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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 current focus of our research is to detect and classify the plant disease in agricultural domain, by implementing image processing techniques. We aim to propose an innovative set of statistical texture features for classification of plant diseases images of leaves. The input images are taken by various mobile cameras. The Scale-invariant feature transform (SIFT) features used as texture feature...
Moving objects' detection in dynamic scenes is a very important task in video processing. In the applications of image processing (for example, video-surveillance), more attention is paid to whether there is an interested object in the scene rather than where the object is located. As a matter of fact, similar to static backgrounds, the statistical histograms of the most dynamic backgrounds have favorable...
Together with the technology advancement, Computer Vision plays an important role in enhancing smart computing systems to help people overcome obstacles in their daily lives. One of the common troublesome problems is human memorization ability, especially memorizing things such as personal items. It is annoying for people to waste their time finding lost items manually by recall or notes. This motivates...
In this paper we address the problem of online video abnormal event detection. A vast number of methods to automatically detect abnormal events in videos have been recently proposed. However, the majority of these recently proposed methods cannot attain online performance; in other words, they cannot detect events as soon as they occur. Thus there is a lack of methods specifically aimed to detect...
In view of the foreground and background similarity of building in some monitoring video and the caliginous environment light, the foreground segmentation is not complete. This paper proposes a background modeling algorithm combining color feature and XCSLBP texture feature. When the color of foreground and background is similar, the foreground is detected by the difference of the texture feature...
Target recognition is a key technology in guided weapon systems. In this paper, an algorithm of target recognition based on local part is presented for the armored target in complex background. By constructing a variable target model to identify the local part of the target, the latent support vector machine is used to find the position of the part, and the position of the whole target is identified...
The proof of human parts has an imperative effect on pose evaluation, and can be effortlessly confused with difficult background due to indefinite part detector. This paper circumvents this predicament by performing a proof supporting approach, where each part also receives confidence from its neighborhood which uses the outline information between connect parts and mitigates the risk of being blindly...
The main challenges of multi-object tracking are the heavy occlusion between targets and targets, in/out the field of surveillance and the data association between objects and candidate objects, etc. In this paper, we proposed a multi-object tracking algorithm by collaborative multi-feature based on Kalman filter, first, in the detection program, we extract pedestrians in every frame, and we adopt...
Video Summarization plays a vital role in the internet user's life, especially for those searching for user specified video of interest for a long time. In order to provide support for users in terms of searching and retrieving video content, it is necessary to segment the video into shots and extract representative frame of each shot which acts as a summary of the video. So, in this paper, an approach...
In this paper, we propose a new local descriptor for action recognition in depth images. The proposed descriptor relies on surface normals in 4D space of depth, time, spatial coordinates and higher-order partial derivatives of depth values along spatial coordinates. In order to classify actions, we follow the traditional Bag-of-words (BoW) approach, and propose two encoding methods termed Multi-Scale...
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...
Discriminating probabilistic graphical models are reliable tools for a sequence labeling task. Conditional Random Fields (CRFs) are discriminative models which will enable us to label a sequence of input data. Other variations of CRFs have been proposed. Hidden Conditional Random Fields (HCRFs) incorporate hidden states to the CRF model and assign a label for the whole input sequence as the model's...
A Regionlet model explored here provides a new object representation strategy for generic object detection, which integrates local deformation handling into object classifier learning and feature extraction. Generic object detection deals with different degrees of variations in discrete object classes with tractable computations and hence faces problems. This generates a need for representational...
This paper introduces an effective active contour model for texture segmentation. To improve the robustness against noise and illumination, a novel descriptor named local statistical variation degree (LSVD) is presented to express textural features, which uses corner point deletion and isolated region detection operations to eliminate image patches unrelated with object regions. And then the fused...
Deep learning-based models have recently been widely successful at outperforming traditional approaches in several computer vision applications such as image classification, object recognition and action recognition. However, those models are not naturally designed to learn structural information that can be important to tasks such as human pose estimation and structured semantic interpretation of...
This paper proposed a new Vehicle Make Recognition (VMR) method using the PCANet features extracted from vehicle front view images. The PCANet architecture processes every input vehicle image through only three very simple data processing components: cascaded principle component analysis (PCA), binary hashing, and block-wise histograms, and generates a sparse vector as the feature representation....
Autonomous Underwater Vehicle (AUV) operations are inherently bandwidth limited but increasingly data intensive. This leads to large latencies between the capture of image data and the time at which operators are able to make informed decisions using the results of a survey. As AUV endurance and reliability continue to improve, there is a greater need for real-time data processing to inform on-board...
A novel approach to spatio-temporal saliency detection in video is proposed. Saliency computation is considered as an optimization problem that maximizes the energy of a fully-connected graphical model based on spatio-temporal feature distinctiveness. Each pixel in a video is modeled by a node, and the spatio-temporal feature distinctiveness between pixels by edges connecting the nodes in the graph...
This paper presents a mathematical analysis of the impact of key-point detection errors on the similarity of local image descriptors that are based on histogram of gradients. First, we derive a closed-form expression for the 𝐿p distance between two descriptors, for general translation, scale and orientation detection errors. Second, we introduce a detailed analysis for the special case where translation...
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