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Utilizing object proposals as a preprocessing procedure has been shown its significance in many multimedia computing tasks. Most state-of-the-art methods devoted to finding a generic objectness measure for rating the possibilities of the initial sliding windows with or without objects. In fact, the object criteria vary from one objectness measure to another, which leads to the definite bottleneck...
Research on pedestrian detection still presents a lot of space for improvements, both on speed and detection accuracy. State-of-the-art object proposals approach has shown the very effective computational efficiency in object detection. In this paper, we present a framework for pedestrian detection based on the object proposals. Instead of scaling the test image to different sizes, we generate a pyramid...
Trust and reputation mechanisms are part of the logical protection of intelligent agents, preventing malicious agents from acting egotistically or with the intention to damage others. Several studies in Psychology, Neurology and Anthropology claim that emotions are part of human's decision making process. However, there is a lack of understanding about how affective aspects, such as emotions, influence...
Feature selection, instance selection and semi-supervised clustering are different challenges for machine learning and data mining communities. While other works have addressed each of these problems separately, in this paper we show how they can be addressed together, simultaneously. We propose an unified framework for semi-supervised co-selection of features and instances, based on weighting constrained...
Least square support vector machines (LSSVMs) are an alternative to SVMs because the training process for LSSVMs is based on solving a linear equation system while the training process for SVMs relies on solving a quadratic programming optimization problem. When LSSVMs are dealing with regression tasks, we refer to them as Least square support vector regressors (LSSVRs). Despite solving a linear system...
This paper presents a method for airport detection from optical satellite images using deep convolutional neural networks (CNN). To achieve fast detection with high accuracy, region proposal by searching adjacent parallel line segments has been applied to select candidate fields with potential runways. These proposals were further classified by a CNN model transfer learned from AlexNet to identify...
With the massive data challenges nowadays and the rapid growing of technology, stream mining has recently received considerable attention. To address the large number of scenarios in which this phenomenon manifests itself suitable tools are required in various research fields. Instance-based data stream algorithms generally employ the Euclidean distance for the classification task underlying this...
Object detection from still images has been among the most active and challenging area in computer vision recently. In contrast, fully supervised object detection from video has rarely been investigated. In this paper, we propose an algorithm to improve the performance of object detection from video. Our proposed method is based on an empirical property that the trajectory of an object is important...
This paper deals with a new kind of fractional-order controllers with nonlinear part in order to eliminate nonlinear characteristic of the controlled system, actuator, wind-up effect, noise and so on, respectively. In this paper is presented the methods for controller implementation in digital form as well as neural network approach to design such kind of the controller. The main advantage of such...
In this paper, we present a system to detect and count the number of vehicles in traffic surveillance videos based on Fast Region-based Convolutional Network (Fast R-CNN). Fast R-CNN is a state-of-the-art object detection network, which takes an entire image and a set of object proposals as input, produces bounding-box positions with probability estimates over object classes as output. First, we fine-tune...
Brand recognition is a very challenging topic with many useful applications in localization recognition, advertisement and marketing. In this paper we present an automatic graphic logo detection system that robustly handles unconstrained imaging conditions. Our approach is based on Fast Region-based Convolutional Networks (FRCN) proposed by Ross Girshick, which have shown state-of-the-art performance...
The task of classifying data has been addressed in various works, and has been utilized in various areas of application, such as medicine, industry, marketing, financial market and many others. This work will present a data classifier proposal that combines the SOM (Self-Organizing Map) neural network with INN (Informative Nearest Neighbors). The combination of these two algorithms will be called...
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