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For remote sensing image understanding, target detection is one of the most important tasks. In this paper, we propose one object detection method based on region proposal detection via active contour model and detection based on one-class classification method. The large scale remote sensing image is split into several connected components. And then, the proposed algorithm detects the object from...
We propose a family of quasi-linear discriminants that outperform current large-margin methods in sliding window visual object detection and open set recognition tasks. In these tasks the classification problems are both numerically imbalanced – positive (object class) training and test windows are much rarer than negative (non-class) ones – and geometrically asymmetric –...
Most approaches for scene parsing, recognition or retrieval use detectors that are either (i) independently trained or (ii) jointly trained for conjunctions of object-object or object-attribute phrases. We posit that neither of these two extremes is uniformly optimal, in terms of performance, across all categories and conjunctions. The choice of whether one should train an independent or composite...
Object detection is a challenging task in the field of pattern recognition. The objective of object detection is to locate the target objects in the testing images. In this paper, we use SVM trained active basis model as a sparse coding model for representing objects. The sparse coding model represents each image as the linear superposition of a small number of Gabor wavelets selected from an over-complete...
Object detection in high resolution remote sensing images is a crucial yet challenging problem for many applications. With the development of satellite and sensor technologies, remote sensing images attain very high spatial resolution, giving rise to the employment of many computer vision algorithms. Therefore, the object detection is usually formalized as a supervised classification task. In this...
Automatic Target Recognition (ATR) technology is of great significance in security inspection, while traditional object detection methods are proved not efficient in human body millimeter-wave images. In this paper, we propose a synthetic objection detection method for millimeter-wave images. We choose saliency, SIFT and HOG features to form image descriptors. According to sparse representation, the...
High-resolution remote sensing images (RSIs) have been adopted in satellites, RSIs processing in satellites will enable new multimedia applications, such as situational awareness. In this paper, we have developed an object detection framework exploiting objectness measurement in which binarized normed gradient (BING) is used to detect some particular objects in RSIs more efficiently. Specifically,...
The growth of marine renewable energy and marine protected areas in France leads to a growing need for animal population knowledge at sea. Offshore energy generator projects (wind turbines for example) must obey these regulations and show their harmlessness to the environment, particularly to the wildlife and to protected species, which are vulnerable and threatened. This paper presents a supervised...
This paper deals with image categorization from weak supervision, e.g. global image labels. We propose to improve the region selection performed in latent variable models such as Latent Support Vector Machine (LSVM) by leveraging human eye movement features collected from an eye-tracker device. We introduce a new model, Gaze Latent Support Vector Machine (G-LSVM), whose region selection during training...
Deep neural networks yield positive object detection results in aerial imaging. To deal with the massive computational time required, we propose to connect an SVM Network to the different feature maps of a CNN. After the training of this SVM Network, we use an activation path to cross the network in a predefined order. We stop the crossing as quickly as possible. This early exit from the CNN allows...
In weakly supervised object detection, conventional methods treat object location in each image as a latent variable and use non-convex optimization to solve the latent variable. However, as the optimization objective is image-level instead of sample-level, the learning procedure tends to choose object parts as false positive samples. Furthermore, when multiple classes of objects appear in the same...
In this paper we propose a multiple layer model for object detection and sketch representation. Unlike most traditional detection models focusing on the object localization, we investigate both the object detection and sketch representation within an unified framework. Based on the multiple layer architecture, our model can provide the sketch information of the detected object. Meanwhile, we generalize...
Passive Millimeter Wave Images (PMMWI) can be used to detect and localize objects concealed under clothing. Unfortunately, the quality of the acquired images and the unknown position, shape, and size of the hidden objects render difficult this task. In this paper we propose a method that combines image processing and statistical machine learning techniques to solve this localization/detection problem...
ShapeNets is an image representation, which is based on shape, compact structure, hierarchical image structure and appearance characteristic of object contour. In a ShapeNets, the shape of image is a window of containing objects which can be extracted with the method of objectness. The outline of objects can also be extracted in a line boundary detection algorithm based on histogram of gradients direction,...
In recent years, unmanned aerial vehicles (UAVs) have been widely used for civilian remote sensing applications. One of them is to assess damages due to man-made or natural disasters and search for bodies in the debris. In this work, we propose to support avalanche search and rescue (SAR) operation with UAVs. The image acquired by the UAV is processed through a pre-trained convolutional neural network...
This paper presents regional Support Vector Machine (SVM) classifiers with a spatial model for object detection. The conventional SVM maps all the features of training examples into a feature space, treats these features individually, and ignores the spatial relationship of the features. The regional SVMs with a spatial model we propose in this paper take into account a 3-dimentional relationship...
Road safety is influenced by the accurate placement and visibility of road signs, which are maintained based on inventories of traffic signs. These inventories are created (semi-)automatically from street-level images, based on object detection and classification. These systems often neglect the present complimentary signs (subsigns), although clearly important for the meaning and validity of signs...
In this paper, we propose a novel technique for object description. The proposed method is based on investigation of energy distribution (in the image) that describes the properties of objects. The energy distribution is encoded into a vector of features and the vector is then used as an input for the SVM classifier. Generally, the technique can be used for detecting arbitrary objects. In this paper,...
In practice, multiple objects in images are located by consecutively applying one detector for each class and taking the best confident score. In this work, we propose to show the advantage of grouping similar object classes into a hierarchical structure. While this approach has found interest in image classification, it is not analyzed for the object detection task. Each node in the hierarchy represents...
Small infrared target detection is one of the key techniques in infrared searching and tracking applications. A novel small target detection method based on the complex filter bank is proposed in this paper. Firstly, a training sample dataset is built and the complex filter bank is utilized to extract the feature representation of each pixel. Then, the support vector machine is used to detect the...
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