8,044 research outputs found
Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach
Object recognition in the video sequence or images is one of the sub-field of
computer vision. Moving object recognition from a video sequence is an
appealing topic with applications in various areas such as airport safety,
intrusion surveillance, video monitoring, intelligent highway, etc. Moving
object recognition is the most challenging task in intelligent video
surveillance system. In this regard, many techniques have been proposed based
on different methods. Despite of its importance, moving object recognition in
complex environments is still far from being completely solved for low
resolution videos, foggy videos, and also dim video sequences. All in all,
these make it necessary to develop exceedingly robust techniques. This paper
introduces multiple moving object recognition in the video sequence based on
LoG Gabor-PCA approach and Angle based distance Similarity measures techniques
used to recognize the object as a human, vehicle etc. Number of experiments are
conducted for indoor and outdoor video sequences of standard datasets and also
our own collection of video sequences comprising of partial night vision video
sequences. Experimental results show that our proposed approach achieves an
excellent recognition rate. Results obtained are satisfactory and competent.Comment: 8,26,conferenc
Controlling redundancy in referring expressions
Krahmer et al.’s (2003) graph-based framework provides an elegant and flexible approach to the generation of referring expressions. In this paper, we present the first reported study that systematically investigates how to tune the parameters of the graph-based framework on the basis of a corpus of human-generated descriptions. We focus in particular on replicating the redundant nature of human referring expressions, whereby properties not strictly necessary for identifying a referent are nonetheless included in descriptions. We show how statistics derived from the corpus data can be integrated to boost the framework’s performance over a non-stochastic baseline
High Resolution Ozone Mapper (HROM)
Using the backscatter ultraviolet instrument (BUV) aboard NIMBUS 4 as a baseline, point scanner mechanisms and spatial multiplex scanning systems were compared on the basis of sensitivity, field of view and simplicity. This comparison included both spectral and spatial scanning and multiplexing techniques. The selected system which optimally met the performance requirements for a shuttle based instrument was a pushbroom spatial scanner using a 15 element photomultiplier tube array and a Hadamard multiplex spectral scan. The selected system was conceptually designed. This design includes ray traces of the monochromator, mechanical layouts and the electronic block diagram
Small and Dim Target Detection in IR Imagery: A Review
While there has been significant progress in object detection using
conventional image processing and machine learning algorithms, exploring small
and dim target detection in the IR domain is a relatively new area of study.
The majority of small and dim target detection methods are derived from
conventional object detection algorithms, albeit with some alterations. The
task of detecting small and dim targets in IR imagery is complex. This is
because these targets often need distinct features, the background is cluttered
with unclear details, and the IR signatures of the scene can change over time
due to fluctuations in thermodynamics. The primary objective of this review is
to highlight the progress made in this field. This is the first review in the
field of small and dim target detection in infrared imagery, encompassing
various methodologies ranging from conventional image processing to
cutting-edge deep learning-based approaches. The authors have also introduced a
taxonomy of such approaches. There are two main types of approaches:
methodologies using several frames for detection, and single-frame-based
detection techniques. Single frame-based detection techniques encompass a
diverse range of methods, spanning from traditional image processing-based
approaches to more advanced deep learning methodologies. Our findings indicate
that deep learning approaches perform better than traditional image
processing-based approaches. In addition, a comprehensive compilation of
various available datasets has also been provided. Furthermore, this review
identifies the gaps and limitations in existing techniques, paving the way for
future research and development in this area.Comment: Under Revie
Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets
In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866
A flexible algorithm for detecting challenging moving objects in real-time within IR video sequences
Real-time detecting moving objects in infrared video sequences may be particularly challenging because of the characteristics of the objects, such as their size, contrast, velocity and trajectory. Many proposed algorithms achieve good performances but only in the presence of some specific kinds of objects, or by neglecting the computational time, becoming unsuitable for real-time applications. To obtain more flexibility in different situations, we developed an algorithm capable of successfully dealing with small and large objects, slow and fast objects, even if subjected to unusual movements, and poorly-contrasted objects. The algorithm is also capable to handle the contemporary presence of multiple objects within the scene and to work in real-time even using cheap hardware. The implemented strategy is based on a fast but accurate background estimation and rejection, performed pixel by pixel and updated frame by frame, which is robust to possible background intensity changes and to noise. A control routine prevents the estimation from being biased by the transit of moving objects, while two noise-adaptive thresholding stages, respectively, drive the estimation control and allow extracting moving objects after the background removal, leading to the desired detection map. For each step, attention has been paid to develop computationally light solution to achieve the real-time requirement. The algorithm has been tested on a database of infrared video sequences, obtaining promising results against different kinds of challenging moving objects and outperforming other commonly adopted solutions. Its effectiveness in terms of detection performance, flexibility and computational time make the algorithm particularly suitable for real-time applications such as intrusion monitoring, activity control and detection of approaching objects, which are fundamental task in the emerging research area of Smart City
Techniques for Improved Space Object Detection Performance from Ground-Based Telescope Systems Using Long and Short Exposure Images
Space object detection is of great importance in the highly dependent yet competitive and congested space domain. Detection algorithms employed play a crucial role in fulfilling the detection component in the space situational awareness mission to detect, track, characterize and catalog unknown space objects. Many current space detection algorithms use a matched filter or a spatial correlator on long exposure data to make a detection decision at a single pixel point of a spatial image based on the assumption that the data follows a Gaussian distribution. This research focuses on improving current space object detection algorithms and developing new algorithms that provide a greater detection performance, specifically with dim and small objects which are inherently difficult to detect. With a greater detection rate, a great number of unknown objects will be detected, tracked and cataloged to deliver safer space operations. Three novel approaches to object detection using long and short exposure images obtained from ground-based telescopes are examined in this dissertation
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