The incidence of cutaneous malignant melanoma, a disease of worldwide distribution and is the deadliest form of skin cancer, has been rapidly increasing over the last few decades. Because advanced cutaneous melanoma is still incurable, early detection is an important step toward a reduction in mortality. Dermoscopy photographs are commonly used in melanoma diagnosis and can capture detailed features of a lesion. A great variability exists in the visual appearance of pigmented skin lesions. Therefore, in order to minimize the diagnostic errors that result from the difficulty and subjectivity of visual interpretation, an automatic detection approach is required. The objectives of this paper were to propose a hybrid method using random forest and Gabor wavelet transformation to accurately differentiate which part belong to lesion area and the other is not in a dermoscopy photographs and analyze segmentation accuracy. A random forest classifier consisting of a set of decision trees was used for classification. Gabor wavelets transformation are the mathematical model of visual cortical cells of mammalian brain and an image can be decomposed into multiple scales and multiple orientations by using it. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain. Texture features based on Gabor wavelets transformation are found by the Gabor filtered image. Experiment results indicate the following: (1) the proposed algorithm based on random forest outperformed the-state-of-the-art in pigmented skin lesions detection (2) and the inclusion of Gabor wavelet transformation based texture features improved segmentation accuracy significantly.
Intelligent video surveillance is to analysis video or image sequences captured by a fixed or mobile surveillance camera, including moving object detection, segmentation and recognition. By using it, we can be notified immediately in an abnormal situation. Pedestrian detection plays an important role in an intelligent video surveillance system, and it is also a key technology in the field of intelligent vehicle. So pedestrian detection has very vital significance in traffic management optimization, security early warn and abnormal behavior detection. Generally, pedestrian detection can be summarized as: first to estimate moving areas; then to extract features of region of interest; finally to classify using a classifier. Redundant wavelet transform (RWT) overcomes the deficiency of shift variant of discrete wavelet transform, and it has better performance in motion estimation when compared to discrete wavelet transform. Addressing the problem of the detection of multi-pedestrian with different speed, we present an algorithm of pedestrian detection based on motion estimation using RWT, combining histogram of oriented gradients (HOG) and support vector machine (SVM). Firstly, three intensities of movement (IoM) are estimated using RWT and the corresponding areas are segmented. According to the different IoM, a region proposal (RP) is generated. Then, the features of a RP is extracted using HOG. Finally, the features are fed into a SVM trained by pedestrian databases and the final detection results are gained. Experiments show that the proposed algorithm can detect pedestrians accurately and efficiently.
With the broad attention of countries in the areas of sea transportation and trade safety, the requirements of efficiency and accuracy of moving ship tracking are becoming higher. Therefore, a systematic design of moving ship tracking onboard based on FPGA is proposed, which uses the Adaptive Inter Frame Difference (AIFD) method to track a ship with different speed. For the Frame Difference method (FD) is simple but the amount of computation is very large, it is suitable for the use of FPGA to implement in parallel. But Frame Intervals (FIs) of the traditional FD method are fixed, and in remote sensing images, a ship looks very small (depicted by only dozens of pixels) and moves slowly. By applying invariant FIs, the accuracy of FD for moving ship tracking is not satisfactory and the calculation is highly redundant. So we use the adaptation of FD based on adaptive extraction of key frames for moving ship tracking. A FPGA development board of Xilinx Kintex-7 series is used for simulation. The experiments show that compared with the traditional FD method, the proposed one can achieve higher accuracy of moving ship tracking, and can meet the requirement of real-time tracking in high image resolution.
Mean shift is a traditional moving target tracking algorithm, which has some deficiencies such as: A tracking window of a target needs to be initialed manually in the first frame; the window size cannot be adaptively changed according to a moving object in the process of tracking; if a target is obscured, it might be lost in the tracking window. In order to solve these problems, a method combining Kalman filter and Scale and Orientation Adaptive Mean Shift Tracking (SOAMST) is proposed. Firstly we use Kalman filter to locate a moving target at the beginning. Then the ratio of the first order moment to the zero order moment is used to estimate its center, and the second order center moment is used to estimate its size and orientation. Meanwhile, whether the target is obscured is determined by the Bhattacharyya coefficient based on the target model and a candidate one. A candidate model is more similar to the target and the estimation result of the target is more reliable when the Bhattacharyya coefficient is closer to 1. On the contrary, if the Bhattacharyya coefficient decreases to 0, the target will be lost for being totally obscured. If the target is partially obscured or not obscured, SOAMST is used directly to track the target; if totally obscured, Kalman filter is imposed to estimate the location of the target in the next frame before SOAMST. The experiments show that the proposed algorithm can track a moving target automatically at the initial frame without prior knowledge. It can also track a completely obscured target accurately by Kalman filtering based location estimation.
Environmental protection is one of the themes of today's world. The forest is a recycler of carbon dioxide and natural
oxygen bar. Protection of forests, monitoring of forest growth is long-term task of environmental protection. It is very
important to automatically statistic the forest coverage rate using optical remote sensing images and the computer, by
which we can timely understand the status of the forest of an area, and can be freed from tedious manual statistics.
Towards the problem of computational complexity of the global optimization using convexification, this paper proposes
a level set segmentation method based on Markov chain Monte Carlo (MCMC) sampling and applies it to forest
segmentation in remote sensing images. The presented method needs not to do any convexity transformation for the
energy functional of the goal, and uses MCMC sampling method with global optimization capability instead. The
possible local minima occurring by using gradient descent method is also avoided. There are three major contributions in
the paper. Firstly, by using MCMC sampling, the convexity of the energy functional is no longer necessary and global
optimization can still be achieved. Secondly, taking advantage of the data (texture) and knowledge (a priori color) to
guide the construction of Markov chain, the convergence rate of Markov chains is improved significantly. Finally, the
level set segmentation method by integrating a priori color and texture for forest is proposed. The experiments show that
our method can efficiently and accurately segment forest in remote sensing images.
Segmenting greenbelts quickly and accurately in remote sensing images is an economic and effective method for the statistics of green coverage rate (GCR). Towards the problem of over-reliance on priori knowledge of the traditional level set segmentation model based on max-flow/min-cut Graph Cut principle and weighted Total Variation (GCTV), this paper proposes a level set segmentation method of combining regional texture features and priori knowledge of color and applies it to greenbelt segmentation in urban remote sensing images. For the color of greenbelts is not reliable for segmentation, Gabor wavelet transform is used to extract image texture features. Then we integrate the extracted features into the GCTV model which contains only priori knowledge of color, and use both the prior knowledge and the targets’ texture to constrain the evolving of the level set which can solve the problem of over-reliance on priori knowledge. Meanwhile, the convexity of the corresponding energy functional is ensured by using relaxation and threshold method, and primal-dual algorithm with global relabeling is used to accelerate the evolution of the level set. The experiments show that our method can effectively reduce the dependence on priori knowledge of GCTV, and yields more accurate greenbelt segmentation results.