Surface defect recognition is one of the key technologies for varistor quality inspection, which can greatly improve detection efficiency and performance. In order to more accurately identify the surface defects of a varistor body and the pins, a method for identifying the surface defects based on deep convolutional neural networks (CNN) is proposed. The proposed method mainly includes four stages: image acquisition and data set construction, convolutional neural network modeling, CNN training and testing. Firstly, varistor images are acquired, and the body and pins of the varistor are segmented by image segmentation method. The number of samples is increased by data augmentation to make a data set of 5 classes. Secondly, according to the appearance characteristics of varistor, a CNN model is designed for varistor surface defect recognition. Third, using the created data set, the training data set with category labels are input to the proposed CNN for training. Finally, 1200 test samples were tested on the trained model in the test phase and the performance of the proposed algorithm was evaluated using mean average precision. The experimental results show that our method can identify the surface defects of the main body and pins of varistor efficiently and accurately.
Segmentation of pigmented lesions is often affected by factors such as hair around the skin lesions, artificial markings, etc., and the complexity of the lesion itself, such as lesions and skin boundaries is not clear, the internal color of lesions is variable, etc., resulting in segmentation difficulties. Aiming at the problem that the segmentation method of pigmented skin lesions using only random forests is not accurate, a segmentation method for pigmented skin lesion using a combination of random forest and fully convolutional neural networks (FCN) is proposed. This method firstly classifies and recognizes skin lesion images based on random forests to obtain a probability distribution of the lesions and the background. Then, the other probability distribution is obtained using FCN based on an improved loss function. Finally, the classification results of random forest and FCN are fused into the final image segmentation results. The experimental results show that the combination of random forest and FCN yields better performances than using random forest alone, in particular, can increase the sensitivity by about 20%.
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.
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.