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Pattern recognition scheme is used for discriminating various classes of hand motion with feature extracted from the surface electromyography signals. However, while using a relatively large feature set for classification process, the computational complexity increases tremendously. To overcome this, the paper implements feature selection technique using wrapper evaluation and four different search...
In this paper, we performed human moving pattern recognition using communication quality: cellular download throughputs, Received Signal Strength Indicators (RSSIs) and cellular base station IDs. We apply three machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) and evaluate recognition accuracy of human moving patterns. Results conclude...
In this paper, we propose a patched-based deep Boltzmann shape priors for visual tracking. The shape priors are generated from deep Boltzmann machine network. The network consists of three layers of hidden and visible units. The generated shapes not only maintain general shapes from a variety of poses, but also entail local modifications with high probability.
This paper proposed a workshop to introduce the use of computational tools and methods to analyze educational data. The workshop will demonstrate three different contexts in which these tools can be used to visualize and characterize patterns within educational data, and validate them using statistical techniques. Participants in this workshop will have the opportunity to learn how to implement these...
This target detection and tracking system is the basis for rescue robots to achieve their independent search and rescue operations. In order to improve their mobile performance and sensing capability, the Kinect camera is employed by rescue robots to obtain environmental visual. The AKAZE(Accelerated-KAZE) feature matching algorithm is adopted to achieve target detection in video frames, combining...
In this paper, a robust visual tracking system by utilizing the images acquired from a color camera and a thermal camera is proposed to track the target with real-time performance. The thermal camera, which can observe the heat originated from the target such as the human body or vehicle, can collaborate with the color camera to track the target in the cluttered environment or under occlusion. Unlike...
The rapid development of three-dimensional (3D) imaging techniques has significantly increased the demand for high resolution (HR) depth video and images. Significant pixel deficiencies and too much noise can be seen in depth images especially taken from Kinect cameras. For this reason, usability in several computer vision applications is restricted. In the acquisition of HR depth images, in traditional...
Plant recognition from their leaves has become a popular area in the machine learning and image processing. In this study 7 different types of apricot trees were determined and classified by using their leaves. At first leaves images were pre-processed. After than each image was scanned by 5×5 overlapping filter and median values of each filter process were recorded to represent the leaves. After...
Saliency detection in images attracts much research attention for its usage in numerous multimedia applications. In this paper, we propose a saliency detection method based on optimization for RGBD images. With RGBD images, our method utilizes the depth channel to enhance the identification of background and foreground regions. We firstly generate new depth image by using non-linear transformation...
This paper presents a methodology for controlling the direction of motor taking a video sample from a camera as input. To control the direction the subject has to move his head in a direction which he would want the motor to rotate. The main challenge would be classifying the test sequence which has the data of the activity performed by the subject The actions are recognized in the frontal view by...
Although the recent success of convolutional neural network (CNN) advances state-of-the-art saliency prediction in static images, few work has addressed the problem of predicting attention in videos. On the other hand, we find that the attention of different subjects consistently focuses on a single face in each frame of videos involving multiple faces. Therefore, we propose in this paper a novel...
One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason about the visual world. Humans can learn about the characteristics of objects and the relationships that occur between them to learn a large variety of visual concepts, often with few examples. This paper investigates the...
Cross-modal retrieval has attracted intensive attention in recent years. Measuring the semantic similarity between heterogeneous data objects is an essential yet challenging problem in cross-modal retrieval. In this paper, we propose an online learning method to learn the similarity function between heterogeneous modalities by preserving the relative similarity in the training data, which is modeled...
We address personalization issues of image captioning, which have not been discussed yet in previous research. For a query image, we aim to generate a descriptive sentence, accounting for prior knowledge such as the users active vocabularies in previous documents. As applications of personalized image captioning, we tackle two post automation tasks: hashtag prediction and post generation, on our newly...
Many computer vision problems require optimization of binary non-submodular energies. In this context, iterative submodularization techniques based on trust region (LSA-TR) and auxiliary functions (LSA-AUX) have been recently proposed [9]. They achieve state-of-the-art-results on a number of computer vision applications. In this paper we extend the LSA-AUX framework in two directions. First, unlike...
Multi-view subspace clustering aims to partition a set of multi-source data into their underlying groups. To boost the performance of multi-view clustering, numerous subspace learning algorithms have been developed in recent years, but with rare exploitation of the representation complementarity between different views as well as the indicator consistency among the representations, let alone considering...
A method based on cosegmentation is applied to change detection to segment image patches belonging to each image. The image patches have the characteristics of spatial correspondence in multi-temporal images and precise boundary in its own image. By construction and optimization of energy function that consists of change feature item and image feature item, both of spectrum and shape change can successfully...
Several recent works have used deep convolutional networks to generate realistic imagery. These methods sidestep the traditional computer graphics rendering pipeline and instead generate imagery at the pixel level by learning from large collections of photos (e.g. faces or bedrooms). However, these methods are of limited utility because it is difficult for a user to control what the network produces...
In this paper a method for separation of partially overlapping particle is proposed based on circular mask and the framework including image segmentation concave points detection point pair matching and shape estimation is realized. First the color image is preprocessed to obtain a binary image. Then the comer detection algorithm is adopted to achieve rough estimation on potential concave points and...
In this paper, we present ResNet-based vehicle classification and localization methods using real traffic surveillance recordings. We utilize a MIOvision traffic dataset, which comprises 11 categories including a variety of vehicles, such as bicycle, bus, car, motorcycle, and so on. To improve the classification performance, we exploit a technique called joint fine-tuning (JF). In addition, we propose...
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