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Using spatio-temporal features is popular for action recognition. However, existing methods embed these local features into a global representation. Orders and correlations among local motions of each action are missing. This can make it difficult to distinguish closely related actions. This paper proposes a solution to address this challenge by encoding correlations of movements. Space-time interest...
Histogram of Oriented Gradient (HOG) is a popular feature description for the purpose of object detection. However, HOG algorithm requires a high performance system because of its complex operation set. In HOG algorithm, the cell histogram generation is one of the most complex part, it uses inverse tangent, square, square root, floating point multiplication. In this paper, we propose an accurate and...
The algorithm of extracting pedestrian features based on Local Binary Pattern (LBP) has the problems of being unable to depict the human visual sensitivity. We proposed an Significant Local Binary Pattern (SLBP) which fused the characteristics of human visual pedestrian system. We extracted the significant factor based on Weber's law, and added the significant factor as a weight to the LBP eigenvalue...
In the world, many currencies have been issued and some of them were counterfeited. Especially, by the advances in copying technology and computer ability it is rather easy to make counterfeiting bills. In this paper, we develop a method to use spectral property of the bills and classify the bill whether it is true or fake using spectral sub-band analysis for Singapore dollars.
Future humanoid robots will be expected to carry out a wide range of tasks for which they had not been originally equipped by learning new skills and adapting to their environment. A crucial requirement towards that goal is to be able to take advantage of external elements as tools to perform tasks for which their own manipulators are insufficient; the ability to autonomously learn how to use tools...
In this paper we propose a new method for scene representation and recognition based on the concept of Region Subspaces. Each image is pre-segmented into semantically meaningful regions and local features are extracted at different scales from each such region. The Region Subspaces are the low-dimensional linear subspaces calculated from the set of local features inside each region. We also define...
Plenty of video stuff is created, broadcasted, shared and stored each and every day by industry experts, beginners, and hobbyists. Video summaries aim at showcasing the semantics and content of a clip in reduced time and space to enable a quick overview of video clip relevance. This paper focuses on static summaries showing key frames from the video. The key frames are extracted by leveraging the...
Medical Image retrieval and similar image classification are the two important aspects in decision making and automatic medical image annotation. These approaches help the doctors and radiologists in their decision making during decease identification and decision making. Image classification is usually done by checking image visual content or semantic content similarity. Image content may be represented...
In this paper, a variation in the Local Binary Pattern (LBP) called Modified Dominant Directional LBP (MDDLBP) is proposed. In this method, the direction of the feature with respect to its central pixel in the LBP is preserved by comparing the neighborhood of the pixel in the four dominant directions such as horizontal, vertical, diagonal and anti diagonal. This method captures complete structure...
The performance of an object detection system relies heavily on two components: an object model to capture the compositional relationship among the object body and its parts, and a feature representation to describe object appearance. In this work, we present an empirical study of combining two state-of-the-art such components: Deformable Part Model (DPM), a proven effective and flexible part-based...
Face recognition has became one of the most popular application in computer vision, due to a large demand for security. In recent years, major advances have been reported in the face recognition and, therefore, the methods are becoming more accurate and efficient. Recent research pointed that feature extraction using activity in directed graphs showed excellent results in feature extraction. In this...
Target tracking is a challenging task in computer vision. It aims to detect and track particular objects in sequences. Illumination variation, motion of target, occlusion and background clutter make target tracking extremely challenging. We propose an novel online target tracking method which based on extreme learning machine(ELM). This tracking method consists of three modules: training, tracking...
Handwriting has been known to be a very strong identifying characteristic of an individual and can be considered a behavioural biometric trait. This has made hand writer identification an important area of research. In this paper, a novel offline writer identification system is proposed using ensemble of multi-scale local ternary pattern histogram features. Features are extracted at multiple scales...
Texture analysis has been an important research area due to wide range of applications in the field of image processing, machine vision and pattern recognition. In this paper, we present a comprehensive analysis of texture descriptors for texture classification. We focus on state of the art texture descriptors which have been widely used for classification in literature and shown promising results...
Ultrasonography (USG) is one of the best imaging modalities for early detection of malignancy in the thyroid gland. Computer Aided Diagnosis (CAD) plays an important role in classifying thyroid nodules. CAD is used as an objective consideration, this is due to the high subjectivity in physician's interpretation of ultrasound images that can lead to difference scanning results. This research proposes...
We present a fully trainable solution for binarization of degraded document images using extremely randomized trees. Unlike previous attempts that often use simple features, our method encodes all heuristics about whether or not a pixel is foreground text into a high-dimensional feature vector and learns a more complicated decision function. We introduce two novel features, the Logarithm Intensity...
An adaptive threshold shot detection algorithm based on improved block color features is proposed in this paper. This paper adopts an improved block color feature extraction method based on equal area of rectangular ring. Sub-block accumulative color histogram is extracted as color features and different weight for different rectangle rings is set in order to highlight the central part of frame. Then,...
Cartoons are an informative way for creating awareness; children take keen interest in watching cartoons and spend leisure time in front of television. Unfortunately there is an inclination towards violence and other objectionable scenes in cartoon videos that have very bad impact on the developing personality of children. Extensive use of such violent scenes is one of the factors of increase of violence...
Nowadays, many steganographic tools have been developed, and secret messages can be imperceptibly transmitted through public networks. This paper concentrates on steganalysis against spatial least significant bit (LSB) matching, which is the prototype of many advanced information hiding methods. Many existing algorithms deal with steganalysis problems by using the dependencies between adjacent pixels...
Target tracking is one of the important tasks in computer vision. It aims to detect and track one or more particular objects in videos. The target and background may change in the process of tracking. In order to solve this problem, this paper proposes an online learning target tracking method based on extreme learning machine (ELM). First of all, we capture the target and background regions in the...
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