Neural Attention-Driven Non-Maximum Suppression for Person Detection.

Non-maximum suppression (NMS) is a post-processing step in almost every visual object detector. NMS aims to prune the number of overlapping detected candidate regions-of-interest (RoIs) on an image, in order to assign a single and spatially accurate detection to each object. The default NMS algorithm (GreedyNMS) is fairly simple and suffers from severe drawbacks, due to its need for manual tuning.

Public opinion monitoring through collective semantic analysis of tweets.

The high popularity of Twitter renders it an excellent tool for political research, while opinion mining through semantic analysis of individual tweets has proven valuable. However, exploiting relevant scientific advances for collective analysis of Twitter messages in order to quantify general public opinion has not been explored. This paper presents such a novel, automated public opinion monitoring mechanism, consisting of a semantic descriptor that relies on Natural Language Processing algorithms.

Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms.

Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based. The former approaches typically employ 2D image analysis/ understanding or 3D point cloud modeling and segmentation algorithms to detect (i.e.

Rethinking Road Surface 3-D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation.

Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety, and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. However, this task is not only hazardous for the personnel but also extremely time consuming.

Domain-Translated 3D Object Pose Estimation.

Synthetic 3D object models have been proven crucial in object pose estimation, as they are utilized to generate a huge number of accurately annotated data. The object pose estimation problem is usually solved for images originating from the real data domain by employing synthetic images for training data enrichment, without fully exploiting the fact that synthetic and real images may have different data distributions. In this work, we argue that 3D object pose estimation problem is easier to solve for images originating from the synthetic domain, rather than the real data domain.

Digital subtraction radiography in detection of vertical root fractures: accuracy evaluation for root canal filling, fracture orientation and width variables. An ex-vivo study.

Objective: Ex-vivo evaluation of the detectability of vertical root fractures (VRFs) using digital subtraction radiography (DSR) and conventional digital periapical radiography (CDPR); investigation of the effect of root canal filling, x-ray angulation, and thickness of the VRF in the diagnostic accuracy.

Materials And Methods: Sixty root canals were mechanically prepared and radiographed either with a gutta-percha root canal filling or without, at 0 and ± 10. VRFs were introduced with a universal testing machine.

K-Anonymity inspired adversarial attack and multiple one-class classification defense.

A novel adversarial attack methodology for fooling deep neural network classifiers in image classification tasks is proposed, along with a novel defense mechanism to counter such attacks. Two concepts are introduced, namely the K-Anonymity-inspired Adversarial Attack (K-A) and the Multiple Support Vector Data Description Defense (M-SVDD-D). The proposed K-A introduces novel optimization criteria to standard adversarial attack methodologies, inspired by the K-Anonymity principles.

Pothole Detection Based on Disparity Transformation and Road Surface Modeling.

Pothole detection is one of the most important tasks for road maintenance. Computer vision approaches are generally based on either 2D road image analysis or 3D road surface modeling. However, these two categories are always used independently.

Neurons With Paraboloid Decision Boundaries for Improved Neural Network Classification Performance.

In mathematical terms, an artificial neuron computes the inner product of a d -dimensional input vector x with its weight vector w , compares it with a bias value w and fires based on the result of this comparison. Therefore, its decision boundary is given by the equation wx+w=0 . In this paper, we propose replacing the linear hyperplane decision boundary of a neuron with a curved, paraboloid decision boundary.

A new methodology for the measurement of the root canal curvature and its 3D modification after instrumentation.

Objective: In this study, the three-dimensional (3D) modification of root canal curvature was measured, after the application of Reciproc instrumentation technique, by using cone beam computed tomography (CBCT) imaging and a special algorithm developed for the 3D measurement of the curvature of the root canal.

Materials And Methods: Thirty extracted upper molars were selected. Digital radiographs for each tooth were taken.

Diagnosis of vertical root fractures in endodontically treated teeth utilising Digital Subtraction Radiography: A case series report.

Vertical root fractures are commonly associated with root-filled teeth. Diagnosis is challenging because the clinical signs are not completely pathognomonic, and conventional periapical radiography is often unreliable. Digital subtraction radiography (DSR) is able to detect small radiographic changes between two successive radiographs by subtracting out consistent radiographic elements.

Multimodal Stereoscopic Movie Summarization Conforming to Narrative Characteristics.

Video summarization is a timely and rapidly developing research field with broad commercial interest, due to the increasing availability of massive video data. Relevant algorithms face the challenge of needing to achieve a careful balance between summary compactness, enjoyability, and content coverage. The specific case of stereoscopic 3D theatrical films has become more important over the past years, but not received corresponding research attention.

Stereoscopic medical data video quality issues.

Stereoscopic medical videos are recorded, e.g., in stereo endoscopy or during video recording medical/dental operations.

Graph Embedded Extreme Learning Machine.

In this paper, we propose a novel extension of the extreme learning machine (ELM) algorithm for single-hidden layer feedforward neural network training that is able to incorporate subspace learning (SL) criteria on the optimization process followed for the calculation of the network's output weights. The proposed graph embedded ELM (GEELM) algorithm is able to naturally exploit both intrinsic and penalty SL criteria that have been (or will be) designed under the graph embedding framework. In addition, we extend the proposed GEELM algorithm in order to be able to exploit SL criteria in arbitrary (even infinite) dimensional ELM spaces.

Symmetric subspace learning for image analysis.

Subspace learning (SL) is one of the most useful tools for image analysis and recognition. A large number of such techniques have been proposed utilizing a priori knowledge about the data. In this paper, new subspace learning techniques are presented that use symmetry constraints in their objective functions.

Maximum margin projection subspace learning for visual data analysis.

Visual pattern recognition from images often involves dimensionality reduction as a key step to discover a lower dimensional image data representation and obtain a more manageable problem. Contrary to what is commonly practiced today in various recognition applications where dimensionality reduction and classification are independently treated, we propose a novel dimensionality reduction method appropriately combined with a classification algorithm. The proposed method called maximum margin projection pursuit, aims to identify a low dimensional projection subspace, where samples form classes that are better discriminated, i.

Projected gradients for subclass discriminant nonnegative subspace learning.

Current discriminant nonnegative matrix factorization (NMF) methods either do not guarantee convergence to a stationary limit point or assume a compact data distribution inside classes, thus ignoring intra class variance in extracting discriminant data samples representations. To address both limitations, we regard that data inside each class has a multimodal distribution, forming various subclasses and perform optimization using a projected gradients framework to ensure limit point stationarity. The proposed method combines appropriate clustering-based discriminant criteria in the NMF decomposition cost function, in order to find discriminant projections that enhance class separability in the reduced dimensional projection space, thus improving classification performance.

On the optimal class representation in linear discriminant analysis.

Linear discriminant analysis (LDA) is a widely used technique for supervised feature extraction and dimensionality reduction. LDA determines an optimal discriminant space for linear data projection based on certain assumptions, e.g.

3D geometric split-merge segmentation of brain MRI datasets.

In this paper, a novel method for MRI volume segmentation based on region adaptive splitting and merging is proposed. The method, called Adaptive Geometric Split Merge (AGSM) segmentation, aims at finding complex geometrical shapes that consist of homogeneous geometrical 3D regions. In each volume splitting step, several splitting strategies are examined and the most appropriate is activated.

Multiplicative update rules for concurrent nonnegative matrix factorization and maximum margin classification.

The state-of-the-art classification methods which employ nonnegative matrix factorization (NMF) employ two consecutive independent steps. The first one performs data transformation (dimensionality reduction) and the second one classifies the transformed data using classification methods, such as nearest neighbor/centroid or support vector machines (SVMs). In the following, we focus on using NMF factorization followed by SVM classification.

View-invariant action recognition based on artificial neural networks.

In this paper, a novel view invariant action recognition method based on neural network representation and recognition is proposed. The novel representation of action videos is based on learning spatially related human body posture prototypes using self organizing maps. Fuzzy distances from human body posture prototypes are used to produce a time invariant action representation.

Improving subspace learning for facial expression recognition using person dependent and geometrically enriched training sets.

In this paper, the robustness of appearance-based subspace learning techniques in geometrical transformations of the images is explored. A number of such techniques are presented and tested using four facial expression databases. A strong correlation between the recognition accuracy and the image registration error has been observed.

Automatic color based reassembly of fragmented images and paintings.

The problem of reassembling image fragments arises in many scientific fields, such as forensics and archaeology. In the field of archaeology, the pictorial excavation findings are almost always in the form of painting fragments. The manual execution of this task is very difficult, as it requires great amount of time, skill and effort.

Color texture segmentation based on the modal energy of deformable surfaces.

This paper presents a new approach for the segmentation of color textured images, which is based on a novel energy function. The proposed energy function, which expresses the local smoothness of an image area, is derived by exploiting an intermediate step of modal analysis that is utilized in order to describe and analyze the deformations of a 3-D deformable surface model. The external forces that attract the 3-D deformable surface model combine the intensity of the image pixels with the spatial information of local image regions.

Novel multiclass classifiers based on the minimization of the within-class variance.

In this paper, a novel class of multiclass classifiers inspired by the optimization of Fisher discriminant ratio and the support vector machine (SVM) formulation is introduced. The optimization problem of the so-called minimum within-class variance multiclass classifiers (MWCVMC) is formulated and solved in arbitrary Hilbert spaces, defined by Mercer's kernels, in order to find multiclass decision hyperplanes/surfaces. Afterwards, MWCVMCs are solved using indefinite kernels and dissimilarity measures via pseudo-Euclidean embedding.