human computer interaction. Advanced user interfaces are widely integrated into modern computing devices enabling more effective human interaction with data sets. However, limited control and display modalities of computing devices often still do not easily facilitate exploration of the multidimensionality and heterogeneity of large data sets. Virtual Reality (VR) technologies have demonstrated their potential to unlock human understanding of complex data sets through immersion and natural spatial interaction. In this position paper, we share our views on how VR-based HDI can support exploration of multidimensional large data sets with the aim at providing direction for open research areas that may serve the design of a VR-based HDI system in this emerging field of research.
An Artificial Neural Network for Quality Assessment in Wireless Imaging Based on Extraction of Structural Information

In digital transmission, images may undergo quality degradation due to lossy compression and error-prone channels. Efficient measurement tools are needed to quantify induced distortions and to predict their impact on perceived quality. In this paper, an artificial neural network (ANN) is proposed for perceptual image quality assessment. The quality prediction is based on structural image features such as blocking, blur, image activity, and intensity masking. Training and testing of the ANN is performed with reference to subjective experiments and the obtained mean opinion scores (MOS). It is shown that the proposed ANN is capable of predicting MOS over a wide range of image distortions. This applies to both cases, when reference information about the structure of the original image is available to the ANN but also in absence of this knowledge. The considered ANN would therefore be well suited for combination with link adaption techniques.
Comparative Study of Fixation Density Maps

Fixation density maps (FDM) created from eye tracking experiments are widely used in image processing applications. The FDM are assumed to be reliable ground truths of human visual attention and as such, one expects a high similarity between FDM created in different laboratories. So far, no studies have analyzed the degree of similarity between FDM from independent laboratories and the related impact on the applications. In this paper, we perform a thorough comparison of FDM from three independently conducted eye tracking experiments. We focus on the effect of presentation time and image content and evaluate the impact of the FDM differences on three applications: visual saliency modeling, image quality assessment, and image retargeting. It is shown that the FDM are very similar and that their impact on the applications is low. The individual experiment comparisons, however, are found to be significantly different, showing that inter-laboratory differences strongly depend on the experimental conditions of the laboratories. The FDM are publicly available to the research community.
Region Proposal for Pattern Spotting in Historical Document Images

Pattern spotting consists in searching in a document image for the occurrences of a queried graphical object. The main challenge in pattern spotting is that the query image is generally small and the occurrences may be located at any random places in the image. Rather than exhaustively indexing all possible subwindows extracted from the document images, the common way is to rely on a segmentation or a document layout analysis to limit the search space. However, there is no segmentation nor document layout analysis technique reliable enough for historical document images. Region proposal, a technique used to generate a set of regions potentially containing an object, has contributed to many state of the art object detection systems recently. Although it is initially proposed for object detection, we will show that region proposal also offers promising results for document images, particularly in the case of pattern spotting. In this paper, we aim at investigating the use of region proposal to produce high quality subwindows to replace the usual document layout analysis step and the blind sliding windowing step. From experiments conducted on the DocExplore dataset, we show that region proposal generates a comparable number of subwindows while helping the system to achieve significant better results than the system built with commonly used layout analysis techniques.
Robust real time tracking of 3D objects

In this article the problem of tracking rigid 3D objects is addressed. The contribution of the proposed approach is an algorithm which combines: efficiency (i.e. the algorithm is designed before all to be real time using standard architecture), robustness (occlusions are allowed) and accuracy (sub-pixel accuracy is obtained). It is devoted to the tracking of 3D rigid objects, assuming that the 3D geometry as well as the texture of the surface is known. Such performances can be obtained through a two levels scheme: the core of the approach consists in an efficient 2D patch tracker whose results are combined robustly to compute the 3D object pose. This article provides experimental results proving the soundness of the proposed approach.
Discriminative Autoencoders for Small Targets Detection

This paper introduces the new concept of discriminative auto encoders. In contrast with the standard auto encoders - which are artificial neural networks used to learn compressed representation for a set of data - discriminative auto encoders aim at learning low-dimensional discriminant encodings using two classes of data (denoted such as the positive and the negative classes). More precisely, the discriminative auto encoders build a latent space (manifold) under the constraint that the positive data should be better reconstructed than the negative data. It can therefore be seen as a generative model of the discriminative data and hence can be used favorably in classification tasks. This new representation is validated on a target detection task, on which the discriminative auto encoders not only give better results than the standard auto encoders but are also competitive when compared to standard classifiers such as the Support Vector Machine.
Groups of Adjacent Contour Segments for Object Detection

We present a family of scale-invariant local shape features formed by chains of k connected roughly straight contour segments (kAS), and their use for object class detection. kAS are able to cleanly encode pure fragments of an object boundary without including nearby clutter. Moreover, they offer an attractive compromise between information content and repeatability and encompass a wide variety of local shape structures. We also define a translation and scale invariant descriptor encoding the geometric configuration of the segments within a kAS, making kAS easy to reuse in other frameworks, for example, as a replacement or addition to interest points (IPs). Software for detecting and describing kAS is released at http://lear.inrialpes.fr/software. We demonstrate the high performance of kAS within a simple but powerful sliding-window object detection scheme. Through extensive evaluations, involving eight diverse object classes and more than 1,400 images, we (1) study the evolution of performance as the degree of feature complexity k varies and determine the best degree, (2) show that kAS substantially outperform IPs for detecting shape-based classes, and (3) compare our object detector to the recent state-of-the-art system by Dalal and Triggs (2005).
Learning shape prior models for object matching

The aim of this work is to learn a shape prior model for an object class and to improve shape matching with the learned shape prior. Given images of example instances, we can learn a mean shape of the object class as well as the variations of non-affine and affine transformations separately based on the thin plate spline (TPS) parameterization. Unlike previous methods, for learning, we represent shapes by vector fields instead of features which makes our learning approach general. During shape matching, we inject the shape prior knowledge and make the matching result consistent with the training examples. This is achieved by an extension of the TPS-RPM algorithm which finds a closed form solution for the TPS transformation coherent with the learned transformations. We test our approach by using it to learn shape prior models for all the five object classes in the ETHZ Shape Classes. The results show that the learning accuracy is better than previous work and the learned shape prior models are helpful for object matching in real applications such as object classification.
Model-based object tracking in cluttered scenes with occlusions

We propose an efficient method for tracking 3D modelled objects in cluttered scenes. Rather than tracking objects in the image, our approach relies on the object recognition aspect of tracking. Candidate matches between image and model features define volumes in the space of transformations. The volumes of the pose space satisfying the maximum number of correspondences are those that best align the model with the image. Object motion defines a trajectory in the pose space. We give some results showing that the presented method allows tracking of objects even when they are totally occluded for a short while, without supposing any motion model and with a low computational cost (below 200 ms per frame on a basic workstation). Furthermore, this algorithm can also be used to initialize the tracking.
Real time 3D face tracking from appearance

We propose a real time 3D tracking algorithm dedicated to the tracking of human faces in video sequences. A face is represented by a collection of 2D images called reference views. In our approach, a pattern is a region of the image defined in an area of interest and its sampling gives a grey level vector. The tracking technique involves two stages. An off-line learning stage is devoted to the computation of an interaction matrix for every reference view. This matrix relates the grey level difference between the tracked reference pattern and the current pattern sampled inside the area of interest to its "fronto parallel" movement (which do not modify its aspect in the image). The on-line stage consists in using this matrix to track the reference pattern in the current image. During this stage, appearance changes due to movements in roll are managed by switching between the different reference patterns. The reference pattern, after motion correction, giving the smallest grey level difference is supposed to be the new tracked reference pattern. We present experimental results showing the efficiency and the robustness of our approach.
Real time tracking of 3D objects with occultations

We present an efficient method for real time tracking of 3D objects. Within this approach, target objects are modeled by sets of 2D patterns including all of the possible object appearances. The tracker is based on two tasks: a 2D tracker estimates the position of the current appearance in the current image; in real time, a second tracker looks for the change of appearance of the object. We experimentally show the efficiency of the algorithm, as well as its ability to resist occultations and changes of brightness.
Unsupervised Part Learning for Visual Recognition

Part-based image classification aims at representing categories by small sets of learned discriminative parts, upon which an image representation is built. Considered as a promising avenue a decade ago, this direction has been neglected since the advent of deep neural networks. In this context, this paper brings two contributions: first, this work proceeds one step further compared to recent part-based models (PBM), focusing on how to learn parts without using any labeled data. Instead of learning a set of parts per class, as generally performed in the PBM literature, the proposed approach both constructs a partition of a given set of images into visually similar groups, and subsequently learns a set of discriminative parts per group in a fully unsupervised fashion. This strategy opens the door to the use of PBM in new applications where labeled data are typically not available, such as instance-based image retrieval. Second, this paper shows that despite the recent success of end-to-end models, explicit part learning can still boost classification performance. We experimentally show that our learned parts can help building efficient image representations, which outperform state-of-the art Deep Convolutional Neural Networks (DCNN) on both classification and retrieval tasks.
Puzzling face verification algorithms for privacy protection

This paper presents a new approach for de-identifying face images, i.e. for preventing automatic matching with public face collections. The overall motivation is to offer tools for privacy protection on social networks. We address this question by drawing a parallel between face de-identification and oracle attacks in digital watermarking. In our case, the identity of the face is seen as the watermark to be removed. Inspired by oracle attacks, we forge de-identified faces by superimposing a collection of carefully designed noise patterns onto the original face. The modification of the image is controlled to minimize the probability of good recognition while minimizing the distortion. In addition, these de-identified images are - by construction - made robust to counter attacks such as blurring. We present an experimental validation in which we de-identify LFW faces and show that resulting images are still recognized by human beings while deceiving a state-of-the-art face recognition algorithm.
Combining appearance models and Markov Random Fields for category level object segmentation

Object models based on bag-of-words representations can achieve state-of-the-art performance for image classification and object localization tasks. However, as they consider objects as loose collections of local patches they fail to accurately locate object boundaries and are not able to produce accurate object segmentation. On the other hand, Markov random field models used for image segmentation focus on object boundaries but can hardly use the global constraints necessary to deal with object categories whose appearance may vary significantly. In this paper we combine the advantages of both approaches. First, a mechanism based on local regions allows object detection using visual word occurrences and produces a rough image segmentation. Then, a MRF component gives clean boundaries and enforces label consistency, guided by local image cues (color, texture and edge cues) and by long-distance dependencies. Gibbs sampling is used to infer the model. The proposed method successfully segments object categories with highly varying appearances in the presence of cluttered backgrounds and large view point changes. We show that it outperforms published results on the Pascal VOC 2007 dataset.
Randomized Clustering Forests for Image Classification

Some of the most effective recent methods for content-based image classification work by quantizing image descriptors, and accumulating histograms of the resulting visual word codes. Large numbers of descriptors and large codebooks are required for good results and this becomes slow using k-means. We introduce Extremely Randomized Clustering Forests-ensembles of randomly created clustering trees-and show that they provide more accurate results, much faster training and testing, and good resistance to background clutter. Second, an efficient image classification method is proposed. It combines ERC-Forests and saliency maps very closely with the extraction of image information. For a given image, a classifier builds a saliency map online and uses it to classify the image. We show in several state-of-the-art image classification tasks that this method can speed up the classification process enormously. Finally, we show that the proposed ERC-Forests can also be used very successfully for learning distance between images. The distance computation algorithm consists of learning the characteristic differences between local descriptors sampled from pairs of same or different objects. These differences are vector quantized by ERC-Forests and the similarity measure is computed from this quantization. The similarity measure has been evaluated on four very different datasets and always outperforms the state-of-the-art competitive approaches.
Histograms of Pattern Sets for Image Classification and Object Recognition

This paper introduces a novel image representation capturing feature dependencies through the mining of meaningful combinations of visual features. This representation leads to a compact and discriminative encoding of images that can be used for image classification, object detection or object recognition. The method relies on (i) multiple random projections of the input space followed by local binarization of projected histograms encoded as sets of items, and (ii) the representation of images as Histograms of Pattern Sets (HoPS). The approach is validated on four publicly available datasets (Daimler Pedestrian, Oxford Flowers, KTH Texture and PASCAL VOC2007), allowing comparisons with many recent approaches. The proposed image representation reaches state-of-the-art performance on each one of these datasets.
Real time 3D template matching

One of the most popular methods to extract useful information from an image sequence is the template matching approach. In this well known method the tracking of a certain feature or target over time is based on the comparison of the content of each image with a sample template. We propose a 3D template matching algorithm that is able to track targets corresponding to the projection of 3D surfaces. With only a few hundred subtractions and multiplications per frame, our algorithm provides, in real time, an estimation of the 3D surface pose. The key idea is to compute the difference between the current image content and the visual aspect of the target under the predicted spatial attitude. This difference image is converted into corrections on the 3D location parameters.
Hybrid multi-layer deep CNN/aggregator feature for image classification

Deep Convolutional Neural Networks (DCNN) have established a remarkable performance benchmark in the field of image classification, displacing classical approaches based on hand-tailored aggregations of local descriptors. Yet DCNNs impose high computational burdens both at training and at testing time, and training them requires collecting and annotating large amounts of training data. Supervised adaptation methods have been proposed in the literature that partially re-learn a transferred DCNN structure from a new target dataset. Yet these require expensive bounding-box annotations and are still computationally expensive to learn. In this paper, we address these shortcomings of DCNN adaptation schemes by proposing a hybrid approach that combines conventional, unsupervised aggregators such as Bag-of-Words (BoW), with the DCNN pipeline by treating the output of intermediate layers as densely extracted local descriptors. We test a variant of our approach that uses only intermediate DCNN layers on the standard PASCAL VOC 2007 dataset and show performance significantly higher than the standard BoW model and comparable to Fisher vector aggregation but with a feature that is 150 times smaller. A second variant of our approach that includes the fully connected DCNN layers significantly outperforms Fisher vector schemes and performs comparably to DCNN approaches adapted to Pascal VOC 2007, yet at only a small fraction of the training and testing cost.
Scale-invariant shape features for recognition of object categories

We introduce a new class of distinguished regions based on detecting the most salient convex local arrangements of contours in the image. The regions are used in a similar way to the local interest points extracted from gray-level images, but they capture shape rather than texture. Local convexity is characterized by measuring the extent to which the detected image contours support circle or arc-like local structures at each position and scale in the image. Our saliency measure combines two cost functions defined on the tangential edges near the circle: a tangential-gradient energy term, and an entropy term that ensures local support from a wide range of angular positions around the circle. The detected regions are invariant to scale changes and rotations, and robust against clutter, occlusions and spurious edge detections. Experimental results show very good performance for both shape matching and recognition of object categories.