In this paper, we propose Local Adaptive Hybrid Pattern (LAHP) to use intrinsic properties, both edge and color, of each pixel adaptively (inspired from LHP based methods) while using a single feature representation (inspired from LOBSTER). The proposed LAHP encodes edge and color information, and an adaptive factor based on gradient magnitude together as a single feature. We introduce a way to calculate the code distance of LNESP to reduce sensitive to edge distortions. Furthermore, we modify ADM (Adaptive Dictionary Model) to manage LAHP and extend a feature matching scheme of ADM to adjacent background models to reduce sensitivity to background motions. Results show promising performance than other methods in standard datasets.

The following topics are dealt with: feature extraction; object detection; learning (artificial intelligence); video signal processing; video surveillance; object tracking; neural nets; image classification; image sequences; image motion analysis.

This paper presents a new face descriptor, local directional ternary pattern (LDTP), for facial expression recognition. LDTP efficiently encodes information of emotion-related features (ı.e., eyes, eyebrows, upper nose, and mouth) by using the directional information and ternary pattern in order to take advantage of the robustness of edge patterns in the edge region while overcoming weaknesses of edge-based methods in smooth regions. Our proposal, unlike existing histogram-based face description methods that divide the face into several regions and sample the codes uniformly, uses a two-level grid to construct the face descriptor while sampling expression-related information at different scales. We use a coarse grid for stable codes (highly related to non-expression), and a finer one for active codes (highly related to expression). This multi-level approach enables us to do a finer grain description of facial motions while still characterizing the coarse features of the expression. Moreover, we learn the active LDTP codes from the emotion-related facial regions. We tested our method by using person-dependent and independent cross-validation schemes to evaluate the performance. We show that our approaches improve the overall accuracy of facial expression recognition on six data sets.

In this paper, we propose a novel edge descriptor method for background modeling. In comparison to previous edge-based local-pattern methods, it is more robust to noise and illumination variations due to the use of principal gradient information in a local neighborhood. For the background modeling problem, we combined the proposed method with the Local Hybrid Pattern and experimented with an adaptive-dictionary-model based background modeling method. We show in the quantitative evaluations that the proposed methods is better than other local edge descriptors when applied to the same framework. Furthermore, we show that our proposed method is more powerful than other state of the art methods on standard datasets for the background modeling problem.

In this paper, we propose a hybrid background model that relies on edge and non-edge features of the image to produce the model. We encode these features into a coding scheme, that we called Local Hybrid Pattern (LHP), that selectively models edges and non-edges features of each pixel. Furthermore, we model each pixel with an adaptive code dictionary to represent the background dynamism, and update it by adding stable codes and discarding unstable ones. We weight each code in the dictionary to enhance its description of the pixel it models. The foreground is detected as the incoming codes that deviate from the dictionary. We can detect (as foreground or background) and classify (as edge or inner region) each pixel simultaneously. We tested our proposed method in existing databases with promising results.

A spatial detection technique, based on the extrema of current pixel's neighborhood, is proposed for Digital Dropout error, evident in the archived media. Digital dropout is a major type of damage occurred due to the physical error in original tape and tends to occur in block by block basis. As long as the presence of fast moving object is observed in video sequences where current state-of-the-art methods fail to detect accurate error information, such spatial detection offers a much better solution in terms of quality and complexity. Experiments are performed on video archives to evaluate the efficacy of the proposed technique.

Unattended object detection is an important task in surveillance. Thus, we propose a new method to detect unattended object by modeling the objects as newly learned temporal background. We use edge-segments to model the structural changes in the scene. Specifically, we construct distributions of these edge-segments to analyze the scene, and to segment its different components: background, fore-ground, and the interesting new objects. Additionally, we propose a clustering algorithm to recover the unattended objects from a set of edges based on the assumption that spatially close edges come from the same object. Our experiments on several datasets validate our proposed method.

For background-subtraction-based moving object detection, reliable background modeling is the most important component. Pixel-based methods are sensitive to illumination change, and edge-based methods can solve illumination-related problems, but have shape distortion problems. In this paper, we propose an edge-segment-based statistical background modeling algorithm and an online update mechanism to detect moving objects from consecutive frames, which creates a balance between the pixel- and edge-based methods. Our background modeling method uses a statistical map to model the frequency of the background-edges, as distributions that comprise support regions approximated with a quadratic function and enhanced with color and gradient information, to overcome the edge-distortion problem by matching the edge-segments to the modeled distributions. To adjust the changing background in the scenes, we propose an online-background update step for every incoming frame that updates the statistical map and enhances the information held by the distributions support regions. Furthermore, our experiments show that the proposed method obtains better results and detects moving edges efficiently.

Background modeling is challenging due to background dynamism. Most background modeling methods fail in the presence of intensity changes, because the model cannot handle sudden changes. A solution to this problem is to use intensity-robust features. Despite the changes of an edge's shape and position among frames, edges are less sensitive than a pixel's intensity to illumination changes. Furthermore, background models in the presence of moving objects produce ghosts in the detected output, because high quality models require ideal backgrounds. In this paper, we propose a robust statistical edge-segment-based method for background modeling of non-ideal sequences. The proposed method learns the structure of the scene using the edges' behaviors through the use of kernel-density distributions. Moreover, it uses segment features to overcome the shape and position variations of the edges. Hence, the use of segments gives us local information of the scene, and that helps us to predict the objects and background precisely. Furthermore, we accumulate segments to build edge distributions, which allow us to perform unconstrained training and to overcome the ghost effect. In addition, the proposed method uses adaptive thresholding (in the segments) to detect the moving objects. Therefore, this approach increases the accuracy over previous methods, which use fixed thresholds.