Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively.
KEYWORDS: Principal component analysis, Video, Feature extraction, Magnetorheological finishing, Data modeling, Dubnium, Stochastic processes, Video processing, Data processing, Video surveillance
Background subtraction is an important task for various computer vision applications. The task becomes more critical when the background scene contains more variations, such as swaying trees and abruptly changing lighting conditions. Recently, robust principal component analysis (RPCA) has been shown to be a very efficient framework for moving-object detection. However, due to its batch optimization process, high-dimensional data need to be processed. As a result, computational complexity, lack of features, weak performance, real-time processing, and memory issues arise in traditional RPCA-based approaches. To handle these, a background subtraction algorithm robust against global illumination changes via online robust PCA (OR-PCA) using multiple features together with continuous constraints, such as Markov random field (MRF), is presented. OR-PCA with automatic parameter estimation using multiple features improves the background subtraction accuracy and computation time, making it attractive for real-time systems. Moreover, the application of MRF to the foreground mask exploits structural information to improve the segmentation results. In addition, global illumination changes in scenes are tackled by using sum of the difference of similarity measure among features, followed by a parameter update process using a low-rank, multiple features model. Evaluation using challenging datasets demonstrated that the proposed scheme is a top performer for a wide range of complex background scenes.
A color correction algorithm for stereoscopic video that uses a locality-based color correspondence search is presented. The algorithm overcomes the common failure problems that affect conventional local methods by introducing two techniques. First, an adaptive locality selection technique is introduced to determine a local image window, which is used to define an affine color correction model. The color correction model is then refined iteratively using locality-based color correspondence search between stereo video frames. Second, the color correspondence samples from the current video frame are used for the temporally stable color correction of the next frame. The experimental results show that the proposed algorithm outperforms the conventional local methods in terms of accuracy and time.
Measuring the crowdedness of a public area can be very useful for preventing from the multitudinous situation in advance and for properly scheduling the frequency of services. We have developed a vision-based crowdedness measuring system for Taejon Expo '93. The system identifies human bodies by using the vision technique that detects moving objects through a series of differencing processes, and, in turn, estimates the distribution of human in wide regions. To ensure robustness on the real outdoor environment, the human detection algorithm exploits three key concepts: multiple features fusion approach, image sequence generation with varied time intervals, and high-level knowledge about the geometry of the scene. The entire venue is divided into several meaningful regions and each region is also divided into several scenes for the realtime analysis. Each scene is obtained from one of twenty-five CCD cameras which cover the critical ares of the venue. Crowdedness analysis algorithm calculates the crowdedness of each scene and combines the results into the region crowdedness. The system was fully functional during the entire period of Taejon EXPO '93.
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