Color Distribution Features Research Articles

A Fully Automatic Feature-Based Real-Time Traffic Surveillance System Using Data Association in the Probabilistic Framework

Multi-object tracking involves maintaining several trajectories of different objects moving in the scene throughout the video. With this objective, in this article, a fully automatic and real-time tracking algorithm to track multiple vehicles in a video is proposed. The proposed method specifically tries to address the challenges of occlusion and fast-motion in traffic surveillance. The algorithm begins with automatic detection of the moving targets by an adaptive GMM-based background subtraction method. The trajectories of these detected targets are then built using a three-level multi-motion modeled particle filter framework which allows to deal with the challenges of occlusion and fast-motions of the target. The likelihood model for targets is based on their color distribution and edge oriented histogram features. It is contended that the color distribution feature, which can represent the target appearance and the edge oriented histogram, which can describe the target structure are sufficient to represent it in an unique feature space. Based on the similarity of target likelihood, the locations of the targets are filtered. These filtered locations are then associated with the most likely detections using the proposed low-cost and fast data association algorithm based on Euclidean distance and prevailing motion vector. The performance evaluation of the proposed scheme is carried out based on the six measures: Multi Object Tracking Accuracy, Multi Object Tracking Precision, Mostly Tracked trajectories, Mostly Lost trajectories, Identity Switches and Frames Per Second. The results evaluated on stationary camera shot sequences from benchmark datasets as well as real-time shot videos indicate that the proposed algorithm ensures robust tracking and can be used effectively for real-time surveillance of highways.

A pillar-based microfluidic chip for T-cells and B-cells isolation and detection with machine learning algorithm

Absolute counting of total leukocytes and specific subset (such as T-cells and B-cells) within small amounts of whole blood is difficult due to the lack of techniques that enables separation of leukocytes from limited volume of whole blood. In this study, a microfluidic chip equipped with a size controlled micropillar array for highly separation of T-cells and B-cells from sub-microliter of whole blood was studied. Based on the difference in size and deformability, leukocytes were separated from other blood cells by micropillar arrays. However, the variability of cells in size, morphology and color intensity along with the spectrum crosstalk between fluorescence dyes make cell detection among pillars extremely difficult. In this paper, an support vector machine supervised machine learning classifier based on both Histogram of Oriented Gradients (HOG) and color distribution features was proposed to distinguish T-cells and B-cells fast and robustly. HOG features were utilized to detect cells from background and noise; color distribution features were employed to alleviate the effect of fluorescence spectrum crosstalk. Experiment showed we achieved average detection accuracy of 94% for detecting T-cells and B-cells from the background. Furthermore, we also got 96% accuracy with cross validation to detect T-cells from B-cells. Both theoretical analysis and experiments demonstrated the proposed method and system has high performance in T-cells and B-cells counting. And our microfluidic cell counting system has great potential as a tool for point-of-care leukocyte analysis system.

Saliency detection by selective color features

This paper is concerned with selective color feature for detecting salient regions. In contrast to most existing methods related to detection in one color space, the proposed algorithm pre-segments the input image into superpixels in both RGBY color space and Lab color space. Next, to calculate color contrast we not only consider the local feature, but also compute the difference between the pixel and the whole image. In the meanwhile, based on the center-surrounding scheme, a new computational model of color distribution features is presented to detect salient regions. Finally, 2D entropy is employed as an evaluation criterion to select and integrate the proper color features. Experimental results show that the proposed method outperforms the state-of-the-art methods on salient region detection.

Color Distribution Features in the Cover of Female Fashion Magazines

Color Distribution Features in the Cover of Female Fashion Magazines

영역분할과 컬러 특징을 이용한 건물 인식기법

This paper proposes a building recognition algorithm using watershed image segmentation algorithm and integrated region matching (IRM). To recognize a building, a preprocessing algorithm which is using Gaussian filter to remove noise and using canny edge extraction algorithm to extract edges is applied to input building image. First, images are segmented by watershed algorithm. Next, a region adjacency graph (RAG) based on the information of segmented regions is created. And then similar and small regions are merged. Second, a color distribution feature of each region is extracted. Finally, similar building images are obtained and ranked. The building recognition algorithm was evaluated by experiment. It is verified that the result from the proposed method is superior to color histogram matching based results.

Adaptive multifeature visual tracking in a probability-hypothesis-density filtering framework

Probability hypothesis density (PHD) based trackers have enjoyed growing popularity in recent years, particularly in the field of nonlinear non-Gaussian visual tracking scenarios. These visual trackers can be degraded by a variety of factors, including changes of illumination, occlusion, poor image contrast, shape and appearance variation, clutter and other unmodeled changes of tracked objects. In this paper, for enhancing the performance of PHD based trackers, both scale invariant feature and color distribution feature are used as descriptors of targets of interest. To adaptively adjust the weights of each feature in the tracking process, a confidence measure, i.e., a quantitative measure for the spatial uncertainty of each feature is incorporated into the multifeature tracking algorithm. Experimental results show that the proposed multifeature tracker can improve the reliability and robustness of state estimation and the number estimation in tracking a variable number of objects of varying scales even when background region was similar to the object's appearance.

Salient object detection based on regions

Salient object detection aims to automatically localize the attractive objects with respect to surrounding background in an image. It can be applied to image browsing, image cropping, image compression, content-based image retrieval, and etc. In the literature, the low-level (pixel-based) features (e.g., color and gradient) were usually adopted for modeling and computing visual attention; these methods are straightforward and efficient but limited by performance, due to losing global organization and inference. Some recent works attempt to use the region-based features but often lead to incomplete object detection. In this paper, we propose an efficient approach of salient object detection using region-based representation, in which two novel region-based features are extracted for proposing salient map and the salient object are localized with a region growing algorithm. Its brief procedure includes: 1) image segmentation to get disjoint regions with characteristic consistency; 2) region clustering; 3) computation of the region-based center-surround feature and color-distribution feature; 4) combination of the two features to propose the saliency map; 5) region growing for detecting salient object. In the experiments, we evaluate our method with the public dataset provided by Microsoft Research Asia. The experimental results show that the new approach outperforms other four state-of-the-arts methods with regard to precision, recall and F-measure.

Extracting PCB components based on color distribution of highlight areas

This paper investigates methodologies for locating and identifying the components on a printed circuit board (PCB) used for surface mount device inspection. The proposed scheme consists of two stages: solder joint extraction and protective coating extraction. Solder joints are extracted by first detecting all the highlight areas, and then recognizing and removing the invalid highlight areas which are mainly markings and via-holes. We sum up three color distribution features. And the invalid highlight areas are recognized and removed by comparing the features of the target objects and the reference objects. The sequence of color distribution as a new clue has been applied to clustering solder joints. Each protective coating is extracted by the positions of the clustered solder joints. Experimental results show that the proposed method can extract most of components effectively.

基于核及尺度不变特征的视觉跟踪

The kernel based tracking has two disadvantages: the tracking window size cannot be adjusted efficiently, and the kernel based color distribution may not have enough ability to discriminate object from clutter background. For boosting up the feature's discriminating ability, both scale invariant features and kernel based color distribution features are used as descriptors of tracked object. The proposed algorithm can keep tracking object of varying scales even when the surrounding background is similar to the object's appearance.

Interactive image retrieval by color distribution content

An interactive system for content-based retrieval in a pictorial database using color distribution features is discussed. Images are internally represented and indexed by a set of histograms reflecting homogeneous chromatic properties of image struct

Color quantization by preserving color distribution features

A color quantization method is proposed. The method hierarchically uses a bisection tool that preserves certain color-distribution features. The preserved features are average color, variance in each color dimension, and average color radius. An algorithm is given that includes the histogram simplification procedure, color palette generation, and pixel mapping. Experiments show that the proposed method usually obtains acceptable quantized images with competitive speed.