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Gioacchino Vino and Angel Sappa. 2013. Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach. 10th International Conference on Image Analysis and Recognition. Springer Berlin Heidelberg, 354–363. (LNCS.)
Abstract: This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach.
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Alejandro Gonzalez Alzate, Gabriel Villalonga, Jiaolong Xu, David Vazquez, Jaume Amores and Antonio Lopez. 2015. Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium IV2015.356–361.
Abstract: Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy.
Keywords: Pedestrian Detection
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Jiaolong Xu, Sebastian Ramos, David Vazquez and Antonio Lopez. 2013. DA-DPM Pedestrian Detection. ICCV Workshop on Reconstruction meets Recognition.
Keywords: Domain Adaptation; Pedestrian Detection
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Alejandro Gonzalez Alzate, Gabriel Villalonga, German Ros, David Vazquez and Antonio Lopez. 2015. 3D-Guided Multiscale Sliding Window for Pedestrian Detection. Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015.560–568.
Abstract: The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multimodal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy.
Keywords: Pedestrian Detection
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Hanne Kause and 6 others. 2015. Quality Assessment of Optical Flow in Tagging MRI. 5th Dutch Bio-Medical Engineering Conference BME2015.
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M. Cruz, Cristhian A. Aguilera-Carrasco, Boris X. Vintimilla, Ricardo Toledo and Angel Sappa. 2015. Cross-spectral image registration and fusion: an evaluation study. 2nd International Conference on Machine Vision and Machine Learning.
Abstract: This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented.
Keywords: multispectral imaging; image registration; data fusion; infrared and visible spectra
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Cristhian A. Aguilera-Carrasco, Angel Sappa and Ricardo Toledo. 2015. LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations. 22th IEEE International Conference on Image Processing.178–181.
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Dennis G.Romero, Anselmo Frizera, Angel Sappa, Boris X. Vintimilla and Teodiano F.Bastos. 2015. A predictive model for human activity recognition by observing actions and context. Advanced Concepts for Intelligent Vision Systems, Proceedings of 16th International Conference, ACIVS 2015. Springer International Publishing, 323–333. (LNCS.)
Abstract: This paper presents a novel model to estimate human activities — a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach.
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Miguel Oliveira, Victor Santos, Angel Sappa and P. Dias. 2015. Scene Representations for Autonomous Driving: an approach based on polygonal primitives. 2nd Iberian Robotics Conference ROBOT2015.503–515.
Abstract: In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques.
Keywords: Scene reconstruction; Point cloud; Autonomous vehicles
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J.Poujol, Cristhian A. Aguilera-Carrasco, E.Danos, Boris X. Vintimilla, Ricardo Toledo and Angel Sappa. 2015. Visible-Thermal Fusion based Monocular Visual Odometry. 2nd Iberian Robotics Conference ROBOT2015. Springer International Publishing, 517–528.
Abstract: The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.
Keywords: Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion.
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