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Object recognition is a very important task in computer vision and different techniques have been presented to solve it. In this paper a Hough-type low-computational algorithm for detection of objects in cluttered scenes is presented. The approach is based on the detection of the shape of an object, modeled by means of a set of corners. An automatically model learning method is introduced. The method...
Moving object detection is one of the important researches in computer vision. The traditional background subtraction approaches need to get an ideal background image which does not include any moving target. However, it is difficult to obtain an ideal background image in reality. The main idea of updating background model is using an update rate to reflect the impact of outside scene changes on the...
To detect the moving objects in a video sequence based on background subtraction approaches, a background model should be generated at the first time before subtract it from each image of the sequence and then segmenting the moving objects. But this detection can be difficult when the environment is influenced by illumination and weather changes. In The goal to solve the problem of environmental illumination...
As laser scanners become widely used in 3D data acquisition of industrial sites, one challenging problem emerges: given two data of the same site scanned/modeled at different times, how can we tell the difference between the two? In this paper, we formulate this problem as the 3D change detection problem, and propose a novel method for detecting object-level changes. In general, we notice that the...
A method of real-time object detection using Radial Reach Correlation (RRC) has been developed. We also apply a statistical background estimation to cope with dynamic and complex environments. In order to reduce the computational cost of RRC for an embedded image processing device, we apply two techniques: fast RRC algorithm and background estimation based on statistical approach with cumulative averaging...
Since the 1970, object recognition technologies have matured to a point at which exciting applications are becoming possible for visual substitution. In fact, industry has created a variety of computer vision products and services by developing new electronic aids for the blind in order to overcome the difficulties that the dog and cane do not respond. This paper provides an overview of various visual...
We present a moving objection detection method robust to shadow effects and illumination changes on a static camera. This study addresses the effect of the declining sun at evening, which produces elongated shadow of objects. The illumination condition of the evening time is more rapidly changed. These two factors can affect the quality of the moving object detection. We deal with these artifacts...
In this paper, an efficient unsupervised approach for extracting objects from maritime background using solely still video images is proposed. Its main idea is that maritime background (sea) has the main particularity of absorbing only hot light frequencies (red and green), while an object has not this property. Therefore if a timely vector of class features is considered, then two distinct statistical...
This paper presents an improved method for removing cast shadows from multiple objects in a static background using gradient amendment. Shadow can decrease object detection rate and increase the likelihood of tracking failure, which are the two most important measure of algorithm in intelligent video system. The drawback of existed shadow detection methods is the fracture problem of objects, especially...
Unstructured detectors such as KGLRT, ACE and AMF are widely applied for target detection in hyperspectral imagery (HSI). However, conventional global and local approaches construct background model without considering the contamination caused by anomalies and suspected targets. This paper proposes a local ACE algorithm based on the minimum covariance determinant (MCD) estimator. In the proposed algo-rithm,...
In this paper we present a multi-frame motion detection approach for aerial platforms with a two-folded contribution. First, we propose a novel image registration method, which can robustly cope with a large variety of aerial imagery. We show that it can benefit from a hardware accelerated implementation using graphic cards, allowing processing at high frame rate. Second, to handle the inaccuracy...
Object recognition, which consists of classification and detection, has two important attributes for robustness: (1) Closeness: detection windows should be close to object locations, and (2) Adaptiveness: object matching should be adaptive to object variations in classification. It is difficult to satisfy both attributes by considering classification and detection separately, thus recent studies combine...
In this paper, we propose a new object localization method named sparse representation based object localization (SROL), which is based on the generalized Hough-transform-based approach using sparse representations for parts detection. The proposed method was applied to car and ship detection in remote sensing images and its performance was compared to those of state-of-the-art methods. Experimental...
In this paper we present the experimental work on object detection in the Smartphone visible light communications (SVLC) system. In SVLC, the Smartphone emits encoded images using its panel light under different conditions (link range and angles) and the transmitted data is captured by a receiving phone. The captured image is processed using the Speeded Up Robust Features (SURF) technique, which enables...
We propose a novel unified framework for the initial detection of possible targets within the aerial images using saliency detection. Our method is a bottom-up approach and computes Locally Adaptive Regression Kernel (LARK) from the given image, which measures the likeness of a pixel to its surroundings. Visual saliency is then computed using the self-resemblance measure. The framework results in...
People counting has many important applications in practice. The two key techniques in video based people counting system are people detection and people tracking. Most of the current people counting methods use body detection or motion detection to detect the people, which can produce some good results in sparse situations but fail in crowded scenes. In the crowded scenes, we know that the head is...
This paper presents a robust background generation method using a modified mixture of Gaussian model. Traditional background generation methods become unstable when the camera viewpoint suddenly changes. To solve this problem, the proposed method robustly extracts the background by mixing multiple Gaussian models.
We present a method for efficiently detecting natural landmarks that can handle scenes with highly repetitive patterns and targets progressively changing its appearance. At the core of our approach lies a Random Ferns classifier, that models the posterior probabilities of different views of the target using multiple and independent Ferns, each containing features at particular positions of the target...
This paper presents a vision-based moving objects detection work which attracts much attention in intelligent automobile applications recently. Vision-based objects detection provides object behavior information of objects and is an intuitive detection method similar to human visual perception. Besides, vision-based objects detection methods are much low-cost compared with detection methods such as...
This paper focuses on detecting parts in laser-scanned data of a cluttered industrial scene. To achieve the goal, we propose a robust object detection system based on segmentation and matching, as well as an adaptive segmentation algorithm and an efficient pose extraction algorithm based on correspondence filtering. We also propose an overlapping-based criterion that exploits more information of the...
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