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This paper introduces a part-based two-stage pedestrian detector. The system finds pedestrian candidates with an AdaBoost cascade on Haar-like features. It then verifies each candidate using a part-based HOG-SVM doing first a regression and then a classification based on the estimated function output from the regression. It uses the Histogram of Oriented Gradients (HOG) computed on both the full,...
In this paper, an affine invariance feature detection method based on Scale Invariant Feature Transform (SIFT) and Maximally Stable Extremal Regions (MSER) is proposed. Classical SIFT algorithm is not robust to affine deformations, because it is based on DOG detector which extracts circle regions for keypoint location. In order to overcome this disadvantage, DOG detector in conventional SIFT algorithm...
Local spatiotemporal detectors and descriptors have recently become very popular for video analysis in many applications. They do not require any preprocessing steps and are invariant to spatial and temporal scales. Despite their computational simplicity, they have not been evaluated and tested for video analysis of facial data. This paper considers two space-time detectors and four descriptors and...
Foreground detection is a key procedure in video analysis such as object detection and tracking. Several foreground detection techniques and edge detectors have been developed until now but the problem is, usually it is difficult to obtain an optimal foreground due to weather, light, shadow and clutter interference. Background subtract is a common method in foreground detection. In background subtract...
This paper presents a fuzzy traffic controller that in an autonomous, centralized and optimal way, manages traffic flow in a group of intersections. The system obtains information from a network of cameras and through machine vision algorithms can detect the number of vehicles in each of the roads. Using this information, the fuzzy system selects the sequence of phases that optimize traffic flow globally...
In this paper, we present a novel and computationally efficient pruning technique to speed up the Shi-Tomasi and Harris corner detectors. The proposed technique quickly prunes non-corners and selects a small corner candidate set by approximating the complex corner measure of Shi-Tomasi and Harris. The actual corner measure is then applied only to the reduced candidate set. Experimental results on...
A novel low complexity feature extraction algorithm, only performing by a single comparison per pixel on the average during detection is proposed. While single-scale version of the algorithm remains quite efficient compared against the complexity of the state-of-the-art algorithms, a multi-scale version is also proposed to handle blur and scale changes. The performance tests on the repeatability of...
This paper proposes a novel non-linear filter, named rank order LoG (ROLG) filter, and a new interest point detector, named ROLG detector. The ROLG filter is a weighted rank order filter. It is used to detect image structures whose significant majority of pixels are brighter (or darker) than the significant majority of pixels in their corresponding surroundings. The ROLG detector is built on this...
This paper presents an efficient algorithm to set adaptive ROI for detecting pedestrians in a moving vehicle environment. The algorithm analyzes the centroid of detected pedestrian in current frame and define centroid region where centroids of detected pedestrian are concentrated. Based on centroid region, adaptive ROI is updated for each different size of detection window in next frame. Experiments...
This paper proposes an unsupervised bottom-up boundary detection algorithm, which is an improved surround suppression model based on orientation contrast. First, the candidate boundary set is obtained by the edge focusing algorithm. Second, the orientation contrast map is constructed using the response of Gabor filter. The suppression term is computed on orientation contrast map using steerable filter,...
In this paper, we present a new method for a locally adaptive region detector called Bilateral kernel-based Region Detector (BIRD). This work is to detect stable regions from images by consecutively computing a multiscale decomposition based on the bilateral kernel. The BIRD regards a region as covariant if it exhibits predictability in its photometric distance over spatial distance. Distinctiveness...
Tracking individuals in video sequences, especially in crowded scenes, is still a challenging research topic in the area of pattern recognition and computer vision. However, current single camera tracking approaches are mostly based on visual features only. The novelty of the approach proposed in this paper is the integration of evidences from a crowd simulation algorithm into a pure vision based...
In this paper, we present an algorithm to detect and describe features of surface textures, similar to SIFT and SURF. In contrast to approaches solely based on the intensity image, it uses depth information to achieve invariance with respect to arbitrary changes of the camera pose. The algorithm works by constructing a scale space representation of the image which conserves the real-world size and...
Intuitive and easily interpretable performance measures, repeatability and matching performance, for local feature detectors and descriptors were introduced by Mikolajczyk et al. [10, 9]. They, however, measured performance in a wide baseline setting that does not correspond to the visual object categorisation problem which is a popular application of the detectors and descriptors. The limitation...
Affine transformation detection can be used in many computer vision and other applications. This paper presents a new method for affine transformation detection. The state-of-the-art methods are mainly divided into two classes. One class is based on complicated descriptors. But this kind of methods need a lot of time to establish and matching the complicated descriptors. The second class is based...
The “bag of words” model has enjoyed much attention in the studies of object categorization. As implied by the name, the images under consideration are modeled as a bag containing multiple features. Despite its simplicity, this model has been able to achieve great performances in many state of the art object categorization datasets. Using this model, we extract patches from an image and categorize...
in this paper, we propose a new framework in pedestrian detection by combining the HOG and uniform LBP feature on blocks. Contrast experiment result shows that detector using combined features is more powerful than one single feature. To further improve the detection performance, we make a contrast experiment that the HOG-LBP features are calculated at variable-size blocks to find the most efficient...
The automated detection of cell nuclei, which is an important step in the pipeline of quantitative histopathological analysis, has received considerable attentions in recent years. However, biological variations, uneven staining and illumination, non-rigid deformations and touching or overlapping of the cell nuclei have made the detection procedure a major hurdle. In this paper, we consider the problem...
Unmanned Air Vehicles (UAVs) have become an intelligent asset for surveillance, target tracking and reconnaissance in both urban and battlefield settings. This paper gives a framework for scale weight selection during feature extraction in aerial images from UAV. Dual-Tree Complex Waveform technique is used to extract rich feature descriptors of keypoints in images so that full phase and amplitude...
Undoubtedly, a key feature in the popularity of smartphones is the numerous applications one can install. Frequently, we learn about applications we desire by seeing them on someone else's mobile device. A user-friendly way to obtain these particular applications could be by taking a photo of their corresponding icons as displayed on our friend's screen. We then need to develop methods for automatic...
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