Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis

The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). Bilateral posterior-anterior knee radiographs were analyzed from the baseline of OAI and MOST datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. We used an oversegmentation strategy for partitioning knee images into the compact regions that follow natural texture boundaries. LBP, Fractal Dimension (FD), Haralick features, Shannon entropy, and HOG methods were computed within the standard ROI and within the proposed adaptive ROIs. Subsequently, we built logistic regression models to identify and compare the performances of each texture descriptor and each ROI placement method using 5-fold cross validation setting. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset.We used area under the receiver operating characteristic (ROC) curve (AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results. We found that the adaptive ROI improves the classification performance (OA vs. non-OA) over the commonly used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, LBP yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA

MoWLD: a robust motion image descriptor for violence detection

© 2015, Springer Science+Business Media New York. Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in designing an algorithm that can detect violence in surveillance videos with high performance. Existing methods typically apply the Bag-of-Words (BoW) model on local spatiotemporal descriptors. However, traditional spatiotemporal features are not discriminative enough, and also the BoW model roughly assigns each feature vector to only one visual word and therefore ignores the spatial relationships among the features. To tackle these problems, in this paper we propose a novel Motion Weber Local Descriptor (MoWLD) in the spirit of the well-known WLD and make it a powerful and robust descriptor for motion images. We extend the WLD spatial descriptions by adding a temporal component to the appearance descriptor, which implicitly captures local motion information as well as low-level image appear information. To eliminate redundant and irrelevant features, the non-parametric Kernel Density Estimation (KDE) is employed on the MoWLD descriptor. In order to obtain more discriminative features, we adopt the sparse coding and max pooling scheme to further process the selected MoWLDs. Experimental results on three benchmark datasets have demonstrated the superiority of the proposed approach over the state-of-the-arts

Novel Multi-Feature Bag-of-Words Descriptor via Subspace Random Projection for Efficient Human-Action Recognition

Human-action recognition through local spatio-temporal features have been widely applied because of their simplicity and its reasonable computational complexity. The most common method to represent such features is the well-known Bag-of-Words approach, which turns a Multiple-Instance Learning problem into a supervised learning one, which can be addressed by a standard classifier. In this paper, a learning framework for human-action recognition that follows the previous strategy is presented. First, spatio-temporal features are detected. Second, they are described by HOG-HOF descriptors, and then represented by a Bag of Words approach to create a feature vector representation. The resulting high dimensional features are reduced by means of a subspace-random-projection technique that is able to retain almost all the original information. Lastly, the reduced feature vectors are delivered to a classifier called Citation K-Nearest Neighborhood, especially adapted to Multiple-Instance Learning frameworks. Excellent results have been obtained, outperforming other state-of-the art approaches in a public database

Object matching using boundary descriptors

The problem of object recognition is of immense practical importance and potential, and the last decade has witnessed a number of breakthroughs in the state of the art. Most of the past object recognition work focuses on textured objects and local appearance descriptors extracted around salient points in an image. These methods fail in the matching of smooth, untextured objects for which salient point detection does not produce robust results. The recently proposed bag of boundaries (BoB) method is the first to directly address this problem. Since the texture of smooth objects is largely uninformative, BoB focuses on describing and matching objects based on their post-segmentation boundaries. Herein we address three major weaknesses of this work. The first of these is the uniform treatment of all boundary segments. Instead, we describe a method for detecting the locations and scales of salient boundary segments. Secondly, while the BoB method uses an image based elementary descriptor (HoGs + occupancy matrix), we propose a more compact descriptor based on the local profile of boundary normals’ directions. Lastly, we conduct a far more systematic evaluation, both of the bag of boundaries method and the method proposed here. Using a large public database, we demonstrate that our method exhibits greater robustness while at the same time achieving a major computational saving – object representation is extracted from an image in only 6% of the time needed to extract a bag of boundaries, and the storage requirement is similarly reduced to less than 8%

Automatic detection of pipe-flange reflections in GPR data sections using supervised learning

Ground Penetrating radar (GPR) is a method widely used to study the near-surface subsoil. Many GPR applications require the acquisition of large volumes of data. In these cases, the processing and analysis of the data involve considerable amounts of time and human effort, and the possibility of errors increases. Considering this, the implementation of dependable methods for the automatic detection of GPR response-patterns of the targeted structures becomes clear, because they can contribute to the efficiency and reliability of the interpretation. In this work, we present three methods for automatic detection of pipe-flange signals in constant-offset reflection-GPR images. These methods were obtained using well-known supervised machine learning techniques, and data acquired during a previous study of an extensive section of a pipeline. The first two methods are based on support vector machines (SVM), combined with the image descriptors local binary patterns (LBP) and histogram of oriented gradients (HOG), and the third, on artificial neural networks (ANN). The training and validation of these types of algorithms require large numbers of positive and negative samples. From the mentioned study, we had only 16 experimental flange-patterns. Then, in this work, they were taken as references, together with available documentation on the geometry and materials of the pipe and flanges, for building a broad database of synthetic patterns corresponding to different depths of the pipe and characteristics of the environment. These patterns constitute the set of positive samples used for training and validation. They were also used for the final test of the algorithms. The negative samples for the three stages were directly extracted from the profiles. The results obtained indicate the usefulness of the proposed methodologies to identify the flanges. The best performance corresponded to the ANN, closely followed by SVM combined with HOG, and finally SVM with LBP. In particular, the ANN provided rates of false positive (FP) predictions for the validation and test samples of about 3%, and rates of false negative (FN) predictions of 1.67% for the validation samples and 18.75% for the test samples. Greater FN rates for the test experimental samples, in comparison to those obtained for the validation synthetic samples, were also observed for both SVM algorithms. The detection failures mainly originated in that some complex features of the experimental flange responses could not be appropriately reproduced through the performed numerical simulations, and therefore, some of the patterns were not satisfactorily represented in the sets of positive samples used for training and validation. A first option to improve the results is to obtain a significant number and variety of experimental samples of flange responses and use them to train and validate the algorithms. Other alternatives are to use more sophisticated numerical simulation environments and to find more efficient attributes of the data.Fil: Bordón, Pablo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Bonomo, Nestor Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Martinelli, Hilda Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentin

Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions