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Accuracy and efficiency in geoscientific data interpretation is critically important for the resource industry, as based on these interpretations, significant financial decisions are made for exploration and extraction of resources. This study aims to understand image characteristics that impact interpreters' ability to detect geological targets within magnetic geophysics images. We use the Brain...
Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. Such automatic object detection soft wares have many applications in surveillance, auto navigation, traffic monitoring and robotics. While identifying the objects it is some time essential to identify the objects individually. Individual object detection is very critical in many...
Foreground extraction and moving object detection are often used in human tracking systems. However those methods are not able to produce accurate results when objects are too close or when occlusions happen since the result is generally a single big blob which contains all the different objects. In this paper we propose a novel and efficient moving object detection enhancement method. Indeed, by...
Background subtraction is the important part of moving object detection. The problem of background subtraction is threshold selection strategy. This paper proposed a Fuzzy C-Means (FCM) algorithm to produce an adaptive threshold for background subtraction in moving object detection. To evaluate the performance, FCM were compared against standard Otsu algorithm as threshold selection strategy. Mean...
The background subtraction is an important method to detect the moving objects, and effective background reconstruction is the key for the background subtraction. Based on the idea that the pixel average appearing with high frequency in an image series is the background points, the pixel intensity classification (PIC) algorithm can reconstruct background accurately. In this paper, a new background...
In this paper we propose a way of using the Audio-Visual Description Profile (AVDP) of the MPEG-7 standard for stereo video content description. Our aim is to provide means of using AVDP in such a way that 3D video content can be correctly and consistently described. Since, AVDP semantics do not include ways for dealing with 3D video content, a new semantic framework within AVDP is proposed. Finally,...
In this paper we presents preliminary results about the development of an artificial vision system for Human Robot Interaction (HRI) based on methods for recognition of facial features, object detection and tracking. The purpose is to implement a vision system in order to process perceptual information as a result of human-robot social interaction in a hybrid architecture for an omni directional test...
Building robots capable of long term autonomy has been a long standing goal of robotics research. Such systems must be capable of performing certain tasks with a high degree of robustness and repeatability. In the context of personal robotics, these tasks could range anywhere from retrieving items from a refrigerator, loading a dishwasher, to setting up a dinner table. Given the complexity of tasks...
We present a robust system for large-scale abandoned object detection (AOD) with low false positive rates and good detection accuracy under complex realistic scenarios. The robustness of our system is largely attributed to an approach we develop for foreground analysis, which can effectively differentiate foreground objects from background under challenging conditions such as lighting changes, low...
In this paper we present a salient object detection model from an over-segmented image. The input image is initially segmented by the mean-shift segmentation algorithm and then over-segmented by a quad mesh to even smaller segments. Such segmented regions overcome the disadvantage of using patches or single pixels to compute saliency. Segments that are similar and spread over the image receive low...
In this work, a perceptual quality-regulable H.264 video encoder system has been developed. Exploiting the relationship between the reconstructed macro block and its best predicted macro block from mode decision, a novel quantization parameter prediction method is built and used to regulate the video quality according to a target perceptual quality. An automatic quality refinement scheme is also developed...
Image co-occurrence has shown great powers on object classification because it captures the characteristic of individual features and spatial relationship between them simultaneously. For example, Co-occurrence Histogram of Oriented Gradients (CoHOG) has achieved great success on human detection task. However, the gradient orientation in CoHOG is sensitive to noise. In addition, CoHOG does not take...
In recent years, the rise of digital image and video data available has led to an increasing demand for image annotation. In this paper, we propose an interactive object annotation method that incrementally trains an object detector while the user provides annotations. In the design of the system, we have focused on minimizing human annotation time rather than pure algorithm learning performance....
Pedestrian detection is one of the fundamental tasks of an intelligent transportation system. Differences in illumination, posture and point of view make pedestrian detection confront with great challenges. In this paper, we focus on the main defect in the existing methods: the interference of the non-person area. Firstly, we use mapping vectors to map the original feature matrix to the different...
This paper presents a new generic framework for human visual system inspired object detection and recognition and introduces the idea of feature extraction based on the human visual sensitivity. These methods can greatly enhance robotic vision applications. Additionally a new computationally effective object detection algorithm is presented based on image morphology and visual sensitivity. This new...
The problem of human detection is challenging, more so, when faced with adverse conditions such as occlusion and background clutter. This paper addresses the problem of human detection by representing an extracted feature of an image using a sparse linear combination of chosen dictionary atoms. The detection along with the scale finding, is done by using the coefficients obtained from sparse representation...
In object detection, the offline trained detector's performance may be degraded in a particular deployed environment, because of the large variation of different environments. In this work, we propose a data level object detector adaptation method to new environments. By recording a small amount of offline data, it's fully compatible with offline training method and easy to implement. We re-derive...
Significant progress has been made towards learning a generalized offline object detector. However, when a generalized offline detector is applied on new datasets, it often misses some instances of the object or produces false alarms in the background scene. we propose a novel and efficient incremental learning method, which improves the performance of an offline trained detector. Our approach adjusts...
The paper proposes a novel method for human face extraction from color images, characterized by uncontrolled illumination conditions and complex backgrounds. The method incorporates stationary Haar wavelet transform & proximity influence for prominent boundaries detection and watershed transform, proximity influence & morphological operations to separate foreground / background along with...
In this paper, we propose to detect human centric objects, including face, head shoulder, upper body, left body, right body and whole body, which can provide essential information to locate humans in highly crowed scenes. In the literature, the approaches to detect multi-class objects are either taking each class independently to learn and apply its classifier successively or taking all classes as...
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