nils-herrmann/clean_scirepeval huggingface dataset:
Multidisciplinary Learning through Implementation of the DVB-S2 Standard

Telecommunication standards are documents that contain consolidated knowledge about communication systems and implementation best practices. They are created based on long consensus processes in order to meet practical constraints. This article describes how the DVB-S2 standard is used in the electrical engineering curricula at KU Leuven within a design and implementation course called EAGLE. The goal of the course is to teach third-year Bachelor students how to apply the abstract knowledge gathered in the theoretical courses (control theory, communication theory, software, and hardware) to the design of a complex and real engineering project. Based on their progress during the academic year, we illustrate how standards can be used to teach a broad skill set and act as an accelerator to abstract system implementation complexity. We give an overview of the objectives of the EAGLE project and the relevance of the DVB-S2 standard, the design and implementation task that the students need to accomplish, and how they finally learn to master the complexity of a real communication standard, VHDL coding, and system verification and integration.
Efficient grouping under perspective skew

We present an efficient grouping strategy for the detection of regular repetitions of planar (but not necessarily coplanar) patterns. At the heart of our system lie the fixed structures that typify the geometric transformations of the regularities. The approach unifies a number of grouping types that have traditionally been dealt with separately. It avoids the use of combinatorics in the search for pattern repetitions, through the combined use of invariants for hashing-based pattern matching on the one hand, and Hough transforms for the detection of the fixed structures on the other hand. In this paper we concentrate on planar homologies and elations in particular Results on real-world scenes demonstrate the performance of the approach.
Real-time affine region tracking and coplanar grouping

We present a novel approach for tracking locally planar regions in an image sequence and their grouping into larger planar surfaces. The tracker recovers the affine transformation of the region and therefore yields reliable point correspondences between frames. Both edges and texture information are exploited in an integrated way, while not requiring the complete region's contour. The tracker withstands zoom, out-of-plane rotations, discontinuous motion and changes in illumination conditions while achieving real-time performance for a region. Multiple tracked regions are grouped into disjoint coplanarity classes. We first define a coplanarity score between each pair of regions, based on motion and texture cues. The scores are then analyzed by a clique-partitioning algorithm yielding the coplanarity classes that best fit the data. The method works in the presence of perspective distortions, discontinuous planar surfaces and considerable amounts of measurement noise.
Using spatio-temporal interest points (STIP) for myoclonic jerk detection in nocturnal video

In this study we introduce a method for detecting myoclonic jerks during the night with video. Using video instead of the traditional method of using EEG-electrodes, permits patients to sleep without any attached sensors. This improves the comfort during sleep and it makes long term home monitoring possible. The algorithm for the detection of the seizures is based on spatio-temporal interest points (STIPs), proposed by Ivan Laptev, which is the state-of-the-art in action recognition [8].We applied this algorithm on a group of patients suffering from myoclonic jerks. With an optimal parameter setting this resulted in a sensitivity of over 75% and a PPV of over 85%, on the patients' combined data.
Codebook-free exemplar models for object detection

Traditional bag-of-features approaches often vector-quantise the features into a visual codebook. This process inevitably causes loss of information. Recently codebook-free methods that avoid the vector-quantisation step have become more popular. Used in conjunction with nearest-neighbour approaches these methods have shown remarkable classification performance. In this paper we show how to exploit the concept of nearest neighbour based classification for object detection. Our codebook-free exemplar model combines the classification power of nearest neighbour methods with a detection concept based on exemplar models. We demonstrate the performance of our proposed system on a real-world dataset of images of motorbikes.
Towards sign language recognition based on body parts relations

Over the years, hand gesture recognition has been mostly addressed considering hand trajectories in isolation. However, in most sign languages, hand gestures are defined on a particular context (body region). We propose a pipeline which models hand movements in the context of other parts of the body captured in the 3D space using the Kinect sensor. In addition, we perform sign recognition based on the different hand postures that occur during a sign. Our experiments show that considering different body parts brings improved performance when compared with methods which only consider global hand trajectories. Finally, we demonstrate that the combination of hand postures features with hand gestures features helps to improve the prediction of a given sign.
The NBNN kernel

Naive Bayes Nearest Neighbor (NBNN) has recently been proposed as a powerful, non-parametric approach for object classification, that manages to achieve remarkably good results thanks to the avoidance of a vector quantization step and the use of image-to-class comparisons, yielding good generalization. In this paper, we introduce a kernelized version of NBNN. This way, we can learn the classifier in a discriminative setting. Moreover, it then becomes straightforward to combine it with other kernels. In particular, we show that our NBNN kernel is complementary to standard bag-of-features based kernels, focussing on local generalization as opposed to global image composition. By combining them, we achieve state-of-the-art results on Caltech101 and 15 Scenes datasets. As a side contribution, we also investigate how to speed up the NBNN computations.
The cascaded Hough transform

When using the original slope-intercept parameterisation for the Hough transform, the resulting parameter space actually corresponds to the dual space. Indeed, lines are transformed into points, and for every point there is also a corresponding line. This paper presents a way of exploiting this special property, by the introduction of the cascaded Hough transform (CHT). This allows to look for the overall structure in an image, such as lines intersecting in a point or intersection points lying on a line. An interesting example is the detection of vanishing points and vanishing lines.
Depth-From-Recognition: Inferring Meta-data by Cognitive Feedback

Thanks to recent progress in category-level object recognition, we have now come to a point where these techniques have gained sufficient maturity and accuracy to succesfully feed back their output to other processes. This is what we refer to as cognitive feedback. In this paper, we study one particular form of cognitive feedback, where the ability to recognize objects of a given category is exploited to infer meta-data such as depth cues, 3D points, or object decomposition in images of previously unseen object instances. Our approach builds on the implicit shape model of Leibe and Schiele, and extends it to transfer annotations from training images to test images. Experimental results validate the viability of our approach.
A Scalable 3D HOG Model for Fast Object Detection and Viewpoint Estimation

In this paper we present a scalable way to learn and detect objects using a 3D representation based on HOG patches placed on a 3D cuboid. The model consists of a single 3D representation that is shared among views. Similarly to the work of Fidler et al. , at detection time this representation is projected on the image plane over the desired viewpoints. However, whereas in  the projection is done at image-level and therefore the computational cost is linear in the number of views, in our model every view is approximated at feature level as a linear combination of the pre-computed fron to-parallel views. As a result, once the fron to-parallel views have been computed, the cost of computing new views is almost negligible. This allows the model to be evaluated on many more viewpoints. In the experimental results we show that the proposed model has a comparable detection and pose estimation performance to standard multiview HOG detectors, but it is faster, it scales very well with the number of views and can better generalize to unseen views. Finally, we also show that with a procedure similar to label propagation it is possible to train the model even without using pose annotations at training time.
Action in chains: A chains model for action localization and classification

In this paper we present a method for action classification in videos using trajectory features. The novelty of our approach is in formulating the problem of simultaneous detection and localization as a probabilistic chains model. In our formulation, chains are sets of regions in the video that are connected based on their joint probabilities. We describe our approach for connecting subvolumes in the video into chains, and using them as spatio-temporal detectors for actions. Our approach allows the detection and localization of multiple actions occurring simultaneously or at different locations in a single video. We test the performance of our method on two challenging action recognition datasets, and compare to state of the art methods.
Naming persons in news video with label propagation

Labeling persons appearing in video frames with names detected from the video transcript helps improving the video content identification and search task. We develop a face naming method that learns from labeled and unlabeled examples using iterative label propagation in a graph of connected faces or name-face pairs. The advantage of this method is that it can use very few labeled data points and incorporate the unlabeled data points during the learning process. Anchor detection and metric learning for face classification techniques are incorporated into the label propagation process to help boosting the face naming performance. On BBC News videos, the label propagation algorithm yields better results than a Support Vector Machine classifier trained on the same labeled data.
Reflectance and Natural Illumination from Single-Material Specular Objects Using Deep Learning

In this paper, we present a method that estimates reflectance and illumination information from a single image depicting a single-material specular object from a given class under natural illumination. We follow a data-driven, learning-based approach trained on a very large dataset, but in contrast to earlier work we do not assume one or more components (shape, reflectance, or illumination) to be known. We propose a two-step approach, where we first estimate the object’s reflectance map, and then further decompose it into reflectance and illumination. For the first step, we introduce a Convolutional Neural Network (CNN) that directly predicts a reflectance map from the input image itself, as well as an indirect scheme that uses additional supervision, first estimating surface orientation and afterwards inferring the reflectance map using a learning-based sparse data interpolation technique. For the second step, we suggest a CNN architecture to reconstruct both Phong reflectance parameters and high-resolution spherical illumination maps from the reflectance map. We also propose new datasets to train these CNNs. We demonstrate the effectiveness of our approach for both steps by extensive quantitative and qualitative evaluation in both synthetic and real data as well as through numerous applications, that show improvements over the state-of-the-art.
Coupling video segmentation and action recognition

Recently a lot of progress has been made in the field of video segmentation. The question then arises whether and how these results can be exploited for this other video processing challenge, action recognition. In this paper we show that a good segmentation is actually very important for recognition. We propose and evaluate several ways to integrate and combine the two tasks: i) recognition using a standard, bottom-up segmentation, ii) using a top-down segmentation geared towards actions, iii) using a segmentation based on inter-video similarities (co-segmentation), and iv) tight integration of recognition and segmentation via iterative learning. Our results clearly show that, on the one hand, the two tasks are interdependent and therefore an iterative optimization of the two makes sense and gives better results. On the other hand, comparable results can also be obtained with two separate steps but mapping the feature-space with a non-linear kernel.
Feature based omnidirectional sparse visual path following

Vision sensors are attractive for autonomous robots because they are a rich source of environment information. The main challenge in using images for mobile robots is managing this wealth of information. A relatively recent approach is the use of fast wide baseline local features, which we developed and used in the novel approach to sparse visual path following described in this paper. These local features have the great advantage that they can be recognized even if the viewpoint differs significantly. This opens the door to a memory efficient description of a path by descriptors of sparse images. We propose a method for re-execution of these paths by a series of visual homing operations which yield a navigation method with unique properties: it is accurate, robust, fast, and without odometry error build-up.
Dataset fingerprints: Exploring image collections through data mining

As the amount of visual data increases, so does the need for summarization tools that can be used to explore large image collections and to quickly get familiar with their content. In this paper, we propose dataset fingerprints, a new and powerful method based on data mining that extracts meaningful patterns from a set of images. The discovered patterns are compositions of discriminative mid-level features that co-occur in several images. Compared to earlier work, ours stands out because i) it's fully unsupervised, ii) discovered patterns cover large parts of the images, often corresponding to full objects or meaningful parts thereof, and iii) different patterns are connected based on co-occurrence, allowing a user to “browse” the images from one pattern to the next and to group patterns in a semantically meaningful manner.
Content-based analysis for accessing audiovisual archives: Alternatives for concept-based indexing and search

Huge amounts of audiovisual material have been digitized recently, resulting in a great source of information relevant both from a cultural and historical point of view. However, in spite of millions of man hours spent on manual annotation and recent advances in (semi-)automatic metadata generation, accessing these archives and retrieving relevant information from them remains a difficult task. Up to recently, the main paradigm to open up archives by automatic tools for audiovisual analysis has been a concept-based indexing and retrieval oriented approach. However, this approach has its limitations, in that it does not scale well, it requires strong supervision, and does not really match well to the user's needs. In this paper, we discuss some upcoming alternative approaches that try to overcome or circumvent some of these issues. This includes i) the use of knowledge modeling to bridge the semantic gap; ii) on-the-fly learning of new, user-defined concepts; and iii) weakly supervised methods that learn from associated text data. We also discuss what we consider important open issues at this time that deserve more attention from the research community.
Vector Quantizing Feature Space with a Regular Lattice

Most recent class-level object recognition systems work with visual words, i.e., vector quantized local descriptors. In this paper we examine the feasibility of a data- independent approach to construct such a visual vocabulary, where the feature space is discretized using a regular lattice. Using hashing techniques, only non-empty bins are stored, and fine-grained grids become possible in spite of the high dimensionality of typical feature spaces. Based on this representation, we can explore the structure of the feature space, and obtain state-of-the-art pixelwise classification results. In the case of image classification, we introduce a class-specific feature selection step, which takes the spatial structure of SIFT-like descriptors into account. Results are reported on the Graz02 dataset.
Weakly supervised object detection with convex clustering

Weakly supervised object detection, is a challenging task, where the training procedure involves learning at the same time both, the model appearance and the object location in each image. The classical approach to solve this problem is to consider the location of the object of interest in each image as a latent variable and minimize the loss generated by such latent variable during learning. However, as learning appearance and localization are two interconnected tasks, the optimization is not convex and the procedure can easily get stuck in a poor local minimum, i.e. the algorithm “misses” the object in some images. In this paper, we help the optimization to get close to the global minimum by enforcing a “soft” similarity between each possible location in the image and a reduced set of “exemplars”, or clusters, learned with a convex formulation in the training images. The help is effective because it comes from a different and smooth source of information that is not directly connected with the main task. Results show that our method improves a strong baseline based on convolutional neural network features by more than 4 points without any additional features or extra computation at testing time but only adding a small increment of the training time due to the convex clustering.
Matching of affinely invariant regions for visual servoing

This paper develops new image matching techniques for visual servoing based on affine invariants which allow one to deal with large viewpoint changes and that do not rely on specific markers. The only assumption is that there are some locally planar and unoccluded scene regions that have enough structure to be detected in the image. Those regions are classified by a set of illumination and viewpoint invariant features. The features represent the image in a very compact way and allow fast comparison and feature matching between quite different viewpoints. The matching procedure is embedded in a visual servoing system for a mobile robot. Experiments show its potential for navigation with large camera rotations and view point changes in a cluttered environment without the need for artificial landmarks.
Object Classification with Adaptable Regions

In classification of objects substantial work has gone into improving the low level representation of an image by considering various aspects such as different features, a number of feature pooling and coding techniques and considering different kernels. Unlike these works, in this paper, we propose to enhance the semantic representation of an image. We aim to learn the most important visual components of an image and how they interact in order to classify the objects correctly. To achieve our objective, we propose a new latent SVM model for category level object classification. Starting from image-level annotations, we jointly learn the object class and its context in terms of spatial location (where) and appearance (what). Furthermore, to regularize the complexity of the model we learn the spatial and co-occurrence relations between adjacent regions, such that unlikely configurations are penalized. Experimental results demonstrate that the proposed method can consistently enhance results on the challenging Pascal VOC dataset in terms of classification and weakly supervised detection. We also show how semantic representation can be exploited for finding similar content.
Noncombinatorial Detection of Regular Repetitions under Perspective Skew

We present a geometric framework for the efficient detection of regular repetitions of planar (but not necessarily coplanar) patterns. At the heart of our system, lie the fixed structures of the transformations that describe these regular configurations. The approach detects a number of symmetric configurations that have traditionally been dealt with separately, in that all configurations corresponding to planar homologies are detected. These include important cases such as periodicities, mirror symmetries, and reflections about a point. The approach can handle perspective distortions. It avoids to get trapped in combinatorics; through invariant-based hashing for pattern matching and through Hough transforms for the detection of fixed structures. Additional efficiency and robustness are obtained from the system's ability to "reason" about the consistency of multiple homologies. The performance of the system is demonstrated with several examples.
Fast indexing for image retrieval based on local appearance with re-ranking

This paper describes an approach to retrieve images containing specific objects, scenes or buildings. The image content is captured by a set of local features. More precisely, we use so-called invariant regions. These are features with shapes that self-adapt to the viewpoint. The physical parts on the object surface that they carve out are the same in all views, even though the extraction proceeds from a single view only. The surface patterns within the regions are then characterized by a feature vector of moment invariants. Invariance is under affine geometric deformations and scaled color bands with an offset added. This allows regions from different views to be matched efficiently. An indexing technique based on vantage point tree organizes the feature vectors in such a way that a naive sequential search can be avoided. This results in sublinear computation times to retrieve images from a database. In order to get sufficient certainty about the correctness of the retrieved images, a method to increase the number of matched regions is introduced. This way, the system is both efficient and discriminant. It is demonstrated how scenes or buildings are recognized, even in case of partial visibility and under a large variety of viewing condition changes.
Allocentric Pose Estimation

The task of object pose estimation has been a challenge since the early days of computer vision. To estimate the pose (or viewpoint) of an object, people have mostly looked at object intrinsic features, such as shape or appearance. Surprisingly, informative features provided by other, external elements in the scene, have so far mostly been ignored. At the same time, contextual cues have been shown to be of great benefit for related tasks such as object detection or action recognition. In this paper, we explore how information from other objects in the scene can be exploited for pose estimation. In particular, we look at object configurations. We show that, starting from noisy object detections and pose estimates, exploiting the estimated pose and location of other objects in the scene can help to estimate the objects' poses more accurately. We explore both a camera-centered as well as an object-centered representation for relations. Experiments on the challenging KITTI dataset show that object configurations can indeed be used as a complementary cue to appearance-based pose estimation. In addition, object-centered relational representations can also assist object detection.