In this paper, a new embedded intelligent monitoring system based on face recognition is proposed. The system uses Pi Raspberry as the central processor. A sensors group has been designed with Zigbee module in order to assist the system to work better and the two alarm modes have been proposed using the Internet and 3G modem. The experimental results show that the system can work under various light intensities to recognize human face and send alarm information in real time.
The high speed attitude maneuver of Unmanned Aerial Vehicle (UAV) always causes large motion between adjacent frames of the video stream produced from the camera fixed on the UAV body, which will severely disrupt the performance of image object tracking process. To solve this problem, this paper proposes a method that using a gyroscope fixed on the camera to measure the angular velocity of camera, and then the object position’s substantial change in the video stream is predicted. We accomplished the object tracking based on template matching. Experimental result shows that the object tracking algorithm’s performance is improved in its efficiency and robustness with embedded gyroscope information.
In this paper, we propose a novel 30-dimension descriptor named SIFTRO(SIFT of Ring Order) to promote the matching speed, which is generated from 3 local ring areas. A new element reordering method is presented to ensure the descriptor’s rotation invariance. To obtain the best scale factor for SIFTRO descriptor, the weight hierarchy decision model based on AHP is designed. The experiments show that the SIFTRO descriptor inherits the advantages of the invariance to image scaling, rotation and affine, and it also speeds up greatly in image matching, while the precision is improved compared with that of original SIFT.
This paper proposes a novelty dense stereo matching method based on TC-MST (Threshold Constrained Minimum Spanning Tree), which aims to improve the accuracy of distance measuring. Due to the threshold has a great impact on the results of image segments, to select a better threshold, we adopt iteration threshold method. And then we uses MST to calculate the cost aggregation, and utilize the winner-take-all algorithm for the cost aggregation to obtain the disparity. Finally the method proposed is used in a distance measuring system. The experiment results show that this method improves the distance measuring accuracy compared with BM (block matching).
In the conventional face recognition, most researchers focused on enhancing the precision which input data was already the member of database. However, they paid less necessary attention to confirm whether the input data belonged to database. This paper proposed an approach of face recognition using two-dimensional principal component analysis (2DPCA). It designed a novel composite classifier founded by statistical technique. Moreover, this paper utilized the advantages of SVM and Logic Regression in field of classification and therefore made its accuracy improved a lot. To test the performance of the composite classifier, the experiments were implemented on the ORL and the FERET database and the result was shown and evaluated.
In this paper, we present a novel binary descriptor with orientation, which called Intensity-Centroid LDB (IC-LDB). This descriptor resolves the problems that the current non-binary descriptors are too compute-expensive to achieve real-time performance in the nonlinear scale space and that the original Local Difference Binary (LDB) descriptors do not have an orientation component to keep rotation invariant. Experimental results demonstrate that IC-LDB proposed in this paper was faster than previously non-binary descriptors which were used in nonlinear scale space, while performing as well in many situations.
In order to meet the accuracy requirement of a target recognition system, a target recognition algorithm based on support
vector machine is proposed in this paper. In the algorithm, firstly, a fast image multi-threshold segmentation method is
accomplished by using a novel searching path of particle swarm optimization to separate the target from the background.
Then some characteristics of target samples such as moment feature, affine invariant feature and texture feature based on
co-occurrence matrix are extracted. Thus, the parameter optimizing selection is achieved according to the corresponding
rule. After comparing with other kernel functions, the radial basis function kernel is selected to build a target classifier
for one particular typical target. Meanwhile, a BP neural network based target recognition system is implemented to
facilitate comparison. Finally, the target recognition method presented in this paper is applied to the airplane recognition.
The experimental results show that the algorithm given in this paper can effectively detect and recognize the image
target automatically. It can be applied to both single target and multi-objective recognition. Moreover, real-time target
recognition can be achieved for single target.
Wavelet transform is a new branch of mathematics, which is developing rapidly in recent years. Because it gets rid of some defects of Fourier transform. Wavelet analysis method has been paid more and more attention and widely used in various fields of engineering application, especially in image processing. In this paper, after the wavelet pyramidal decomposition of the image, the different quantization and coding schemes for each subimage are then carried out in accordance with its statistical properties and distributed properties of the coefficients. The computer simulation result shows that this compression system can attain good reconstructed image while assuring satisfying compression ratio.