Chengzhuan Yang

Bio: Chengzhuan Yang is an academic researcher from Zhejiang University of Finance and Economics. The author has contributed to research in topics: Probability distribution & Convolution. The author has an hindex of 2, co-authored 2 publications receiving 6 citations.

Latent-MVCNN: 3D Shape Recognition Using Multiple Views from Pre-defined or Random Viewpoints

Abstract: The Multi-view Convolution Neural Network (MVCNN) has achieved considerable success in 3D shape recognition. However, 3D shape recognition using view-images from random viewpoints has not been yet exploited in depth. In addition, 3D shape recognition using a small number of view-images remains difficult. To tackle these challenges, we developed a novel Multi-view Convolution Neural Network, “Latent-MVCNN” (LMVCNN), that recognizes 3D shapes using multiple view-images from pre-defined or random viewpoints. The LMVCNN consists of three types of sub Convolution Neural Networks. For each view-image, the first type of CNN outputs multiple category probability distributions and the second type of CNN outputs a latent vector to help the first type of CNN choose the decent distribution. The third type of CNN outputs the transition probabilities from the category probability distributions of one view to the category probability distributions of another view, which further helps the LMVCNN to find the decent category probability distributions for each pair of view-images. The three CNNs cooperate with each other to the obtain satisfactory classification scores. Our experimental results show that the LMVCNN achieves competitive performance in 3D shape recognition on ModelNet10 and ModelNet40 for both the pre-defined and the random viewpoints and exhibits promising performance when the number of view-images is quite small.

Bag of contour fragments for improvement of object segmentation

Abstract: Many state-of-the-art shape features have been proposed for the shape recognition task. In this paper, to explore whether a shape feature influences object segmentation, we propose a specific shape feature, Fisher shape (a form of bag of contour fragments), and we combine this with the appearance feature with multiple kernel learning to create a pipeline of object segmentation system. The experimental results on benchmark datasets clearly demonstrate that the pipeline of object segmentation is effective and that the Fisher shape can improve object segmentation with only the appearance feature.

Scene Semantic Recognition Based on Modified Fuzzy C-Mean and Maximum Entropy Using Object-to-Object Relations

Abstract: With advances in machine vision systems (e.g., artificial eye, unmanned aerial vehicles, surveillance monitoring) scene semantic recognition (SSR) technology has attracted much attention due to its related applications such as autonomous driving, tourist navigation, intelligent traffic and remote aerial sensing. Although tremendous progress has been made in visual interpretation, several challenges remain (i.e., dynamic backgrounds, occlusion, lack of labeled data, changes in illumination, direction, and size). Therefore, we have proposed a novel SSR framework that intelligently segments the locations of objects, generates a novel Bag of Features, and recognizes scenes via Maximum Entropy. First, denoising and smoothing are applied on scene data. Second, modified Fuzzy C-Means integrates with super-pixels and Random Forest for the segmentation of objects. Third, these segmented objects are used to extract a novel Bag of Features that concatenate different blobs, multiple orientations, Fourier transform and geometrical points over the objects. An Artificial Neural Network recognizes the multiple objects using the different patterns of objects. Finally, labels are estimated via Maximum Entropy model. During experimental evaluation, our proposed system illustrated a remarkable mean accuracy rate of 90.07% over the MSRC dataset and 89.26% over the Caltech 101 for object recognition, and 93.53% over the Pascal-VOC12 dataset for scene recognition, respectively. The proposed system should be applicable to various emerging technologies, such as augmented reality, to represent the real-world environment for military training and engineering design, as well as for entertainment, artificial eyes for visually impaired people and traffic monitoring to avoid congestion or road accidents.

Iterated graph cut method for automatic and accurate segmentation of finger-vein images

Abstract: Recent advances in computer vision and machine intelligence have facilitated biometric technologies, which increasingly rely on image data in security practices. As an important biometric identifier, the near-infrared (NIR) finger-vein pattern is favoured by non-contact, high accuracy, and enhanced security systems. However, large stacks of low-contrast and complex finger-vein images present barriers to manual image segmentation, which locates the objects of interest. Although some headway in computer-aided segmentation has been made, state-of-the-art approaches often require user interaction or prior training, which are tedious, time-consuming and prone to operator bias. Recognizing this deficiency, the present study exploits structure-specific contextual clues and proposes an iterated graph cut (IGC) method for automatic and accurate segmentation of finger-vein images. To this end, the geometric structures of the image-acquisition system and the fingers provide the hard (centreline along the finger) and shape (rectangle around the finger) constraints. A node-merging scheme is applied to reduce the computational burden. The Gaussian probability model determines the initial labels. Finally, the maximum a posteriori Markov random field (MAP-MRF) framework is tasked with iteratively updating the data models of the object and the background. Our approach was extensively evaluated on 4 finger-vein databases and compared with some benchmark methods. The experimental results indicate that the proposed IGC method outperforms the state-of-the-practice approaches in finger-vein image segmentation. Specifically, the IGC method, relative to its level set deep learning (LSDL) counterpart, can increase the average F-measure value by 5.03%, 6.56%, 49.91%, and 22.89% when segmenting images from four different finger-vein databases. Therefore, this work can provide a feasible path towards fully automatic image segmentation.