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Target detection is one of important roles of radar systems. In this paper, we present a detection method using total correlation based on information theory for noise radar systems which enables a system detects multiple targets at low signal to noise ratio regimes. The proposed method utilizes the largest eigenvalue of the sample covariance matrix to extract information from replica of the transmitted...
SIFT is one of the feature region extraction methods. It is widely used in various fields including object recognition, mosaic object tracking, etc. However, since SIFT requires lots of arithmetic operations, it is not appropriate for real-time use. Therefore, many researchers has been tried to prepare other measures. SIFT using PCA or SURF is one of them. This paper is to study about the real-time...
It is well known that the special objectives such as military cave storage is hard to be recognized and located by routine method because of camouflage technology. This paper presents a new algorithm basing on differential image detection and edge detection technology. Firstly, extracting some target images at different period while in the same position. Then using preprocessing technology to define...
Appearance variation is a big challenge for object tracking. To deal with this problem, we propose a robust tracking method by online appearance modeling and sparse representation. In this method, we use the intensity matrix of image to represent the object, and learn a low dimensional subspace online to model the object appearance variations during tracking. Then applying the recent theory of sparse...
In this paper, a multistage algorithm, which is applicable to vision-based vehicle detection system, for detecting and removing cast shadows is proposed. In the first stage, a novel noise level adapted method is presented to segment foreground which consists the moving objects and moving shadows. In the second stage, the foreground is partitioned into sub-regions using multilevel thresholding. Then,...
As cameras become cheaper and gain popularity, vision-based input devices are highly desired and may now become a feasible solution. In this paper, a vision-based virtual piano mechanism is proposed. By tacking and analyzing the motion of the fingertips, the system can detect keystrokes and play the corresponding note on a seven-key virtual piano. Experimental results show the precision and efficiency...
In this paper, a novel optimized genetic algorithm based on morphology for target detection from infrared images is proposed. In our improved algorithm, a new fitness measurement method based on target characteristics value is introduced to meet specific target detection needs. Male and female parent dynamic clustering methods are put forward to make crossover operator more reasonable. Besides, multi-population...
To deal with the problem of inaccuracy in current object contour extraction algorithms, a new approach based on difference multiplication and Gradient Vector Flow (GVF) Snake is proposed. It firstly eliminates most background boundaries through difference multiplication of four consecutive frames. Then median filer is used to remove residual strong noise in the image. Finally, it operates the GVF...
In the vision-based traffic system for the moving vehicle detection, the accuracy of vehicle detection is heavily based on exact acquirement of the background, this paper presents a T-distribution background reconstruction algorithm of moving vehicle detection to obtain background pixels, that is, the background image which doesn't contain any moving objects is restored by integrating of several background...
A new background estimation method is proposed to improve iterative restoration of microscope images. The method is based on a sequence of subtractions performed in the beginning of an iterative restoration procedure. A series of experiments using background intensity detection methods were carried out in order to analyse the influence of a correct estimation of the background. The restoration results...
The feature extraction and target recognition of low-velocity targets near the port is difficult in signal processing. After the spectral characteristics of low-velocity targets are analyzed, the paper uses 1(1/2)-spectrum as the method of feature extraction. The advantages are as follow. 1(1/2)-spectrum can suppress the Gaussian white noise. It can enhance weak fundamental frequency components of...
In this paper we provide a framework of detection and localization of multiple similar shapes or object instances from an image based on shape matching. There are three challenges about the problem. The first is the basic shape matching problem about how to find the correspondence and transformation between two shapes; second how to match shapes under occlusion; and last how to recognize and locate...
The problem of object detection and tracking has received relatively less attention in low frame rate and low resolution videos. Here we focus on motion segmentation in videos where objects appear small (less than 30-pixel tall people) and have low frame rate (less than 5 Hz). We study challenging cases where some of the, otherwise successful, approaches may break down. We investigate a number of...
Traditional methods of detection tend to under perform in the presence of the strong and variable background clutter that characterize a medical ultrasound image. In this paper, we present a novel diffusion based technique to localize acoustically dense objects in an ultrasound image. The approach is premised on the observation that the topology of noise in ultrasound images is more sensitive to diffusion...
This paper proposes a new ego-motion estimation and background/foreground classification method to effectively segment moving objects from videos captured by a moving camera on a moving platform. Existing methods for moving-camera detecting impose serious constraints. In our approach, ellipsoid scene shape is applied in the motion model and a complicated ego-motion estimation formula is derived. Genetic...
Shadow detection and removal is an important step employed after foreground detection, in order to improve the segmentation of objects for tracking. Methods reported in the literature typically have a significant trade-off between the shadow detection rate (classifying true shadow areas as shadows) and the shadow discrimination rate (discrimination between shadows and foreground). We propose a method...
We present I-FAC, a novel fuzzy associative classification algorithm for object class detection in images using interest points. In object class detection, the negative class CN is generally vague (CN = U - CP ; where U and CP are the universal and positive classes respectively). But, image classification necessarily requires both positive and negative classes for training. I-FAC is a single class...
We propose an edge segment based statistical background modeling algorithm and a moving edge detection framework for the detection of moving objects. We analyze the performance of the proposed segment based statistical background model with traditional pixel based, edge pixel based and edge segment based approaches. Existing edge based moving object detection algorithms fetches difficulty due to the...
Statistical background subtraction has proved to be a robust and effective approach for segmenting and extracting objects without any prior information of the foreground objects. This paper presents two contributions on this topic. The first contribution of this paper proposes a novel approach which introduces the motion mask into the Gaussian Mixture Models to reduce the errors of classical GMMs,...
Merging and splitting of objects cause challenges for visual tracking. This is due to observation ambiguity, object lost, and tracking errors when objects are close together. In this paper, we propose a method to combine the joint probabilistic data association (JPDA) and the particle filter to maintain tracks of objects. The results of JPDA are employed to improve the observation model in the particle...
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