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This article develops a geometric framework for detecting targets, in the form of regions of interest, from certain sonar imagery. The main idea is to extract level sets from voxel images and compute local geometric features of the resulting surfaces. Examples include Gaussian and principal curvatures, radial distances, patch areas etc. These features are then compressed into histograms, or estimated...
Small infrared target detection in complex backgrounds is a challenging task. Due to dynamic background clutter and low signal-to-clutter ratio, most conventional methods fail to produce satisfactory results. In this paper, an effective spatial and temporal filter is proposed. The spatial filter is used to remove cloud edge, and the temporal filter is used to remove point-like background clutter....
Different types of traffic signs has different colors and shapes located in uncontrolled traffic environments. The detection of different types of traffic signs is a difficult problem in pattern recognition and computer vision. In our study, a region of interest (ROI) extraction method is proposed to extract ROI using color contrast in local regions. We utilize the high contrast in local regions to...
Current object detection approaches predict bounding boxes that provide little instance-specific information beyond location, scale and aspect ratio. In this work, we propose to regress directly to objects shapes in addition to their bounding boxes and categories. It is crucial to find an appropriate shape representation that is compact and decodable, and in which objects can be compared for higher-order...
We present a method for 3D object detection and pose estimation from a single image. In contrast to current techniques that only regress the 3D orientation of an object, our method first regresses relatively stable 3D object properties using a deep convolutional neural network and then combines these estimates with geometric constraints provided by a 2D object bounding box to produce a complete 3D...
In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image. A robust convolutional network is introduced for simultaneous vehicle detection, part localization, visibility characterization and 3D dimension estimation. Its architecture is based on a new coarse-to-fine object proposal that boosts the vehicle detection. Moreover,...
Recently, object recognition techniques have been rapidly developed. Most of existing object recognition focused on recognizing several independent concepts. The relationship of objects is also an important problem, which shows in-depth semantic information of images. In this work, toward general visual relationship detection, we propose a method to integrate spatial distribution of object to facilitate...
It is difficult to apply object recognition technology to manufacturing industry because the intrinsic characteristics of an object is easily influenced by the surrounding environment such as lighting condition, background complexity and object shape. This paper proposes a precise object detection method for assembling components by more stable feature extraction. To accomplish it, two images are...
The development of moving object detection systems has become of great interest in the video surveillance field. Although many foreground detection methods have been proposed, the problems of incomplete object shapes and misclassified shadow regions remain. We propose a new approach by combining bit-planes representation with gray-level co-occurrence matrix (GLCM). This allows the system to exploit...
Small unmanned aerial vehicles (UAVs) have become increasingly popular in the last several years. This paper explores numerous methods to detect and track small UAVs using computer vision.
Influenced by the optics point spread function of the thermal imaging system at a remote distance, the small target is analogous to isotropic Gaussian-like shape, whereas background clutters are usually the shape of the local orientation clutter. To tackle this problem, an improved infrared target detection method is proposed in this paper, which adopts multiple channels in the process of image enhancement...
This paper proposes a model for detecting objects with geometric shapes in an image whose file format is SVG (Scalable Vector Graphics). The SVG is known as a type of vector images. A major interesting feature of SVG is that components in victor images are created using mathematical theories and equations, in addition, vector images have many more advantages over raster images in various aspects....
Image processing is a method of extracting some useful information by converting image into digital inform by performing some operations on it. Object detection and tracking are the task that is important and challenging such as video surveillance and vehicle navigation. Video surveillance is a technology which works in dynamic environment in various events such as sports, public safety, and management...
Determining visual saliency is one of the fundamental problems in computer vision as the saliency not only identifies the most informative parts of a visual scene but may also reduce computational complexity by filtering out irrelevant segments of the scene. In this paper, we propose a novel saliency object detection method that combines a shape-preserving saliency prediction driven by a convolutional...
Video summarization is the process to extract informative events of a video and represent in the condensed form. The paper proposes a new method for extracting important contents of a video for summarization using geometric primitives, such as line segments, angles, and conic parts. The primitives have the capabilities to represent complex shapes and structures of objects in a video frame. Therefore,...
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...
Given object instances belonging to the same class, a novel topic model is proposes to learn the part-based object model by a semi-supervised manner. The proposed model, called segment-layout topic model, automatically partitions the instances into several subclasses, discovers the component segments in each instance as the possible parts, and learns the part-based model for each subclass. Unlike...
The concept of vector summation of a target response to two orthogonal polarized signals for enhancing its delectability, even under concealed conditions, have been demonstrated. A dual polarized 94 GHz radiometer mounted on a dual axis scanning systems has been utilized to collect the field data for the study of the concept. Emissivity gradient due to the presence of target embedded in a given background...
Optical remote sensing satellite holds great potential for ship detection. However, it is challenging for real-time detection due to the relatively low resolution and complicated background. We propose a real-time on-board ship detection method based on statistical analysis and shape identification. First, Gaussian and median filter are used to reduce the periodical and pepper noise generated by the...
We describe a technique for object detection that uses a combination of global shape descriptors and local point descriptors. Our system is able to represent pose using a global shape descriptor, rather than the commonly used part based representation. This approach considerably reduces computational complexity and achieves a significant performance improvement on an extensive dataset: CUB-200-2011...
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