In this paper, we examine the main advances registered in the last ten years in Pattern Recognition methodologies based on graph matching and related techniques, analyzing more than 180 papers; the...

Graph matching and learning in pattern recognition in the last 10 years

Accurate and efficient tracking, analysis and visualization of as-built (actual) status of buildings under construction are critical components of a successful project monitoring. Such information directly supports control decision-making and if automated, can significantly impact management of a project. This paper presents a new automated approach for recognition of physical progress based on two emerging sources of information: (1) unordered daily construction photo collections, which are currently collected at almost no cost on all construction sites; and (2) building information models (BIMs), which are increasingly turning into binding components of architecture/engineering/construction contracts. First, given a set of unordered and uncalibrated site photographs, an approach based on structure-from-motion, multiview stereo, and voxel coloring and labeling algorithms is presented that calibrates cameras, photorealistically reconstructs a dense as-built point cloud model in four dimensions (th...

Automated Progress Monitoring Using Unordered Daily Construction Photographs and IFC-Based Building Information Models

In recent years, the detection of voluntary motor intentions from electroencephalogram (EEG) has been used for triggering external devices in closed-loop brain-computer interface (BCI) research. Movement-related cortical potentials (MRCP), a type of slow cortical potentials, have been recently used for detection. In order to enhance the efficacy of closed-loop BCI systems based on MRCPs, a manifold method called Locality Preserving Projection, followed by a linear discriminant analysis (LDA) classifier (LPP-LDA) is proposed in this paper to detect MRCPs from scalp EEG in real time. In an online experiment on nine healthy subjects, LPP-LDA statistically outperformed the classic matched filter approach with greater true positive rate (79 ± 11% versus 68 ± 10%; p = 0.007) and less false positives (1.4 ± 0.8/min versus 2.3 ± 1.1/min; p = 0.016). Moreover, the proposed system performed detections with significantly shorter latency (315 ± 165 ms versus 460 ± 123 ms; p = 0.013), which is a fundamental characteristics to induce neuroplastic changes in closed-loop BCIs, following the Hebbian principle. In conclusion, the proposed system works as a generic brain switch, with high accuracy, low latency, and easy online implementation. It can thus be used as a fundamental element of BCI systems for neuromodulation and motor function rehabilitation.

/pdf/enhanced-low-latency-detection-of-motor-intention-from-eeg-3g17ev68g3.pdf

Enhanced Low-Latency Detection of Motor Intention From EEG for Closed-Loop Brain-Computer Interface Applications

The significant advancement in digital imaging and widespread popularity of digital cameras for capturing a comprehensive visual record of construction performance in the architecture, engineering, construction, and facility management (AEC/FM) industries have triggered an extensive growth in the rate of site photography, allowing hundreds of images to be stored for a project on a daily basis. Meanwhile, collaborative AEC technologies centering around building information models (BIMs) are becoming widely applied to support various architectural, structural, and preconstruction decision-making tasks. These models, if integrated with the as-built perspective of a construction, have great potential to extensively add value during the construction phase of a project. This paper reports recent developments from research efforts in (1) automated acquisition of as-built point-cloud models from unordered daily site photo collections and geo-registration of site images, (2) automated generation of four-dimensiona...

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CO.1943-7862.0000371

Integrated Sequential As-Built and As-Planned Representation with D4AR Tools in Support of Decision-Making Tasks in the AEC/FM Industry

A brief of the 18th International Conference on Pattern Recognition is introduced in this pa- per,And more details about the organized workshops,tutorials,Plenary speaks and invited pa- per are presented also.

International Trends of Pattern Recognition Research A Brief Introduction to the 18th International Conference on Pattern Recognition

The ICP algorithm is accurate and fast for registration between two point sets in a same scale, but it doesn't handle the case with different scales. This paper instead introduces a novel approach named the scaling iterative closest point (SICP) algorithm which integrates a scale matrix with boundaries into the original ICP algorithm for scaling registration. This method uses a simple iterative algorithm with the SVD algorithm and the properties of parabola incorporated to compute the translation, rotation and scale transformations at each iterative step, and its convergence is rapid with only a few iterations. The SICP algorithm is independent of shape representation and feature extraction; thereby it is general for scaling registration. Experimental results demonstrate its robustness and fast speed compared with the standard ICP algorithm.

/pdf/an-extension-of-the-icp-algorithm-considering-scale-factor-3zwsh69b9w.pdf

AN Extension of the ICP Algorithm Considering Scale Factor

This paper extends the upright face detection framework proposed by Viola et al. 2001 to handle in-plane rotated faces. These haar-like features work inefficiently on rotated faces, so this paper proposes a new set of ±26.565 ° haar-like features which can be calculated quickly to represent the features of rotated faces. Unlike previous face detection techniques in training quantities of samples to build different rotated detectors, with these new features, we address to build different rotated detectors by rotating an upright face detector directly so as to achieve in-plane rotated face detection. This approach is selected because of its computational efficiency, simplicity and training time saving. This proposed method is tested on CMU-MIT rotated test data and yields good results in accuracy and maintains speed advantage.

Rotated haar-like features for face detection with in-plane rotation

We propose a novel manifold learning approach, called neighborhood discriminant projection (NDP), for robust face recognition. The purpose of NDP is to preserve the within-class neighboring geometry of the image space, while keeping away the projected vectors of the samples of different classes. For representing the intrinsic within-class neighboring geometry and the similarity of the samples of different classes, the within-class affinity weight and the between-class affinity weight are used to model the within-class submanifold and the between-class submanifold of the samples, respectively. Several experiments on face recognition are conducted to demonstrate the effectiveness and robustness of our proposed method

Neighborhood Discriminant Projection for Face Recognition

Shape and texture are two aspects in facial expression synthesis. For the requirements of synthesized shape and texture are different, it is unsuitable to use same method to synthesize shape and texture. In this paper, we propose a Statistical Shape and Radio texture fusion model for facial expression sequence synthesis. In this framework, facial shape and texture had been processed separately, then they had been fused together to synthesize final result. The main contributes of this paper are: First propose statistical shape and radio texture fusion model, process shape and texture separately. Second introduce ASM model into facial shape synthesis, and construct expression model of ASM parameters. Third put forward a shape based sample select mechanism, fusion shape and texture process together. Experiment results demonstrate that the proposed model is more expressive and realistic than traditional methods, and can process batch input well.

/pdf/facial-expression-sequence-synthesis-based-on-shape-and-2zo59t2nxr.pdf

Facial Expression Sequence Synthesis Based on Shape and Texture Fusion Model

An age-converting method of human face based on average face and ageing scale map includes dividing human face to be shape vector and vein vector based on dense feature presentation, applying human face cartoon synthetic technique to finalize young conversion of specific human face by calculating difference between specific human face to average human face, combining human face image vector modeling with human face image ageing conversion for realizing human face image ageing analog in accordance with physiological feature.

Qubo You

Papers

AN Extension of the ICP Algorithm Considering Scale Factor

Rotated haar-like features for face detection with in-plane rotation

Neighborhood Discriminant Projection for Face Recognition

Facial Expression Sequence Synthesis Based on Shape and Texture Fusion Model

Human face image age changing method based on average face and senile proportional image